Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field
Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template‐free protein structure prediction. Query sequences are first broken into fragments of...
Saved in:
| Published in | Proteins, structure, function, and bioinformatics Vol. 80; no. 7; pp. 1715 - 1735 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.07.2012
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template‐free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full‐length structure models are then assembled from fragments using replica‐exchange Monte Carlo simulations, which are guided by a composite knowledge‐based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one‐third cases of short proteins up to 100 residues. In the ninth community‐wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z‐score of global distance test‐total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Proteins 2012; © 2012 Wiley Periodicals, Inc. |
|---|---|
| AbstractList | Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Proteins 2012; copyright 2012 Wiley Periodicals, Inc. Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Proteins 2012; © 2012 Wiley Periodicals, Inc. [PUBLICATION ABSTRACT] Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template‐free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full‐length structure models are then assembled from fragments using replica‐exchange Monte Carlo simulations, which are guided by a composite knowledge‐based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one‐third cases of short proteins up to 100 residues. In the ninth community‐wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z‐score of global distance test‐total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Proteins 2012; © 2012 Wiley Periodicals, Inc. Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field.Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Ab initio protein folding is one of the major unsolved problems in computational biology due to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 non-homologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score (TM-score) >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in 1/3 cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction (CASP9) experiment, QUARK server outperformed the second and third best servers by 18% and 47% based on the cumulative Z-score of global distance test-total (GDT-TS) scores in the free modeling (FM) category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress towards the solution of the most important problem in the field. Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template‐free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full‐length structure models are then assembled from fragments using replica‐exchange Monte Carlo simulations, which are guided by a composite knowledge‐based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one‐third cases of short proteins up to 100 residues. In the ninth community‐wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z ‐score of global distance test‐total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Proteins 2012; © 2012 Wiley Periodicals, Inc. |
| Author | Xu, Dong Zhang, Yang |
| AuthorAffiliation | 1 Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA 2 Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA |
| AuthorAffiliation_xml | – name: 1 Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA – name: 2 Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA |
| Author_xml | – sequence: 1 givenname: Dong surname: Xu fullname: Xu, Dong organization: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109 – sequence: 2 givenname: Yang surname: Zhang fullname: Zhang, Yang email: zhng@umich.edu organization: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109 |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22411565$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkt1r1TAYxoNM3Ife-AdIwRsROt80SdPcCHPoFA9OxsTLkLZvj9na5Cxpnce_3tSzHeYQvQov-T0Pz_uxT3acd0jIUwqHFKB4tQp-PCw4lOIB2aOgZA6U8R2yB1UlcyYqsUv2Y7wAgFKx8hHZLQpOqSjFHolHdWadHa3PZhu0LotjmJpxCpiZGHGo-3U2ReuWWePdaN3kp3iH6YJZDujGmBnXZn412sH-xDa7dP66x3aJeW1iqjsfmkRb7NvH5GFn-ohPbt4D8uXd2_Pj9_ni9OTD8dEib1JkkbOCs05RjsZQKOpKFsixTi1J0ymDjejKTjRVBRJaDi3UpmorlKXhQLGoOTsgrze-q6kesG1SymB6vQp2MGGtvbH6zx9nv-ml_64ZkwByNnhxYxD81YRx1IONDfa9cZimoKkolOJplOz_KFBVUq6ETOjze-iFn4JLk0iGVHKulJgNn90Nv019u7oEvNwATfAxBuy2CAU934WeF6p_30WC4R7c2NGkrc-N2_7vErqRXNse1_8w15_PTs9vNflGY-OIP7YaEy51KZkU-uunE119XEh4oxb6jP0Cn_7clg |
| CitedBy_id | crossref_primary_10_1371_journal_ppat_1007056 crossref_primary_10_3390_ijms21010366 crossref_primary_10_1093_bioinformatics_btw668 crossref_primary_10_1093_bioinformatics_btv459 crossref_primary_10_1093_bioinformatics_btx514 crossref_primary_10_3390_ijms21010006 crossref_primary_10_1007_s11047_014_9427_8 crossref_primary_10_1016_j_jmb_2015_09_024 crossref_primary_10_1016_S2222_1808_14_60492_8 crossref_primary_10_1093_bib_bbab493 crossref_primary_10_20537_2076_7633_2013_5_4_737_747 crossref_primary_10_1371_journal_pone_0065770 crossref_primary_10_1016_j_jmgm_2017_09_003 crossref_primary_10_1016_j_ijbiomac_2020_12_192 crossref_primary_10_1016_j_bbamem_2016_11_011 crossref_primary_10_1016_j_compbiolchem_2014_10_001 crossref_primary_10_2174_1871526518666171215123214 crossref_primary_10_1016_j_celrep_2014_05_034 crossref_primary_10_3390_md19040181 crossref_primary_10_1016_j_jsb_2015_06_009 crossref_primary_10_1016_j_devcel_2023_05_006 crossref_primary_10_1016_j_isci_2021_102059 crossref_primary_10_1016_j_pnmrs_2018_03_002 crossref_primary_10_1186_s12859_016_1348_3 crossref_primary_10_1016_j_gene_2015_04_070 crossref_primary_10_1109_TEVC_2021_3095481 crossref_primary_10_1002_prot_26355 crossref_primary_10_1021_acs_jctc_8b00303 crossref_primary_10_1002_yea_3537 crossref_primary_10_1021_bi400606b crossref_primary_10_2174_0929866527666200129141116 crossref_primary_10_1016_j_fochx_2024_101275 crossref_primary_10_1021_acs_jpcb_1c00902 crossref_primary_10_1016_j_abb_2018_05_020 crossref_primary_10_1093_bioinformatics_btv235 crossref_primary_10_1093_molbev_mst207 crossref_primary_10_1002_prot_24179 crossref_primary_10_1002_prot_26478 crossref_primary_10_1109_TCBB_2022_3168676 crossref_primary_10_1093_bioinformatics_btab620 crossref_primary_10_1016_j_jmb_2021_167127 crossref_primary_10_1093_bioinformatics_btab500 crossref_primary_10_1111_cmi_12288 crossref_primary_10_3390_v14102307 crossref_primary_10_3390_molecules26216566 crossref_primary_10_1002_prot_25278 crossref_primary_10_1038_srep45224 crossref_primary_10_1038_s41596_021_00628_9 crossref_primary_10_1371_journal_pone_0053571 crossref_primary_10_1038_srep44150 crossref_primary_10_1016_j_ijbiomac_2020_10_211 crossref_primary_10_1093_bioinformatics_btw316 crossref_primary_10_1109_TNB_2017_2749243 crossref_primary_10_1093_bioinformatics_btz943 crossref_primary_10_3390_life10070109 crossref_primary_10_1021_acs_cgd_0c01136 crossref_primary_10_3390_toxins7061989 crossref_primary_10_1093_bioinformatics_btab871 crossref_primary_10_1109_TCBB_2017_2705094 crossref_primary_10_1007_s11120_014_0071_z crossref_primary_10_1039_D1SC06825C crossref_primary_10_3390_biology10111113 crossref_primary_10_3390_mca26020039 crossref_primary_10_3389_fmolb_2021_814935 crossref_primary_10_1109_TCBB_2015_2497226 crossref_primary_10_1016_j_bbagen_2021_129889 crossref_primary_10_1002_jbmr_3142 crossref_primary_10_1002_prot_25162 crossref_primary_10_1371_journal_pcbi_1004619 crossref_primary_10_1016_j_micpath_2020_104111 crossref_primary_10_1186_s12858_015_0047_4 crossref_primary_10_1021_acs_jpclett_6b00452 crossref_primary_10_1016_j_tplants_2015_08_004 crossref_primary_10_1111_ejh_12456 crossref_primary_10_1016_j_jmb_2022_167541 crossref_primary_10_1016_j_bpj_2016_12_008 crossref_primary_10_1111_plb_13711 crossref_primary_10_1371_journal_pone_0068954 crossref_primary_10_1002_ange_201707554 crossref_primary_10_1039_D4MD00383G crossref_primary_10_1080_07391102_2015_1077736 crossref_primary_10_1002_prot_25332 crossref_primary_10_1016_j_abb_2017_12_004 crossref_primary_10_1093_bioinformatics_btab777 crossref_primary_10_1098_rsif_2011_0843 crossref_primary_10_1080_07391102_2018_1461688 crossref_primary_10_1080_07391102_2020_1832579 crossref_primary_10_1002_minf_201400108 crossref_primary_10_1021_acs_jcim_9b00948 crossref_primary_10_1155_2018_7963401 crossref_primary_10_1007_s10096_020_04125_x crossref_primary_10_3390_v14020420 crossref_primary_10_1093_bioinformatics_btx781 crossref_primary_10_1016_j_dnarep_2019_102698 crossref_primary_10_1074_jbc_RA118_003490 crossref_primary_10_1002_biot_201900343 crossref_primary_10_1093_nar_gkz266 crossref_primary_10_1002_prot_24374 crossref_primary_10_1002_prot_24490 crossref_primary_10_1016_j_compbiomed_2021_104380 crossref_primary_10_1016_j_bbrc_2020_08_024 crossref_primary_10_1186_s12859_021_04258_6 crossref_primary_10_1371_journal_pone_0057848 crossref_primary_10_1016_j_antiviral_2017_11_001 crossref_primary_10_1093_bioinformatics_btz876 crossref_primary_10_1002_pro_3348 crossref_primary_10_1038_srep35465 crossref_primary_10_1093_bib_bbab590 crossref_primary_10_1007_s00439_017_1843_2 crossref_primary_10_1016_j_ijbiomac_2020_08_232 crossref_primary_10_3389_fmicb_2016_00509 crossref_primary_10_1016_j_isci_2022_104425 crossref_primary_10_1021_ct500592m crossref_primary_10_1021_acs_jcim_2c01196 crossref_primary_10_1016_j_virusres_2020_198143 crossref_primary_10_1038_s41598_019_39301_3 crossref_primary_10_1016_j_peptides_2013_03_014 crossref_primary_10_1038_nature24282 crossref_primary_10_1124_mol_119_117549 crossref_primary_10_3390_tropicalmed9070140 crossref_primary_10_1124_jpet_114_221143 crossref_primary_10_1126_sciadv_abg3147 crossref_primary_10_1371_journal_ppat_1009884 crossref_primary_10_3390_v7010394 crossref_primary_10_3389_fmicb_2018_00725 crossref_primary_10_1002_cmdc_201300256 crossref_primary_10_1002_bkcs_10421 crossref_primary_10_1007_s11883_023_01154_7 crossref_primary_10_1016_j_virusres_2016_07_013 crossref_primary_10_34248_bsengineering_1402011 crossref_primary_10_1142_S0219720019400134 crossref_primary_10_3390_physiologia3010002 crossref_primary_10_1080_07391102_2020_1841025 crossref_primary_10_1002_prot_25244 crossref_primary_10_1186_1756_0500_7_446 crossref_primary_10_3389_fbinf_2023_1120370 crossref_primary_10_1242_jeb_092288 crossref_primary_10_1002_prot_25769 crossref_primary_10_1016_j_gpb_2022_11_014 crossref_primary_10_1093_jnci_djab019 crossref_primary_10_3390_v15112144 crossref_primary_10_1371_journal_pgen_1003874 crossref_primary_10_1099_jgv_0_001730 crossref_primary_10_1128_JB_00445_20 crossref_primary_10_3390_life9010008 crossref_primary_10_1021_acs_jproteome_5b00734 crossref_primary_10_3390_biom9100612 crossref_primary_10_1155_2013_594319 crossref_primary_10_1002_minf_201500044 crossref_primary_10_12688_f1000research_12486_1 crossref_primary_10_12688_f1000research_12486_2 crossref_primary_10_1080_07391102_2020_1790423 crossref_primary_10_3389_fmolb_2021_649990 crossref_primary_10_1021_acs_jpclett_6b00362 crossref_primary_10_1093_database_bay090 crossref_primary_10_1002_prot_25414 crossref_primary_10_1016_j_jmb_2016_01_007 crossref_primary_10_1128_JB_00623_18 crossref_primary_10_3389_fmicb_2018_02846 crossref_primary_10_1016_j_jmb_2020_07_027 crossref_primary_10_1016_j_bbrep_2016_07_018 crossref_primary_10_1021_acs_jafc_3c05860 crossref_primary_10_3390_plants10102181 crossref_primary_10_3390_pharmaceutics15051433 crossref_primary_10_7554_eLife_24109 crossref_primary_10_1371_journal_pone_0211897 crossref_primary_10_1371_journal_pone_0191162 crossref_primary_10_1016_j_jbior_2012_12_001 crossref_primary_10_1016_j_sbi_2022_102354 crossref_primary_10_1002_anie_201707554 crossref_primary_10_3390_ijms23073721 crossref_primary_10_1016_j_biochi_2020_04_026 crossref_primary_10_1016_j_csbj_2019_07_010 crossref_primary_10_1002_1873_3468_13704 crossref_primary_10_1002_prot_24459 crossref_primary_10_1002_prot_25306 crossref_primary_10_1021_acsinfecdis_4c00240 crossref_primary_10_1126_science_aar2781 crossref_primary_10_1002_prot_24336 crossref_primary_10_1007_s00436_017_5408_z crossref_primary_10_1371_journal_pone_0183548 crossref_primary_10_3389_fmicb_2015_01397 crossref_primary_10_1016_j_knosys_2018_01_028 crossref_primary_10_1016_j_dci_2018_02_021 crossref_primary_10_1074_mcp_M116_060806 crossref_primary_10_1016_j_cjche_2021_08_017 crossref_primary_10_1016_j_jinorgbio_2019_110960 crossref_primary_10_1016_j_jmgm_2021_108046 crossref_primary_10_1186_s12862_015_0337_x crossref_primary_10_1109_TAI_2021_3062771 crossref_primary_10_3389_fbinf_2023_1157956 crossref_primary_10_1002_prot_25792 crossref_primary_10_1002_prot_24341 crossref_primary_10_1016_j_cels_2019_03_006 crossref_primary_10_1186_s12859_019_2794_5 crossref_primary_10_1002_ctm2_1789 crossref_primary_10_7554_eLife_01584 crossref_primary_10_1021_acs_chemrev_6b00163 crossref_primary_10_1111_cns_13627 crossref_primary_10_1093_bioinformatics_btz544 crossref_primary_10_1016_j_pbiomolbio_2017_02_006 crossref_primary_10_1002_prot_24587 crossref_primary_10_1007_s00439_016_1741_z crossref_primary_10_1128_JB_00399_21 crossref_primary_10_1021_jacs_0c02954 crossref_primary_10_1093_bib_bbac507 crossref_primary_10_1021_acs_jctc_4c00067 crossref_primary_10_1073_pnas_1905068116 crossref_primary_10_3390_molecules29040832 crossref_primary_10_1002_prot_25442 crossref_primary_10_1038_srep23105 crossref_primary_10_1107_S2052252521011891 crossref_primary_10_1108_BPMJ_11_2023_0902 crossref_primary_10_1007_s00894_014_2357_7 crossref_primary_10_7554_eLife_92379_3 crossref_primary_10_3390_molecules24050854 crossref_primary_10_1039_D0SM00491J crossref_primary_10_1161_CIRCRESAHA_122_320882 crossref_primary_10_3390_molecules25092228 crossref_primary_10_1074_jbc_M116_771675 crossref_primary_10_1093_jb_mvw064 crossref_primary_10_1089_regen_2023_0012 crossref_primary_10_1093_bioinformatics_btab484 crossref_primary_10_1093_bioinformatics_btv092 crossref_primary_10_1371_journal_pone_0082985 crossref_primary_10_1093_bib_bbw070 crossref_primary_10_3390_ijms22094408 crossref_primary_10_1109_ACCESS_2022_3176945 crossref_primary_10_1016_j_bbagen_2020_129633 crossref_primary_10_1016_j_peptides_2012_07_017 crossref_primary_10_1016_j_jsb_2016_03_004 crossref_primary_10_1016_j_bbabio_2017_11_001 crossref_primary_10_1016_j_jmgm_2016_08_007 crossref_primary_10_1109_TCBB_2019_2921958 crossref_primary_10_1002_advs_202102592 crossref_primary_10_1021_ct5007828 crossref_primary_10_1038_s41467_023_41669_w crossref_primary_10_1016_j_crstbi_2022_01_001 crossref_primary_10_1007_s00430_020_00663_5 crossref_primary_10_1162_EVCO_a_00176 crossref_primary_10_1016_j_compbiomed_2022_105695 crossref_primary_10_1007_s10969_012_9146_2 crossref_primary_10_1128_AEM_00367_20 crossref_primary_10_1021_acsbiomaterials_8b00819 crossref_primary_10_1074_mcp_TIR118_001233 crossref_primary_10_1371_journal_pone_0221347 crossref_primary_10_3390_ijms160715136 crossref_primary_10_1038_s41598_019_43926_9 crossref_primary_10_1016_j_asoc_2015_06_024 crossref_primary_10_3389_fcimb_2020_00405 crossref_primary_10_1073_pnas_1607178113 crossref_primary_10_1093_ofid_ofz013 crossref_primary_10_3390_md17060376 crossref_primary_10_1016_j_jes_2015_03_035 crossref_primary_10_1016_j_jid_2021_01_033 crossref_primary_10_1016_j_ncrna_2019_10_002 crossref_primary_10_1002_2211_5463_13908 crossref_primary_10_1016_j_peptides_2014_02_010 crossref_primary_10_1089_ars_2019_7931 crossref_primary_10_1124_jpet_114_217976 crossref_primary_10_1002_bit_26865 crossref_primary_10_1111_cea_12028 crossref_primary_10_1038_s41598_018_31891_8 crossref_primary_10_1021_acs_jcim_1c00175 crossref_primary_10_1002_prot_24782 crossref_primary_10_1016_j_future_2019_04_011 crossref_primary_10_1016_j_xpro_2021_100505 crossref_primary_10_1109_TCBB_2020_3000758 crossref_primary_10_1007_s10462_022_10350_x crossref_primary_10_1016_j_ijbiomac_2021_02_210 crossref_primary_10_1038_s41598_021_90313_4 crossref_primary_10_1016_j_jprot_2013_04_007 crossref_primary_10_1038_s41467_020_17568_9 crossref_primary_10_1016_j_jmb_2020_07_002 crossref_primary_10_3390_ijms20030606 crossref_primary_10_1002_jez_1930 crossref_primary_10_1093_bioinformatics_btx164 crossref_primary_10_3390_biom14091083 crossref_primary_10_1016_j_compbiolchem_2018_02_003 crossref_primary_10_3390_molecules25225405 crossref_primary_10_1016_j_chroma_2018_04_067 crossref_primary_10_3390_bioengineering9030118 crossref_primary_10_1126_scisignal_abe9613 crossref_primary_10_3390_ijms23073862 crossref_primary_10_1371_journal_pone_0071610 crossref_primary_10_1016_j_molliq_2021_117778 crossref_primary_10_12688_f1000research_130443_1 crossref_primary_10_1016_j_bbapap_2013_04_022 crossref_primary_10_1002_prot_24950 crossref_primary_10_1093_bib_bbx006 crossref_primary_10_1021_acs_jmedchem_7b01831 crossref_primary_10_1136_jmedgenet_2018_105299 crossref_primary_10_1016_j_cels_2020_08_016 crossref_primary_10_1109_TCBB_2013_29 crossref_primary_10_1515_cmb_2019_0001 crossref_primary_10_1039_D4ME00073K crossref_primary_10_1002_jcc_23543 crossref_primary_10_1073_pnas_2208275120 crossref_primary_10_1016_j_molliq_2021_116899 crossref_primary_10_1002_wcms_1307 crossref_primary_10_1016_j_ijbiomac_2020_07_293 crossref_primary_10_1371_journal_pcbi_1010539 crossref_primary_10_1038_s41588_023_01403_0 crossref_primary_10_1371_journal_pone_0093313 crossref_primary_10_1007_s00018_016_2131_3 crossref_primary_10_1021_acs_jctc_6b00819 crossref_primary_10_1107_S2059798322003576 crossref_primary_10_1098_rsob_130172 crossref_primary_10_1002_prot_24600 crossref_primary_10_1371_journal_pone_0138787 crossref_primary_10_1016_j_bbapap_2014_07_019 crossref_primary_10_3389_fimmu_2020_00076 crossref_primary_10_1042_BCJ20190687 crossref_primary_10_1371_journal_pone_0080493 crossref_primary_10_1016_j_heliyon_2020_e04115 crossref_primary_10_1038_s41596_022_00728_0 crossref_primary_10_1016_j_procbio_2015_06_010 crossref_primary_10_1371_journal_pone_0154923 crossref_primary_10_1186_s12859_020_3491_0 crossref_primary_10_1016_j_jsb_2022_107870 crossref_primary_10_1111_mmi_12759 crossref_primary_10_3390_ijms24076262 crossref_primary_10_1016_j_dci_2014_11_008 crossref_primary_10_1186_s12859_020_3523_9 crossref_primary_10_1007_s12010_014_0757_x crossref_primary_10_3390_molecules21081062 crossref_primary_10_1094_MPMI_02_20_0028_R crossref_primary_10_1016_j_cor_2017_11_015 crossref_primary_10_1002_prot_24973 crossref_primary_10_1016_j_bbagen_2018_06_011 crossref_primary_10_1021_acs_jcim_3c01324 crossref_primary_10_1016_j_compbiolchem_2022_107693 crossref_primary_10_1111_jcmm_16660 crossref_primary_10_1111_cbdd_13329 crossref_primary_10_1530_JME_14_0050 crossref_primary_10_1074_jbc_M115_676221 crossref_primary_10_1016_j_peptides_2014_03_017 crossref_primary_10_1002_jcc_24415 crossref_primary_10_1099_vir_0_055673_0 crossref_primary_10_1007_s13353_014_0265_2 crossref_primary_10_1002_prot_25956 crossref_primary_10_1109_TCBB_2019_2917452 crossref_primary_10_1002_prot_24744 crossref_primary_10_7498_aps_65_178701 crossref_primary_10_1007_s00232_021_00187_w crossref_primary_10_1016_j_csbj_2015_04_004 crossref_primary_10_1016_j_jmgm_2013_05_012 crossref_primary_10_1002_pro_2961 crossref_primary_10_3389_fgene_2015_00348 crossref_primary_10_1109_TCBB_2020_3049088 crossref_primary_10_1016_j_foodhyd_2024_110154 crossref_primary_10_1371_journal_pone_0170131 crossref_primary_10_1042_ETLS20200253 crossref_primary_10_1038_srep44948 crossref_primary_10_1093_nar_gkt462 crossref_primary_10_1126_science_aaz3041 crossref_primary_10_1038_srep38031 crossref_primary_10_1016_j_asej_2019_03_004 crossref_primary_10_1016_j_bbamem_2018_03_002 crossref_primary_10_1186_s12934_017_0706_9 crossref_primary_10_1074_jbc_M112_343756 crossref_primary_10_1016_j_micpath_2018_09_004 crossref_primary_10_1016_j_bbcan_2013_02_001 crossref_primary_10_1016_j_jacl_2021_01_002 crossref_primary_10_1093_nar_gku369 crossref_primary_10_1002_prot_24917 crossref_primary_10_1093_bioinformatics_btac056 crossref_primary_10_1002_prot_24918 crossref_primary_10_1371_journal_pone_0251459 crossref_primary_10_1016_j_jocs_2020_101087 crossref_primary_10_1016_j_ijbiomac_2021_03_199 crossref_primary_10_1155_2021_9542038 crossref_primary_10_1016_j_jmb_2018_12_013 crossref_primary_10_1126_sciadv_abd0263 crossref_primary_10_3390_v12050570 crossref_primary_10_1016_j_jmb_2018_12_016 crossref_primary_10_1007_s00894_019_4232_z crossref_primary_10_1007_s00239_016_9738_8 crossref_primary_10_1242_jcs_188516 crossref_primary_10_3390_genes9080386 crossref_primary_10_1016_j_jmb_2019_05_044 crossref_primary_10_1038_s41467_021_21667_6 crossref_primary_10_1186_s12885_021_08370_x crossref_primary_10_1016_j_actbio_2022_04_025 crossref_primary_10_1021_acschembio_7b00055 crossref_primary_10_3390_ijms141020635 crossref_primary_10_3390_v11020143 crossref_primary_10_1002_prot_24924 crossref_primary_10_1002_cem_3026 crossref_primary_10_3390_ph17020158 crossref_primary_10_1093_nar_gkv342 crossref_primary_10_1371_journal_pone_0121074 crossref_primary_10_3390_app11188315 crossref_primary_10_1080_07391102_2022_2050301 crossref_primary_10_15406_jbmoa_2022_10_00330 crossref_primary_10_1016_j_cub_2017_08_019 crossref_primary_10_1126_sciadv_adh9812 crossref_primary_10_1002_jcc_23339 crossref_primary_10_1021_acs_biochem_7b01073 crossref_primary_10_1016_j_foodhyd_2023_108638 crossref_primary_10_1021_acsomega_0c00189 crossref_primary_10_7554_eLife_17029 crossref_primary_10_7717_peerj_3139 crossref_primary_10_1016_j_bbamem_2019_183138 crossref_primary_10_1002_prot_24930 crossref_primary_10_3390_computation6020039 crossref_primary_10_1271_bbb_130424 crossref_primary_10_1016_j_npep_2023_102390 crossref_primary_10_1007_s10570_020_03194_7 crossref_primary_10_1186_s12859_019_2652_5 crossref_primary_10_2217_fvl_2016_0083 crossref_primary_10_1111_cbdd_14588 crossref_primary_10_3390_jcm10050984 crossref_primary_10_1142_S2737416523300018 crossref_primary_10_1007_s10989_015_9459_7 crossref_primary_10_1371_journal_pone_0188520 crossref_primary_10_3390_ijms20153756 crossref_primary_10_1016_j_sbi_2021_01_007 crossref_primary_10_1021_acs_biomac_6b01810 crossref_primary_10_1038_s41467_024_48843_8 crossref_primary_10_1016_j_ins_2020_06_003 crossref_primary_10_1073_pnas_2110828118 crossref_primary_10_1038_s42003_022_03886_9 crossref_primary_10_1021_acs_jcim_6b00407 crossref_primary_10_1109_TCBB_2016_2566617 crossref_primary_10_1007_s10930_021_10016_7 crossref_primary_10_1007_s13337_021_00688_1 crossref_primary_10_1016_j_jsb_2016_05_004 crossref_primary_10_1111_febs_13795 crossref_primary_10_3390_life5021141 crossref_primary_10_1158_0008_5472_CAN_17_2876 crossref_primary_10_1002_jbmr_4711 crossref_primary_10_1016_j_compbiolchem_2016_01_009 crossref_primary_10_1016_j_intimp_2019_01_016 crossref_primary_10_1016_j_compbiolchem_2016_01_008 crossref_primary_10_1186_s12936_021_03598_1 crossref_primary_10_1128_JVI_01785_16 crossref_primary_10_3390_jof9010103 crossref_primary_10_1186_s12915_020_0751_4 crossref_primary_10_3389_fcimb_2015_00040 crossref_primary_10_3390_biology9090294 crossref_primary_10_1074_jbc_M117_794230 crossref_primary_10_1016_j_jmgm_2017_12_023 crossref_primary_10_1016_j_biochi_2020_12_017 crossref_primary_10_3389_fmicb_2015_00958 crossref_primary_10_1371_journal_pcbi_1007411 crossref_primary_10_1016_j_mimet_2015_06_003 crossref_primary_10_1371_journal_pcbi_1003298 crossref_primary_10_1016_j_drudis_2014_08_011 crossref_primary_10_3989_arbor_2015_772n2006 crossref_primary_10_1016_j_jmb_2023_168146 crossref_primary_10_1016_j_ejps_2022_106324 crossref_primary_10_1093_bioinformatics_btv601 crossref_primary_10_1111_brv_12170 crossref_primary_10_1021_bi5004052 crossref_primary_10_1186_s12864_015_2218_5 crossref_primary_10_1002_ajmg_a_61168 crossref_primary_10_1038_s41598_023_45875_w crossref_primary_10_1002_prot_26193 crossref_primary_10_1007_s12539_024_00626_x crossref_primary_10_1016_j_cels_2017_11_014 crossref_primary_10_1016_j_jbc_2021_100441 crossref_primary_10_7554_eLife_61069 crossref_primary_10_7554_eLife_92379 crossref_primary_10_1021_acs_jctc_1c00129 crossref_primary_10_1172_JCI83817 crossref_primary_10_1109_TEVC_2015_2505317 crossref_primary_10_1111_nph_17739 crossref_primary_10_1021_ja409608r crossref_primary_10_1016_j_tibtech_2013_10_008 crossref_primary_10_1016_j_pbiomolbio_2022_05_002 crossref_primary_10_1371_journal_pone_0178272 crossref_primary_10_1093_bioinformatics_btt578 crossref_primary_10_3390_v8030061 crossref_primary_10_1093_nar_gkw329 crossref_primary_10_1016_j_bpj_2016_08_004 crossref_primary_10_1038_s41598_019_45942_1 crossref_primary_10_1080_09205063_2020_1816870 crossref_primary_10_1128_AEM_02655_13 crossref_primary_10_1038_s43588_022_00232_1 crossref_primary_10_1007_s10723_015_9353_8 crossref_primary_10_1107_S2059798321004629 crossref_primary_10_1128_mBio_00950_17 crossref_primary_10_1371_journal_pone_0050200 crossref_primary_10_2139_ssrn_4013270 crossref_primary_10_1038_s41467_021_25316_w crossref_primary_10_1016_j_foodchem_2022_133589 crossref_primary_10_1021_acs_jpcb_0c02720 crossref_primary_10_1016_j_sbi_2017_03_012 crossref_primary_10_1126_science_aay0262 crossref_primary_10_1155_2018_5486403 crossref_primary_10_1016_j_biosystems_2018_04_002 crossref_primary_10_1080_17460441_2021_1918097 crossref_primary_10_1371_journal_pone_0180456 crossref_primary_10_1002_bip_22436 crossref_primary_10_1186_s12976_015_0014_1 crossref_primary_10_1016_j_meegid_2021_104826 crossref_primary_10_1093_nar_gkaa1198 crossref_primary_10_1016_j_compbiomed_2024_108620 crossref_primary_10_1002_prot_25049 crossref_primary_10_1021_acscentsci_3c00158 crossref_primary_10_1007_s10989_020_10017_7 crossref_primary_10_1016_j_biotechadv_2017_02_001 crossref_primary_10_1093_bib_bbab296 crossref_primary_10_3390_v12121463 crossref_primary_10_1109_TNB_2019_2922101 crossref_primary_10_1016_j_jbc_2021_100870 crossref_primary_10_7554_eLife_39340 crossref_primary_10_1016_j_bpj_2020_06_025 crossref_primary_10_1016_j_virol_2020_01_018 crossref_primary_10_3390_v11030231 crossref_primary_10_2174_1574893615999200504103643 crossref_primary_10_1002_pro_4606 crossref_primary_10_1021_acs_jctc_7b01079 crossref_primary_10_1142_S0219720018500051 crossref_primary_10_1007_s10930_020_09944_7 crossref_primary_10_2139_ssrn_3239970 crossref_primary_10_1007_s12033_023_00885_x crossref_primary_10_3390_axioms8040136 crossref_primary_10_1007_s00436_021_07190_z crossref_primary_10_1007_s00894_020_04490_6 crossref_primary_10_1016_j_molliq_2022_119321 crossref_primary_10_1080_07391102_2020_1860826 crossref_primary_10_1038_srep25138 crossref_primary_10_1016_j_celrep_2021_109567 crossref_primary_10_1016_j_jtbi_2017_05_011 crossref_primary_10_1016_j_molimm_2021_04_016 crossref_primary_10_1080_07391102_2020_1860827 crossref_primary_10_3762_bjoc_12_267 crossref_primary_10_4204_EPTCS_130_9 crossref_primary_10_3390_molecules23010216 crossref_primary_10_1093_nar_gkx366 crossref_primary_10_1109_TCBB_2016_2635143 crossref_primary_10_1021_acs_jcim_5b00246 crossref_primary_10_1093_bib_bbad217 crossref_primary_10_1016_j_asoc_2018_07_042 crossref_primary_10_1080_07391102_2020_1784287 crossref_primary_10_1080_07391102_2024_2308763 crossref_primary_10_3389_fmicb_2016_02080 crossref_primary_10_1134_S1990750819030053 crossref_primary_10_1002_prot_25186 crossref_primary_10_1007_s00894_019_4285_z crossref_primary_10_1002_pep2_24087 crossref_primary_10_3892_mmr_2017_8314 crossref_primary_10_1021_acs_chemrev_8b00731 crossref_primary_10_1016_j_celrep_2019_07_036 crossref_primary_10_1016_j_compbiolchem_2022_107773 crossref_primary_10_1093_bib_bbac045 crossref_primary_10_1371_journal_pcbi_1008954 crossref_primary_10_1016_j_bbamem_2018_10_005 crossref_primary_10_1002_1873_3468_13060 crossref_primary_10_1109_TCBB_2018_2873691 crossref_primary_10_1016_j_biochi_2013_06_027 crossref_primary_10_1016_j_bbapap_2017_06_003 crossref_primary_10_3390_ijms25158426 crossref_primary_10_1002_humu_23041 crossref_primary_10_3390_antibiotics11030363 crossref_primary_10_1093_bioinformatics_btx722 crossref_primary_10_1098_rspb_2018_1589 crossref_primary_10_1098_rsif_2013_1147 crossref_primary_10_1186_s13104_016_2149_5 crossref_primary_10_1021_acs_molpharmaceut_0c00312 crossref_primary_10_1186_1471_2105_14_62 |
| Cites_doi | 10.1093/nar/gkm251 10.1002/pro.5560031206 10.1126/science.181.4096.223 10.1126/science.1113801 10.1093/nar/28.1.235 10.1006/jmbi.1996.0720 10.1002/prot.22589 10.1103/PhysRevLett.57.2607 10.1002/prot.21702 10.1002/prot.10056 10.1107/S0567739476001873 10.1093/nar/gkg571 10.1145/63039.63042 10.1002/pro.5560010313 10.1006/jmbi.1993.1626 10.1110/ps.0242703 10.1529/biophysj.104.041541 10.1002/prot.20338 10.1016/j.str.2011.05.004 10.1002/prot.21767 10.1038/323533a0 10.1002/prot.23190 10.1073/pnas.0611593104 10.1016/S0006-3495(03)74551-2 10.1093/bioinformatics/btq066 10.1002/prot.20264 10.1093/bioinformatics/btg224 10.1002/prot.1170 10.1016/S0022-2836(05)80269-4 10.1093/nar/gki524 10.1002/jcc.20011 10.1093/nar/25.17.3389 10.1371/journal.pone.0008140 10.1006/jmbi.1997.0959 10.1002/(SICI)1097-0134(1999)37:3 <171::AID-PROT21>3.0.CO;2-Z 10.1038/nprot.2010.5 10.1006/jmbi.1994.1334 10.1002/prot.21945 10.1002/jcc.540040211 10.1093/bioinformatics/16.9.776 10.1002/bip.360221211 10.1006/jmbi.1999.3091 10.1002/pro.5560050204 10.1016/S0076-6879(04)83004-0 10.1073/pnas.0305695101 10.1016/j.sbi.2006.02.004 10.1021/jp065380a 10.1002/prot.23181 10.1002/(SICI)1097-0134(1999)37:3 <149::AID-PROT20>3.0.CO;2-H 10.1016/S0065-3233(08)60402-7 10.1002/prot.22538 10.1002/prot.22591 10.1063/1.1699114 10.1016/j.bpj.2011.10.024 10.1002/prot.21791 10.1110/ps.0217002 |
| ContentType | Journal Article |
| Copyright | Copyright © 2012 Wiley Periodicals, Inc. |
| Copyright_xml | – notice: Copyright © 2012 Wiley Periodicals, Inc. |
| DBID | BSCLL AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QL 7QO 7QP 7QR 7TK 7TM 7U9 8FD C1K FR3 H94 K9. M7N P64 RC3 7X8 5PM |
| DOI | 10.1002/prot.24065 |
| DatabaseName | Istex CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Neurosciences Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database AIDS and Cancer Research Abstracts ProQuest Health & Medical Complete (Alumni) Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Virology and AIDS Abstracts Technology Research Database Nucleic Acids Abstracts ProQuest Health & Medical Complete (Alumni) Neurosciences Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Genetics Abstracts Biotechnology Research Abstracts Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) AIDS and Cancer Research Abstracts Chemoreception Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts MEDLINE - Academic |
| DatabaseTitleList | Engineering Research Database MEDLINE Virology and AIDS Abstracts MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology Chemistry Biology |
| EISSN | 1097-0134 |
| EndPage | 1735 |
| ExternalDocumentID | PMC3370074 3278880201 22411565 10_1002_prot_24065 PROT24065 ark_67375_WNG_8KL70B9L_R |
| Genre | article Research Support, U.S. Gov't, Non-P.H.S Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: The NSF Career Award funderid: DBI 1027394 – fundername: The National Institute of General Medical Sciences funderid: GM083107; GM084222 – fundername: NIGMS NIH HHS grantid: GM083107 – fundername: NIGMS NIH HHS grantid: R01 GM083107 – fundername: NIGMS NIH HHS grantid: GM084222 – fundername: NIGMS NIH HHS grantid: R01 GM084222 |
| GroupedDBID | -~X .3N .GA .GJ .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 31~ 33P 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5RE 5VS 66C 6TJ 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AANLZ AAONW AASGY AAXRX AAZKR ABCQN ABCUV ABEML ABIJN ABLJU ACAHQ ACBWZ ACCFJ ACCZN ACFBH ACGFS ACIWK ACPOU ACPRK ACSCC ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADZMN AEEZP AEIGN AEIMD AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFRAH AFZJQ AHBTC AHMBA AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BLYAC BMNLL BNHUX BROTX BRXPI BSCLL BY8 CS3 D-E D-F D0L DCZOG DPXWK DR1 DR2 DRFUL DRSTM EBD EBS EJD EMOBN F00 F01 F04 F5P FA8 FEDTE G-S G.N GNP GODZA H.T H.X HBH HF~ HGLYW HHY HHZ HVGLF HZ~ IX1 JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LH6 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NDZJH NF~ NNB O66 O9- P2P P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QRW R.K RBB RIWAO RJQFR RNS ROL RWI RX1 SAMSI SUPJJ SV3 UB1 V2E W8V W99 WBFHL WBKPD WIB WIH WIK WJL WOHZO WQJ WRC WSB WXSBR WYISQ XG1 XPP XV2 ZGI ZXP ZZTAW ~IA ~WT AAHQN AAMNL AANHP AAYCA ACRPL ACYXJ ADNMO AFWVQ ALVPJ AAYXX AEYWJ AGHNM AGQPQ AGYGG CITATION CGR CUY CVF ECM EIF NPM 7QL 7QO 7QP 7QR 7TK 7TM 7U9 8FD C1K FR3 H94 K9. M7N P64 RC3 7X8 5PM |
| ID | FETCH-LOGICAL-c5855-3243f914eaa102b872e4eb8877af9aec5f6f5c88070d40d0ba8d8e76a401e2b43 |
| IEDL.DBID | DR2 |
| ISSN | 0887-3585 1097-0134 |
| IngestDate | Thu Aug 21 13:56:32 EDT 2025 Fri Jul 11 07:15:12 EDT 2025 Fri Jul 11 11:01:22 EDT 2025 Fri Jul 25 10:19:07 EDT 2025 Thu Apr 03 07:09:20 EDT 2025 Thu Apr 24 22:51:07 EDT 2025 Tue Jul 01 00:45:03 EDT 2025 Wed Jan 22 16:38:33 EST 2025 Wed Oct 30 10:05:04 EDT 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor Copyright © 2012 Wiley Periodicals, Inc. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c5855-3243f914eaa102b872e4eb8877af9aec5f6f5c88070d40d0ba8d8e76a401e2b43 |
| Notes | ArticleID:PROT24065 The NSF Career Award - No. DBI 1027394 ark:/67375/WNG-8KL70B9L-R The National Institute of General Medical Sciences - No. GM083107; No. GM084222 istex:C7EC21E364CFDA33330E4B2A96763F32E39B4E09 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/3370074 |
| PMID | 22411565 |
| PQID | 1517449953 |
| PQPubID | 1016441 |
| PageCount | 21 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_3370074 proquest_miscellaneous_1529944113 proquest_miscellaneous_1019614957 proquest_journals_1517449953 pubmed_primary_22411565 crossref_primary_10_1002_prot_24065 crossref_citationtrail_10_1002_prot_24065 wiley_primary_10_1002_prot_24065_PROT24065 istex_primary_ark_67375_WNG_8KL70B9L_R |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | July 2012 |
| PublicationDateYYYYMMDD | 2012-07-01 |
| PublicationDate_xml | – month: 07 year: 2012 text: July 2012 |
| PublicationDecade | 2010 |
| PublicationPlace | Hoboken |
| PublicationPlace_xml | – name: Hoboken – name: United States – name: Hokoben |
| PublicationTitle | Proteins, structure, function, and bioinformatics |
| PublicationTitleAlternate | Proteins |
| PublicationYear | 2012 |
| Publisher | Wiley Subscription Services, Inc., A Wiley Company Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc., A Wiley Company – name: Wiley Subscription Services, Inc |
| References | Wu S,Szilagyi A,Zhang Y. Improving protein structure prediction using multiple sequence-based contact predictions. Structure 2011; 19: 1182-1191. Kinch L,Yong Shi S,Cong Q,Cheng H,Liao Y,Grishin NV. CASP9 assessment of free modeling target predictions. Proteins 2011; 79: 59-73. Wu S,Zhang Y. MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 2008; 72: 547-556. Kabsch W. A solution for the best rotation to relate two sets of vectors. Acta Cryst 1976; 32A: 922-923. Rumelhart DE,Hinton GE,Williams RJ. Learning representations by back-propagating errors. Nature 1986; 323: 533-536. Ben-David M,Noivirt-Brik O,Paz A,Prilusky J,Sussman JL,Levy Y. Assessment of CASP8 structure predictions for template free targets. Proteins 2009; 77: 50-65. Skolnick J,Kolinski A,Ortiz AR. MONSSTER: a method for folding globular proteins with a small number of distance restraints. J Mol Biol 1997; 265: 217-241. Case DA,Pearlman DA,Caldwell JA,Cheatham TE,Ross WSea. AMBER 5.0. 1997; University of California, San Francisco, San Francisco. Skolnick J. In quest of an empirical potential for protein structure prediction. Curr Opin Struct Biol 2006; 16: 166-171. Summa CM,Levitt M. Near-native structure refinement using in vacuo energy minimization. Proc Natl Acad Sci USA 2007; 104: 3177-3182. Rohl CA,Strauss CE,Misura KM,Baker D. Protein structure prediction using Rosetta. Methods Enzymol 2004; 383: 66-93. Xu J,Zhang Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 2010; 26: 889-895. Zhang Y,Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004; 57: 702-710. Wang G,Dunbrack RL,Jr. PISCES: a protein sequence culling server. Bioinformatics 2003; 19: 1589-1591. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292: 195-202. Zhang Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 2007; 69: 108-117. Zemla A. LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res 2003; 31: 3370-3374. Zhang Y,Skolnick J. Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci USA 2004; 101: 7594-7599. Moult J,Fidelis K,Kryshtafovych A,Rost B,Tramontano A. Critical assessment of methods of protein structure prediction-Round VIII. Protein Struct Funct Bioinformatics 2009; 77: 1-4. Shackelford G,Karplus K. Contact prediction using mutual information and neural nets. Proteins 2007; 69: 159-164. Anfinsen CB. Principles that govern the folding of protein chains. Science 1973; 181: 223-230. Bradley P,Misura KM,Baker D. Toward high-resolution de novo structure prediction for small proteins. Science 2005; 309: 1868-1871. Wu S,Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res 2007; 35: 3375-3382. Kabsch W,Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983; 22: 2577-2637. Siew N,Elofsson A,Rychlewski L,Fischer D. MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics 2000; 16: 776-785. Liwo A,Khalili M,Czaplewski C,Kalinowski S,Oldziej S,Wachucik K,Scheraga HA. Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins. J Phys Chem B 2007; 111: 260-285. Sali A,Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993; 234: 779-815. Hobohm U,Scharf M,Schneider R,Sander C. Selection of representative protein data sets. Protein Sci 1992; 1: 409-417. MacCallum JL,Hua L,Schnieders MJ,Pande VS,Jacobson MP,Dill KA. Assessment of the protein-structure refinement category in CASP8. Proteins 2009; 77: 66-80. Swendsen RH,Wang JS. Replica Monte Carlo simulation of spin glasses. Phys Rev Lett 1986; 57: 2607-2609. Klepeis JL,Wei Y,Hecht MH,Floudas CA. Ab initio prediction of the three-dimensional structure of a de novo designed protein: a double-blind case study. Proteins 2005; 58: 560-570. Zhang Y,Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 2005; 33: 2302-2309. Xu D,Zhang Y. Generating triangulated macromolecular surfaces by Euclidean distance transform. PLoS One 2009; 4: e8140. Bonneau R,Tsai J,Ruczinski I,Chivian D,Rohl C,Strauss CE,Baker D. Rosetta in CASP4: progress in ab initio protein structure prediction. Proteins 2001; S5: 119-126. Hutchinson EG,Thornton JM. A revised set of potentials for beta-turn formation in proteins. Protein Sci 1994; 3: 2207-2216. Simons KT,Bonneau R,Ruczinski I,Baker D. Ab initio protein structure prediction of CASP III targets using ROSETTA. Proteins 1999; 37: 171-176. Berman HM,Westbrook J,Feng Z,Gilliland G,Bhat TN,Weissig H,Shindyalov IN,Bourne PE. The Protein Data Bank. Nucleic Acids Res 2000; 28: 235-242. Moult J,Fidelis K,Kryshtafovych A,Rost B,Hubbard T,Tramontano A. Critical assessment of methods of protein structure prediction-Round VII. Proteins 2007; 69: 3-9. Simons KT,Kooperberg C,Huang E,Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997; 268: 209-225. Zhou H,Zhou Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 2002; 11: 2714-2726. Wu S,Skolnick J,Zhang Y. Ab initio modeling of small proteins by iterative TASSER simulations. Biomed Chromatogr Biol 2007; 5: 17. Brooks BR,Bruccoleri RE,Olafson BD,States DJ,Swaminathan S,Karplus M. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 1983; 4: 187-217. Zhang Y,Skolnick J. SPICKER: A clustering approach to identify near-native protein folds. J Comput Chem 2004; 25: 865-871. McDonald IK,Thornton JM. Satisfying hydrogen bonding potential in proteins. J Mol Biol 1994; 238: 777-793. Kinch LN,Shi S,Cheng H,Cong Q,Pei J,Mariani V,Schwede T,Grishin NV. CASP9 target classification. Proteins 2011; 79: 21-36. Ramachandran GN,Sasisekharan V. Conformation of polypeptides and proteins. Adv Protein Chem 1968; 23: 283-438. Hutchinson EG,Thornton JM. PROMOTIF-a program to identify and analyze structural motifs in proteins. Protein Sci 1996; 5: 212-220. Orengo CA,Bray JE,Hubbard T,LoConte L,Sillitoe I. Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction. Proteins 1999; S3: 149-170. Roy A,Kucukural A,Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 2010; 5: 725-738. Sippl MJ. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J Mol Biol 1990; 213: 859-883. Park SK,Miller KW. Random number generators: good ones are hard to find. Commun ACM 1988; 31: 1192-1201. Canutescu AA,Dunbrack RL,Jr. Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci 2003; 12: 963-972. Lesk AM,Lo Conte L,Hubbard TJ. Assessment of novel fold targets in CASP4: predictions of three-dimensional structures, secondary structures, and interresidue contacts. Proteins 2001; S5: 98-118. Zhang Y,Kolinski A,Skolnick J. TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophys J 2003; 85: 1145-1164. Xu D,Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 2011; 101: 2525-2534. da Silva RA,Degreve L,Caliri A. LMProt: an efficient algorithm for Monte Carlo sampling of protein conformational space. Biophys J 2004; 87: 1567-1577. Metropolis N,Rosenbluth AW,Rosenbluth MN,Teller AH,Teller E. Equation of state calculations by fast computing machines. J Chem Phys 1953; 21: 1087-1092. Altschul SF,Madden TL,Schaffer AA,Zhang J,Zhang Z,Miller W,Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389-3402. 2007; 104 1968; 23 1973; 181 2004; 25 1999; 292 1983; 4 2002; 11 1976; 32A 2003; 19 1988; 31 2008; 72 2011; 19 2007; 35 2003; 12 1997; 268 2010; 26 1990; 213 2000; 16 1997; 265 2005; 309 2007; 5 1996; 5 2010; 5 2003; 85 1992; 1 2005; 33 2007; 69 1953; 21 1983; 22 2004; 101 2004; 87 1994; 238 2000; 28 2006; 16 2004; 383 1997; 25 1986; 57 1997 1999; S3 2011; 79 2001; S5 2003; 31 2009; 77 1986; 323 2007; 111 1999; 37 2004; 57 2009; 4 1993; 234 1994; 3 2011; 101 2005; 58 e_1_2_6_51_2 e_1_2_6_53_2 e_1_2_6_30_2 e_1_2_6_19_2 e_1_2_6_13_2 e_1_2_6_34_2 e_1_2_6_59_2 e_1_2_6_11_2 e_1_2_6_32_2 e_1_2_6_17_2 e_1_2_6_38_2 e_1_2_6_55_2 e_1_2_6_15_2 e_1_2_6_36_2 e_1_2_6_57_2 e_1_2_6_20_2 e_1_2_6_41_2 e_1_2_6_7_2 e_1_2_6_3_2 e_1_2_6_5_2 e_1_2_6_24_2 e_1_2_6_47_2 e_1_2_6_22_2 e_1_2_6_49_2 e_1_2_6_28_2 e_1_2_6_43_2 e_1_2_6_26_2 e_1_2_6_45_2 e_1_2_6_50_2 e_1_2_6_52_2 e_1_2_6_31_2 Case DA (e_1_2_6_9_2) 1997 e_1_2_6_18_2 e_1_2_6_12_2 e_1_2_6_35_2 e_1_2_6_58_2 e_1_2_6_10_2 e_1_2_6_33_2 e_1_2_6_16_2 e_1_2_6_39_2 e_1_2_6_54_2 e_1_2_6_14_2 e_1_2_6_37_2 e_1_2_6_56_2 e_1_2_6_42_2 e_1_2_6_40_2 e_1_2_6_8_2 e_1_2_6_29_2 e_1_2_6_4_2 Wu S (e_1_2_6_6_2) 2007; 5 e_1_2_6_23_2 e_1_2_6_48_2 e_1_2_6_2_2 e_1_2_6_21_2 e_1_2_6_27_2 e_1_2_6_44_2 e_1_2_6_25_2 e_1_2_6_46_2 4124164 - Science. 1973 Jul 20;181(4096):223-30 12381853 - Protein Sci. 2002 Nov;11(11):2714-26 17478507 - Nucleic Acids Res. 2007;35(10):3375-82 17932918 - Proteins. 2007;69 Suppl 8:159-64 17918729 - Proteins. 2007;69 Suppl 8:3-9 19774550 - Proteins. 2009;77 Suppl 9:50-65 16166519 - Science. 2005 Sep 16;309(5742):1868-71 17360625 - Proc Natl Acad Sci U S A. 2007 Feb 27;104(9):3177-82 6667333 - Biopolymers. 1983 Dec;22(12):2577-637 11108700 - Bioinformatics. 2000 Sep;16(9):776-85 9149153 - J Mol Biol. 1997 Apr 25;268(1):209-25 12717019 - Protein Sci. 2003 May;12(5):963-72 10526364 - Proteins. 1999;Suppl 3:149-70 9254694 - Nucleic Acids Res. 1997 Sep 1;25(17):3389-402 12912846 - Bioinformatics. 2003 Aug 12;19(12):1589-91 16524716 - Curr Opin Struct Biol. 2006 Apr;16(2):166-71 15063647 - Methods Enzymol. 2004;383:66-93 12885659 - Biophys J. 2003 Aug;85(2):1145-64 15609306 - Proteins. 2005 Feb 15;58(3):560-70 2359125 - J Mol Biol. 1990 Jun 20;213(4):859-83 19774620 - Proteins. 2009;77 Suppl 9:1-4 9020984 - J Mol Biol. 1997 Jan 17;265(2):217-41 20164152 - Bioinformatics. 2010 Apr 1;26(7):889-95 19956577 - PLoS One. 2009;4(12):e8140 15011258 - J Comput Chem. 2004 Apr 30;25(6):865-71 11835487 - Proteins. 2001;Suppl 5:98-118 7756980 - Protein Sci. 1994 Dec;3(12):2207-16 10493868 - J Mol Biol. 1999 Sep 17;292(2):195-202 10526365 - Proteins. 1999;Suppl 3:171-6 17894355 - Proteins. 2007;69 Suppl 8:108-17 17201450 - J Phys Chem B. 2007 Jan 11;111(1):260-85 11835488 - Proteins. 2001;Suppl 5:119-26 8182748 - J Mol Biol. 1994 May 20;238(5):777-93 4882249 - Adv Protein Chem. 1968;23:283-438 20360767 - Nat Protoc. 2010 Apr;5(4):725-38 1304348 - Protein Sci. 1992 Mar;1(3):409-17 10033814 - Phys Rev Lett. 1986 Nov 24;57(21):2607-2609 12824330 - Nucleic Acids Res. 2003 Jul 1;31(13):3370-4 15849316 - Nucleic Acids Res. 2005;33(7):2302-9 22098752 - Biophys J. 2011 Nov 16;101(10):2525-34 17488521 - BMC Biol. 2007;5:17 21997778 - Proteins. 2011;79 Suppl 10:21-36 19714776 - Proteins. 2009;77 Suppl 9:66-80 15476259 - Proteins. 2004 Dec 1;57(4):702-10 21827953 - Structure. 2011 Aug 10;19(8):1182-91 8254673 - J Mol Biol. 1993 Dec 5;234(3):779-815 21997521 - Proteins. 2011;79 Suppl 10:59-73 10592235 - Nucleic Acids Res. 2000 Jan 1;28(1):235-42 8745398 - Protein Sci. 1996 Feb;5(2):212-20 18247410 - Proteins. 2008 Aug;72(2):547-56 15345537 - Biophys J. 2004 Sep;87(3):1567-77 15126668 - Proc Natl Acad Sci U S A. 2004 May 18;101(20):7594-9 |
| References_xml | – reference: Kabsch W. A solution for the best rotation to relate two sets of vectors. Acta Cryst 1976; 32A: 922-923. – reference: Zhang Y,Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 2005; 33: 2302-2309. – reference: Wu S,Skolnick J,Zhang Y. Ab initio modeling of small proteins by iterative TASSER simulations. Biomed Chromatogr Biol 2007; 5: 17. – reference: Kinch L,Yong Shi S,Cong Q,Cheng H,Liao Y,Grishin NV. CASP9 assessment of free modeling target predictions. Proteins 2011; 79: 59-73. – reference: Siew N,Elofsson A,Rychlewski L,Fischer D. MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics 2000; 16: 776-785. – reference: Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292: 195-202. – reference: Wu S,Szilagyi A,Zhang Y. Improving protein structure prediction using multiple sequence-based contact predictions. Structure 2011; 19: 1182-1191. – reference: Brooks BR,Bruccoleri RE,Olafson BD,States DJ,Swaminathan S,Karplus M. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 1983; 4: 187-217. – reference: Skolnick J,Kolinski A,Ortiz AR. MONSSTER: a method for folding globular proteins with a small number of distance restraints. J Mol Biol 1997; 265: 217-241. – reference: Rohl CA,Strauss CE,Misura KM,Baker D. Protein structure prediction using Rosetta. Methods Enzymol 2004; 383: 66-93. – reference: Anfinsen CB. Principles that govern the folding of protein chains. Science 1973; 181: 223-230. – reference: Skolnick J. In quest of an empirical potential for protein structure prediction. Curr Opin Struct Biol 2006; 16: 166-171. – reference: Zhang Y,Kolinski A,Skolnick J. TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophys J 2003; 85: 1145-1164. – reference: Canutescu AA,Dunbrack RL,Jr. Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci 2003; 12: 963-972. – reference: Kinch LN,Shi S,Cheng H,Cong Q,Pei J,Mariani V,Schwede T,Grishin NV. CASP9 target classification. Proteins 2011; 79: 21-36. – reference: Simons KT,Kooperberg C,Huang E,Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997; 268: 209-225. – reference: Berman HM,Westbrook J,Feng Z,Gilliland G,Bhat TN,Weissig H,Shindyalov IN,Bourne PE. The Protein Data Bank. Nucleic Acids Res 2000; 28: 235-242. – reference: McDonald IK,Thornton JM. Satisfying hydrogen bonding potential in proteins. J Mol Biol 1994; 238: 777-793. – reference: Ben-David M,Noivirt-Brik O,Paz A,Prilusky J,Sussman JL,Levy Y. Assessment of CASP8 structure predictions for template free targets. Proteins 2009; 77: 50-65. – reference: Swendsen RH,Wang JS. Replica Monte Carlo simulation of spin glasses. Phys Rev Lett 1986; 57: 2607-2609. – reference: Sali A,Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993; 234: 779-815. – reference: Moult J,Fidelis K,Kryshtafovych A,Rost B,Tramontano A. Critical assessment of methods of protein structure prediction-Round VIII. Protein Struct Funct Bioinformatics 2009; 77: 1-4. – reference: Bonneau R,Tsai J,Ruczinski I,Chivian D,Rohl C,Strauss CE,Baker D. Rosetta in CASP4: progress in ab initio protein structure prediction. Proteins 2001; S5: 119-126. – reference: Shackelford G,Karplus K. Contact prediction using mutual information and neural nets. Proteins 2007; 69: 159-164. – reference: Lesk AM,Lo Conte L,Hubbard TJ. Assessment of novel fold targets in CASP4: predictions of three-dimensional structures, secondary structures, and interresidue contacts. Proteins 2001; S5: 98-118. – reference: Roy A,Kucukural A,Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 2010; 5: 725-738. – reference: MacCallum JL,Hua L,Schnieders MJ,Pande VS,Jacobson MP,Dill KA. Assessment of the protein-structure refinement category in CASP8. Proteins 2009; 77: 66-80. – reference: Zemla A. LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res 2003; 31: 3370-3374. – reference: Summa CM,Levitt M. Near-native structure refinement using in vacuo energy minimization. Proc Natl Acad Sci USA 2007; 104: 3177-3182. – reference: Orengo CA,Bray JE,Hubbard T,LoConte L,Sillitoe I. Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction. Proteins 1999; S3: 149-170. – reference: Zhang Y,Skolnick J. SPICKER: A clustering approach to identify near-native protein folds. J Comput Chem 2004; 25: 865-871. – reference: Zhang Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 2007; 69: 108-117. – reference: Hutchinson EG,Thornton JM. A revised set of potentials for beta-turn formation in proteins. Protein Sci 1994; 3: 2207-2216. – reference: Wu S,Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res 2007; 35: 3375-3382. – reference: Simons KT,Bonneau R,Ruczinski I,Baker D. Ab initio protein structure prediction of CASP III targets using ROSETTA. Proteins 1999; 37: 171-176. – reference: Liwo A,Khalili M,Czaplewski C,Kalinowski S,Oldziej S,Wachucik K,Scheraga HA. Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins. J Phys Chem B 2007; 111: 260-285. – reference: Case DA,Pearlman DA,Caldwell JA,Cheatham TE,Ross WSea. AMBER 5.0. 1997; University of California, San Francisco, San Francisco. – reference: Klepeis JL,Wei Y,Hecht MH,Floudas CA. Ab initio prediction of the three-dimensional structure of a de novo designed protein: a double-blind case study. Proteins 2005; 58: 560-570. – reference: da Silva RA,Degreve L,Caliri A. LMProt: an efficient algorithm for Monte Carlo sampling of protein conformational space. Biophys J 2004; 87: 1567-1577. – reference: Hutchinson EG,Thornton JM. PROMOTIF-a program to identify and analyze structural motifs in proteins. Protein Sci 1996; 5: 212-220. – reference: Zhang Y,Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004; 57: 702-710. – reference: Bradley P,Misura KM,Baker D. Toward high-resolution de novo structure prediction for small proteins. Science 2005; 309: 1868-1871. – reference: Moult J,Fidelis K,Kryshtafovych A,Rost B,Hubbard T,Tramontano A. Critical assessment of methods of protein structure prediction-Round VII. Proteins 2007; 69: 3-9. – reference: Ramachandran GN,Sasisekharan V. Conformation of polypeptides and proteins. Adv Protein Chem 1968; 23: 283-438. – reference: Sippl MJ. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J Mol Biol 1990; 213: 859-883. – reference: Park SK,Miller KW. Random number generators: good ones are hard to find. Commun ACM 1988; 31: 1192-1201. – reference: Kabsch W,Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983; 22: 2577-2637. – reference: Zhang Y,Skolnick J. Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci USA 2004; 101: 7594-7599. – reference: Metropolis N,Rosenbluth AW,Rosenbluth MN,Teller AH,Teller E. Equation of state calculations by fast computing machines. J Chem Phys 1953; 21: 1087-1092. – reference: Xu J,Zhang Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 2010; 26: 889-895. – reference: Altschul SF,Madden TL,Schaffer AA,Zhang J,Zhang Z,Miller W,Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389-3402. – reference: Rumelhart DE,Hinton GE,Williams RJ. Learning representations by back-propagating errors. Nature 1986; 323: 533-536. – reference: Zhou H,Zhou Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 2002; 11: 2714-2726. – reference: Xu D,Zhang Y. Generating triangulated macromolecular surfaces by Euclidean distance transform. PLoS One 2009; 4: e8140. – reference: Hobohm U,Scharf M,Schneider R,Sander C. Selection of representative protein data sets. Protein Sci 1992; 1: 409-417. – reference: Xu D,Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 2011; 101: 2525-2534. – reference: Wang G,Dunbrack RL,Jr. PISCES: a protein sequence culling server. Bioinformatics 2003; 19: 1589-1591. – reference: Wu S,Zhang Y. MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 2008; 72: 547-556. – volume: 57 start-page: 2607 year: 1986 end-page: 2609 article-title: Replica Monte Carlo simulation of spin glasses publication-title: Phys Rev Lett – volume: 69 start-page: 108 year: 2007 end-page: 117 article-title: Template‐based modeling and free modeling by I‐TASSER in CASP7 publication-title: Proteins – volume: 85 start-page: 1145 year: 2003 end-page: 1164 article-title: TOUCHSTONE II: a new approach to ab initio protein structure prediction publication-title: Biophys J – volume: 101 start-page: 2525 year: 2011 end-page: 2534 article-title: Improving the physical realism and structural accuracy of protein models by a two‐step atomic‐level energy minimization publication-title: Biophys J – volume: 31 start-page: 1192 year: 1988 end-page: 1201 article-title: Random number generators: good ones are hard to find publication-title: Commun ACM – volume: 16 start-page: 776 year: 2000 end-page: 785 article-title: MaxSub: an automated measure for the assessment of protein structure prediction quality publication-title: Bioinformatics – volume: 79 start-page: 21 year: 2011 end-page: 36 article-title: CASP9 target classification publication-title: Proteins – volume: 101 start-page: 7594 year: 2004 end-page: 7599 article-title: Automated structure prediction of weakly homologous proteins on a genomic scale publication-title: Proc Natl Acad Sci USA – volume: 77 start-page: 1 year: 2009 end-page: 4 article-title: Critical assessment of methods of protein structure prediction‐Round VIII publication-title: Protein Struct Funct Bioinformatics – volume: 19 start-page: 1182 year: 2011 end-page: 1191 article-title: Improving protein structure prediction using multiple sequence‐based contact predictions publication-title: Structure – volume: 111 start-page: 260 year: 2007 end-page: 285 article-title: Modification and optimization of the united‐residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins publication-title: J Phys Chem B – volume: 57 start-page: 702 year: 2004 end-page: 710 article-title: Scoring function for automated assessment of protein structure template quality publication-title: Proteins – volume: 4 start-page: 187 year: 1983 end-page: 217 article-title: CHARMM: a program for macromolecular energy, minimization, and dynamics calculations publication-title: J Comput Chem – volume: 26 start-page: 889 year: 2010 end-page: 895 article-title: How significant is a protein structure similarity with TM‐score = 0.5? publication-title: Bioinformatics – volume: 268 start-page: 209 year: 1997 end-page: 225 article-title: Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions publication-title: J Mol Biol – volume: 383 start-page: 66 year: 2004 end-page: 93 article-title: Protein structure prediction using Rosetta publication-title: Methods Enzymol – volume: 31 start-page: 3370 year: 2003 end-page: 3374 article-title: LGA: A method for finding 3D similarities in protein structures publication-title: Nucleic Acids Res – volume: 21 start-page: 1087 year: 1953 end-page: 1092 article-title: Equation of state calculations by fast computing machines publication-title: J Chem Phys – volume: 292 start-page: 195 year: 1999 end-page: 202 article-title: Protein secondary structure prediction based on position‐specific scoring matrices publication-title: J Mol Biol – year: 1997 – volume: 37 start-page: 171 year: 1999 end-page: 176 article-title: Ab initio protein structure prediction of CASP III targets using ROSETTA publication-title: Proteins – volume: 234 start-page: 779 year: 1993 end-page: 815 article-title: Comparative protein modelling by satisfaction of spatial restraints publication-title: J Mol Biol – volume: 69 start-page: 159 year: 2007 end-page: 164 article-title: Contact prediction using mutual information and neural nets publication-title: Proteins – volume: 5 start-page: 212 year: 1996 end-page: 220 article-title: PROMOTIF—a program to identify and analyze structural motifs in proteins publication-title: Protein Sci – volume: 77 start-page: 66 year: 2009 end-page: 80 article-title: Assessment of the protein‐structure refinement category in CASP8 publication-title: Proteins – volume: 58 start-page: 560 year: 2005 end-page: 570 article-title: Ab initio prediction of the three‐dimensional structure of a de novo designed protein: a double‐blind case study publication-title: Proteins – volume: 25 start-page: 865 year: 2004 end-page: 871 article-title: SPICKER: A clustering approach to identify near‐native protein folds publication-title: J Comput Chem – volume: 79 start-page: 59 year: 2011 end-page: 73 article-title: CASP9 assessment of free modeling target predictions publication-title: Proteins – volume: 35 start-page: 3375 year: 2007 end-page: 3382 article-title: LOMETS: a local meta‐threading‐server for protein structure prediction publication-title: Nucleic Acids Res – volume: 28 start-page: 235 year: 2000 end-page: 242 article-title: The Protein Data Bank publication-title: Nucleic Acids Res – volume: 1 start-page: 409 year: 1992 end-page: 417 article-title: Selection of representative protein data sets publication-title: Protein Sci – volume: 213 start-page: 859 year: 1990 end-page: 883 article-title: Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge‐based prediction of local structures in globular proteins publication-title: J Mol Biol – volume: 22 start-page: 2577 year: 1983 end-page: 2637 article-title: Dictionary of protein secondary structure: pattern recognition of hydrogen‐bonded and geometrical features publication-title: Biopolymers – volume: 16 start-page: 166 year: 2006 end-page: 171 article-title: In quest of an empirical potential for protein structure prediction publication-title: Curr Opin Struct Biol – volume: 11 start-page: 2714 year: 2002 end-page: 2726 article-title: Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction publication-title: Protein Sci – volume: 323 start-page: 533 year: 1986 end-page: 536 article-title: Learning representations by back‐propagating errors publication-title: Nature – volume: 5 start-page: 17 year: 2007 article-title: Ab initio modeling of small proteins by iterative TASSER simulations publication-title: Biomed Chromatogr Biol – volume: 77 start-page: 50 year: 2009 end-page: 65 article-title: Assessment of CASP8 structure predictions for template free targets publication-title: Proteins – volume: 5 start-page: 725 year: 2010 end-page: 738 article-title: I‐TASSER: a unified platform for automated protein structure and function prediction publication-title: Nat Protoc – volume: 104 start-page: 3177 year: 2007 end-page: 3182 article-title: Near‐native structure refinement using in vacuo energy minimization publication-title: Proc Natl Acad Sci USA – volume: 87 start-page: 1567 year: 2004 end-page: 1577 article-title: LMProt: an efficient algorithm for Monte Carlo sampling of protein conformational space publication-title: Biophys J – volume: 3 start-page: 2207 year: 1994 end-page: 2216 article-title: A revised set of potentials for beta‐turn formation in proteins publication-title: Protein Sci – volume: 25 start-page: 3389 year: 1997 end-page: 3402 article-title: Gapped BLAST and PSI‐BLAST: a new generation of protein database search programs publication-title: Nucleic Acids Res – volume: S5 start-page: 98 year: 2001 end-page: 118 article-title: Assessment of novel fold targets in CASP4: predictions of three‐dimensional structures, secondary structures, and interresidue contacts publication-title: Proteins – volume: 238 start-page: 777 year: 1994 end-page: 793 article-title: Satisfying hydrogen bonding potential in proteins publication-title: J Mol Biol – volume: 69 start-page: 3 year: 2007 end-page: 9 article-title: Critical assessment of methods of protein structure prediction‐Round VII publication-title: Proteins – volume: 33 start-page: 2302 year: 2005 end-page: 2309 article-title: TM‐align: a protein structure alignment algorithm based on the TM‐score publication-title: Nucleic Acids Res – volume: 12 start-page: 963 year: 2003 end-page: 972 article-title: Cyclic coordinate descent: a robotics algorithm for protein loop closure publication-title: Protein Sci – volume: 265 start-page: 217 year: 1997 end-page: 241 article-title: MONSSTER: a method for folding globular proteins with a small number of distance restraints publication-title: J Mol Biol – volume: 181 start-page: 223 year: 1973 end-page: 230 article-title: Principles that govern the folding of protein chains publication-title: Science – volume: 23 start-page: 283 year: 1968 end-page: 438 article-title: Conformation of polypeptides and proteins publication-title: Adv Protein Chem – volume: S3 start-page: 149 year: 1999 end-page: 170 article-title: Analysis and assessment of ab initio three‐dimensional prediction, secondary structure, and contacts prediction publication-title: Proteins – volume: 19 start-page: 1589 year: 2003 end-page: 1591 article-title: PISCES: a protein sequence culling server publication-title: Bioinformatics – volume: S5 start-page: 119 year: 2001 end-page: 126 article-title: Rosetta in CASP4: progress in ab initio protein structure prediction publication-title: Proteins – volume: 72 start-page: 547 year: 2008 end-page: 556 article-title: MUSTER: Improving protein sequence profile‐profile alignments by using multiple sources of structure information publication-title: Proteins – volume: 309 start-page: 1868 year: 2005 end-page: 1871 article-title: Toward high‐resolution de novo structure prediction for small proteins publication-title: Science – volume: 4 start-page: e8140 year: 2009 article-title: Generating triangulated macromolecular surfaces by Euclidean distance transform publication-title: PLoS One – volume: 32A start-page: 922 year: 1976 end-page: 923 article-title: A solution for the best rotation to relate two sets of vectors publication-title: Acta Cryst – ident: e_1_2_6_23_2 doi: 10.1093/nar/gkm251 – ident: e_1_2_6_41_2 doi: 10.1002/pro.5560031206 – ident: e_1_2_6_14_2 doi: 10.1126/science.181.4096.223 – ident: e_1_2_6_4_2 doi: 10.1126/science.1113801 – ident: e_1_2_6_10_2 doi: 10.1093/nar/28.1.235 – ident: e_1_2_6_57_2 doi: 10.1006/jmbi.1996.0720 – ident: e_1_2_6_20_2 doi: 10.1002/prot.22589 – ident: e_1_2_6_38_2 doi: 10.1103/PhysRevLett.57.2607 – volume-title: AMBER 5.0 year: 1997 ident: e_1_2_6_9_2 – ident: e_1_2_6_17_2 doi: 10.1002/prot.21702 – ident: e_1_2_6_55_2 doi: 10.1002/prot.10056 – ident: e_1_2_6_48_2 doi: 10.1107/S0567739476001873 – ident: e_1_2_6_49_2 doi: 10.1093/nar/gkg571 – ident: e_1_2_6_29_2 doi: 10.1145/63039.63042 – ident: e_1_2_6_35_2 doi: 10.1002/pro.5560010313 – ident: e_1_2_6_56_2 doi: 10.1006/jmbi.1993.1626 – ident: e_1_2_6_39_2 doi: 10.1110/ps.0242703 – ident: e_1_2_6_40_2 doi: 10.1529/biophysj.104.041541 – ident: e_1_2_6_7_2 doi: 10.1002/prot.20338 – ident: e_1_2_6_59_2 doi: 10.1016/j.str.2011.05.004 – ident: e_1_2_6_21_2 doi: 10.1002/prot.21767 – ident: e_1_2_6_27_2 doi: 10.1038/323533a0 – ident: e_1_2_6_53_2 doi: 10.1002/prot.23190 – ident: e_1_2_6_11_2 doi: 10.1073/pnas.0611593104 – ident: e_1_2_6_5_2 doi: 10.1016/S0006-3495(03)74551-2 – ident: e_1_2_6_52_2 doi: 10.1093/bioinformatics/btq066 – ident: e_1_2_6_30_2 doi: 10.1002/prot.20264 – ident: e_1_2_6_22_2 doi: 10.1093/bioinformatics/btg224 – ident: e_1_2_6_46_2 doi: 10.1002/prot.1170 – ident: e_1_2_6_32_2 doi: 10.1016/S0022-2836(05)80269-4 – ident: e_1_2_6_24_2 doi: 10.1093/nar/gki524 – ident: e_1_2_6_31_2 doi: 10.1002/jcc.20011 – ident: e_1_2_6_26_2 doi: 10.1093/nar/25.17.3389 – ident: e_1_2_6_36_2 doi: 10.1371/journal.pone.0008140 – ident: e_1_2_6_15_2 doi: 10.1006/jmbi.1997.0959 – ident: e_1_2_6_45_2 doi: 10.1002/(SICI)1097-0134(1999)37:3 <171::AID-PROT21>3.0.CO;2-Z – ident: e_1_2_6_19_2 doi: 10.1038/nprot.2010.5 – ident: e_1_2_6_51_2 doi: 10.1006/jmbi.1994.1334 – ident: e_1_2_6_28_2 doi: 10.1002/prot.21945 – ident: e_1_2_6_8_2 doi: 10.1002/jcc.540040211 – ident: e_1_2_6_50_2 doi: 10.1093/bioinformatics/16.9.776 – ident: e_1_2_6_34_2 doi: 10.1002/bip.360221211 – ident: e_1_2_6_47_2 doi: 10.1006/jmbi.1999.3091 – ident: e_1_2_6_42_2 doi: 10.1002/pro.5560050204 – ident: e_1_2_6_44_2 doi: 10.1016/S0076-6879(04)83004-0 – ident: e_1_2_6_16_2 doi: 10.1073/pnas.0305695101 – ident: e_1_2_6_13_2 doi: 10.1016/j.sbi.2006.02.004 – ident: e_1_2_6_18_2 doi: 10.1021/jp065380a – ident: e_1_2_6_3_2 doi: 10.1002/prot.23181 – ident: e_1_2_6_54_2 doi: 10.1002/(SICI)1097-0134(1999)37:3 <149::AID-PROT20>3.0.CO;2-H – ident: e_1_2_6_37_2 doi: 10.1016/S0065-3233(08)60402-7 – ident: e_1_2_6_12_2 doi: 10.1002/prot.22538 – volume: 5 start-page: 17 year: 2007 ident: e_1_2_6_6_2 article-title: Ab initio modeling of small proteins by iterative TASSER simulations publication-title: Biomed Chromatogr Biol – ident: e_1_2_6_2_2 doi: 10.1002/prot.22591 – ident: e_1_2_6_43_2 doi: 10.1063/1.1699114 – ident: e_1_2_6_25_2 doi: 10.1016/j.bpj.2011.10.024 – ident: e_1_2_6_58_2 doi: 10.1002/prot.21791 – ident: e_1_2_6_33_2 doi: 10.1110/ps.0217002 – reference: 4124164 - Science. 1973 Jul 20;181(4096):223-30 – reference: 17360625 - Proc Natl Acad Sci U S A. 2007 Feb 27;104(9):3177-82 – reference: 20360767 - Nat Protoc. 2010 Apr;5(4):725-38 – reference: 1304348 - Protein Sci. 1992 Mar;1(3):409-17 – reference: 8254673 - J Mol Biol. 1993 Dec 5;234(3):779-815 – reference: 2359125 - J Mol Biol. 1990 Jun 20;213(4):859-83 – reference: 12885659 - Biophys J. 2003 Aug;85(2):1145-64 – reference: 21827953 - Structure. 2011 Aug 10;19(8):1182-91 – reference: 22098752 - Biophys J. 2011 Nov 16;101(10):2525-34 – reference: 11108700 - Bioinformatics. 2000 Sep;16(9):776-85 – reference: 7756980 - Protein Sci. 1994 Dec;3(12):2207-16 – reference: 8182748 - J Mol Biol. 1994 May 20;238(5):777-93 – reference: 19774550 - Proteins. 2009;77 Suppl 9:50-65 – reference: 18247410 - Proteins. 2008 Aug;72(2):547-56 – reference: 19956577 - PLoS One. 2009;4(12):e8140 – reference: 19714776 - Proteins. 2009;77 Suppl 9:66-80 – reference: 15476259 - Proteins. 2004 Dec 1;57(4):702-10 – reference: 12381853 - Protein Sci. 2002 Nov;11(11):2714-26 – reference: 10526365 - Proteins. 1999;Suppl 3:171-6 – reference: 21997778 - Proteins. 2011;79 Suppl 10:21-36 – reference: 10493868 - J Mol Biol. 1999 Sep 17;292(2):195-202 – reference: 20164152 - Bioinformatics. 2010 Apr 1;26(7):889-95 – reference: 21997521 - Proteins. 2011;79 Suppl 10:59-73 – reference: 11835487 - Proteins. 2001;Suppl 5:98-118 – reference: 15126668 - Proc Natl Acad Sci U S A. 2004 May 18;101(20):7594-9 – reference: 10526364 - Proteins. 1999;Suppl 3:149-70 – reference: 4882249 - Adv Protein Chem. 1968;23:283-438 – reference: 17478507 - Nucleic Acids Res. 2007;35(10):3375-82 – reference: 15063647 - Methods Enzymol. 2004;383:66-93 – reference: 17918729 - Proteins. 2007;69 Suppl 8:3-9 – reference: 15609306 - Proteins. 2005 Feb 15;58(3):560-70 – reference: 17201450 - J Phys Chem B. 2007 Jan 11;111(1):260-85 – reference: 10033814 - Phys Rev Lett. 1986 Nov 24;57(21):2607-2609 – reference: 12912846 - Bioinformatics. 2003 Aug 12;19(12):1589-91 – reference: 9149153 - J Mol Biol. 1997 Apr 25;268(1):209-25 – reference: 15849316 - Nucleic Acids Res. 2005;33(7):2302-9 – reference: 12717019 - Protein Sci. 2003 May;12(5):963-72 – reference: 10592235 - Nucleic Acids Res. 2000 Jan 1;28(1):235-42 – reference: 17932918 - Proteins. 2007;69 Suppl 8:159-64 – reference: 8745398 - Protein Sci. 1996 Feb;5(2):212-20 – reference: 16524716 - Curr Opin Struct Biol. 2006 Apr;16(2):166-71 – reference: 16166519 - Science. 2005 Sep 16;309(5742):1868-71 – reference: 11835488 - Proteins. 2001;Suppl 5:119-26 – reference: 6667333 - Biopolymers. 1983 Dec;22(12):2577-637 – reference: 19774620 - Proteins. 2009;77 Suppl 9:1-4 – reference: 12824330 - Nucleic Acids Res. 2003 Jul 1;31(13):3370-4 – reference: 17894355 - Proteins. 2007;69 Suppl 8:108-17 – reference: 9020984 - J Mol Biol. 1997 Jan 17;265(2):217-41 – reference: 17488521 - BMC Biol. 2007;5:17 – reference: 9254694 - Nucleic Acids Res. 1997 Sep 1;25(17):3389-402 – reference: 15011258 - J Comput Chem. 2004 Apr 30;25(6):865-71 – reference: 15345537 - Biophys J. 2004 Sep;87(3):1567-77 |
| SSID | ssj0006936 |
| Score | 2.5864947 |
| Snippet | Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational... Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational... Ab initio protein folding is one of the major unsolved problems in computational biology due to the difficulties in force field design and conformational... |
| SourceID | pubmedcentral proquest pubmed crossref wiley istex |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1715 |
| SubjectTerms | Computer Simulation Databases, Protein Hydrogen Bonding Models, Chemical Models, Molecular Monte Carlo Method Monte Carlo simulation Protein Conformation Protein Folding protein structure prediction Proteins - chemistry Sequence Alignment - methods solvent accessibility Solvents - chemistry statistical potential |
| Title | Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field |
| URI | https://api.istex.fr/ark:/67375/WNG-8KL70B9L-R/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fprot.24065 https://www.ncbi.nlm.nih.gov/pubmed/22411565 https://www.proquest.com/docview/1517449953 https://www.proquest.com/docview/1019614957 https://www.proquest.com/docview/1529944113 https://pubmed.ncbi.nlm.nih.gov/PMC3370074 |
| Volume | 80 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1db9MwFLWmIQQvfGx8BAYyAk0CKZ2bxHEs8dJNjAnGQNUm9oIs23FGtDVF7SKxPfET-I38Eu51mnSFaRK8RcpplVxf2-c6x8eEvIgYFNKCyTCXmQsTY3hosiQKUya1zLmNM4e7kT_spTsHybtDfrhEXrd7YRp_iG7BDXuGH6-xg2sz3ZibhqKPQQ_nI9xhjmItZETDuXdUKv35gE0vAlLceZNGG_OfLsxG1zCw3y-jmn8rJi8yWT8Vbd8mX9qXaBQox7361PTs-R_-jv_7lnfIrRlHpYMmqe6SJVetkNVBBfX56IyuU68a9cvxK-T6Znt1Y6s9O26V1ANDS5Qljak3gigr2hjV1hNHga67kTk5o6i5P6Ioli-relxPL2CKiT7ym--ornI6hnFtVJ67nHZLgL9-_MQJOKdAui3gUYh3jxxsv9nf2glnBzyEFhqEh0Dm4kL2E6c18ByTicglzkCDCV1I7Swv0oJbGGEEyxOWM6OzPHMi1VAUusgk8X2yXI0r95BQHjNjdVpAfQnQOM_SxAK5SVjSt5IZE5CXbUMrO3M_x0M4TlTj2xwpDIfykQ7I8w77rfH8uBS17vOlg-jJMarkBFef996q7P2uYJtyVw0DstYmlJoNEFPVR4dwqDZ5HJBn3W1oJfxeoysHQUf1nUyxghVXYDjwCaC0ffifB02Odg-E7AzKc3hSsZC9HQCtxRfvVOVXbzEexwLJZUBe-eS8Igzq0_Djvr969C_gx-QmUM-Z8HmNLEN-uSdA707NU9-NfwNNs0-3 |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtNAEB6VVqhc-Gn5CRRYBFQCyalje_1z4JC2lJSkAUWp6G3ZtdclauOg_AjaE4_Ai_AqPARPwsw6dhqoKnHogZslj-zV7MzuN7sz3wA8c2wMpAM7spIo1JanFLdU6DmWb0cySnjshpqqkffafmPfe3vADxbgR1ELk_NDlAdu5BlmvSYHpwPpjRlrKBEZVGlDKnIqm_rkC0Zso1e72zi9zx1n53V3q2FNmwpYMSJjbiGAcNOo5mkpcW9VYeBoTyt0tUCmkdQxT_2Ux2jVgZ14dmIrGSahDnyJgYh2lOfid6_AErUQJ6r-7c6MrcqPTEfC3G_xZyUbqrMxG-vc_rdEU_n1PHD7d47mWexsNr-dG_CzUFue83JUnYxVNT79g1Hyv9HrTbg-heGsnvvNLVjQ2Qqs1jM5HvRP2DozibHmxmEFrm4WT8tbRXu8VZjUFetR5tWAGa6LXsZyLt7JUDOMSHRfHZ8wKis4ZFQP0Msmg8nojEw6lIemvpDJLGEDXLr7vVOdsPKU89e374QxEoZxRYzylGt4G_YvRS13YDEbZPoeMO7aKpZ-iiE0irpJ6Hsx4jfP9mpxZCtVgReFZYl4SvBOfUaORU5N7QhShzAzW4GnpeznnNbkXKl1Y6CliBweUSJgwMWH9hsRNluBvRm1RKcCa4UFi-kaOBI1IkHHgJq7FXhSvsZZoispmWlUOiUYRj4F6cEFMhwhE6L2Gn7nbu4U5YAIgNY4jTSYc5dSgNjT599kvU-GRd11A8LPFXhpvOECNYj3nXdd83T_X4Qfw3Kju9cSrd128wFcQ6Q9zfNeg0W0Nf0Q0exYPTJrCIOPl-1cvwG80q2Q |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VVjwuPFoegQKLgEpFcurHrh8HDmlDaEkIVdSK3pZde12iNk6VhyA98RP4IfwV_gS_hJl17DRQVeLQAzdLHtnr2Zndb9Yz3xDywrUhkA7syEqiUFtMKW6pkLmWb0cySnjshRqrkd-3_e199u6AHyyQH0UtTM4PUR64oWeY9Rod_CRJN2akochjUMX9qEipbOrJFwjYhq936jC7L1238WZva9ua9hSwYgDG3AL84KWRw7SUsLWqMHA10wo8LZBpJHXMUz_lMRh1YCfMTmwlwyTUgS8hDtGuYh489wpZYvAt2Cii3pmRVfmRaUiYuy28rCRDdTdmY53b_pZwJr-eh23_TtE8C53N3te4RX4WWstTXo6q45Gqxqd_EEr-L2q9TW5OQTit5V5zhyzobJms1DI56vcmdI2atFjzv2GZXN0srq5vFc3xVsi4pmgX86761DBddDOaM_GOB5pCPKJ76nhCsajgkGI1QDcb98fDMzLpQB6a6kIqs4T2YeHudU91Qsszzl_fviPCSChEFTHIY6bhXbJ_KWq5RxazfqYfEMo9W8XSTyGABlEvCX0WA3pjNnPiyFaqQtYLwxLxlN4du4wci5yY2hWoDmFmtkKel7InOanJuVJrxj5LETk4wjTAgIuP7bcibLYCezNqiU6FrBYGLKYr4FA4SIEO4TT3KuRZeRtmCX9IyUyD0jG9MPIxRA8ukOEAmACzO_Cc-7lPlANC-OlwHGkw5y2lAHKnz9_Jup8Nh7rnBYieK-SVcYYL1CB2Ox_2zNXDfxF-Sq7t1huitdNuPiI3AGZPk7xXySKYmn4MUHaknpgVhJJPl-1bvwELlaw_ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Ab+initio+protein+structure+assembly+using+continuous+structure+fragments+and+optimized+knowledge-based+force+field&rft.jtitle=Proteins%2C+structure%2C+function%2C+and+bioinformatics&rft.au=Xu%2C+Dong&rft.au=Zhang%2C+Yang&rft.date=2012-07-01&rft.eissn=1097-0134&rft.volume=80&rft.issue=7&rft.spage=1715&rft_id=info:doi/10.1002%2Fprot.24065&rft_id=info%3Apmid%2F22411565&rft.externalDocID=22411565 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0887-3585&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0887-3585&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0887-3585&client=summon |