Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement
Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimental...
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| Published in | Nature communications Vol. 12; no. 1; pp. 2777 - 11 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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London
Nature Publishing Group UK
13.05.2021
Nature Publishing Group Nature Portfolio |
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| Abstract | Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques.
Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures. |
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| AbstractList | Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures. Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years' efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined "native" structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base-base, base-oxygen and oxygen-oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base-base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques.Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years' efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined "native" structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base-base, base-oxygen and oxygen-oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base-base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques.Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures. Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures. |
| ArticleNumber | 2777 |
| Author | Zhan, Jian Xiong, Peng Wu, Ruibo Zhou, Yaoqi |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33986288$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1126/sciadv.aar5316 10.1093/nar/gkv141 10.1038/s41467-019-13395-9 10.1073/pnas.1835769100 10.1038/nmeth.1433 10.1007/s00214-007-0310-x 10.1093/nar/gkx1269 10.1016/j.str.2020.05.011 10.1261/rna.060368.116 10.1073/pnas.0703836104 10.1146/annurev-biophys-070816-033920 10.1073/pnas.1713027115 10.1093/bioinformatics/btn328 10.1038/srep00734 10.1261/rna.049502.114 10.1261/rna.074179.119 10.1021/ja00525a004 10.1016/j.cell.2016.03.030 10.1038/nsb0594-334 10.1006/jmbi.1999.3001 10.1126/science.1219021 10.1093/nar/gki541 10.1021/jp112059y 10.1261/rna.069872.118 10.1371/journal.pone.0074830 10.1093/nar/gkm216 10.1093/nar/gku991 10.3389/fgene.2017.00231 10.1021/acs.jpcb.7b10102 10.1261/rna.1700409 10.1093/nar/gku1214 10.1002/wcms.1307 10.1385/CBB:46:2:165 10.1261/rna.075341.120 10.1021/acs.jctc.7b00125 10.1093/bioinformatics/btv221 10.1038/nature06684 10.1093/nar/gkx386 10.1093/nar/gkq1320 10.1093/nar/gkv1479 10.1261/rna.657708 10.1186/s12900-019-0103-1 10.5281/zenodo.4661144 10.1093/bioinformatics/btab165 10.3390/ijms20174116 10.1142/9789814295291_0024 10.1089/cmb.2019.0251 10.3390/ijms20092301 |
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| References | Singh, Hanson, Paliwal, Zhou (CR43) 2019; 10 Das, Karanicolas, Baker (CR23) 2010; 7 Dunbrack, Karplus (CR33) 1994; 1 Murray, Arendall, Richardson, Richardson (CR31) 2003; 100 Das, Baker (CR7) 2007; 104 Miao (CR17) 2017; 23 Rose (CR1) 2015; 43 Miao (CR19) 2020; 26 Wang, Zhao, Zhu, Xiao (CR27) 2015; 43 Miao (CR18) 2015; 21 Dill, MacCallum (CR32) 2012; 338 Krokhotin, Houlihan, Dokholyan (CR10) 2015; 31 Westhof, Sundaralingam (CR49) 1980; 102 CR5 Tinoco, Bustamante (CR30) 1999; 293 CR46 Davis (CR37) 2007; 35 CR44 Antunes, Jorge, Caffarena, Passetti (CR4) 2018; 8 Parisien, Major (CR8) 2008; 452 Watkins (CR34) 2018; 4 Rother, Rother, Puton, Bujnicki (CR6) 2011; 39 Poblete, Bottaro, Bussi (CR45) 2018; 46 Weinreb (CR40) 2016; 165 Zhao (CR14) 2012; 2 Cuturello, Tiana, Bussi (CR42) 2020; 26 Sharma, Ding, Dokholyan (CR11) 2008; 24 CR15 Alford (CR25) 2017; 13 Boniecki (CR16) 2016; 44 Gardner, Wilm, Washietl (CR38) 2005; 33 CR50 Das (CR9) 2013; 8 Petrov (CR2) 2015; 43 Stasiewicz, Mukherjee, Nithin, Bujnicki (CR22) 2019; 19 Tan, Piana, Dirks, Shaw (CR35) 2018; 115 Tan (CR29) 2019; 25 Parisien, Cruz, Westhof, Major (CR36) 2009; 15 De Leonardis (CR39) 2015; 43 Wang (CR41) 2017; 45 CR28 Sun, Zhang, Chen (CR3) 2017; 46 CR21 Richardson (CR48) 2008; 14 Watkins, Rangan, Das (CR24) 2020; 28 Zhao, Truhlar (CR47) 2008; 120 Zhou, Zhou, Zhang, Liu (CR26) 2006; 46 Feig (CR20) 2017; 7 Cao, Chen (CR12) 2011; 115 Xu, Chen (CR13) 2018; 122 J Wang (23100_CR27) 2015; 43 23100_CR46 MJ Boniecki (23100_CR16) 2016; 44 IW Davis (23100_CR37) 2007; 35 A Krokhotin (23100_CR10) 2015; 31 F Cuturello (23100_CR42) 2020; 26 YL Tan (23100_CR29) 2019; 25 23100_CR50 J Wang (23100_CR41) 2017; 45 PW Rose (23100_CR1) 2015; 43 Y Zhao (23100_CR47) 2008; 120 LJ Murray (23100_CR31) 2003; 100 23100_CR15 Z Miao (23100_CR17) 2017; 23 AI Petrov (23100_CR2) 2015; 43 AM Watkins (23100_CR24) 2020; 28 M Parisien (23100_CR36) 2009; 15 R Das (23100_CR23) 2010; 7 J Singh (23100_CR43) 2019; 10 R Das (23100_CR7) 2007; 104 PP Gardner (23100_CR38) 2005; 33 23100_CR21 RF Alford (23100_CR25) 2017; 13 E De Leonardis (23100_CR39) 2015; 43 Y Zhao (23100_CR14) 2012; 2 M Feig (23100_CR20) 2017; 7 RL Dunbrack Jr. (23100_CR33) 1994; 1 D Antunes (23100_CR4) 2018; 8 D Tan (23100_CR35) 2018; 115 E Westhof (23100_CR49) 1980; 102 23100_CR28 AM Watkins (23100_CR34) 2018; 4 Y Zhou (23100_CR26) 2006; 46 C Weinreb (23100_CR40) 2016; 165 J Stasiewicz (23100_CR22) 2019; 19 Z Miao (23100_CR18) 2015; 21 R Das (23100_CR9) 2013; 8 KA Dill (23100_CR32) 2012; 338 M Rother (23100_CR6) 2011; 39 X Xu (23100_CR13) 2018; 122 S Poblete (23100_CR45) 2018; 46 Z Miao (23100_CR19) 2020; 26 I Tinoco (23100_CR30) 1999; 293 23100_CR5 M Parisien (23100_CR8) 2008; 452 S Cao (23100_CR12) 2011; 115 S Sharma (23100_CR11) 2008; 24 JS Richardson (23100_CR48) 2008; 14 LZ Sun (23100_CR3) 2017; 46 23100_CR44 |
| References_xml | – volume: 4 start-page: eaar5316 year: 2018 ident: CR34 article-title: Blind prediction of noncanonical RNA structure at atomic accuracy publication-title: Sci. Adv. doi: 10.1126/sciadv.aar5316 – volume: 43 start-page: e63 year: 2015 ident: CR27 article-title: 3dRNAscore: a distance and torsion angle dependent evaluation function of 3D RNA structures publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv141 – volume: 10 start-page: 5407 year: 2019 ident: CR43 article-title: RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning publication-title: Nat. Commun. doi: 10.1038/s41467-019-13395-9 – volume: 100 start-page: 13904 year: 2003 end-page: 13909 ident: CR31 article-title: RNA backbone is rotameric publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1835769100 – volume: 7 start-page: 291 year: 2010 end-page: 294 ident: CR23 article-title: Atomic accuracy in predicting and designing noncanonical RNA structure publication-title: Nat. Methods doi: 10.1038/nmeth.1433 – volume: 120 start-page: 215 year: 2008 end-page: 241 ident: CR47 article-title: The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals publication-title: Theor. Chem. Acc. doi: 10.1007/s00214-007-0310-x – volume: 46 start-page: 1674 year: 2018 end-page: 1683 ident: CR45 article-title: A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1269 – volume: 28 start-page: 963 year: 2020 end-page: 976 e6 ident: CR24 article-title: FARFAR2: improved de novo Rosetta prediction of complex global RNA folds publication-title: Structure doi: 10.1016/j.str.2020.05.011 – volume: 23 start-page: 655 year: 2017 end-page: 672 ident: CR17 article-title: RNA-puzzles round III: 3D RNA structure prediction of five riboswitches and one ribozyme publication-title: RNA doi: 10.1261/rna.060368.116 – ident: CR21 – ident: CR46 – volume: 104 start-page: 14664 year: 2007 end-page: 14669 ident: CR7 article-title: Automated de novo prediction of native-like RNA tertiary structures publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.0703836104 – volume: 46 start-page: 227 year: 2017 end-page: 246 ident: CR3 article-title: Theory and modeling of RNA structure and interactions with metal ions and small molecules publication-title: Annu. Rev. Biophys. doi: 10.1146/annurev-biophys-070816-033920 – ident: CR15 – ident: CR50 – volume: 43 start-page: 10444 year: 2015 end-page: 10455 ident: CR39 article-title: Direct-coupling analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction publication-title: Nucleic Acids Res. – volume: 115 start-page: E1346 year: 2018 end-page: E1355 ident: CR35 article-title: RNA force field with accuracy comparable to state-of-the-art protein force fields publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1713027115 – volume: 24 start-page: 1951 year: 2008 end-page: 1952 ident: CR11 article-title: iFoldRNA: three-dimensional RNA structure prediction and folding publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn328 – volume: 2 year: 2012 ident: CR14 article-title: Automated and fast building of three-dimensional RNA structures publication-title: Sci. Rep. doi: 10.1038/srep00734 – volume: 21 start-page: 1066 year: 2015 end-page: 1084 ident: CR18 article-title: RNA-puzzles round II: assessment of RNA structure prediction programs applied to three large RNA structures publication-title: RNA doi: 10.1261/rna.049502.114 – volume: 26 start-page: 637 year: 2020 end-page: 647 ident: CR42 article-title: Assessing the accuracy of direct-coupling analysis for RNA contact prediction publication-title: RNA doi: 10.1261/rna.074179.119 – volume: 102 start-page: 1493 year: 1980 end-page: 1500 ident: CR49 article-title: Interrelationships between the pseudorotation parameters P and taum and the geometry of the furanose ring publication-title: J. Am. Chem. Soc. doi: 10.1021/ja00525a004 – volume: 165 start-page: 963 year: 2016 end-page: 975 ident: CR40 article-title: 3D RNA and functional interactions from evolutionary couplings publication-title: Cell doi: 10.1016/j.cell.2016.03.030 – volume: 1 start-page: 334 year: 1994 end-page: 340 ident: CR33 article-title: Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains publication-title: Nat. Struct. Biol. doi: 10.1038/nsb0594-334 – volume: 293 start-page: 271 year: 1999 end-page: 281 ident: CR30 article-title: How RNA folds publication-title: J. Mol. Biol. doi: 10.1006/jmbi.1999.3001 – ident: CR5 – volume: 338 start-page: 1042 year: 2012 end-page: 1046 ident: CR32 article-title: The protein-folding problem, 50 years on publication-title: Science doi: 10.1126/science.1219021 – volume: 33 start-page: 2433 year: 2005 end-page: 2439 ident: CR38 article-title: A benchmark of multiple sequence alignment programs upon structural RNAs publication-title: Nucleic Acids Res. doi: 10.1093/nar/gki541 – volume: 115 start-page: 4216 year: 2011 end-page: 4226 ident: CR12 article-title: Physics-based de novo prediction of RNA 3D structures publication-title: J. Phys. Chem. B doi: 10.1021/jp112059y – volume: 25 start-page: 793 year: 2019 end-page: 812 ident: CR29 article-title: What is the best reference state for building statistical potentials in RNA 3D structure evaluation? publication-title: RNA doi: 10.1261/rna.069872.118 – volume: 8 start-page: e74830 year: 2013 ident: CR9 article-title: Atomic-accuracy prediction of protein loop structures through an RNA-inspired Ansatz publication-title: PLos ONE doi: 10.1371/journal.pone.0074830 – volume: 35 start-page: W375 year: 2007 end-page: W383 ident: CR37 article-title: MolProbity: all-atom contacts and structure validation for proteins and nucleic acids publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkm216 – volume: 43 start-page: D123 year: 2015 end-page: D129 ident: CR2 article-title: RNAcentral: an international database of ncRNA sequences publication-title: Nucleic Acids Res. doi: 10.1093/nar/gku991 – volume: 8 start-page: 231 year: 2018 ident: CR4 article-title: Using RNA sequence and structure for the prediction of riboswitch aptamer: a comprehensive review of available software and tools publication-title: Front. Genet. doi: 10.3389/fgene.2017.00231 – volume: 122 start-page: 5327 year: 2018 end-page: 5335 ident: CR13 article-title: Hierarchical assembly of RNA three-dimensional structures based on loop templates publication-title: J. Phys. Chem. B doi: 10.1021/acs.jpcb.7b10102 – volume: 15 start-page: 1875 year: 2009 end-page: 1885 ident: CR36 article-title: New metrics for comparing and assessing discrepancies between RNA 3D structures and models publication-title: RNA doi: 10.1261/rna.1700409 – volume: 43 start-page: D345 year: 2015 end-page: D356 ident: CR1 article-title: The RCSB Protein Data Bank: views of structural biology for basic and applied research and education publication-title: Nucleic Acids Res. doi: 10.1093/nar/gku1214 – volume: 7 start-page: e1307 year: 2017 ident: CR20 article-title: Computational protein structure refinement: almost there, yet still so far to go publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. doi: 10.1002/wcms.1307 – volume: 46 start-page: 165 year: 2006 end-page: 174 ident: CR26 article-title: What is a desirable statistical energy function for proteins and how can it be obtained? publication-title: Cell Biochem. Biophys. doi: 10.1385/CBB:46:2:165 – volume: 26 start-page: 982 year: 2020 end-page: 995 ident: CR19 article-title: RNA-puzzles round IV: 3D structure predictions of four ribozymes and two aptamers publication-title: RNA doi: 10.1261/rna.075341.120 – volume: 13 start-page: 3031 year: 2017 end-page: 3048 ident: CR25 article-title: The Rosetta all-atom energy function for macromolecular modeling and design publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.7b00125 – ident: CR44 – volume: 31 start-page: 2891 year: 2015 end-page: 2893 ident: CR10 article-title: iFoldRNA v2: folding RNA with constraints publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv221 – volume: 452 start-page: 51 year: 2008 end-page: 55 ident: CR8 article-title: The MC-fold and MC-Sym pipeline infers RNA structure from sequence data publication-title: Nature doi: 10.1038/nature06684 – volume: 45 start-page: 6299 year: 2017 end-page: 6309 ident: CR41 article-title: Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx386 – volume: 39 start-page: 4007 year: 2011 end-page: 4022 ident: CR6 article-title: ModeRNA: a tool for comparative modeling of RNA 3D structure publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkq1320 – volume: 44 start-page: e63 year: 2016 ident: CR16 article-title: SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv1479 – volume: 14 start-page: 465 year: 2008 end-page: 481 ident: CR48 article-title: RNA backbone: consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution) publication-title: RNA doi: 10.1261/rna.657708 – ident: CR28 – volume: 19 year: 2019 ident: CR22 article-title: QRNAS: software tool for refinement of nucleic acid structures publication-title: BMC Struct. Biol. doi: 10.1186/s12900-019-0103-1 – ident: 23100_CR46 – volume: 120 start-page: 215 year: 2008 ident: 23100_CR47 publication-title: Theor. Chem. Acc. doi: 10.1007/s00214-007-0310-x – volume: 10 start-page: 5407 year: 2019 ident: 23100_CR43 publication-title: Nat. Commun. doi: 10.1038/s41467-019-13395-9 – ident: 23100_CR50 doi: 10.5281/zenodo.4661144 – ident: 23100_CR44 doi: 10.1093/bioinformatics/btab165 – volume: 45 start-page: 6299 year: 2017 ident: 23100_CR41 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx386 – volume: 23 start-page: 655 year: 2017 ident: 23100_CR17 publication-title: RNA doi: 10.1261/rna.060368.116 – volume: 28 start-page: 963 year: 2020 ident: 23100_CR24 publication-title: Structure doi: 10.1016/j.str.2020.05.011 – volume: 46 start-page: 227 year: 2017 ident: 23100_CR3 publication-title: Annu. Rev. Biophys. doi: 10.1146/annurev-biophys-070816-033920 – volume: 293 start-page: 271 year: 1999 ident: 23100_CR30 publication-title: J. Mol. Biol. doi: 10.1006/jmbi.1999.3001 – volume: 26 start-page: 982 year: 2020 ident: 23100_CR19 publication-title: RNA doi: 10.1261/rna.075341.120 – volume: 104 start-page: 14664 year: 2007 ident: 23100_CR7 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.0703836104 – ident: 23100_CR15 doi: 10.3390/ijms20174116 – volume: 1 start-page: 334 year: 1994 ident: 23100_CR33 publication-title: Nat. Struct. Biol. doi: 10.1038/nsb0594-334 – volume: 338 start-page: 1042 year: 2012 ident: 23100_CR32 publication-title: Science doi: 10.1126/science.1219021 – volume: 44 start-page: e63 year: 2016 ident: 23100_CR16 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv1479 – volume: 43 start-page: e63 year: 2015 ident: 23100_CR27 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv141 – volume: 13 start-page: 3031 year: 2017 ident: 23100_CR25 publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.7b00125 – volume: 26 start-page: 637 year: 2020 ident: 23100_CR42 publication-title: RNA doi: 10.1261/rna.074179.119 – volume: 115 start-page: 4216 year: 2011 ident: 23100_CR12 publication-title: J. Phys. Chem. B doi: 10.1021/jp112059y – volume: 15 start-page: 1875 year: 2009 ident: 23100_CR36 publication-title: RNA doi: 10.1261/rna.1700409 – volume: 46 start-page: 1674 year: 2018 ident: 23100_CR45 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1269 – volume: 165 start-page: 963 year: 2016 ident: 23100_CR40 publication-title: Cell doi: 10.1016/j.cell.2016.03.030 – volume: 8 start-page: 231 year: 2018 ident: 23100_CR4 publication-title: Front. Genet. doi: 10.3389/fgene.2017.00231 – volume: 31 start-page: 2891 year: 2015 ident: 23100_CR10 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv221 – volume: 43 start-page: 10444 year: 2015 ident: 23100_CR39 publication-title: Nucleic Acids Res. – volume: 14 start-page: 465 year: 2008 ident: 23100_CR48 publication-title: RNA doi: 10.1261/rna.657708 – volume: 39 start-page: 4007 year: 2011 ident: 23100_CR6 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkq1320 – ident: 23100_CR5 doi: 10.1142/9789814295291_0024 – volume: 7 start-page: e1307 year: 2017 ident: 23100_CR20 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. doi: 10.1002/wcms.1307 – ident: 23100_CR28 doi: 10.1089/cmb.2019.0251 – volume: 33 start-page: 2433 year: 2005 ident: 23100_CR38 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gki541 – volume: 2 year: 2012 ident: 23100_CR14 publication-title: Sci. Rep. doi: 10.1038/srep00734 – volume: 100 start-page: 13904 year: 2003 ident: 23100_CR31 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1835769100 – volume: 21 start-page: 1066 year: 2015 ident: 23100_CR18 publication-title: RNA doi: 10.1261/rna.049502.114 – ident: 23100_CR21 doi: 10.3390/ijms20092301 – volume: 43 start-page: D345 year: 2015 ident: 23100_CR1 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gku1214 – volume: 43 start-page: D123 year: 2015 ident: 23100_CR2 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gku991 – volume: 25 start-page: 793 year: 2019 ident: 23100_CR29 publication-title: RNA doi: 10.1261/rna.069872.118 – volume: 24 start-page: 1951 year: 2008 ident: 23100_CR11 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn328 – volume: 102 start-page: 1493 year: 1980 ident: 23100_CR49 publication-title: J. Am. Chem. Soc. doi: 10.1021/ja00525a004 – volume: 452 start-page: 51 year: 2008 ident: 23100_CR8 publication-title: Nature doi: 10.1038/nature06684 – volume: 115 start-page: E1346 year: 2018 ident: 23100_CR35 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1713027115 – volume: 122 start-page: 5327 year: 2018 ident: 23100_CR13 publication-title: J. Phys. Chem. B doi: 10.1021/acs.jpcb.7b10102 – volume: 46 start-page: 165 year: 2006 ident: 23100_CR26 publication-title: Cell Biochem. Biophys. doi: 10.1385/CBB:46:2:165 – volume: 35 start-page: W375 year: 2007 ident: 23100_CR37 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkm216 – volume: 4 start-page: eaar5316 year: 2018 ident: 23100_CR34 publication-title: Sci. Adv. doi: 10.1126/sciadv.aar5316 – volume: 8 start-page: e74830 year: 2013 ident: 23100_CR9 publication-title: PLos ONE doi: 10.1371/journal.pone.0074830 – volume: 19 year: 2019 ident: 23100_CR22 publication-title: BMC Struct. Biol. doi: 10.1186/s12900-019-0103-1 – volume: 7 start-page: 291 year: 2010 ident: 23100_CR23 publication-title: Nat. Methods doi: 10.1038/nmeth.1433 |
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| Snippet | Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the... Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years' efforts, the... Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the... |
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| SubjectTerms | 631/114/2397 631/114/2411 631/45/535 Accuracy Algorithms Energy Humanities and Social Sciences Knowledge Methods Model accuracy Molecular biology multidisciplinary Nucleotide sequence Oxygen Protein structure Proteins Ribonucleic acid RNA Sampling Science Science (multidisciplinary) Statistical analysis Statistics Three dimensional models |
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| Title | Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement |
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