Predicting substrate specificity of adenylation domains of nonribosomal peptide synthetases and other protein properties by latent semantic indexing

Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the...

Full description

Saved in:
Bibliographic Details
Published inJournal of industrial microbiology & biotechnology Vol. 41; no. 2; pp. 461 - 467
Main Authors Baranašić, Damir, Zucko, Jurica, Diminic, Janko, Gacesa, Ranko, Long, Paul F, Cullum, John, Hranueli, Daslav, Starcevic, Antonio
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer-Verlag 01.02.2014
Oxford University Press
Springer Berlin Heidelberg
Subjects
amino acid sequences
Amino Acids
Amino Acids - chemistry
Analysis
Biochemistry
Bioinformatics
Biomedical and Life Sciences
Biosynthesis
Biotechnology
Catalytic Domain
chemistry
classification
Decomposition
Documents
Genetic Engineering
Genomes
Genomics
Inorganic Chemistry
Kinases
Life Sciences
ligases
Linear algebra
metabolism
Metabolites
methods
Microbiology
Original Article
Peptide Synthases
Peptide Synthases - chemistry
Peptide Synthases - classification
Peptide Synthases - metabolism
Peptides
prediction
Proteins
Semantics
Sequence Alignment
Sequence Analysis, Protein
Sequence Analysis, Protein - methods
Studies
Substrate Specificity
Substrates
Adenylation domains
Functional subtype
LSI
NRPS
Protein tokenization
Online AccessGet full text

Cover

Abstract Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.
AbstractList Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.
Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.
Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ''documents'' and it is necessary to encode properties of the amino acid sequence as ''terms'' in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ''concept'' dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Aa of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.
Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ''documents'' and it is necessary to encode properties of the amino acid sequence as ''terms'' in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ''concept'' dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ''documents'' and it is necessary to encode properties of the amino acid sequence as ''terms'' in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ''concept'' dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.
Issue Title: Special Issue: Microbial Genome Mining Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ''documents'' and it is necessary to encode properties of the amino acid sequence as ''terms'' in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ''concept'' dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.[PUBLICATION ABSTRACT]
Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ''documents'' and it is necessary to encode properties of the amino acid sequence as ''terms'' in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ''concept'' dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.
Abstract Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.
Author Cullum, John
Long, Paul F
Zucko, Jurica
Hranueli, Daslav
Starcevic, Antonio
Diminic, Janko
Gacesa, Ranko
Baranašić, Damir
Author_xml – sequence: 1
  fullname: Baranašić, Damir
– sequence: 2
  fullname: Zucko, Jurica
– sequence: 3
  fullname: Diminic, Janko
– sequence: 4
  fullname: Gacesa, Ranko
– sequence: 5
  fullname: Long, Paul F
– sequence: 6
  fullname: Cullum, John
– sequence: 7
  fullname: Hranueli, Daslav
– sequence: 8
  fullname: Starcevic, Antonio
BackLink https://www.ncbi.nlm.nih.gov/pubmed/24104398$$D View this record in MEDLINE/PubMed
BookMark eNqNkstu1TAQhiNURC_wAGzAEhsWBHxPskQVN6kSSNC15TiTg6scO9iOxHkPHpgJKaiqRGFle_L9k9-e_7Q6CjFAVT1m9CWjtHmVGeWdqikTNROc1_xedcJko2ulhDrCvdBNraRQx9VpzleUUtU0_EF1zCWjUnTtSfXjU4LBu-LDjuSlzyXZAiTP4PzonS8HEkdiBwiHyRYfAxni3vqQ1zK6Sb6PGSsTmWEufkDpIZSvUGyGTGwYSMRTInOKBXxY1xlS8fixPxBsCaGQDHsbinfEhwG-o5OH1f3RThkeXa9n1eXbN1_O39cXH999OH99UTstVak7xaHthWNsHDvVt2CVxiu6odMOq7pzou04bx1limqEqGwdZ6zRbSe1HMRZ9Xzri7a-LZCL2fvsYJpsgLhkwxQ-lGItPuE_Udlh11Zo-j8oQztSNog-u4VexSUFvPNKUS24YB1ST66ppd_DYObk9zYdzO8pIsA2wKWYc4LxD8KoWZNitqQYTIpZk2I4appbGpz2rxFjBPx0p5Jvyox_CTtIN0zfIXqxieIy_8XdjRwj_nTDRxuN3SWfzeVnTplcM8x1I8VPsBzoTg
CitedBy_id crossref_primary_10_1016_S1875_5364_24_60585_6
crossref_primary_10_1016_j_bbapap_2023_140972
crossref_primary_10_1093_nar_gkx319
crossref_primary_10_3390_md22080349
crossref_primary_10_1039_C6NP00025H
crossref_primary_10_1039_D3NP00064H
crossref_primary_10_1021_acs_jnatprod_5b00932
crossref_primary_10_1038_nchembio_1884
crossref_primary_10_1016_j_ijmm_2014_02_001
crossref_primary_10_3389_fchem_2018_00394
crossref_primary_10_3762_bjoc_20_253
crossref_primary_10_1007_s00253_023_12585_2
crossref_primary_10_1016_j_synbio_2015_12_002
crossref_primary_10_1002_prot_24922
crossref_primary_10_1016_j_cbpa_2022_102212
crossref_primary_10_1021_acs_jnatprod_0c01186
crossref_primary_10_1093_bioinformatics_btx400
crossref_primary_10_1038_nchembio_1890
crossref_primary_10_1126_science_aaw6732
crossref_primary_10_4103_epj_epj_46_23
crossref_primary_10_1007_s12602_020_09688_x
crossref_primary_10_1039_D1OB00772F
crossref_primary_10_1007_s10295_018_2084_7
crossref_primary_10_1016_S1875_5364_20_60006_1
crossref_primary_10_1039_D4NP00073K
crossref_primary_10_1099_mgen_0_000621
crossref_primary_10_1007_s12038_017_9663_z
crossref_primary_10_1186_s12915_020_00873_6
crossref_primary_10_1039_C6NP00030D
crossref_primary_10_1021_acschembio_2c00761
crossref_primary_10_1016_j_biocontrol_2020_104530
crossref_primary_10_1093_nar_gkv1012
crossref_primary_10_1038_s41598_018_37792_0
crossref_primary_10_1128_Spectrum_00571_21
Cites_doi 10.1371/journal.pcbi.1000069
10.1186/1471-2105-10-421
10.1016/S1074-5521(99)80082-9
10.1016/S1074-5521(00)00091-0
10.1093/nar/gki885
10.1016/j.sbi.2010.01.009
10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
10.1006/jmbi.2000.4036
10.1093/nar/gkr323
10.1007/s10295-013-1252-z
10.1186/1471-2105-10-335
10.1093/bioinformatics/btm404
10.1093/nar/gkn685
ContentType Journal Article
Copyright Society for Industrial Microbiology 2014 2014
Society for Industrial Microbiology and Biotechnology 2013
Society for Industrial Microbiology and Biotechnology 2014
Copyright_xml – notice: Society for Industrial Microbiology 2014 2014
– notice: Society for Industrial Microbiology and Biotechnology 2013
– notice: Society for Industrial Microbiology and Biotechnology 2014
DBID FBQ
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7QL
7QR
7T7
7WY
7WZ
7X7
7XB
87Z
88A
88E
88I
8AO
8FD
8FE
8FH
8FI
8FJ
8FK
8FL
8G5
ABUWG
AEUYN
AFKRA
AZQEC
BBNVY
BENPR
BEZIV
BHPHI
C1K
CCPQU
DWQXO
FR3
FRNLG
FYUFA
F~G
GHDGH
GNUQQ
GUQSH
HCIFZ
K60
K6~
K9.
L.-
LK8
M0C
M0S
M1P
M2O
M2P
M7N
M7P
MBDVC
P64
PHGZM
PHGZT
PJZUB
PKEHL
PPXIY
PQBIZ
PQBZA
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
Q9U
7X8
7QO
7S9
L.6
DOI 10.1007/s10295-013-1322-2
DatabaseName AGRIS
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
ProQuest Central (Corporate)
Bacteriology Abstracts (Microbiology B)
Chemoreception Abstracts
Industrial and Applied Microbiology Abstracts (Microbiology A)
ABI/INFORM Collection
ABI/INFORM Global (PDF only)
ProQuest Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
ABI/INFORM Collection
Biology Database (Alumni Edition)
Medical Database (Alumni Edition)
Science Database (Alumni Edition)
ProQuest Pharma Collection
Technology Research Database
ProQuest SciTech Collection
ProQuest Natural Science Collection
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ABI/INFORM Collection (Alumni)
ProQuest Research Library
ProQuest Central (Alumni)
ProQuest One Sustainability (subscription)
ProQuest Central UK/Ireland
ProQuest Central Essentials
Biological Science Collection
ProQuest Central
Business Premium Collection
Natural Science Collection
Environmental Sciences and Pollution Management
ProQuest One
ProQuest Central Korea
Engineering Research Database
Business Premium Collection (Alumni)
Health Research Premium Collection
ABI/INFORM Global (Corporate)
Health Research Premium Collection (Alumni)
ProQuest Central Student
Research Library Prep
SciTech Premium Collection
ProQuest Business Collection (Alumni Edition)
ProQuest Business Collection
ProQuest Health & Medical Complete (Alumni)
ABI/INFORM Professional Advanced
Biological Sciences
ABI/INFORM Global
Health & Medical Collection (Alumni)
Medical Database
Research Library
Science Database
Algology Mycology and Protozoology Abstracts (Microbiology C)
Biological Science Database
Research Library (Corporate)
Biotechnology and BioEngineering Abstracts
ProQuest Central Premium
ProQuest One Academic
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Business
ProQuest One Business (Alumni)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
ProQuest Central Basic
MEDLINE - Academic
Biotechnology Research Abstracts
AGRICOLA
AGRICOLA - Academic
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
ProQuest Business Collection (Alumni Edition)
Research Library Prep
ProQuest Central Student
ProQuest Central Essentials
SciTech Premium Collection
ProQuest Central China
ABI/INFORM Complete
Environmental Sciences and Pollution Management
ProQuest One Applied & Life Sciences
ProQuest One Sustainability
Health Research Premium Collection
Natural Science Collection
Health & Medical Research Collection
Biological Science Collection
Chemoreception Abstracts
Industrial and Applied Microbiology Abstracts (Microbiology A)
ProQuest Central (New)
ProQuest Medical Library (Alumni)
Business Premium Collection
ABI/INFORM Global
ProQuest Science Journals (Alumni Edition)
ProQuest Biological Science Collection
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
Biological Science Database
ProQuest Business Collection
ProQuest Hospital Collection (Alumni)
Biotechnology and BioEngineering Abstracts
ProQuest Health & Medical Complete
ProQuest One Academic UKI Edition
Engineering Research Database
ProQuest One Academic
ProQuest One Academic (New)
ABI/INFORM Global (Corporate)
ProQuest One Business
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest One Health & Nursing
Research Library (Alumni Edition)
ProQuest Natural Science Collection
ProQuest Pharma Collection
ProQuest Biology Journals (Alumni Edition)
ProQuest Central
ABI/INFORM Professional Advanced
ProQuest Health & Medical Research Collection
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
Bacteriology Abstracts (Microbiology B)
Algology Mycology and Protozoology Abstracts (Microbiology C)
ProQuest Research Library
ABI/INFORM Complete (Alumni Edition)
ABI/INFORM Global (Alumni Edition)
ProQuest Central Basic
ProQuest Science Journals
ProQuest SciTech Collection
ProQuest Medical Library
ProQuest One Business (Alumni)
ProQuest Central (Alumni)
Business Premium Collection (Alumni)
MEDLINE - Academic
Biotechnology Research Abstracts
AGRICOLA
AGRICOLA - Academic
DatabaseTitleList
CrossRef
Engineering Research Database
MEDLINE - Academic
ProQuest Business Collection (Alumni Edition)
MEDLINE


AGRICOLA
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
– sequence: 3
  dbid: BENPR
  name: ProQuest Central
  url: https://www.proquest.com/central
  sourceTypes: Aggregation Database
– sequence: 4
  dbid: FBQ
  name: AGRIS
  url: http://www.fao.org/agris/Centre.asp?Menu_1ID=DB&Menu_2ID=DB1&Language=EN&Content=http://www.fao.org/agris/search?Language=EN
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Biology
Chemistry
DocumentTitleAlternate Special Issue: Microbial Genome Mining
EISSN 1476-5535
EndPage 467
ExternalDocumentID 3186315721
24104398
10_1007_s10295_013_1322_2
10.1007/s10295-013-1322-2
US201400052674
Genre Research Support, Non-U.S. Gov't
Journal Article
GroupedDBID ---
-4W
-56
-5G
-BR
-EM
-Y2
-~C
.86
.VR
06C
06D
0R~
0VY
199
1N0
1SB
2.D
203
28-
29K
29~
2J2
2JN
2JY
2KG
2KM
2LR
2P1
2VQ
2~H
30V
36B
3SX
3V.
4.4
408
409
40D
40E
4P2
53G
5GY
5QI
5VS
67N
67Z
6NX
78A
7WY
7X7
88A
88E
88I
8AO
8CJ
8FE
8FH
8FI
8FJ
8FL
8G5
8TC
8UJ
95-
95.
95~
96X
A8Z
AAAVM
AABHQ
AABYN
AAIAL
AAJKR
AANXM
AAPXW
AARHV
AARTL
AATVU
AAVAP
AAWCG
AAYIU
AAYQN
AAYTO
ABBBX
ABBXA
ABDBF
ABDZT
ABFGW
ABFTV
ABHLI
ABJOX
ABKCH
ABKTR
ABMNI
ABNWP
ABPLI
ABPTD
ABPTK
ABQBU
ABTEG
ABTHY
ABTMW
ABULA
ABUWG
ABXPI
ACBMV
ACBRV
ACBXY
ACBYP
ACGFS
ACGOD
ACHXU
ACIHN
ACIPQ
ACIWK
ACKNC
ACMLO
ACOKC
ACOMO
ACPRK
ACREN
ACSNA
ADBBV
ADHHG
ADHIR
ADINQ
ADKPE
ADMDM
ADOAH
ADRFC
ADURQ
ADYFF
ADYOE
ADYPR
ADZKW
AEAQA
AEBTG
AEFIE
AEGAL
AEGNC
AEJHL
AEJRE
AEKMD
AENEX
AEOHA
AEPYU
AETLH
AEVTX
AEXYK
AFEXP
AFGCZ
AFKRA
AFLOW
AFNRJ
AFRAH
AFULF
AFWTZ
AFYQB
AFZKB
AGAYW
AGDGC
AGGBP
AGGDS
AGJBK
AGQMX
AGWIL
AGWZB
AGYKE
AHAVH
AHBYD
AHKAY
AHMBA
AHSBF
AHYZX
AI.
AIIXL
AILAN
AIMYW
AITGF
AJBLW
AJRNO
AJZVZ
AKMHD
AKQUC
ALMA_UNASSIGNED_HOLDINGS
ALWAN
AMKLP
AMTXH
AMYQR
AOSHJ
ARMRJ
AXYYD
AZFZN
AZQEC
B-.
B0M
BA0
BBNVY
BDATZ
BENPR
BEZIV
BGNMA
BHPHI
BPHCQ
BVXVI
CAG
CCPQU
COF
CS3
CSCUP
D-I
D1J
DL5
DU5
DWQXO
EAD
EAP
EAS
EBC
EBD
EBLON
EBS
EDH
EIOEI
EJD
EMB
EMK
EMOBN
EN4
EPAXT
EPL
ESBYG
EST
ESTFP
ESX
F5P
FBQ
FEDTE
FERAY
FFXSO
FIGPU
FINBP
FNLPD
FRNLG
FRRFC
FSGXE
FWDCC
FYUFA
G-Y
G-Z
GGCAI
GGRSB
GJIRD
GNUQQ
GNWQR
GQ6
GQ7
GQ8
GROUPED_ABI_INFORM_COMPLETE
GROUPED_DOAJ
GUQSH
GXS
HCIFZ
HF~
HG5
HG6
HMCUK
HMJXF
HQYDN
HRMNR
HVGLF
HZ~
I-F
I09
IHE
IJ-
ITM
IXC
IZIGR
IZQ
I~X
I~Z
J-C
J0Z
JBSCW
JCJTX
K60
K6~
KDC
KOV
KOW
KPH
KSI
LAS
LK8
M0C
M0L
M1P
M2O
M2P
M4Y
M7P
MA-
ML0
MM.
N2Q
NB0
NDZJH
NQJWS
NU0
O9-
O93
O9G
O9I
O9J
OAM
OJZSN
P19
PF0
PQBIZ
PQQKQ
PROAC
PSQYO
PT5
Q2X
QOK
QOR
QOS
R89
R9I
RHV
RNI
RNS
ROL
ROX
RPM
RPX
RRX
RSV
RZK
S16
S1Z
S26
S27
S28
S3A
S3B
SAP
SBL
SBY
SCLPG
SDH
SDM
SHX
SISQX
SNE
SNX
SOJ
SPISZ
SSXJD
STPWE
SV3
SZN
T13
T16
TOX
TSG
TSK
TSV
TUC
TUS
U2A
U9L
UG4
UKHRP
UNUBA
UOJIU
UTJUX
UZXMN
VC2
VFIZW
VH1
W23
W48
WJK
WK6
WK8
XFK
YLTOR
Z45
Z5O
Z7R
Z7U
Z7V
Z7W
Z7X
Z7Y
Z7Z
Z83
Z86
Z87
Z88
Z8M
Z8O
Z8P
Z8Q
Z8R
Z8S
Z8T
Z8W
Z91
ZOVNA
~8M
~EX
~KM
AAHBH
AAYZH
ABEJV
ABGNP
ABMOR
ABQSL
ABXVV
ACUHS
AEUYN
AFBBN
ALIPV
AMNDL
H13
IAO
IEP
IHR
PQBZA
ZMTXR
AAYXX
ABFSG
ACSTC
AEZWR
AFHIU
AGQPQ
AHWEU
AIXLP
CITATION
PHGZM
PHGZT
ADHKG
CGR
CUY
CVF
ECM
EIF
NPM
7QL
7QR
7T7
7XB
8FD
8FK
C1K
FR3
K9.
L.-
M7N
MBDVC
P64
PJZUB
PKEHL
PPXIY
PQEST
PQGLB
PQUKI
PRINS
Q9U
7X8
7QO
7S9
L.6
ID FETCH-LOGICAL-c645t-952e8b3c11ff95b8ea56005cd96c3c169c389228c015061ff048c2117689464d3
IEDL.DBID 7X7
ISSN 1367-5435
1476-5535
IngestDate Fri Jul 11 05:19:59 EDT 2025
Tue Aug 05 11:05:57 EDT 2025
Thu Aug 07 14:44:00 EDT 2025
Wed Aug 13 10:40:03 EDT 2025
Thu Apr 03 06:52:45 EDT 2025
Tue Jul 01 04:30:35 EDT 2025
Thu Apr 24 23:04:02 EDT 2025
Fri Feb 21 02:42:34 EST 2025
Tue Feb 04 08:58:36 EST 2025
Wed Dec 27 19:27:01 EST 2023
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Keywords Adenylation domains
Functional subtype
LSI
NRPS
Protein tokenization
Language English
License This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c645t-952e8b3c11ff95b8ea56005cd96c3c169c389228c015061ff048c2117689464d3
Notes http://dx.doi.org/10.1007/s10295-013-1322-2
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://academic.oup.com/jimb/article-pdf/41/2/461/36813158/jimb0461.pdf
PMID 24104398
PQID 1490632319
PQPubID 54631
PageCount 7
ParticipantIDs proquest_miscellaneous_1524151854
proquest_miscellaneous_1496898360
proquest_miscellaneous_1491061447
proquest_journals_1490632319
pubmed_primary_24104398
crossref_primary_10_1007_s10295_013_1322_2
crossref_citationtrail_10_1007_s10295_013_1322_2
springer_journals_10_1007_s10295_013_1322_2
oup_primary_10_1007_s10295-013-1322-2
fao_agris_US201400052674
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2014-02-01
PublicationDateYYYYMMDD 2014-02-01
PublicationDate_xml – month: 02
  year: 2014
  text: 2014-02-01
  day: 01
PublicationDecade 2010
PublicationPlace Berlin/Heidelberg
PublicationPlace_xml – name: Berlin/Heidelberg
– name: Germany
– name: Fairfax
PublicationSubtitle Official Journal of the Society for Industrial Microbiology and Biotechnology
PublicationTitle Journal of industrial microbiology & biotechnology
PublicationTitleAbbrev J Ind Microbiol Biotechnol
PublicationTitleAlternate J Ind Microbiol Biotechnol
PublicationYear 2014
Publisher Springer-Verlag
Oxford University Press
Springer Berlin Heidelberg
Publisher_xml – name: Springer-Verlag
– name: Oxford University Press
– name: Springer Berlin Heidelberg
References Eddy (CR6) 2008; 4
Deerwester, Dumais, Furnas, Landauer, Harshman (CR4) 1990; 41
Challis, Ravel, Townsend (CR2) 2000; 7
Larkin, Blackshields, Brown, Chenna, McGettigan, McWilliam, Valentin, Wallace, Wilm, Lopez, Thompson, Gibson, Higgins (CR9) 2007; 23
Camacho, Coulouris, Avagyan, Ma, Papadopoulos, Bealer, Madden (CR1) 2009; 10
CR11
Stachelhaus, Mootz, Marahiel (CR14) 1999; 6
CR10
Rausch, Weber, Kohlbacher, Wohlleben, Huson (CR12) 2005; 33
Röttig, Medema, Blin, Weber, Rausch, Kohlbacher (CR13) 2011; 39
Starcevic, Zucko, Simunkovic, Long, Cullum, Hranueli (CR15) 2008; 36
Couto, Ladeira, Santos (CR3) 2007; 6
Goldstein, Zucko, Vujaklija, Krisko, Hranueli, Long, Etchebest, Basrak, Cullum (CR7) 2009; 10
Diminic, Zucko, Trninic Ruzic, Gacesa, Hranueli, Long, Cullum, Starcevic (CR5) 2013; 40
Strieker, Tanovic, Marahiel (CR16) 2010; 20
Hannenhalli, Russell (CR8) 2000; 303
Larkin (2021033103400090300_CR9) 2007; 23
Deerwester (2021033103400090300_CR4) 1990; 41
Hannenhalli (2021033103400090300_CR8) 2000; 303
Camacho (2021033103400090300_CR1) 2009; 10
Eddy (2021033103400090300_CR6) 2008; 4
Couto (2021033103400090300_CR3) 2007; 6
Strieker (2021033103400090300_CR16) 2010; 20
Röttig (2021033103400090300_CR13) 2011; 39
Challis (2021033103400090300_CR2) 2000; 7
Goldstein (2021033103400090300_CR7) 2009; 10
Diminic (2021033103400090300_CR5) 2013; 40
Stachelhaus (2021033103400090300_CR14) 1999; 6
2021033103400090300_CR10
2021033103400090300_CR11
Rausch (2021033103400090300_CR12) 2005; 33
Starcevic (2021033103400090300_CR15) 2008; 36
References_xml – volume: 4
  start-page: e1000069
  year: 2008
  ident: CR6
  article-title: A probabilistic model of local sequence alignment that simplifies statistical significance estimation
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1000069
– volume: 10
  start-page: 421
  year: 2009
  ident: CR1
  article-title: BLAST + : architecture and applications
  publication-title: BMC Bioinf
  doi: 10.1186/1471-2105-10-421
– volume: 6
  start-page: 493
  year: 1999
  end-page: 505
  ident: CR14
  article-title: The specificity-conferring code of adenylation domains in nonribosomal peptide synthetases
  publication-title: Chem Biol
  doi: 10.1016/S1074-5521(99)80082-9
– volume: 7
  start-page: 211
  year: 2000
  end-page: 224
  ident: CR2
  article-title: Predictive, structure-based model of amino acid recognition by nonribosomal peptide synthetase adenylation domains
  publication-title: Chem Biol
  doi: 10.1016/S1074-5521(00)00091-0
– ident: CR10
– ident: CR11
– volume: 33
  start-page: 5799
  year: 2005
  end-page: 5808
  ident: CR12
  article-title: Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs)
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gki885
– volume: 20
  start-page: 234
  year: 2010
  end-page: 240
  ident: CR16
  article-title: Nonribosomal peptide synthetases: structures and dynamics
  publication-title: Curr Opin Struct Biol
  doi: 10.1016/j.sbi.2010.01.009
– volume: 41
  start-page: 391
  year: 1990
  end-page: 407
  ident: CR4
  article-title: Indexing by latent semantic analysis
  publication-title: J Am Soc Inform Sci
  doi: 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
– volume: 303
  start-page: 61
  year: 2000
  end-page: 76
  ident: CR8
  article-title: Analysis and prediction of functional sub-types from protein sequence alignments
  publication-title: J Mol Biol
  doi: 10.1006/jmbi.2000.4036
– volume: 6
  start-page: 983
  year: 2007
  end-page: 999
  ident: CR3
  article-title: Application of latent semantic indexing to evaluate the similarity of sets of sequences without multiple alignments character-by-character
  publication-title: Genet Mol Res
– volume: 39
  start-page: W362
  year: 2011
  end-page: W367
  ident: CR13
  article-title: NRPSpredictor2–a web server for predicting NRPS adenylation domain specificity
  publication-title: Nucleic Acids Res (Web Server issue)
  doi: 10.1093/nar/gkr323
– volume: 40
  start-page: 653
  year: 2013
  end-page: 659
  ident: CR5
  article-title: Databases of the Thiotemplate Modular Systems ( ) and their in silico recombinants ( - )
  publication-title: J Ind Microbiol Biotechnol
  doi: 10.1007/s10295-013-1252-z
– volume: 10
  start-page: 335
  year: 2009
  ident: CR7
  article-title: Clustering of protein domains for functional and evolutionary studies
  publication-title: BMC Bioinformatics
  doi: 10.1186/1471-2105-10-335
– volume: 23
  start-page: 2947
  year: 2007
  end-page: 2948
  ident: CR9
  article-title: Clustal W and Clustal X version 2.0
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btm404
– volume: 36
  start-page: 6882
  year: 2008
  end-page: 6892
  ident: CR15
  article-title: ClustScan: an integrated program package for the semi-automatic annotation of modular biosynthetic gene clusters and in silico prediction of novel chemical structures
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkn685
– volume: 23
  start-page: 2947
  year: 2007
  ident: 2021033103400090300_CR9
  article-title: Clustal W and Clustal X version 2.0
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btm404
– volume: 20
  start-page: 234
  year: 2010
  ident: 2021033103400090300_CR16
  article-title: Nonribosomal peptide synthetases: structures and dynamics
  publication-title: Curr Opin Struct Biol
  doi: 10.1016/j.sbi.2010.01.009
– volume: 41
  start-page: 391
  year: 1990
  ident: 2021033103400090300_CR4
  article-title: Indexing by latent semantic analysis
  publication-title: J Am Soc Inform Sci
  doi: 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
– volume: 40
  start-page: 653
  year: 2013
  ident: 2021033103400090300_CR5
  article-title: Databases of the Thiotemplate Modular Systems (CSDB) and their in silico recombinants (r-CSDB)
  publication-title: J Ind Microbiol Biotechnol
  doi: 10.1007/s10295-013-1252-z
– volume: 10
  start-page: 421
  year: 2009
  ident: 2021033103400090300_CR1
  article-title: BLAST + : architecture and applications
  publication-title: BMC Bioinf
  doi: 10.1186/1471-2105-10-421
– volume: 33
  start-page: 5799
  year: 2005
  ident: 2021033103400090300_CR12
  article-title: Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs)
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gki885
– volume: 10
  start-page: 335
  year: 2009
  ident: 2021033103400090300_CR7
  article-title: Clustering of protein domains for functional and evolutionary studies
  publication-title: BMC Bioinformatics
  doi: 10.1186/1471-2105-10-335
– volume: 6
  start-page: 493
  year: 1999
  ident: 2021033103400090300_CR14
  article-title: The specificity-conferring code of adenylation domains in nonribosomal peptide synthetases
  publication-title: Chem Biol
  doi: 10.1016/S1074-5521(99)80082-9
– ident: 2021033103400090300_CR10
– ident: 2021033103400090300_CR11
– volume: 4
  start-page: e1000069
  year: 2008
  ident: 2021033103400090300_CR6
  article-title: A probabilistic model of local sequence alignment that simplifies statistical significance estimation
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1000069
– volume: 6
  start-page: 983
  year: 2007
  ident: 2021033103400090300_CR3
  article-title: Application of latent semantic indexing to evaluate the similarity of sets of sequences without multiple alignments character-by-character
  publication-title: Genet Mol Res
– volume: 39
  start-page: W362
  year: 2011
  ident: 2021033103400090300_CR13
  article-title: NRPSpredictor2–a web server for predicting NRPS adenylation domain specificity
  publication-title: Nucleic Acids Res (Web Server issue)
  doi: 10.1093/nar/gkr323
– volume: 36
  start-page: 6882
  year: 2008
  ident: 2021033103400090300_CR15
  article-title: ClustScan: an integrated program package for the semi-automatic annotation of modular biosynthetic gene clusters and in silico prediction of novel chemical structures
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkn685
– volume: 7
  start-page: 211
  year: 2000
  ident: 2021033103400090300_CR2
  article-title: Predictive, structure-based model of amino acid recognition by nonribosomal peptide synthetase adenylation domains
  publication-title: Chem Biol
  doi: 10.1016/S1074-5521(00)00091-0
– volume: 303
  start-page: 61
  year: 2000
  ident: 2021033103400090300_CR8
  article-title: Analysis and prediction of functional sub-types from protein sequence alignments
  publication-title: J Mol Biol
  doi: 10.1006/jmbi.2000.4036
SSID ssj0005772
Score 2.2504594
Snippet Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional...
Abstract Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into...
Issue Title: Special Issue: Microbial Genome Mining Successful genome mining is dependent on accurate prediction of protein function from sequence. This often...
SourceID proquest
pubmed
crossref
springer
oup
fao
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 461
SubjectTerms amino acid sequences
Amino Acids
Amino Acids - chemistry
Analysis
Biochemistry
Bioinformatics
Biomedical and Life Sciences
Biosynthesis
Biotechnology
Catalytic Domain
chemistry
classification
Decomposition
Documents
Genetic Engineering
Genomes
Genomics
Inorganic Chemistry
Kinases
Life Sciences
ligases
Linear algebra
metabolism
Metabolites
methods
Microbiology
Original Article
Peptide Synthases
Peptide Synthases - chemistry
Peptide Synthases - classification
Peptide Synthases - metabolism
Peptides
prediction
Proteins
Semantics
Sequence Alignment
Sequence Analysis, Protein
Sequence Analysis, Protein - methods
Studies
Substrate Specificity
Substrates
SummonAdditionalLinks – databaseName: SpringerLink Journals (ICM)
  dbid: U2A
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fi9QwEB70RNAH0fPHVU-JoC9KYZtN0-bxEI9DUARduLfSJqks3LXLdu9h_w__YL9J27364xZ8KiTTEDLJ5Btm5gvRG6ZIk9pmsasSEyupbGywDeLEWTMvS62z8MbS5y_6bKE-nafnQx13N2a7jyHJYKknxW6Sq4kTDMQeFOzunRSuO-dxLeTJdV5HFl5sYiqyOAUYGEOZ_xrit8vodl22f5S5TdDmX5HScAGdPqQHA3IUJ72qH9Et3xzS3f4tye0h3Z8wCz6mn1_XHIHhnGbRwTYEDlrBZZWcGgTkLdpalLA52z4XTrj2slw2HTc3bbNeVm2Hlgux4qwXh1-3DaDiBndeJ8rGiVC4JQLLw7Lh74oTtNFZbQWGxFUmOn8JtS2tCIyMmMkTWpx-_P7hLB4eYIitVukmNqn0eTW3SVLXJq1yXzI-Sq0z2qJVGwu4I2VuA08hhGAOLDxKuDBGaeXmT-kAc_ZHJGxloX3pnZo7NfNMSeNmtamchj-j8iSi2aiJwg7s5PxIxkVxzavMyiugvIKVV8iI3u1-WfXUHPuEj6DeovwB01ksvkl2LAPXTaYiegud3zDEZLNEdDzuimI46B08JwOQB5BsInq968YR5bhL2fj2Ksj0jne2VwaLxhU1e2RShlsAWJjys35X7maNHi5yziN6P27TySRvWpXn_yX9gu7xuvU568d0sFlf-ZeAZJvqVTiCvwB6xCfe
  priority: 102
  providerName: Springer Nature
Title Predicting substrate specificity of adenylation domains of nonribosomal peptide synthetases and other protein properties by latent semantic indexing
URI https://link.springer.com/article/10.1007/s10295-013-1322-2
https://www.ncbi.nlm.nih.gov/pubmed/24104398
https://www.proquest.com/docview/1490632319
https://www.proquest.com/docview/1491061447
https://www.proquest.com/docview/1496898360
https://www.proquest.com/docview/1524151854
Volume 41
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3fb9MwED6xTUjwgGD8WGBMRoIXUETjOj_8hArqmEBME1CpPEWJnaBKW1Ka7qH_B38w3zlJW0DrUyTHiRzf-fxdfPcd0UumSJORiX2bB9pXUhlfQw38wBo9zLIoil2NpS_n0dlEfZqG0-6HW9OFVfY20RlqWxv-R_4WSB67KdCIfjf_5XPVKD5d7Upo7NEBU5exVsfTeBPiEbviTcxK5ofABf2pZps6Jzk3OcCw2B-Tf-1Le2VW_5PxtgU8_zs0dXvR6X2614FIMWql_oBuFdUh3W7LSq4O6e4WyeBD-n2x4MMYDm8WDcyEo6MVnGHJUUIA4aIuRQbzs2rD4oStr7JZ1XBzVVeLWV43aLkUcw6AsXh0VQE1LrH9NSKrrHA5XMIRPswqvs45Vhs385XAK7Griaa4ggRnRjhyRozkEU1Ox98_nPldLQbfRCpc-jqURZIPTRCUpQ7zpMgYKoXG6sigNdIGyEfKxDjKQnSCZTBwLuHNaBUpO3xM-xhzcUTC5AaKIAurhlYNCmansYNS5zaCa6OSwKNBL4nUdETlXC_jMt1QLLPwUggvZeGl0qPX60fmLUvHrs5HEG-a_YQVTSffJPuYjvYmVh69gsxveMWWsnh03GtF2q35Jt1oqEcv1rexWvkIJquK-tr1aX3weGcfTBon1-zoEzLyAtbCkJ-0WrkeNe5wvnPi0ZteTbcGedOsPN39Sc_oDk9UG69-TPvLxXXxHHBsmZ-4NXdCB6OPPz6PcX0_Pr_4itaJHP0B-dUvhA
linkProvider ProQuest
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB61RQg4ICiPBgoYiV5AERtvngeEEFBt6UNIdKW9uYmdoJXaZNlshfI_-B38Rr5xNrsLqHvrKZLtWI5nPI945huiVwyRJkMduSbzEteXvnYTsIHrGZ300zQMI1tj6fgkHAz9L6NgtEG_u1wYDqvsZKIV1KbS_I_8LSx5aFNYI8n7yQ-Xq0bx7WpXQqNli8O8-QmXrX538An03ZNy__Ppx4E7ryrg6tAPZm4SyDzO-trziiIJsjhPWekH2iShRmuYaOhwKWNtwfcwCDyu4SbBLk_80Dd9zLtJN6B4e-zsRaNoGVIS2WJRjILmBrBDulvUNlVPci60h21g_0_-pQc3i7T6J8NuxdD975LW6r79e3R3brSKDy2X3aeNvNymm20Zy2ab7qyAGj6gX1-nfPnD4dSihliy8LeCMzo5KglGv6gKkULcNW0YnjDVRToua24uq3I6zqoaLediwgE3Bq82JazUGdRtLdLSCJszJizAxLjk54Rjw9GZNQJTQouKOr8Ax4y1sGCQWMlDGl4LlR7RFtac75DQmQbjydz4feP3ckbDMb0iyUwIV8qPPYd6HSWUngOjc32Oc7WEdGbiKRBPMfGUdOj14pVJiwqybvAOyKvS75DaavhNsk9rYXYi36E90PyKKVaYxaHdjivUXMbUankiHHq56IZ04CuftMyrSzum9fmjtWOwaZzMs2ZMwJYebDss-XHLlYtVo4fzq2OH3nRsurLIq3blyfpPekG3BqfHR-ro4OTwKd3mTWtj5Xdpaza9zJ_BFJxlz-35E3R23Qf-DxszZew
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB61qUBwQFAeNRRYJHoBWY03fh4QAtqopRBFQKTeXHvXRpFaO8SpUP4Hv4ZfxzdrOwmg5taTpd31ar0zOw_vzDdELxkiTfoqsHXqRLYrXWVHYAPb0SrqJYnvB6bG0ueBfzRyP556pxv0u82F4bDKViYaQa1Lxf_I92HJQ5vCGon28yYsYnjQfzv5YXMFKb5pbctp1Cxyks1_wn2r3hwfgNZ7UvYPv304spsKA7byXW9mR57MwrSnHCfPIy8Ns4QNAE_pyFdo9SMFfS5lqAwQHwaB3xVcJtjokeu7uod5N2krYK-oQ1vvDwfDL8sAk8CUjmJMNNuDVdLeqdaJe5Izox1sCnuD8i-tuJkn5T_5ditm739XtkYT9u_SncaEFe9qnrtHG1mxTTfqopbzbbq9AnF4n34Np3wVxMHVooKQMmC4gvM7OUYJLoAoc5FA-M3roDyhy4tkXFTcXJTFdJyWFVrOxYTDbzRenRewWWdQvpVICi1MBpkwcBPjgp8TjhRHZzoXmBI6VVTZBfhnrISBhsRKHtDoWuj0kDpYc7ZDQqUKbCgz7fa0280YG0d38yjVPhwrN3Qs6raUiFUDk87VOs7jJcAzEy8G8WImXiwterV4ZVJjhKwbvAPyxsl3yPB49FWyh2tAdwLXoj3Q_IopVpjFot2WK-JG4lTx8nxY9GLRDVnBF0BJkZWXZkz9ByBYOwabxqk9a8Z4bPfB0sOSH9VcuVg1ejjbOrTodcumK4u8alcer_-k53QThz3-dDw4eUK3eM_qwPld6syml9lT2IWz9FlzAAWdXfeZ_wONLmuH
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=Predicting+substrate+specificity+of+adenylation+domains+of+nonribosomal+peptide+synthetases+and+other+protein+properties+by+latent+semantic+indexing&rft.jtitle=Journal+of+industrial+microbiology+%26+biotechnology&rft.au=Barana%C5%A1i%C4%87%2C+Damir&rft.au=Zucko%2C+Jurica&rft.au=Diminic%2C+Janko&rft.au=Gacesa%2C+Ranko&rft.date=2014-02-01&rft.eissn=1476-5535&rft.volume=41&rft.issue=2&rft.spage=461&rft_id=info:doi/10.1007%2Fs10295-013-1322-2&rft_id=info%3Apmid%2F24104398&rft.externalDocID=24104398
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-5435&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-5435&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-5435&client=summon