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Pregled bibliografske jedinice broj: 667486

Prediction of the substrate specificity of adenylation domains of non-ribosomal peptide synthetases and other protein properties by latent semantic indexing


Baranašić, Damir; Zucko, Jurica; Diminic, Janko; Gacesa, Ranko; Long, F Paul; Cullum, John; Hranueli, Daslav; Starcevic, Antonio
Prediction of the substrate specificity of adenylation domains of non-ribosomal peptide synthetases and other protein properties by latent semantic indexing // Journal of industrial microbiology & biotechnology, 41 (2013), 2; 461-467 doi:10.1007/s10295-013-1322-2 (međunarodna recenzija, članak, znanstveni)


Naslov
Prediction of the substrate specificity of adenylation domains of non-ribosomal peptide synthetases and other protein properties by latent semantic indexing

Autori
Baranašić, Damir ; Zucko, Jurica ; Diminic, Janko ; Gacesa, Ranko ; Long, F Paul ; Cullum, John ; Hranueli, Daslav ; Starcevic, Antonio

Izvornik
Journal of industrial microbiology & biotechnology (1367-5435) 41 (2013), 2; 461-467

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Adenylation domains; nonribosomal peptide synthetases; latent semantic indexing; term-document matrix; protein similarity; support vector machine

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Biotehnologija



POVEZANOST RADA


Projekt / tema
058-0000000-3475 - Generiranje potencijalnih lijekova u uvjetima in silico (Daslav Hranueli, )

Ustanove
Prehrambeno-biotehnološki fakultet, Zagreb

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Uključenost u ostale bibliografske baze podataka:


  • MEDLINE


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