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  An automated protocol for modelling peptide substrates to proteases

Ochoa, R., Magnitov, M., Laskowski, R. A., Cossio, P., & Thornton, J. M. (2020). An automated protocol for modelling peptide substrates to proteases. BMC Bioinformatics, 21(1):. doi:10.1186/s12859-020-03931-6.

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アイテムのパーマリンク: https://hdl.handle.net/21.11116/0000-0007-9F9C-7 版のパーマリンク: https://hdl.handle.net/21.11116/0000-0007-D46B-2
資料種別: 学術論文

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 作成者:
Ochoa, Rodrigo1, 2, 著者
Magnitov, Mikhail 2, 著者
Laskowski, Roman A.2, 著者
Cossio, Pilar1, 3, 著者           
Thornton, Janet M.2, 著者
所属:
1Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, 050010, Medellín, Colombia, ou_persistent22              
2European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, ou_persistent22              
3Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              

内容説明

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キーワード: Bioinformatics; Peptides; Promiscuity; Proteases; Structure
 要旨: Background: Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. However, depending on the family and molecular function, they can also display substrate promiscuity which can also be essential. Databases compiling specificity matrices derived from experimental assays have provided valuable insights into protease substrate recognition. Despite this, there are still gaps in our knowledge of the structural determinants. Here, we compile a set of protease crystal structures with bound peptide-like ligands to create a protocol for modelling substrates bound to protease structures, and for studying observables associated to the binding recognition.

Results: As an application, we modelled a subset of protease-peptide complexes for which experimental cleavage data are available to compare with informational entropies obtained from protease-specificity matrices. The modelled complexes were subjected to conformational sampling using the Backrub method in Rosetta, and multiple observables from the simulations were calculated and compared per peptide position. We found that some of the calculated structural observables, such as the relative accessible surface area and the interaction energy, can help characterize a protease's substrate recognition, giving insights for the potential prediction of novel substrates by combining additional approaches.

Conclusion: Overall, our approach provides a repository of protease structures with annotated data, and an open source computational protocol to reproduce the modelling and dynamic analysis of the protease-peptide complexes.

資料詳細

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言語: eng - English
 日付: 2020-09-072020-12-092020-12-29
 出版の状態: オンラインで出版済み
 ページ: 20
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): DOI: 10.1186/s12859-020-03931-6
PMID: 33375946
その他: https://libkey.io/libraries/2385/10.1186/s12859-020-03931-6
 学位: -

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出版物 1

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出版物名: BMC Bioinformatics
種別: 学術雑誌
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出版社, 出版地: BioMed Central
ページ: - 巻号: 21 (1) 通巻号: 586 開始・終了ページ: - 識別子(ISBN, ISSN, DOIなど): ISSN: 1471-2105
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905000