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PLoS One. 2019 Jan 8;14(1):e0199270. doi: 10.1371/journal.pone.0199270. eCollection 2019.

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery.

Author information

1
Pompeu Fabra University, Barcelona, Spain.
2
Lead Molecular Design, S. L, Sant Cugat del Vallés, Spain.
3
Molecular Discovery Ltd, London, United Kingdom.

Abstract

Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.

Conflict of interest statement

Luca Morettoni is employed by Molecular Discovery Ltd. There are no patents. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. WebMetabase is a marketed product of the Molecular Discovery Ltd. But the functionality described in the article and the algorithm described is not a part of the marketed product. Described algorithm was developed as a part of the research PhD project. Luca Morettoni was participating only in development of the algorithm as a consultant programmer and did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. Tatiana Radchenko, Fabien Fontaine, Ismael Zamora are employed by Lead Molecular Design S.L. There are no patents. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. The functionality described in the article and the algorithm described is not a part of the marketed product. Described algorithm was developed as a part of the research PhD project. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

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