An automated protocol for modelling peptide substrates to proteases

Rodrigo Ochoa,Roman A Laskowski,Pilar Cossio,Janet M Thornton,Mikhail Magnitov

doi:10.1186/s12859-020-03931-6

Abstract

BackgroundProteases 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.ResultsAs 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.ConclusionOverall, 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.

Highlights

Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates
Annotation and modelling results Based on the criteria used to annotate the proteins, we obtained a list of enzyme structures in complex to peptide-like ligands
Here we provide an open source protocol to model peptide substrates bound to wellannotated structures of proteases, which can be applied to help gain an insight of their binding recognition using a structure and dynamic-based approach

Summary

Introduction

Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. Proteases are enzymes present in all species, from bacteria to vertebrates, and they account for approximately 2% of the genes in humans, second in number only to transcription factors [1] These enzymes are involved in almost all fundamental processes in the cell, catalysing the cleavage of peptide bonds both in proteins and oligomeric peptides [2, 3]. Recognition and binding of a polypeptide substrate, which is cleaved at a specific peptide bond in the active site, occurs via pockets which accommodate specific amino acid side chains [3, 4]. The serine proteases are the largest and the best-studied class of proteases [5, 6] They contain the classic Asp-His-Ser catalytic triad, displaying a generally accepted cleavage mechanism for a large number of diverse amino acidic substrates

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