Rodrigo Ochoa1,2, Mikhail Magnitov3,4, Roman A Laskowski3, Pilar Cossio5,6, Janet M Thornton3. 1. Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, 050010, Medellín, Colombia. rodrigo.ochoa@udea.edu.co. 2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. rodrigo.ochoa@udea.edu.co. 3. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. 4. Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia, 141701. 5. Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, 050010, Medellín, Colombia. 6. Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438, Frankfurt am Main, Germany.
Abstract
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.
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.
Authors: Mari Enoksson; Jingwei Li; Melanie M Ivancic; John C Timmer; Eric Wildfang; Alexey Eroshkin; Guy S Salvesen; W Andy Tao Journal: J Proteome Res Date: 2007-06-05 Impact factor: 4.466
Authors: Rebecca F Alford; Andrew Leaver-Fay; Jeliazko R Jeliazkov; Matthew J O'Meara; Frank P DiMaio; Hahnbeom Park; Maxim V Shapovalov; P Douglas Renfrew; Vikram K Mulligan; Kalli Kappel; Jason W Labonte; Michael S Pacella; Richard Bonneau; Philip Bradley; Roland L Dunbrack; Rhiju Das; David Baker; Brian Kuhlman; Tanja Kortemme; Jeffrey J Gray Journal: J Chem Theory Comput Date: 2017-05-12 Impact factor: 6.006