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Journal Article

An automated protocol for modelling peptide substrates to proteases


Cossio,  Pilar
Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, 050010, Medellín, Colombia;
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;

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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): 586. doi:10.1186/s12859-020-03931-6.

Cite as: http://hdl.handle.net/21.11116/0000-0007-9F9C-7
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.