An automated protocol for modelling peptide substrates to proteases

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.

Protocol for iterative optimization of modified peptides bound to protein targets

“…To test mPARCE we selected a well-characterized protease system (PDB id 3tjv) bound to a 9-mer peptide substrate. The peptide covers the cleavage binding site from position S4′ to S4, including the catalytic region between S1′ and S1 [ 51 ]. The peptide consists of 9 natural amino acids, and the goal was to allow changes in four positions, covering both the flanking and core amino acids close to the catalytic site.…”

PepFun: Open Source Protocols for Peptide-Related Computational Analysis

“…On average the protease-binding peptides tend to be negatively charged, are smaller than the MHC peptide binders, and less hydrophobic. However, based on a study of proteases specificity profiles [ 27 ], the sequences can be diverse in terms of their physico-chemical properties such as the hydrophobicity and net charge. Therefore, challenges remain in the prediction of substrate cleavage patterns by machine learning and sequence-based methodologies, which can be aided by tools like PepFun.…”

“…In ref. [27], we found that several structural descriptors, which are normally not used for cleavage predictions (because of the lack of protease structures bound to complete substrates), can provide relevant insights about the enzyme specificity. The second type of interactions calculated with PepFun are the potential hydrogen bonds.…”

Embracing enzyme promiscuity with activity-based compressed biosensing

“…To accelerate the process of designing specific substrates, methods to generate and screen libraries of peptide sequences have been developed, including positional scanning libraries [27][28][29] , peptide microarrays 30,31 , fluorogenic peptides 32,33 , and other mixture-based peptide libraries 34,35 . These libraries are either degenerate or diversified at certain positions based on consensus cleavage motifs from the literature 36 or computational approaches to predict peptide sequences based on the structure of the active site of a target protease 37,38 (Fig. 1, step 2).…”