A Generalized Attraction–Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Abstract

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for nonbonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

A drug delivery system constructed by a fusion peptide capturing exosomes targets to titanium implants accurately resulting the enhancement of osseointegration peri-implant

“…The result of CLSM also indicated large proportion of PEP3 adsorbed exosomes and PEP1 and PEP2 adsorbed exosomes in a low proportion. It may be because the different peptides structures influenced the function of CP05 motifs that three peptides showed different binding force with CD63 [ 55 ].…”

A refined pH-dependent coarse-grained model for peptide structure prediction in aqueous solution

“…The sOPEP2 potential, to be used in a discrete space, originates from the OPEP potential which uses an explicit representation of the backbone (N, H, C α , N and H atoms) and one bead for each side chain, whose position to C α and Van der Walls radius depend on the amino acid type ( Maupetit et al, 2007 ; Sterpone et al, 2014 ). The sOPEP2 is expressed as a sum of local, non-bonded and hydrogen-bond (H-bond) terms, with all parameters described in ( Binette et al, 2022 ). …”

Binding and Kinetic Analysis of Human Protein Phosphatase PP2A Interactions with Caspase 9 Protein and the Interfering Peptide C9h

“…To generate initial poses, we have used PEP-FOLD using the sOPEP2 force-field [ 16 ], taking as constraints positions generated from the amino acids of PP2A identified by PEPscan as being at the interface between catalytic subunits alpha of the PP2A (PP2Acα and Caspase 9) similarly to the protocol previously described [ 17 ]. One hundred models were generated and clustered using ligand RMSD as criterion.…”