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

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 non-bonded 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.