Low-energy conformation search on biological macromolecules remains a
challenge in biochemical experiments and theoretical studies. Finding efficient
approaches to minimize the energy of peptide structures is critically needed for
researchers either studying peptide-protein interactions or designing peptide drugs. In this
study, we aim to develop a heuristic-based algorithm to efficiently minimize a promising
PD-L1 inhibiting polypeptide, TPP-1, and build its low-energy conformer pool to
advance its subsequent structure optimization and molecular docking studies. Through
our study, we find that, using backbone dihedral angles as the decision variables, both
PSO and GA can outperform other existing heuristic approaches in optimizing the
structure of Met-enkephalin, a benchmarking pentapeptide for evaluating the efficiency
of conformation optimizers. Using the established algorithm pipeline, hybridizing PSO
and GA minimized TPP-1 structure efficiently and a low-energy pool was built with an
acceptable computational cost (a couple days using a single laptop). Remarkably, the
efficiency of hybrid PSO-GA is hundreds-fold higher than the conventional Molecular
Dynamic simulations running under the force filed. Meanwhile, the stereo-chemical
quality of the minimized structures was validated using Ramachandran plot. In summary,
hybrid PSO-GA minimizes TPP-1 structure efficiently and yields a low-energy
conformer pool within a reasonably short time period. Overall, our approach can be
extended to biochemical research to speed up the peptide conformation determinations
and hence can facilitate peptide-involved drug development
