Abstract

DEEPSAM is a relatively new global optimization algorithm aimed to predict the structure of bio-molecules from sequence, without any additional preliminary assumption. It is an evolutionary algorithm whose mutation operators are built by hybridizing the diffusion equation method, molecular dynamics simulated annealing, and a quasi-Newton local minimization method. The goal of this study was to evaluate the structure prediction capabilities of DEEPSAM by running it upon NMR structures of linear peptides (10–20 residues). The results indicate that DEEPSAM successfully predicted the conformations of these peptides, using modest computing resources.

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