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Rapid and accurate structure‐based therapeutic peptide design using GPU accelerated thermodynamic integration
Author(s) -
Garton Michael,
CorbiVerge Carles,
Hu Yuan,
Nim Satra,
Tarasova Nadya,
Sherborne Brad,
Kim Philip M.
Publication year - 2019
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25644
Subject(s) - peptide , chemistry , combinatorial chemistry , amino acid , thermodynamic integration , molecular dynamics , computer science , materials science , computational chemistry , biochemistry
Peptide‐based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side‐chains was found to be less accurate than for non‐charged, and use of a more complex 3‐step TI protocol was found to boost accuracy in these cases. Using the 3‐step protocol for non‐charged side‐chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non‐canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.