Benchmarking Peptide Structure Prediction with AlphaFold2

Abstract

AbstractAlphaFold2 (AF2) is a computational tool developed for the determination of protein structures with high accuracy. AF2 has been used for the modeling of many soluble and membrane proteins, but its performance in modeling peptide structures has not been systematically investigated so far. We benchmarked the accuracy of AF2 in predicting peptide structures between 16 – 60 amino acids using experimentally-determined peptide structures as reference. Our results show that while AF2 can predict the structures of certain peptide scaffolds with RMSD values below 3 Å, it is less successful in predicting the structures of peptides that have kinks, turns, or have extended flexible regions. Further, AF2 had several shortcomings in predicting rotamer recoveries, disulfide bonds, and the lowest RMSD structures based on pLDDT values. In summary, AF2 can be a powerful tool to determine peptide structures, but additional steps may be necessary to analyze and validate the results.