Issue 4, 2023

Benchmarking protein structure predictors to assist machine learning-guided peptide discovery

Abstract

Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles. Nevertheless, they are primarily trained on sequences where the tridimensional structures of peptides and proteins are often overlooked. We need a fast and reliable approach to estimate the structural diversity of medium-large training sets before building models. This study benchmarked four protein structure prediction methods (Jpred4, PEP2D, PSIPRED, AlphaFold2) using 261 curated and experimentally known structures from the PDBe database. We applied our best predictor to map the structural landscape of GRAMPA, the giant and vastly uncharted repository of 5980 antimicrobial peptides. The dataset was predominantly made of loose helices (65.1%), followed by random coils (17.8%), and β-stranded and mixed structures accounted for the rest.

Graphical abstract: Benchmarking protein structure predictors to assist machine learning-guided peptide discovery

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Article information

Article type
Paper
Submitted
17 Mar 2023
Accepted
02 Jun 2023
First published
02 Jun 2023
This article is Open Access
Creative Commons BY license

Digital Discovery, 2023,2, 981-993

Benchmarking protein structure predictors to assist machine learning-guided peptide discovery

V. D. Aldas-Bulos and F. Plisson, Digital Discovery, 2023, 2, 981 DOI: 10.1039/D3DD00045A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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