Interpreting a black box predictor to gain insights into early folding mechanisms

Protein folding and function are closely connected, but the exact mechanisms by which proteins fold remain elusive. Early folding residues (EFRs) are amino acids within a particular protein that induce the very first stages of the folding process. High-resolution EFR data are only available for few proteins, which has previously enabled the training of a protein sequence-based machine learning 'black box' predictor (EFoldMine). Such a black box approach does not allow a direct extraction of the 'early folding rules' embedded in the protein sequence, whilst such interpretation is essential to improve our understanding of how the folding process works. We here apply and investigate a novel 'grey box' approach to the prediction of EFRs from protein sequence to gain mechanistic residue-level insights into the sequence determinants of EFRs in proteins. We interpret the rule set for three datasets, a default set comprised of natural proteins, a scrambled set comprised of the scrambled default set sequences, and a set of de novo designed proteins. Finally, we relate these data to the secondary structure adopted in the folded protein and provide all information online via http://xefoldmine.bio2byte.be/, as a resource to help understand and steer early protein folding. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study investigates a novel approach to predict early folding residues (EFRs) from protein sequences to gain mechanistic residue-level insights into the sequence determinants of EFRs in proteins.