Encore Abstract: Interpreting a Black-Box Predictor to Gain Insights into Early Folding Mechanisms

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian-Dutch Conference on Machine Learning BNAIC/BeneLearn 2021
Pages674-676
Number of pages3
Publication statusPublished - 2021
Event33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning - , Luxembourg
Duration: 10 Nov 202112 Nov 2021
https://bnaic2021.uni.lu/

Conference

Conference33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
Abbreviated titleBNAIC/BeneLearn 2021
CountryLuxembourg
Period10/11/2112/11/21
Internet address

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