Enzyme structure correlates with variant effect predictability

Floris van der Flier, Dave Estell, Sina Pricelius, Lydia Dankmeyer, Sander van Stigt Thans, Harm Mulder, Rei Otsuka, Frits Goedegebuur, Laurens Lammerts, Diego Staphorst, Aalt D.J. van Dijk, Dick de Ridder, Henning Redestig*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Protein engineering increasingly relies on machine learning models to computationally pre-screen promising novel candidates. Although machine learning approaches have proven effective, their performance on prospective screening data leaves room for improvement; prediction accuracy can vary greatly from one protein variant to the next. So far, it is unclear what characterizes variants that are associated with large prediction error. In order to establish whether structural characteristics influence predictability, we created a novel high-order combinatorial dataset for an enzyme spanning 3,706 variants, that can be partitioned into subsets of variants with mutations at positions exclusively belonging to a particular structural class. By training four different supervised variant effect prediction (VEP) models on structurally partitioned subsets of our data, we found that predictability strongly depended on all four structural characteristics we tested; buriedness, number of contact residues, proximity to the active site and presence of secondary structure elements. These dependencies were also found in several single mutation enzyme variant datasets, albeit with dataset specific directions. Most importantly, we found that these dependencies were similar for all four models we tested, indicating that there are specific structure and function determinants that are insufficiently accounted for by current machine learning algorithms. Overall, our findings suggest that improvements can be made to VEP models by exploring new inductive biases and by leveraging different data modalities of protein variants, and that stratified dataset design can highlight areas of improvement for machine learning guided protein engineering.

Original languageEnglish
Pages (from-to)3489-3497
Number of pages9
JournalComputational and Structural Biotechnology Journal
Volume23
DOIs
Publication statusPublished - Dec 2024

Keywords

  • Machine learning
  • Predictability
  • Protein engineering

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