Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the unin-terrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.(c) 2023 The Authors. 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 license (http://creative-commons.org/licenses/by/4.0/).

Automated summary

Recent breakthroughs in protein structure prediction indicate a new era in structural bioinformatics. Machine learning methods in protein bioinformatics are shifting from sequence-based approaches to utilizing structural information as input. This review explores the diversity of structure-based machine learning approaches, input methods, and typical applications in protein biology, while also discussing the current challenges and opportunities in this important and growing field.