Novel machine learning approaches revolutionize protein knowledge

作者：Nicola Bordin, Christian Dallago, Michael Heinzinger, Stephanie S. Kim, Maria Littmann, Clemens Rauer, Martin Steinegger, Burkhard Rost, Christine A. Orengo · 发表于：Trends in Biochemical Sciences · 年份：2022 · DOI：10.1016/j.tibs.2022.11.001 · 被引用次数：77 · 研究领域：Protein Structure and Dynamics、Genomics and Phylogenetic Studies、Enzyme Structure and Function

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.