From systems to structure — using genetic data to model protein structures

• Published in: Nature reviews. Genetics Vol. 23; no. 6; pp. 342 - 354
• Main Authors: Braberg, Hannes, Echeverria, Ignacia, Kaake, Robyn M., Sali, Andrej, Krogan, Nevan J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.06.2022; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/2163; 631/1647/1513; 631/535; 631/553/2490/1472; Agriculture; Animal Genetics and Genomics; Biomedical and Life Sciences; Biomedicine; Cancer Research; Coevolution; Deep learning; Epistasis, Genetic; Gene Function; Gene mapping; Gene Regulatory Networks; Genetic diversity; Genomes; Human Genetics; Integration; Molecular modelling; Mutation; Peptide mapping; Protein interaction; Protein Interaction Mapping; Protein Interaction Maps; Proteins; Proteins - genetics; Proteins - metabolism; Review; Review Article; Scanning
• ISSN: 1471-0056; 1471-0064; 1471-0064
• DOI: 10.1038/s41576-021-00441-w

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical–genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources. Large-scale genetic datasets and deep learning approaches are being used to model the structures of proteins or protein complexes. This Review describes approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical–genetic interaction mapping and their application and integration to inform structural modelling.