학술논문
From systems to structure - using genetic data to model protein structures.
Document Type
Academic Journal
Author
Braberg H; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.; Echeverria I; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.; Kaake RM; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.; Gladstone Institutes, San Francisco, CA, USA.; Sali A; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.; Krogan NJ; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA. nevan.krogan@ucsf.edu.; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA. nevan.krogan@ucsf.edu.; Gladstone Institutes, San Francisco, CA, USA. nevan.krogan@ucsf.edu.; Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. nevan.krogan@ucsf.edu.
Source
Publisher: Nature Pub. Group Country of Publication: England NLM ID: 100962779 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1471-0064 (Electronic) Linking ISSN: 14710056 NLM ISO Abbreviation: Nat Rev Genet Subsets: MEDLINE
Subject
Language
English
Abstract
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.
(© 2022. Springer Nature Limited.)
(© 2022. Springer Nature Limited.)