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

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 physica...

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Published inNature 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
LanguageEnglish
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
Online AccessGet full text
ISSN1471-0056
1471-0064
1471-0064
DOI10.1038/s41576-021-00441-w

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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. 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.
AbstractList 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.
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.
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.
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.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.
Author Echeverria, Ignacia
Kaake, Robyn M.
Braberg, Hannes
Krogan, Nevan J.
Sali, Andrej
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  organization: Department of Cellular and Molecular Pharmacology, University of California, San Francisco, Quantitative Biosciences Institute, University of California, San Francisco, Gladstone Institutes, Department of Microbiology, Icahn School of Medicine at Mount Sinai
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PublicationDecade 2020
PublicationPlace London
PublicationPlace_xml – name: London
– name: England
PublicationTitle Nature reviews. Genetics
PublicationTitleAbbrev Nat Rev Genet
PublicationTitleAlternate Nat Rev Genet
PublicationYear 2022
Publisher Nature Publishing Group UK
Nature Publishing Group
Publisher_xml – name: Nature Publishing Group UK
– name: Nature Publishing Group
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Snippet Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences...
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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
Title From systems to structure — using genetic data to model protein structures
URI https://link.springer.com/article/10.1038/s41576-021-00441-w
https://www.ncbi.nlm.nih.gov/pubmed/35013567
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Volume 23
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