Coordinated amino acid changes in homologous protein families.

By D. Altschuh, T. Vernet, P. Berti, D. Moras, K. Nagai. 1988

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “The idea that biological function and structure are recorded in the statistics of protein sequences selected through evolution has a long history (Yanofsky et al., 1964; Altschuh et al., 1987; 1988).”
Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[2] D Altschuh, T Vernet, P Berti, D Moras, and K Nagai.”
MSA Transformer (2021, background): “Sequence variation within a protein family conveys information about the structure of the protein (Yanofsky et al., 1964; Altschuh et al., 1988; G¨obel et al., 1994).”
Evaluating Protein Transfer Learning with TAPE (2019, background): “Analogously, there is good reason to believe that unlabelled protein sequences contain significant information about their structure and function [2, 4].”
Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “This suggests that residue correlations could provide information about amino acid residues that are close in structure [4,5,6,7,8,9,10,11].”
Learning generative models for protein fold families (2011, background): “Much of the early work focused on detecting such pairs in order to predict contacts in a protein in the absence of a solved structure [27, 28] and to perform fold recognition.”
Language models enable zero-shot prediction of the effects of mutations on protein function (2021, background): “Statistical patterns in a family of evolutionarily related protein sequences contain information about structure and function [5–7].”
3D Protein Structure Predicted from Sequence (2011, unknown): “This suggests that residue correlation can provide information about amino acid residues that are close in structure [4,5,6,7,8,9,10,11].”
News from the protein mutability landscape. (2013, background): “[73] Altschuh D, Vernet T, Berti P, Moras D, Nagai K.”
Correlated mutations contain information about protein-protein interaction. (1997, background): “Several groups have studied correlated mutations: technical differences between different approaches have led to con¯icting conclusions about the nature and intensity of this phenomena (Altschuh et al., 1987, 1988; Go È bel et al., 1994; Neher, 1994; Shindyalov et al., 1994; Taylor & Hatrick, 1994).”
Finding and Leveraging Structure in Learning Problems (2012, background): “Much of the early work focused on detecting such pairs in order to predict contacts in a protein in the absence of a solved structure [Altschuh et al., 1988, Göbel et al., 1994] and to perform fold recognition.”