Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus.

By D. Altschuh, Arthur M. Lesk, A. Bloomer, A. Klug. 1987

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).”
Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, positive): “…performance in the CASP14 competition 6 in part by advancing the state of the art for inferring patterns of interactions between related sequences in a multiple-sequence alignment (MSA), building on a long history of methods for inferring these patterns 7–10 , often called evolutionary couplings.”
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, background): “residue contacts/distances share the same cornerstones: constructing multiple sequence alignment (MSA) for target protein and then exploiting MSA using the principle of residue co-evolution [8–10].”
Direct-coupling analysis of residue coevolution captures native contacts across many protein families (2011, methodology): “ic implementation of the mean-field approximation is sketched in the following steps: 1. Estimate the frequency counts € f i (A) and € f ij (A,B) from the MSA, using the pseudo-count € λ=M eff in Eq. (1) and (2). 2. Determine the empirical estimate of the connected correlation matrix Eq. (10). 3. De”
Protein structure prediction from sequence variation (2012, methodology): “In the 18-year history of contact-prediction methods using correlated mutations, all methods used local mutual information or other local statistical models 28–33 , with one notable but unnoticed exception 17 .”
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners (2014, neutral): “…of amino-acid sequences in homologous protein families, e.g. via the inference of signals of co-evolution between residues, which may be distant along the sequence, but in contact in the folded protein; cf. [1–6] for a selection of classical works and [7] for a review over recent developments.”
Why similar protein sequences encode similar three-dimensional structures? (2010, unknown): “Such a phenomenon was shown previously [32].”
Influence of conservation on calculations of amino acid covariance in multiple sequence alignments (2004, methodology): “To do this, we constrained the relationship between i and j so that it was always true that j i. So, for example, for columns 1 and 5 in an alignment, we used the most conserved residue in column 1 to form the subalignment and reported the SCA score for SCA(1, 5 ) but not SCA(5, 1). One can imagine”
Determination of network of residues that regulate allostery in protein families using sequence analysis (2006, methodology): “A sequence-based method (Lesk and Chothia, 1980; Altschuh et al., 1987; Neher, 1994; Taylor and Hatrick, 1994; Pollock and Taylor, 1997; Pazos et al., 1997; Olmea et al., 1999; Valencia and Pazos, 2002; Lichtarge et al., 1996; Sowa et al., 2001; Kass and Horovitz, 2002) is desirable because a larger”
Inferring interaction partners from protein sequences (2016, background): “[2] Altschuh D, Lesk AM, Bloomer AC, Klug A (1987) Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus.”
Power law tails in phylogenetic systems (2018, background): “In contrast, for proteins, the signal is less strong, and for many years it was unclear whether any remnant of molecular phenotypes such as protein structure is imprinted on covarying sequence positions (2–4).”