Graphical models of residue coupling in protein families

By John Thomas, Naren Ramakrishnan, C. Bailey‐Kellogg. 2005

76 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods, Computational Biology & Bioinformatics

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Citations in the corpus

Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[5] John Thomas, Naren Ramakrishnan, and Chris Bailey-Kellogg.”
MSA Transformer (2021, neutral): “Correspondence to: Roshan Rao < rmrao@berkeley.edu > , Alexander Rives < arives@fb.com > . signal (Lapedes et al., 1999; Thomas et al., 2008; Weigt et al., 2009).”
Learning generative models for protein fold families (2011, methodology): “A compares our structure learning method with the algorithm in [12].”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, background): “Recent work has demonstrated the accuracy of coevolution-based contact prediction for monomeric proteins using a global statistical model ( Thomas et al., 2008 ) to distinguish between direct and indirect couplings ( Marks et al., 2011 ; Morcos et al., 2011 ; Hopf et al., 2012 ; Nugent and Jones,…”
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, methodology): “ethods than MI, such as maximum entropy and probabilistic graphical models, are developed to infer residue co-evolution from MSA (Balakrishnan, et al., 2011; Jones, et al., 2012; Marks, et al., 2011; Thomas, et al., 2008; Thomas, et al., 2009; Thomas, et al., 2009; Weigt, et al., 2009). These probab”
Transformer protein language models are unsupervised structure learners (2020, background): “This was proposed by Lapedes et al. (1999) and reintroduced by Thomas et al. (2008) and Weigt et al. (2009).”
A structural biology community assessment of AlphaFold2 applications (2021, methodology): “For visualization, we performed a t -distributed stochastic alignments 2–5 , and an understanding of how this information is eventually encoded into a predicted 3D structure 6–8 .”
Unified framework for modeling multivariate distributions in biological sequences (2019, background): “A Markov Random Field, or Potts model, generalizes the PSSMs to also include two-body interaction terms (Lapedes et al., 1999; Thomas et al., 2005; Balakrishnan et al., 2009; Weigt et al., 2009).”
Single Layers of Attention Suffice to Predict Protein Contacts (2020, methodology): “…in MSAs can be used to predict couplings has been widely used to infer structure and function from protein families (Korber et al., 1993; G¨obel et al., 1994; Lapedes et al., 1999; Lockless & Ranganathan, 1999; Fodor & Aldrich, 2004; Thomas et al., 2008; Weigt et al., 2009; Fatakia et al., 2009).”
Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors (2009, methodology): “The prediction of a cohort of functionally important, specificity determining, or coevolving residues, without involving MI, has also been addressed extensively [15,18,19,57,77,78,80–83,83–117] and a few of these strategies were summarized by those previously cited and by Ortiz and colleagues [118] ”
Graphical Models of Residue Coupling in Protein Families (2008, methodology): “Previously, we have found that our results are relatively insensitive to a range of values [37].”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “SCA was successfully applied to G-protein coupled receptors (GPCRs) and heterotrimeric G proteins to identify allosteric networks of residues involved in ligand binding and activation and GPCR dimerization [64,66–68] (Figure 3A).”
Finding and Leveraging Structure in Learning Problems (2012, methodology): “MRFs are widely used in a variety of domains, from statistical physics [Ising, 1925], natural language processing [Manning and Schütze, 1999], to computational biology [Liu et al., 2005, Kamisetty et al., 2008, Thomas et al., 2005], and computer vision [Blake et al., 2011] amongst several others.”