Protein Design by Sampling an Undirected Graphical Model of Residue Constraints

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Learning generative models for protein fold families (2011, methodology): “Thomas and colleagues address that problem using a greedy algorithm, called GMRC , that adds edges between nodes with high mutual information [11, 12, 13, 14].”
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, methodology): “s 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 probabilistic methods can di”
Finding and Leveraging Structure in Learning Problems (2012, methodology): “They then extended this approach to incorporate interaction data to learn models over pairs of interacting proteins [187] and also develop a sampling algorithm for protein design using such models [189].”
MODELING AND INFERENCE OF SEQUENCE-STRUCTURE SPECIFICITY (2009, methodology): “While we focus on modeling sequence-structure specificity, our approach directly supports the design of proteins to maximize this specificity using methods similar to [7] or [24].”