Relationships among amino acids determine stability and function and are also constrained by evolutionary history. We develop a probabilistic hypergraph model of residue relationships that generalizes traditional pairwise contact potentials to account for the statistics of multi-residue interactions. Using this model, we detected non-random associations in protein families and in the protein database. We also use this model in optimizing site-directed recombination experiments to preserve significant interactions and thereby increase the frequency of generating useful recombinants. We formulate the optimization as a sequentially-constrained hypergraph partitioning problem; the quality of recombinant libraries wrt a set of breakpoints is characterized by the total perturbation to edge weights. We prove this problem to be NP-hard in general, but develop exact and heuristic polynomial-time algorithms for a number of important cases. Application to the beta-lactamase family demonstrates the utility of our algorithms in planning site-directed recombination. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Ye, X., Friedman, A. M., & Bailey-Kellogg, C. (2006). Hypergraph model of multi-residue interactions in proteins: Sequentially-constrained partitioning algorithms for optimization of site-directed protein recombination. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3909 LNBI, pp. 15–29). https://doi.org/10.1007/11732990_2
Mendeley helps you to discover research relevant for your work.