IEEE Transactions on Nanobioscience
We describe efficient methods for consistently coloring and visualizing collections of rigid cluster decompositions obtained from variations of a protein structure, and lay the foundation for more complex setups, that may involve different computational and experimental methods. The focus here is on three biological applications: the conceptually simpler problems of visualizing results of dilution and mutation analyses, and the more complex task of matching decompositions of multiple Nucleic Magnetic Resonance (NMR) models of the same protein. Implemented into the KINematics And RIgidity (KINARI) web server application, the improved visualization techniques give useful information about protein folding cores, help examining the effect of mutations on protein flexibility and function, and provide insights into the structural motions of Protein Data Bank proteins solved with solution NMR. These tools have been developed with the goal of improving and validating rigidity analysis as a credible coarse-grained model capturing essential information about a protein's slow motions near the native state.
Bipartite graphs/optimal matching, Computational biology/computational biochemistry, Computational geometry, Nanobiotechnology, Nuclear magnetic resonance (NMR), Proteins
Flynn, Emily and Streinu, Ileana, "Matching Multiple Rigid Domain Decompositions of Proteins" (2017). Computer Science: Faculty Publications, Smith College, Northampton, MA.