Computational protocol: A conservation and rigidity based method for detecting critical protein residues

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[…] Rigidity analysis [] is an efficient graph-based method alternative to molecular simulations, that gives information about the flexibility properties of proteins. Atoms and their chemical interactions are used to construct a mechanical model of a molecule, in which covalent bonds are represented as hinges, and other stabilizing interactions such as hydrogen bonds and hydrophobic interactions are represented as hinges or bars. The mechanical model is used to construct a graph, in which each body is associated to a node, a hinge between two bodies is associated to five edges between two nodes, and a bar is associated to an edge. Efficient algorithms based on the pebble game paradigm [] are used to analyze the rigidity of the graph. The rigidity results are used to infer the rigid and flexible regions of the mechanical model, and hence the protein. In Figure ), we show the cartoon rendering of Staphylococcal Nuclease (PDB ID 1stn). The visualization of its rigidity properties calculated using KINARI-Web are shown in Figure ), where color bodies indicate clusters of atoms that are rigid.In this study, we used KINARI-Mutagen [], which is part of the KINARI [] software, to perform fast evaluation of the effects of mutations that may not be easy to perform in vitro, because it is not always possible to express a protein with a specific amino acid substitution. The publicly available KINARI-Mutagen tool simulates the mutation of a residue to glycine by removing its side-chain hydrogen bonds and hydrophobic interactions from the molecular model and measuring the effect of the removal on the stability of the protein structure. A new, not yet publicly released, feature of KINARI-Mutagen that was developed specifically for this study was its ability to in-silico mutate residues to alanine, as well as to glcyine. Doing so allowed us to compare the rigidity results against a richer dataset of proteins, for which experimental data about the role of mutations to alanine is known. This new feature of in-silico mutating a residue to alanine will be made publicly available during an upcoming update to the KINARI web server. KINARI-Mutagen identifies critical residues based on the degree to which an in silico mutation to glycine affects the protein's rigidity. It has been demonstrated in identifying critical residues in Crambin. Also, its predictive capabilities to identify critical residues were evaluated on a dataset of 48 mutants from 14 proteins; predictions made by KINARI-Mutagen were correlated against experimental stability measurements []. […]

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