From: Jeff Solka <[log in to unmask]>Date: February 10, 2013 7:51:14 AM ESTTo: Tiffany C Sandstrum <[log in to unmask]>Subject: colloquium speaker for 2/12/13*******************************Protein analysis and engineering using computational mutagenesis. (Iosif Vaisman Ph.D., GMU)
Proteins exhibit a wide range of functional consequences upon mutation. Accurate predictive models for the impact of amino acid substitutions on protein stability and activity provide important insights into protein structure, function, and evolution. Such models are also valuable for the study of the effects on phenotype, and can be used to modify protein properties or design new proteins. This approach is based on the computational mutagenesis technique, which utilizes a four-body, knowledge-based, statistical contact potential and machine learning methods. For any mutation due to a single or multiple amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. The predictive mutagenesis models of structure-function relationships provide accurate identification of the positions in protein sequence, where introduced substitutions are likely to result in a protein with desired properties.************************************************************************--Everything is founded on Mind, is made of Mind. To act or speak with a pure mind is happiness.
THE BUDDHA