Statistical Theory for Protein Combinatorial Libraries

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Combinatorial experiments provide new ways to probe the determinants of protein folding and to identify novel folding amino acid sequences. These types of experiments, however, are complicated by both enormous conformational complexity and by large numbers of possible sequences. We present and apply a statistically based, computational approach for identifying the properties of sequences compatible with a given main chain structure. The method yields the likelihood of each of the amino acids at preselected positions in a given protein structure. The theory may be used to quantify the characteristics of sequence space for a chosen structure without explicitly tabulating sequences. We apply the method to consider the energetic separation of a target structure from other possible structures and to identity the monomer probabilities at selected positions of the immunoglobulin light chain-binding domain of protein L, for which many variant folding sequences are available.

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Journal: TechConnect Briefs
Volume: 2, Technical Proceedings of the 2001 International Conference on Computational Nanoscience and Nanotechnology
Published: March 19, 2001
Pages: 33 - 36
Industry sectors: Advanced Materials & Manufacturing | Medical & Biotech
Topics: Biomaterials, Informatics, Modeling & Simulation
ISBN: 0-9708275-3-9