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Protein backbone fragment libraries have been used successfully in a variety of structural biology applications, including homology modeling,〔Kolodny, R., Guibas, L., Levitt, M., and Koehl, P. (2005, March). Inverse Kinematics in Biology: The Protein Loop Closure Problem. The International Journal of Robotics Research 24(2-3), 151-163.〕 de novo structure prediction,〔 Simons, K., Kooperberg, C., Huang, E., and Baker, D. (1997). Assembly of Protein Tertiary Structures from Fragments with Similar Local Sequences using Simulated Annealing and Bayesian Scoring Functions. J Mol Biol 268, 209-225.〕〔Bujnicki, J. (2006) Protein Structure Prediction by Recombination of Fragments. ChemBioChem. 7, 19-27.〕〔Li, S. et al. (2008) Fragment-HMM: A New Approach to Protein Structure Prediction. Protein Science. 17, 1925-1934.〕 and structure determination.〔DiMaio, F., Shavlik, J., Phillips, G. A probabilistic approach to protein backbone tracing in electron density maps (2006). Bioinformatics 22(14), 81-89.〕 By reducing the complexity of the search space, these fragment libraries enable more rapid search of conformational space, leading to more efficient and accurate models. == Motivation == Proteins can adopt an exponential number of states when modeled discretely. Typically, a protein's conformations are represented as sets of dihedral angles, bond lengths, and bond angles between all connected atoms. The most common simplification is to assume ideal bond lengths and bond angles. However, this still leaves the phi-psi angles of the backbone, and up to four dihedral angles for each side chain, leading to a worst case complexity of ''k''6 *''n'' possible states of the protein, where ''n'' is the number of residues and ''k'' is the number of discrete states modeled for each dihedral angle. In order to reduce the conformational space, one can use protein fragment libraries rather than explicitly model every phi-psi angle. Fragments are short segments of the peptide backbone, typically from 5 to 15 residues long, and do not include the side chains. They may specify the location of just the C-alpha atoms if it is a reduced atom representation, or all the backbone heavy atoms (N, C-alpha, C carbonyl, O). Note that side chains are typically not modeled using the fragment library approach. To model discrete states of a side chain, one could use a rotamer library approach.〔Canutescu, A., Shelenkov, A., and Dunbrack, R. (2003). A graph theory algorithm for protein side-chain prediction. Protein Sci. 12, 2001–2014.〕 This approach operates under the assumption that local interactions play a large role in stabilizing the overall protein conformation. In any short sequence, the molecular forces constrain the structure, leading to only a small number of possible conformations, which can be modeled by fragments. Indeed, according to Levinthal's paradox, a protein could not possibly sample all possible conformations within a biologically reasonable amount of time. Locally stabilized structures would reduce the search space and allow proteins to fold on the order of milliseconds. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Protein fragment library」の詳細全文を読む スポンサード リンク
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