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Systematic optimization model and algorithm for binding sequence selection in computational enzyme design
Author(s) -
Huang Xiaoqiang,
Han Kehang,
Zhu Yushan
Publication year - 2013
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.2275
Subject(s) - sequence (biology) , active site , heuristic , saddle point , protein design , energy minimization , optimization problem , computer science , chemistry , algorithm , mathematical optimization , biological system , enzyme , protein structure , mathematics , computational chemistry , biology , biochemistry , geometry
Abstract A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph‐theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions.