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Exploring the energy landscapes of molecular recognition by a genetic algorithm: Analysis of the requirements for robust docking of HIV‐1 protease and FKBP‐12 complexes
Author(s) -
Verkhivker Gennady M.,
Rejto Paul A.,
Gehlhaar Daniel K.,
Freer Stephan T.
Publication year - 1996
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/(sici)1097-0134(199607)25:3<342::aid-prot6>3.0.co;2-h
Subject(s) - docking (animal) , fkbp , human immunodeficiency virus (hiv) , hiv 1 protease , computational biology , protease , genetic algorithm , computer science , algorithm , chemistry , artificial intelligence , biological system , combinatorial chemistry , biology , machine learning , biochemistry , virology , enzyme , medicine , nursing
Energy landscapes of molecular recognition are explored by performing “semi‐rigid” docking of FK‐506 and rapamycin with the Fukisawa binding protein (FKBP‐12), and flexible docking simulations of the Ro‐31‐8959 and AG‐1284 inhibitors with HIV‐1 protease by a genetic algorithm. The requirements of a molecular recognition model to meet thermodynamic and kinetic criteria of ligand‐protein docking simultaneously are investigated using a family of simple molecular recognition energy functions. The critical factor that determines the success rate in predicting the structure of ligand‐protein complexes is found to be the roughness of the binding energy landscape, in accordance with a minimal frustration principle. The results suggest that further progress in structure prediction of ligand‐protein complexes can be achieved by designing molecular recognition energy functions that generate binding landscapes with reduced frustration. © 1996 Wiley‐Liss, Inc.