New trends in computational structure prediction of ligand-protein complexes for receptor-based drug design

A number of challenging computational problems arise in the field of structure-based drug design, including the estimation of ligand binding affinity and the de novo design of novel ligands. An important step toward solutions of these problems is the consistent and rapid prediction of the thermodynamically most favorable structure of a ligand—protein complex from the three-dimensional structures of its unbound ligand and protein components. This fundamental problem in molecular recognition is commonly known as the docking problem [1–3]. To solve this problem, two distinct conditions must be satisfied. The first is a thermodynamic requirement: the energy function used to describe ligand—protein binding must have the crystal structure of ligand—protein complexes as its global energy minimum. The second is a kinetic requirement: it must be possible to locate consistently and rapidly the global energy minimum on the ligand—protein binding energy landscape. While the first condition is necessary for successful structure prediction, it is by no means sufficient. Without kinetic accessibility, the global minimum cannot be reached during docking simulations, and computational structure prediction will fail. Here we review approaches to address both the kinetic and thermodynamic aspects of the docking problem.

[1]  M J Sternberg,et al.  A continuum model for protein-protein interactions: application to the docking problem. , 1995, Journal of molecular biology.

[2]  D. K. Friesen,et al.  A combinatorial algorithm for calculating ligand binding , 1984 .

[3]  J. Onuchic,et al.  Funnels, pathways, and the energy landscape of protein folding: A synthesis , 1994, Proteins.

[4]  R M Knegtel,et al.  MONTY: a Monte Carlo approach to protein-DNA recognition. , 1994, Journal of molecular biology.

[5]  Ruben Abagyan,et al.  Detailed ab initio prediction of lysozyme–antibody complex with 1.6 Å accuracy , 1994, Nature Structural Biology.

[6]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[7]  A. Elofsson,et al.  Local moves: An efficient algorithm for simulation of protein folding , 1995, Proteins.

[8]  C. Aflalo,et al.  Hydrophobic docking: A proposed enhancement to molecular recognition techniques , 1994, Proteins.

[9]  J. Scott Dixon,et al.  Flexible ligand docking using a genetic algorithm , 1995, J. Comput. Aided Mol. Des..

[10]  A. Wlodawer,et al.  Structure-based inhibitors of HIV-1 protease. , 1993, Annual review of biochemistry.

[11]  Richard S. Judson,et al.  Docking flexible molecules: A case study of three proteins , 1995, J. Comput. Chem..

[12]  K. Appelt,et al.  Crystal structures of HIV-1 protease-inhibitor complexes , 1993 .

[13]  I. Kuntz,et al.  Docking flexible ligands to macromolecular receptors by molecular shape. , 1986, Journal of medicinal chemistry.

[14]  E. Shakhnovich,et al.  Engineering of stable and fast-folding sequences of model proteins. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[15]  I. Kuntz,et al.  Protein docking and complementarity. , 1991, Journal of molecular biology.

[16]  P Argos,et al.  Folding the main chain of small proteins with the genetic algorithm. , 1994, Journal of molecular biology.

[17]  K. Dill,et al.  Transition states and folding dynamics of proteins and heteropolymers , 1994 .

[18]  Randy J. Read,et al.  A multiple‐start Monte Carlo docking method , 1992 .

[19]  A Caflisch,et al.  Monte Carlo docking of oligopeptides to proteins , 1992, Proteins.

[20]  P. Wolynes,et al.  Protein tertiary structure recognition using optimized Hamiltonians with local interactions. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[21]  J. Janin,et al.  Computer analysis of protein-protein interaction. , 1978, Journal of molecular biology.

[22]  M. Karplus,et al.  Kinetics of protein folding. A lattice model study of the requirements for folding to the native state. , 1994, Journal of molecular biology.

[23]  P. Wolynes,et al.  Spin glasses and the statistical mechanics of protein folding. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[24]  S. L. Mayo,et al.  DREIDING: A generic force field for molecular simulations , 1990 .

[25]  A. Leach,et al.  Ligand docking to proteins with discrete side-chain flexibility. , 1994, Journal of molecular biology.

[26]  I. Kuntz,et al.  Matching chemistry and shape in molecular docking. , 1993, Protein engineering.

[27]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[28]  K Yue,et al.  Folding proteins with a simple energy function and extensive conformational searching , 1996, Protein science : a publication of the Protein Society.

[29]  D. Goodsell,et al.  Automated docking of substrates to proteins by simulated annealing , 1990, Proteins.

[30]  Yong L. Xiao,et al.  Genetic algorithms for docking of actinomycin D and deoxyguanosine molecules with comparison to the crystal structure of actinomycin D-deoxyguanosine complex , 1994 .

[31]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[32]  R. Nussinov,et al.  A geometry-based suite of molecular docking processes. , 1995, Journal of molecular biology.

[33]  S. Kim,et al.  "Soft docking": matching of molecular surface cubes. , 1991, Journal of molecular biology.

[34]  D E Koshland,et al.  Molecular recognition analyzed by docking simulations: the aspartate receptor and isocitrate dehydrogenase from Escherichia coli. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[35]  J A McCammon,et al.  Combined conformational search and finite-difference Poisson-Boltzmann approach for flexible docking. Application to an operator mutation in the lambda repressor-operator complex. , 1994, Journal of molecular biology.

[36]  R Unger,et al.  Genetic algorithms for protein folding simulations. , 1992, Journal of molecular biology.

[37]  J. Scott Dixon,et al.  A shape- and chemistry-based docking method and its use in the design of HIV-1 protease inhibitors , 1994, J. Comput. Aided Mol. Des..

[38]  S. Yue Distance-constrained molecular docking by simulated annealing. , 1990, Protein Engineering.

[39]  Gennady M Verkhivker,et al.  Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. , 1995, Chemistry & biology.

[40]  Gennady M Verkhivker,et al.  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. , 1996, Proteins.

[41]  Richard S. Judson,et al.  Analysis of the genetic algorithm method of molecular conformation determination , 1993, J. Comput. Chem..

[42]  Richard S. Judson,et al.  Conformational searching methods for small molecules. II. Genetic algorithm approach , 1993, J. Comput. Chem..

[43]  D. Yee,et al.  Principles of protein folding — A perspective from simple exact models , 1995, Protein science : a publication of the Protein Society.

[44]  J. Onuchic,et al.  Folding kinetics of proteinlike heteropolymers , 1994, cond-mat/9404001.

[45]  R. Glen,et al.  Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. , 1995, Journal of molecular biology.

[46]  A Wlodawer,et al.  X-ray crystallographic structure of a complex between a synthetic protease of human immunodeficiency virus 1 and a substrate-based hydroxyethylamine inhibitor. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[47]  P. Argos,et al.  Potential of genetic algorithms in protein folding and protein engineering simulations. , 1992, Protein engineering.

[48]  H J Berendsen,et al.  Molecular dynamics simulation of the docking of substrates to proteins , 1994, Proteins.

[49]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[50]  D K Gehlhaar,et al.  De novo design of enzyme inhibitors by Monte Carlo ligand generation. , 1995, Journal of medicinal chemistry.

[51]  J. Janin,et al.  Protein docking algorithms: simulating molecular recognition , 1993 .

[52]  S. Sun,et al.  Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms , 1993, Protein science : a publication of the Protein Society.

[53]  M J Sternberg,et al.  New algorithm to model protein-protein recognition based on surface complementarity. Applications to antibody-antigen docking. , 1992, Journal of molecular biology.

[54]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[55]  Huajun Wang Grid‐search molecular accessible surface algorithm for solving the protein docking problem , 1991 .