Docking and scoring with alternative side‐chain conformations
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[1] J. Irwin,et al. Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.
[2] Gerhard Klebe,et al. Ligand-supported homology modeling of g-protein-coupled receptor sites: models sufficient for successful virtual screening. , 2004, Angewandte Chemie.
[3] R. Glen,et al. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. , 1995, Journal of molecular biology.
[4] Song Liu,et al. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. , 2005, Journal of medicinal chemistry.
[5] M Rarey,et al. Detailed analysis of scoring functions for virtual screening. , 2001, Journal of medicinal chemistry.
[6] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[7] G. V. Paolini,et al. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes , 1997, J. Comput. Aided Mol. Des..
[8] DAVID G. KENDALL,et al. Introduction to Mathematical Statistics , 1947, Nature.
[9] 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.
[10] G. Klebe,et al. DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. , 2005, Journal of medicinal chemistry.
[11] Roland L. Dunbrack,et al. Bayesian statistical analysis of protein side‐chain rotamer preferences , 1997, Protein science : a publication of the Protein Society.
[12] P Willett,et al. Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.
[13] A. Sali,et al. Protein Structure Prediction and Structural Genomics , 2001, Science.
[14] G. Klebe,et al. Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials. , 2003, Journal of molecular biology.
[15] C. Bron,et al. Algorithm 457: finding all cliques of an undirected graph , 1973 .
[16] Brian K Shoichet,et al. Testing a flexible-receptor docking algorithm in a model binding site. , 2004, Journal of molecular biology.
[17] G. Reinsel,et al. Introduction to Mathematical Statistics (4th ed.). , 1980 .
[18] C. Venkatachalam,et al. LigScore: a novel scoring function for predicting binding affinities. , 2005, Journal of molecular graphics & modelling.
[19] Y. Martin,et al. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.
[20] T. Blundell,et al. Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.
[21] Hans-Joachim Böhm,et al. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..
[22] Brian K Shoichet,et al. Protein–protein docking with multiple residue conformations and residue substitutions , 2002, Protein science : a publication of the Protein Society.
[23] David S. Goodsell,et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998, J. Comput. Chem..
[24] Luhua Lai,et al. Further development and validation of empirical scoring functions for structure-based binding affinity prediction , 2002, J. Comput. Aided Mol. Des..
[25] Thomas Lengauer,et al. FlexE: efficient molecular docking considering protein structure variations. , 2001, Journal of molecular biology.
[26] L. Kuhn,et al. Virtual screening with solvation and ligand-induced complementarity , 2000 .
[27] C. E. Peishoff,et al. A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.
[28] Todd J. A. Ewing,et al. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..
[29] Ingo Muegge. Effect of ligand volume correction on PMF scoring , 2001, J. Comput. Chem..
[30] I. Kuntz,et al. Automated docking with grid‐based energy evaluation , 1992 .
[31] Adrian A Canutescu,et al. Access the most recent version at doi: 10.1110/ps.03154503 References , 2003 .
[32] G. Klebe,et al. Knowledge-based scoring function to predict protein-ligand interactions. , 2000, Journal of molecular biology.
[33] Hans-Joachim Böhm,et al. LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads , 1992, J. Comput. Aided Mol. Des..
[34] Markus Meuwly,et al. How inaccuracies in protein structure models affect estimates of protein–ligand interactions: Computational analysis of HIV‐I protease inhibitor binding , 2006, Proteins.
[35] Renxiao Wang,et al. Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.
[36] B. Shoichet,et al. Flexible ligand docking using conformational ensembles , 1998, Protein science : a publication of the Protein Society.
[37] György M. Keserü,et al. Ensemble Docking into Flexible Active Sites. Critical Evaluation of FlexE against JNK-3 and beta-Secretase , 2006, J. Chem. Inf. Model..
[38] P. Kollman,et al. An all atom force field for simulations of proteins and nucleic acids , 1986, Journal of computational chemistry.
[39] P. Koehl,et al. A self consistent mean field approach to simultaneous gap closure and side-chain positioning in homology modelling , 1995, Nature Structural Biology.
[40] Thomas Lengauer,et al. A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.
[41] Thomas Lengauer,et al. IRECS: A new algorithm for the selection of most probable ensembles of side‐chain conformations in protein models , 2007, Protein science : a publication of the Protein Society.
[42] Maria A Miteva,et al. Fast structure-based virtual ligand screening combining FRED, DOCK, and Surflex. , 2005, Journal of medicinal chemistry.
[43] Arne Elofsson,et al. All are not equal: A benchmark of different homology modeling programs , 2005, Protein science : a publication of the Protein Society.
[44] Thomas Lengauer,et al. Placement of medium-sized molecular fragments into active sites of proteins , 1996, J. Comput. Aided Mol. Des..
[45] U. Singh,et al. A NEW FORCE FIELD FOR MOLECULAR MECHANICAL SIMULATION OF NUCLEIC ACIDS AND PROTEINS , 1984 .
[46] Xiaoqin Zou,et al. An iterative knowledge‐based scoring function to predict protein–ligand interactions: II. Validation of the scoring function , 2006, J. Comput. Chem..
[47] Didier Rognan,et al. Comparative evaluation of eight docking tools for docking and virtual screening accuracy , 2004, Proteins.
[48] G. Klebe,et al. Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model. , 2004, Journal of medicinal chemistry.
[49] Z. Xiang,et al. Extending the accuracy limits of prediction for side-chain conformations. , 2001, Journal of molecular biology.
[50] R. Samudrala,et al. An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. , 1998, Journal of molecular biology.
[51] SHENG-YOU HUANG,et al. An iterative knowledge‐based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials , 2006, J. Comput. Chem..
[52] Adam Godzik,et al. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences , 2006, Bioinform..
[53] M. Sippl. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. , 1990, Journal of molecular biology.
[54] Hans-Joachim Böhm,et al. The computer program LUDI: A new method for the de novo design of enzyme inhibitors , 1992, J. Comput. Aided Mol. Des..
[55] R. Friesner,et al. Novel procedure for modeling ligand/receptor induced fit effects. , 2006, Journal of medicinal chemistry.