Development of a chirality-sensitive flexibility descriptor for 3+3D-QSAR

Journal of Chemical Information and Modeling

Volumn 46, Issue 3, 2006, Pages 1431-1438

  Dervarics, Máté ^a   Ötvös, Ferenc ^b   Martinek, Tamás A ^a  

^a UNIVERSITY OF SZEGED   (Hungary)

^b INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMATIONS; ELECTRONIC STRUCTURE; FREE ENERGY; PHARMACODYNAMICS; SAMPLING;

ACTIVE CONFORMATIONS; ANTINIDATORY ACTIVITY; INTERNAL COORDINATE-BASED FLEXIBILITY DESCRIPTORS; PHARMACOPHORE;

MOLECULAR STRUCTURE;

ARTICLE; CHEMICAL STRUCTURE; DRUG DESIGN; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STEREOISOMERISM;

DRUG DESIGN; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; STEREOISOMERISM;

EID: 33745368561     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci0505574     Document Type: Article

References (43)

1. Goodford, P. J. A Computational Procedure for Determining Energetically Favorable Binding Sites on Biologically Important Macromolecules. J. Med. Chem. 1985, 28, 849-857.
2. Cramer, R. D., III.; Patterson, D. E.; Bunce, J. D. Comparative Molecular-Field Analysis (COMFA): Effect of Shape on Binding of Steroids to Carrier Proteins. J. Am. Chem. Soc. 1988, 110, 5959-5967.
3. Klebe, G.; Abraham, U.; Mietzner, T. Molecular Similarity Indexes in a Comparative-Analysis (COMSIA) of Drug Molecules to Correlate and Predict their Biological Activity. J. Med. Chem. 1994, 37, 4130.
4. Doweyko, A. M. The Hypothetical Active-Site Lattice - an Approach to Modeling Active Sites from Data on Inhibitor Molecules. J. Med. Chem. 1988, 31, 1396-1406.
5. Robinson, D. D.; Winn, P. J.; Lyne, P. D.; Richards W. G. Self-Organizing Molecular Field Analysis: A Tool for Structure-Activity Studies. J. Med. Chem. 1999, 42, 573-583.
6. Cramer, R. D., III.; DePriest, S. A.; Patterson, D. E.; Hecht, P. The Developing Practice of CoMFA. In 3D-QSAR in Drug Design: Theory, Methods and Applications; Kubinyi, H., Ed.; ESCOM: Leiden, 1993; pp 443-485.
7. Doweyko, A. M. 3D-QSAR Illusions. J. Comput.-Aided Mol. Des. 2004, 18, 587-596.
8. Pastor, M.; Cruciani, G.; McLay, I.; Pickett, S.; Clementi, S. GRid-INdependent Descriptors (GRIND): A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors. J. Med. Chem. 2000, 43, 3233-3243.
9. Ferguson, A. M.; Heritage, T.; Jonathon, P.; Pack, S. E.; Philips, L.; Rogan, J.; Snaith, P. J. EVA: A New Theoretically Based Molecular Descriptor for Use in QSAR/QSPR Analysis. J. Comput.-Aided Mol. Des. 1997, 11, 143-152.
10. Stiefl, N.; Baumann, K. Mapping Property Distributions of Molecular Surfaces: Algorithm and Evaluation of a Novel 3D Quantitative Structure - Activity Relationship Technique. J. Med. Chem. 2003, 46, 1390-1407.
11. Schuur, J. H.; Selzer, P.; Gasteiger, J. The Coding of the Three-Dimensional Structure of Molecules by Molecular Transforms and its Application to Structure-Spectra Correlations and Studies of Biological Activity. J. Chem. Inf. Comput. Sci. 1996, 36, 334-344.
12. Gancia, E.; Bravi, G.; Mascagni, P.; Zaliani, A. Global 3D-QSAR Methods: MS - WHIM and Autocorrelation. J. Comput.-Aided Mol. Des. 2000, 14, 293-306.
13. Baumann, K. Distance Profiles (DiP): A Translationally and Rotationally Invariant 3D Structure Descriptor Capturing Steric Properties of Molecules. QSAR Comb. Sci. 2002, 21, 507-519.
14. Silverman, B. D.; Platt, D. E. Comparative Molecular Moment Analysis (CoMMA): 3D-QSAR Without Molecular Superposition. J. Med. Chem. 1996, 39, 2129-2140.
15. Klein, C. T.; Kaiblinger, N.; Wolschann, P. Internally Defined Distances in 3D-Quantitative Structure - Activity Relationships. J. Comput.-Aided Mol. Des. 2002, 16, 79-93.
16. Kovatcheva, A.; Golbraikh, A.; Oloff, S.; Feng, J.; Zheng, W.; Tropsha, A. QSAR Modeling of Datasets with Enantioselective Compounds Using Chirality Sensitive Molecular Descriptors. SAR QSAR Environ. Res. 2005, 16, 93-102.
17. Payne, J. W.; Grail, B. M.; Marshall, N. J. Molecular Recognition Templates of Peptides: Driving Force for Molecular Evolution of Peptide Transporters. Biochem. Biophys. Res. Commun. 2000, 267, 283-289.
18. Kalászi, A.; Farkas. Ö. Lead Conformer Prediction Based on a Library of Flexible Molecules. J. Mol. Struct. (THEOCHEM) 2003, 666-667, 645-649.
19. Hopfinger, A. J.; Wang, S.; Tokarski, J. S.; Jin, B., Albuquerque, M.; Madhav, P. J.; Duraiswami, C. Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. J. Am. Chem. Soc. 1997, 119, 10509-10524.
20. Duca, J. S.; Hopfinger, A. J. Estimation of Molecular Similarity Based on 4D-QSAR Analysis: Formalism and Validation. J. Chem. Inf. Comput. Sci., 2001, 41, 1367-1387.
21. Santos-Filho, O. A.; Hopfinger, A. J. The 4D-QSAR Paradigm: Application to a Novel Set of Nonpeptidic HIV Protease Inhibitors. Quant. Struct. - Act. Relat. 2002, 21, 369-381.
22. Vedani, A.; Briem, K.; Dobler, M.; Dollinger, H.; McMasters, D. R. Multiple-Conformation and Protonation-State Representation in 4D-QSAR: The Neurokinin-1 Receptor System. J. Med. Chem. 2000, 43, 4416-4427.
23. Vedani A.; Dobler, M. 5D-QSAR: The Key for Simulating Induced Fit? J. Med. Chem. 2002, 45, 2139-2149.
24. Vedani A.; Dobler, M.; Lill, M. A. Combining Protein Modeling and 6D-QSAR. Simulating the Binding of Structurally Diverse Ligands to the Estrogen Receptor. J. Med. Chem. 2005, 48, 3700-3703.
25. Martinek, T. A.; Ötvös, F.; Dervarics, M.; Tóth, G.; Fülöp, F. Ligand-based Prediction of Active Conformation by 3D-QSAR Flexibility Descriptors and Their Application in 3+3D-QSAR Models. J. Med. Chem. 2005, 48, 3239-3250.
26. 2, and Examination of their Opioid Receptor Binding Activities and Solution Conformation. Biochem. Biophys. Res. Commun. 2000, 276, 7-11.
27. Halgren, T. A. Merck Molecular Force Field: Basis, Form, Scope, Parameterization, and Performance of MMFF94. J. Comput. Chem. 1996, 17, 490-519, 520-522, 553-586, 587-615, 616-641.
28. Halgren, T. A. MMFF VI. MMFF94s Option for Energy Minimization Studies. J. Comput. Chem. 1999, 20, 720-729.
29. Leitgeb, B.; Ötvös, F.; Tóth, G. Conformational Analysis of Endomorphin-2 by Molecular Dynamics. Biopolymers 2003, 68, 497-511.
30. Rannar, S.: Lindgren, F.; Geladi, P.; Wold, S. A PLS Kernel Algorithm for Data Sets with Many Variables and Fewer Objects: Theory and Algorithm J. Chemom. 1994, 8, 111-125.
31. Heiland, I. S. On the Structure of Partial Least-Squares Regression. Commun. Stat.- Simul. Comput. 1988, 17, 581-607.
32. Fedders, M.; Ponder, J. W. Program QSAR 1996. http://dasher.wustl.edu.
33. Sutter, J. M.; Jurs, P. C. Automated Descriptor Selection for Quantitative Structure - Activity Relationships Using Generalized Simulated Annealing, J. Chem. Inf. Comput. Sci. 1995, 35, 77-84.
34. 2. J. Mol. Graphics Modell. 2002, 20, 269-276.
35. Zadina, J. E.; Hackler, L.; Ge, L.-J.; Kastin, A. J. A Potent and Selective Endogenous Agonist for the Mu-Opiate Receptor. Nature 1997, 386, 499-502.
36. Tóth, G.; Fülöp, F.; Péter, A.; Fábián, G.; Murányi, M.; Horváth, Gy.; Szûcs, M. New Endomorphin Analogues Using Beta-Amino Acids as Proline Mimetics in Position 2. In Peptides 2002; Benedetti, E., Pedone, C., Eds.; Edizione Ziino: Naples, 2002; pp 630-631.
37. Tóth, G.; Keresztes, A.; Tömböly, Cs.; Péter, A.; Fülöp, F.; Tourwé, D.; Navratilova, D.; Varga, É.; Roeske, W. R.; Yamamura, H. J.; Szûcs, M.; Borsodi, A. New Endomorphin Analogs with Mu-Agonist and Delta-Antagonist Properties. Pure Appl. Chem. 2004, 76, 951-957.
38. Pogozheva, I. D.; Lomize, A. L.; Mosberg, H. I. Opioid Receptor Three-Dimensional Structures from Distance Geometry Calculations with Hydrogen Bonding Constraints. Biophys J. 1998, 75, 612-634.
39. Law, P. Y.; Loh, H. H. Regulation of Opioid Receptor Activities. J. Pharmacol. Exp. Ther. 1999, 289, 607-624.
40. Mosberg, H. I.; Fowler, C. B. Development and Validation of Opioid Ligand - Receptor Interaction Models: The Structural Basis of Mu vs. Delta Selectivity. J. Pept. Res. 2002, 60, 329-335.
41. Zhang, Y.; Sham, Y. Y.; Rajamani, R.; Gao, J.; Portoghese, P. S. Homology Modeling and Molecular Dynamics Simulations of the Mu Opioid Receptor in a Membrane-Aqueous System. ChemBioChem 2005, 6, 853-859.
42. Roecker, E. B. Prediction Error And Its Estimation For Subset Selected Models. Technometrics 1991, 33, 459-468.
43. Baumann, K. Chance Correlation in Variable Subset Regression: Influence of the Objective Function, the Selection Mechanism, the Ensemble Averaging. QSAR Comb. Sci. 2005, 24, 1033-1046.