Structure-based 3D-QSAR—merging the accuracy of structure-based alignments with the computational efficiency of ligand-based methods

Abstract One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. The strategies that can be applied for this purpose fall into two major categories—the indirect ligand-based and the direct receptor-based approach. In this contribution, we used a combination of both approaches in order to improve the prediction accuracy for drug molecules. The combined approach was tested on two sets of ligands for which the three-dimensional structure of the target receptor was known—estrogen receptor ligands and acetylcholinesterase inhibitors. The binding modes of the ligands under study were determined using an automated docking program ( AutoDock ) and were compared with available X-ray structures of corresponding protein–ligand complexes. The ligand alignments obtained from the docking simulations were subsequently taken as the basis for a comparative field analysis applying the grid/golpe program. Using the interaction field derived with a water probe and applying the smart region definition variable selection, highly predictive models were obtained. The comparison of our models with interaction energy-based models and with traditional CoMFA models obtained using a ligand-based alignment indicates that the combination of structure-based and 3D-QSAR methods is able to improve the prediction ability of the underlying model.

[1]  C G Wermuth,et al.  Aminopyridazines--an alternative route to potent muscarinic agonists with no cholinergic syndrome. , 1993, Farmaco.

[2]  H. Kubinyi,et al.  3D QSAR in drug design. , 2002 .

[3]  J. Sussman,et al.  Quaternary ligand binding to aromatic residues in the active-site gorge of acetylcholinesterase. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Yuan-Ping Pang,et al.  Structure of acetylcholinesterase complexed with the nootropic alkaloid, (–)-huperzine A , 1997, Nature Structural Biology.

[5]  C. Wermuth,et al.  Aminopyridazines as acetylcholinesterase inhibitors. , 1999, Journal of medicinal chemistry.

[6]  N Heinrich,et al.  Three-dimensional models of estrogen receptor ligand binding domain complexes, based on related crystal structures and mutational and structure-activity relationship data. , 1998, Journal of medicinal chemistry.

[7]  E. Giacobini,et al.  From molecular structure to Alzheimer therapy. , 1997, Japanese journal of pharmacology.

[8]  G. Cruciani,et al.  Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. , 1994, Journal of medicinal chemistry.

[9]  Erwin von Angerer,et al.  The Estrogen Receptor as a Target for Rational Drug Design , 1995 .

[10]  Yun Tang,et al.  Molecular Modeling and 3D-QSAR Studies on the Interaction Mechanism of Tripeptidyl Thrombin Inhibitors with Human α-Thrombin† , 1997 .

[11]  C. Lemmen,et al.  FLEXS: a method for fast flexible ligand superposition. , 1998, Journal of medicinal chemistry.

[12]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[13]  R A Goldstein,et al.  Three-dimensional model for the hormone binding domains of steroid receptors. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[14]  John A. Katzenellenbogen,et al.  The estradiol pharmacophore: Ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site , 1997, Steroids.

[15]  A. Olson,et al.  Modelling of Factor Xa‐inhibitor complexes: a computational flexible docking approach , 1999, Proteins.

[16]  A. Goldman,et al.  Atomic structure of acetylcholinesterase from Torpedo californica: a prototypic acetylcholine-binding protein , 1991, Science.

[17]  I. Kuntz,et al.  Automated docking with grid‐based energy evaluation , 1992 .

[18]  Mark von Itzstein,et al.  A structural and energetics analysis of the binding of a series of N-acetylneuraminic-acid-based inhibitors to influenza virus sialidase , 1996, J. Comput. Aided Mol. Des..

[19]  A Wlodawer,et al.  An approach to rapid estimation of relative binding affinities of enzyme inhibitors: application to peptidomimetic inhibitors of the human immunodeficiency virus type 1 protease. , 1996, Journal of medicinal chemistry.

[20]  R. Evans,et al.  The steroid and thyroid hormone receptor superfamily. , 1988, Science.

[21]  P. Goodford A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[22]  H. Kubinyi QSAR and 3D QSAR in drug design Part 1: methodology , 1997 .

[23]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

[24]  Garland R. Marshall,et al.  3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: A comparison of CoMFA models based on deduced and experimentally determined active site geometries , 1993 .

[25]  Jack D. Dunitz,et al.  Lone-pair directionality in hydrogen-bond potential functions for molecular mechanics calculations: the inhibition of human carbonic anhydrase II by sulfonamides , 1985 .

[26]  Wolfgang Sippl,et al.  Comparative Molecular Field Analysis of Aminopyridazine Acetylcholinesterase Inhibitors , 2000 .

[27]  M. Pastor,et al.  A strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure--activity relationship analysis. , 1997, Journal of medicinal chemistry.

[28]  D. Lewis,et al.  Molecular modelling of the human estrogen receptor and ligand interactions based on site-directed mutagenesis and amino acid sequence homology , 1995, The Journal of Steroid Biochemistry and Molecular Biology.

[29]  K. Chae,et al.  Three-dimensional quantitative structure-activity relationship study of nonsteroidal estrogen receptor ligands using the comparative molecular field analysis/cross-validated r2-guided region selection approach. , 1998, Journal of medicinal chemistry.

[30]  T Lengauer,et al.  CASP2 experiences with docking flexible ligands using FLEXX , 1997, Proteins.

[31]  Tudor I. Oprea,et al.  Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors , 1996, J. Comput. Aided Mol. Des..

[32]  T. Lybrand Ligand-protein docking and rational drug design. , 1995, Current Opinion in Structural Biology.

[33]  Hans-Joachim Böhm,et al.  Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs , 1998, J. Comput. Aided Mol. Des..

[34]  Jeremy R. H. Tame,et al.  Scoring functions: A view from the bench , 1999, J. Comput. Aided Mol. Des..

[35]  Hugo Kubinyi,et al.  3D QSAR in drug design : theory, methods and applications , 2000 .

[36]  Toshio Fujita,et al.  Classical and three-dimensional QSAR in agrochemistry : developed from a symposium sponsored by the Division of Agrochemicals at the 208th National Meeting of the American Chemical Society, Washington, DC, August 21-25, 1994 , 1995 .

[37]  David A. Agard,et al.  The Structural Basis of Estrogen Receptor/Coactivator Recognition and the Antagonism of This Interaction by Tamoxifen , 1998, Cell.

[38]  F. Zeelen Medicinal chemistry of steroids , 1990 .

[39]  G. Cruciani,et al.  Generating Optimal Linear PLS Estimations (GOLPE): An Advanced Chemometric Tool for Handling 3D‐QSAR Problems , 1993 .

[40]  C. Wermuth,et al.  3-aminopyridazine derivatives with atypical antidepressant, serotonergic, and dopaminergic activities. , 1989, Journal of medicinal chemistry.

[41]  G Klebe,et al.  On the prediction of binding properties of drug molecules by comparative molecular field analysis. , 1993, Journal of medicinal chemistry.

[42]  J. Mornon,et al.  A model for the determination of the 3D-spatial distribution of the functions of the hormone-binding domain of receptors that bind 3-keto-4-ene steroids , 1992, The Journal of Steroid Biochemistry and Molecular Biology.

[43]  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..

[44]  Zbigniew Dauter,et al.  Molecular basis of agonism and antagonism in the oestrogen receptor , 1997, Nature.

[45]  A. Maggi,et al.  NEW ACETYLCHOLINESTERASE INHIBITORS , 1997 .

[46]  Ki Hwan Kim,et al.  Nonlinear dependence in comparative molecular field analysis , 1993, J. Comput. Aided Mol. Des..

[47]  I. Kuntz Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[48]  Tudor I. Oprea,et al.  Three-dimensional QSAR of human immunodeficiency virus (I) protease inhibitors. 1. A CoMFA study employing experimentally-determined alignment rules. , 1993, Journal of medicinal chemistry.

[49]  A Tropsha,et al.  Structure-based alignment and comparative molecular field analysis of acetylcholinesterase inhibitors. , 1996, Journal of medicinal chemistry.

[50]  Han van de Waterbeemd,et al.  Computer-Assisted Lead Finding and Optimization , 1997 .

[51]  Larry R. McLean,et al.  Evaluation of proposed modes of binding of (2S)-2-[4-[[(3S)-1-acetimidoyl-3-pyrrolidinyl]oxy]phenyl]-3-(7-amidino-2-naphthyl)propanoic acid hydrochloride and some analogs to Factor Xa using a comparative molecular field analysis , 1998, J. Comput. Aided Mol. Des..

[52]  P. Mecocci,et al.  Cognitive enhancement therapy for Alzheimer's disease. The way forward. , 1997, Drugs.