Partial Charge Calculation Method Affects CoMFA QSAR Prediction Accuracy

The 3D-QSAR method comparative molecular field analysis (CoMFA) involves the estimation of atomic partial charges as part of the process of calculating molecular electrostatic fields. Using 30 data sets from the literature the effect of using different common partial charge calculation methods on the predictivity (cross-validated R2) of CoMFA was studied. The partial charge methods ranged from the popular Gasteiger and the newer MMFF94 electronegativity equalization methods, to the more complex and computationally expensive semiempirical charges AM1, MNDO, and PM3. The MMFF94 and semiempirical MNDO, AM1, and PM3 methods for computing charges were found to result in statistically significantly more predictive CoMFA models than the Gasteiger charges. Although there was a trend toward the semiempirical charges performing better than the MMFF94 charges, the difference was not statistically significant. Thus, semiempirical partial charge calculation methods are suggested for the most predictive CoMFA models, but the MMFF94 charge calculation method is a very good alternative if semiempirical methods are not available or faster calculation speed is important.

[1]  S. Roberts,et al.  High-throughput screening approaches for investigating drug metabolism and pharmacokinetics , 2001, Xenobiotica; the fate of foreign compounds in biological systems.

[2]  Laura Belvisi,et al.  A 3 D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists , .

[3]  Renxiao Wang,et al.  All-Orientation Search and All-Placement Search in Comparative Molecular Field Analysis , 1998 .

[4]  Jonathan D. Hirst,et al.  On the Stability of CoMFA Models , 2004, J. Chem. Inf. Model..

[5]  Rahul Jain,et al.  3D-QSAR study of ring-substituted quinoline class of anti-tuberculosis agents. , 2006, Bioorganic & medicinal chemistry.

[6]  Tudor I. Oprea,et al.  A Different Method for Steric Field Evaluation in CoMFA Improves Model Robustness , 1997, J. Chem. Inf. Comput. Sci..

[7]  Jyrki Taskinen,et al.  CoMFA Modeling of Human Catechol O-Methyltransferase Enzyme Kinetics. , 2004 .

[8]  Ronan Bureau,et al.  Conformational Analysis and 3D QSAR Study on Novel Partial Agonists of 5‐HT3 Receptors , 1996 .

[9]  A. Poso,et al.  A rhodopsin-based model for melatonin recognition at its G protein-coupled receptor. , 1996, European journal of pharmacology.

[10]  Ettore Novellino,et al.  A Critical Review of Recent CoMFA Applications , 1998 .

[11]  Klaus R. Liedl,et al.  Different electrostatic descriptors in comparative molecular field analysis: A comparison of molecular electrostatic and coulomb potentials , 1996, Journal of Computational Chemistry.

[12]  Yong-Jun Jiang,et al.  3D QSAR for GSK-3β inhibition by indirubin analogues , 2006 .

[13]  G. Narahari Sastry,et al.  Understanding the structural requirements of triarylethane analogues towards PDE-IV inhibitors : A molecular modeling study , 2006 .

[14]  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.

[15]  G R Marshall,et al.  Three-dimensional quantitative structure-activity relationship of angiotesin-converting enzyme and thermolysin inhibitors. II. A comparison of CoMFA models incorporating molecular orbital fields and desolvation free energies based on active-analog and complementary-receptor-field alignment rules. , 1993, Journal of medicinal chemistry.

[16]  J. Sutherland,et al.  A comparison of methods for modeling quantitative structure-activity relationships. , 2004, Journal of medicinal chemistry.

[17]  Edmund K. Burke,et al.  Exploring Phase-Transfer Catalysis with Molecular Dynamics and 3D/4D Quantitative Structure-Selectivity Relationships , 2005, J. Chem. Inf. Model..

[18]  Robert D. Clark,et al.  Boosted leave-many-out cross-validation: the effect of training and test set diversity on PLS statistics , 2003, J. Comput. Aided Mol. Des..

[19]  Xin Hu,et al.  Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors. , 2005, Bioorganic & medicinal chemistry.

[20]  T R Stouch,et al.  Three-dimensional quantitative structure-activity relationships of sulfonamide endothelin inhibitors. , 1995, Journal of medicinal chemistry.

[21]  Juan M. Luco,et al.  QSAR Based on Multiple Linear Regression and PLS Methods for the Anti-HIV Activity of a Large Group of HEPT Derivatives , 1997, J. Chem. Inf. Comput. Sci..

[22]  R. Shoemaker,et al.  A Three-Dimensional Quantitative Structure-Activity Relationship Study of the Inhibition of the ATPase Activity and the Strand Passing Catalytic Activity of Topoisomerase IIα by Substituted Purine Analogs , 2006, Molecular Pharmacology.

[23]  Gerhard Klebe,et al.  A 3D QSAR Study on a Set of Dopamine D4 Receptor Antagonists , 2003, J. Chem. Inf. Comput. Sci..

[24]  Yun Tang,et al.  Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists. , 2006, Bioorganic & medicinal chemistry.

[25]  Wesley Schaal,et al.  Improved CoMFA Modeling by Optimization of Settings , 2006, J. Chem. Inf. Model..

[26]  Shagufta,et al.  CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents , 2007, Journal of molecular modeling.

[27]  Maurizio Recanatini,et al.  Comparative molecular field analysis of non-steroidal aromatase inhibitors related to fadrozole , 1996, J. Comput. Aided Mol. Des..

[28]  Edmund K. Burke,et al.  Exploring Phase‐Transfer Catalysis with Molecular Dynamics and 3D/4D Quantitative Structure—Selectivity Relationships. , 2005 .

[29]  Carlo Scolastico,et al.  A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists , 1996, J. Comput. Aided Mol. Des..

[30]  Tommi H. Nyrönen,et al.  Comparing the Quality and Predictiveness between 3D QSAR Models Obtained from Manual and Automated Alignment , 2004, J. Chem. Inf. Model..

[31]  Yoshiaki Nakagawa,et al.  Classical and three-dimensional QSAR for the inhibition of [3H]ponasterone A binding by diacylhydrazine-type ecdysone agonists to insect Sf-9 cells. , 2005, Bioorganic & medicinal chemistry.

[32]  Lirong Chen,et al.  Mapping the Binding Site of a Large Set of Quinazoline Type EGF-R Inhibitors Using Molecular Field Analyses and Molecular Docking Studies. , 2003 .

[33]  W Welch,et al.  Structural determinants of high-affinity binding of ryanoids to the vertebrate skeletal muscle ryanodine receptor: a comparative molecular field analysis. , 1994, Biochemistry.

[34]  Michael J. Sorich,et al.  Systematic Statistical Comparison of Comparative Molecular Similarity Indices Analysis Molecular Fields for Computer-Aided Lead Optimization. , 2006 .

[35]  Antti Poso,et al.  3D-QSAR studies on cannabinoid CB1 receptor agonists: G-protein activation as biological data. , 2006, Journal of medicinal chemistry.

[36]  Marjana Novic,et al.  Variable Selection and Interpretation in Structure-Affinity Correlation Modeling of Estrogen Receptor Binders , 2005, J. Chem. Inf. Model..

[37]  Hongbin Yuan,et al.  CoMFA study of piperidine analogues of cocaine at the dopamine transporter: exploring the binding mode of the 3 alpha-substituent of the piperidine ring using pharmacophore-based flexible alignment. , 2004, Journal of medicinal chemistry.

[38]  A. Poso,et al.  Comparative Molecular Field Analysis (CoMFA) of MX Compounds using different Semi-empirical Methods: LUMO Field and its Correlation with Mutagenic Activity , 1996 .

[39]  Eric Oldfield,et al.  Inhibition of Trypanosoma cruzi hexokinase by bisphosphonates. , 2006, Journal of medicinal chemistry.

[40]  Supa Hannongbua,et al.  Comparative Molecular Field Analysis of HIV‐1 Reverse Transcriptase Inhibitors in the Class of 1[(2‐Hydroxyethoxy)‐methyl]‐6‐(phenylthio)thymine , 1996 .

[41]  Seung Joo Cho,et al.  Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) Study of Mutagen X , 2004 .

[42]  H. Koh,et al.  The 3D-QSAR study of antitumor arylsulfonylimidazolidinone derivatives by CoMFA and CoMSIA. , 2003, Bioorganic & medicinal chemistry.

[43]  Tommi H. Nyrönen,et al.  A structure-activity relationship study of catechol-O-methyltransferase inhibitors combining molecular docking and 3D QSAR methods , 2003, J. Comput. Aided Mol. Des..

[44]  Roberts Sa,et al.  High-throughput screening approaches for investigating drug metabolism and pharmacokinetics. , 2001 .