Feature extraction using molecular planes for fuzzy relational clustering of a flexible dopamine reuptake inhibitor

Amit Banerjee, Milind Misra, Deepa Pai, Liang Yu Shih, Rohan Woodley, Xiang Jun Lu, A. R. Srinivasan, Wilma K. Olson, Rajesh N. Davé, Carol A. Venanzi

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Six rigid-body parameters (Shift, Slide, Rise, Tilt, Roll, Twist) are commonly used to describe the relative displacement and orientation of successive base pairs in a nucleic acid structure. The present work adapts this approach to describe the relative displacement and orientation of any two planes in an arbitrary molecule - specifically, planes which contain important pharmacophore elements. Relevant code from the 3DNA software package (Nucleic Acids Res. 2003, 37, 5108-5121) was generalized to treat molecular fragments other than DNA bases as input for the calculation of me corresponding rigid-body (or "planes") parameters. These parameters were used to construct feature vectors for a fuzzy relational clustering study of over 700 conformations of a flexible analogue of the dopamine reuptake inhibitor, GBR 12909. Several cluster validity measures were used to determine the optimal number of clusters. Translational (Shift, Slide, Rise) rather than rotational (Tilt, Roll, Twist) features dominate clustering based on planes that are relatively far apart, whereas both types of features are important to clustering when the pair of planes are close by. This approach was able to classify the data set of molecular conformations into groups and to identify representative conformers for use as template conformers in future Comparative Molecular Field Analysis studies of GBR 12909 analogues. The advantage of using the planes parameters, rather than the combination of atomic coordinates and angles between molecular planes used in our previous fuzzy relational clustering of the same data set (J. Chem. Inf. Model. 2005, 45, 610-623), is that the present clustering results are independent of molecular superposition and the technique is able to identify clusters in the molecule considered as a whole. This approach is easily generalizable to any two planes in any molecule.

Original languageEnglish (US)
Pages (from-to)2216-2227
Number of pages12
JournalJournal of Chemical Information and Modeling
Volume47
Issue number6
DOIs
StatePublished - Nov 1 2007

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

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    Banerjee, A., Misra, M., Pai, D., Shih, L. Y., Woodley, R., Lu, X. J., Srinivasan, A. R., Olson, W. K., Davé, R. N., & Venanzi, C. A. (2007). Feature extraction using molecular planes for fuzzy relational clustering of a flexible dopamine reuptake inhibitor. Journal of Chemical Information and Modeling, 47(6), 2216-2227. https://doi.org/10.1021/ci7001632