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| Martin Peters, Penn State University |
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| Martin Peters was born in the Republic of Ireland and received his bachelor's degree from the University of Dublin, Trinity College (Moderatorship in Computational Chemistry (B.A. with honors)) in 2002. Martin carried out undergraduate research with Dr. Rozas on DNA triplexes investigating their hydrogen bonds (J. Phys. Chem. B, 2003,107, 323-330). Since then Martin has enrolled in the PhD program at Penn State University and has been working with Prof. Merz on the application of semiempirical quantum mechanics to structure-based drug design. Martin obtained his Master's degree from Penn State in 2005 carrying out research on the new SemiEmpirical COMparative BINding Energy analysis (SE-COMBINE) approach to drug design. Martin has since moved from Penn State to the University of Florida to continue his studies with Prof. Merz.
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Semiempirical Comparative Binding Energy Analysis (SE-COMBINE) of a series of Trypsin Inhibitors
Martin B. Peters and Kenneth M. Merz, Jr., Department of Chemistry, 104 Chemistry Building, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
A scheme to decompose the intermolecular interaction energy of a series of protein-ligand complexes at the semiempirical level of theory has been developed and validated. Extending the work of Wade and Ortiz et al. (J. Med. Chem. 1995, 38, 2681-2691), and Raha et al. (J.Am. Chem. Soc. 2005, 127, 6583-6594), COMBINE and the semiempirical quantum mechanical method PairWise energy Decomposition (PWD) were coupled together to form SE-COMBINE. This approach calculates the residue pairwise electrostatic interaction energies, and QSAR models were built with the energies as descriptors using partial least squares (PLS). The application of SE-COMBINE was used as an investigation of the intermolecular interactions between 88 benzamidine inhibitors and trypsin and to test the ability of this new method to predict binding free energies. The predictive capability of SE-COMBINE is shown to be comparable to those of other QSAR methods, and using graphical intermolecular interaction maps (IMMs) enhances the interpretability of receptor-based QSARs.
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