Molecular mechanics methods for predicting protein-ligand binding. Export

@article{citeulike:1375462, abstract = {Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed.}, address = {Department of Pharmaceutical Chemistry, University of California San Francisco, UCSF MC 2240, Genentech Hall, Room N472C, 600 16th St., San Francisco, CA 94158-2517, USA.}, author = {Huang, Niu and Kalyanaraman, Chakrapani and Bernacki, Katarzyna and Jacobson, Matthew P.}, citeulike-article-id = {1375462}, citeulike-linkout-0 = {http://dx.doi.org/10.1039/b608269f}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17203140}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17203140}, citeulike-linkout-3 = {http://www.rsc.org/Publishing/Journals/article.asp?doi=b608269f}, day = {28}, doi = {10.1039/b608269f}, issn = {1463-9076}, journal = {Physical chemistry chemical physics : PCCP}, keywords = {ligand-protein-binding, molecular\_mechanics}, month = {November}, number = {44}, pages = {5166--5177}, posted-at = {2009-08-05 07:26:14}, priority = {2}, title = {Molecular mechanics methods for predicting protein-ligand binding.}, url = {http://dx.doi.org/10.1039/b608269f}, volume = {8}, year = {2006} }

TY - JOUR ID - citeulike:1375462 L3 - citeulike-article-id:1375462 N2 - Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed. IS - 44 JF - Physical chemistry chemical physics : PCCP SN - 1463-9076 AD - Department of Pharmaceutical Chemistry, University of California San Francisco, UCSF MC 2240, Genentech Hall, Room N472C, 600 16th St., San Francisco, CA 94158-2517, USA. EP - 5177 TI - Molecular mechanics methods for predicting protein-ligand binding. VL - 8 SP - 5166 KW - ligand-protein-binding KW - molecular_mechanics AU - Huang, Niu AU - Kalyanaraman, Chakrapani AU - Bernacki, Katarzyna AU - Jacobson, Matthew PY - 2006/11/28/ UR - http://dx.doi.org/10.1039/b608269f ER -