![\"\"](\"img\/MedusaDock.jpg\" "\\"MedusaDock\\"")
discrete rotamers. We developed an algorithm to build the ligand rotamer library \u201con-the-\ufb02y\u201d during docking simulations. MedusaDock benchmarks demonstrate a rapid sampling efficiency and high prediction accuracy in both self- (to the co-crystallized state) and cross-docking (to a state co-crystallized with a different ligand), the latter of which mimics the virtual screening procedure in computational drug discovery. We also perform a virtual screening test of four \ufb02exible kinase targets, including cyclin-dependent kinase 2, vascular endothelial growth factor receptor 2, HIV reverse transcriptase, and HIV protease. We \ufb01nd signi\ufb01cant improvements of virtual screening enrichments when compared to rigid-receptor methods. The predictive power of MedusaDock in cross-docking and preliminary virtual-screening benchmarks highlights the importance to model both ligand and receptor flexibility simultaneously in computational docking.<\/p>\n <\/p>\n <\/p>\n C. Protein-peptide binding using DMD simulations<\/strong>. Utilizing <\/p>\n <\/p>\n 5. Nedumpully-Govindan P., Domen J., and Ding F., \u201cCSAR Benchmark of flexible MedusaDock in affinity prediction and native-like binding pose selection\u201d, Journal of Chemical Information and Modeling, in press (2015)<\/p>\n 4. Fourche, D., Muratov, E., Ding, F., Dokholyan, N. V., Tropsha, A. \u201cPredicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches\u201d, Journal of Chemical Information and Modeling, in press (2013)<\/p>\n 3. F. Ding. and Dokholyan, N. V., \u201cIncorporating backbone flexibility in MedusaDock improves ligand binding pose prediction in the CSAR2011 docking benchmark\u201d, Journal of Chemical Information and Modeling, in press, (2012)[download]<\/a><\/p>\n 2. Dagliyan, O., Proctor, E. A., D\u2019Auria, K., F. Ding* and Dokholyan N. V.* \u201cStructural and Dynamic Determinants of Protein-peptide Recognition\u201d, Structure, 19:1837 (2011) [download]<\/a><\/p>\n 1. F. Ding, Yin, S., and Dokholyan, N. V. \u201cRapid flexible docking using a stochastic rotamer library of ligands\u201d, Journal of Chemical Information and Modeling, 50:1623-32 (2010)[download]<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" A major challenge in modeling molecular recognition is the conformational flexibility. The structures of the receptors in the bound and un-bound states are often different, known as the induced-fit problem. In these cases, a rigid docking approach will fail to achieve accurate predictions. Existing flexible methods for modeling molecular recognition or docking often adopt the … <\/p>\n![\"\"](\"img\/MedusaDock-flexbb.png\" "\\"Flexbb-Medusa\\"")
B. Incorporating backbone flexibility in MedusaDock<\/strong>. We introduce backbone <\/em>flexibility into MedusaDock by implementing ensemble docking in a sequential manner for a set of distinct receptor backbone conformations. We generate corresponding backbone ensembles to capture backbone changes upon binding to different ligands, as observed experimentally. We develop a simple clustering and ranking approach to select the top poses as blind predictions. We applied our method in the CSAR2011 benchmark exercise. In 28 out of 35 cases (80%) where the ligand-receptor complex structures were released, we were able to predict near-native poses (< 2.5 \u00c5 RMSD), the highest success rate reported for CSAR2011. Thisresult highlights the importance of modeling receptor backbone flexibility to the accurate docking of ligands to flexible targets. We expect a broach application of our fully flexible docking approach in biological studies as well as in rational drug design.<\/p>\n![\"\"](\"img\/DMD-Prot-Pep.png\" "\\"DMD-Prot-Pep\\"")
sampling efficiency of DMD and Medusa force field in describing interaction between amino acids, we apply DMD simulations to modeling protein-peptide recognition. We \ufb01nd that, in most cases, we recapitulate the native binding sites and native-likeposes of protein-peptide complexes. Inclusion ofelectrostatic interactions in simulations signi\ufb01cantly improves the prediction accuracy. Our results alsohighlight the importance of protein conformational\ufb02exibility, especially side-chain movement, which allows the peptide to optimize its conformation. Our\ufb01ndings not only demonstrate the importance of suf\ufb01cient sampling of the protein and peptide conformations, but also reveal the possible effects ofelectrostatics and conformational \ufb02exibility on peptide recognition.<\/p>\n