Applications of the PDBbind-CN database
Selected Applications of the PDBbind Database 2016 (1) Bansal, N.; Zheng, Z.; Merz, K. M. Jr. Incorporation of side chain flexibility into protein binding pockets using MTflex. Bioorg. Med. Chem., 2016, 24, 4978-4987. (2) Greenidge, P.A.; Lewis, R. A.; Ertl, P. Boosting Pose Ranking Performance via Rescoring with MM-GBSA. Chem. Biol. Drug Des., 2016, 88, 317-328. (3) Grudinin, S.; Kadukova, M.; Eisenbarth, A.; Marillet, S.; Cazals, F. Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. J. Comput. Aided. Mol. Des. 2016, 30, 791-804. (4) Grudinin, S.; Popov, P.; Neveu, E.; Cheremovskiy, G. Predicting Binding Poses and Affinities in the CSAR 2013-2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential. J. Chem. Inf. Model. 2016, 56, 1053-1062. (5) Hao, G. F.; Jiang, W.; Ye, Y. N.; Wu, F. X.; Zhu, X. L.; Guo, F. B.; Yang, G. F. ACFIS: a web server for fragment-based drug discovery. Nucleic Acids Res. 2016, 44, W550-W556. (6) Kadukova, M.; Grudinin, S. Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates. J. Chem. Inf. Model. 2016, 56, 1410-1419. (7) Luo, H.; Zhang, P.; Cao, X. H.; Du, D.; Ye, H.; Huang, H.; Li, C.; Qin, S.; Wan, C.; Shi, L.; He, L.; Yang, L. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome. Sci Rep., 2016, 6, 35996. (8) Piotto, S.; Di Biasi, L.; Fino, R.; Parisi, R.; Sessa, L.; Concilio, S. Yada: a novel tool for molecular docking calculations. J. Comput. Aided. Mol. Des. 2016, 30, 753-759. (9) Wang, C.; Nguyen, P. H.; Pham, K.; Huynh, D.; Le, T. B.; Wang, H.; Ren, P.; Luo, R. Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis. J. Comput. Chem., 2016, 37, 2436-2446. (10) Yan, C.; Grinter, S. Z.; Merideth, B. R.; Ma, Z.; Zou, X. Iterative Knowledge-Based Scoring Functions Derived from Rigid and Flexible Decoy Structures: Evaluation with the 2013 and 2014 CSAR Benchmarks. J. Chem. Inf. Model. 2016, 56, 1013-1021. (11) Zang, P.; Gong, A.; Zhang, P.; Yu, J. Targeting druggable enzymome by exploiting natural medicines: An in silico-in vitro integrated approach to combating multidrug resistance in bacterial infection. Pharm Biol., 2016, 54, 604-618. (12) Yan, Z. Q.; Wang, J. Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks. J. Comput. Aided. Mol. Des. 2016, 30, 219-227. (13) Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys., 2016, 18, 12964-12975. (14) Tanchuk, V. Y.; Tanin, V. O.; Vovk, A. I.; Poda, G. A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina. Chem. Biol. Drug Des., 2016, 87, 618-625. (15) Liu, X.; Liu, J.; Zhu, T.; Zhang, L.; He, X.; Zhang, J. Z. PBSA_E: A PBSA-Based Free Energy Estimator for Protein-Ligand Binding Affinity. J. Chem. Inf. Model. 2016, 56, 854-861. (16) Koebel, M. R.; Schmadeke, G.; Posner, R.G.; Sirimulla, S. AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. J. Cheminform., 2016, 8, 27. (17) Kim, S.; Thiessen, P. A.; Cheng, T.; Yu, B.; Shoemaker B. A.; Wang, J.; Bolton, E. E.; Wang, Y.; Bryant, S. H. Literature information in PubChem: associations between PubChem records and scientific articles. J. Cheminform., 2016, 8, 32. (18) Hain, E.; Camacho, C. J.; Koes, D. R. Fragment oriented molecular shapes. J. Mol. Graph. Model., 2016, 66, 143-154. (19) Gong, H. L.; Yuan, Z. C.; Zhan, L. P. High-throughput screening against 6.1 million structurally diverse, lead-like compounds to discover novel ROCK inhibitors for cerebral injury recovery. Mol. Divers., 2016, 20, 537-549. (20) Chen, A. S.; Westwood, N. J.; Brear, P.; Rogers, G. W.; Mavridis, L.; Mitchell, J. B. A Random Forest Model for Predicting Allosteric and Functional Sites on Proteins. Mol. Inform., 2016, 35, 125-135. (21) Bolia, A.; Ozkan, S.B.; Adaptive BP-Dock: An Induced Fit Docking Approach for Full Receptor Flexibility. J. Chem. Inf. Model. 2016, 56, 734-746. (22) Basse, M. J.; Betzi, S.; Morelli, X.; Roche, P. 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions. Database, 2016, 2016, baw007. 2015 (23) Zheng, Z.; Wang, T.; Li, P.; Merz, K. M. Jr. KECSA-Movable Type Implicit Solvation Model (KMTISM). J. Chem. Theory Comput., 2015, 11, 667-682. (24) Yilmazer, N. D.; Heitel, P.; Schwabe, T.; Korth, M. Benchmark of electronic structure methods for protein-ligand interactions based on high-level reference data. J. Theor. Comput. Chem., 2015, 14, 1540001. (25) Yang, Z.; Liu, Y.; Chen, Z.; Xu, Z.; Shi, J.; Chen, K.; Zhu, W. A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions. J. Mol. Model., 2015, 21, 138. (26) Yan, Z. Q.; Wang, J. Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect. Proteins: Struct. Funct. Bioinfor., 2015, 83, 1632-1642. (27) Wojcikowski, M.; Zielenkiewicz, P.; Siedlecki, P. Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J. Cheminform., 2015, 7. (28) Weber, J.; Achenbach, J.; Moser, D.; Proschak, E. VAMMPIRE-LORD: A Web Server for Straightforward Lead Optimization Using Matched Molecular Pairs. J. Chem. Inf. Model. 2015, 55, 207-213. (29) Wang, Y.; Guo, Y.; Kuang, Q.; Pu, X.; Ji, Y.; Zhang, Z.; Li, M. A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach. J. Comput. Aided. Mol. Des. 2015, 29, 349-360. (30) Trepte, P.; Buntru, A.; Klockmeier, K.; Willmore, L.; Arumughan, A.; Secker, C.; Zenkner, M.; Brusendorf, L.; Rau, K.; Redel, A.; Wanker, E. E. DULIP: A Dual Luminescence-Based Co-Immunoprecipitation Assay for Interactome Mapping in Mammalian Cells. J. Mol. Biol., 2015, 427, 3375-3388. (31) Su, H.; Yan, J.; Xu, J.; Fan, X. Z.; Sun, X. L.; Chen, K.Y. Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension. Pharm. Biol., 2015, 53, 1201-1206. (32) Razzaghi-Asl, N.; Razzaghi-Asl, N.; Sepehri, S.; Ebadi, A.; Miri, R.; Shahabipour, S. Effect of Biomolecular Conformation on Docking Simulation: A Case Study on a Potent HIV-1 Protease Inhibitor. Iran. J. Pharm. Res., 2015, 14, 785-802. (33) Qiu, J. X.; Zhou, Z. W.; He, Z. X.; Zhao, R. J.; Zhang, X.; Yang, L.; Zhou, S. F.; Mao, Z. F.; Plumbagin elicits differential proteomic responses mainly involving cell cycle, apoptosis, autophagy, and epithelial-to-mesenchymal transition pathways in human prostate cancer PC-3 and DU145 cells. Drug Des. Devel. Ther., 2015, 9 349-417. (34) Lizunov, A.Y.; Gonchar, A. L.; Zaitseva, N. I.; Zosimov, V. V. Accounting for Intraligand Interactions in Flexible Ligand Docking with a PMF-Based Scoring Function. J. Chem. Inf. Model. 2015, 55, 2121-2137. (35) Li, H. J.; Leung, K. S.; Wong, M. H.; Ballester, P. J. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest. Molecules, 2015, 20, 10947-10962. (36) Kothiwale, S.; Mendenhall, J. L.; Meiler, J. BCL::CONF: small molecule conformational sampling using a knowledge based rotamer library. J. Cheminform., 2015, 7. (37) Khamis, M.A.; Gomaa, W. Comparative assessment of machine-learning scoring functions on PDBbind 2013, Eng. Appl. Artif. Intel., 2015, 45 136-151. (38) He, W. H.; Shi, F.; Zhou, Z. W.; Li, B.; Zhang, K.; Zhang, X.; Ouyang, C.; Zhou, S. F.; Zhu, X. A bioinformatic and mechanistic study elicits the antifibrotic effect of ursolic acid through the attenuation of oxidative stress with the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in human hepatic stellate cells and rat liver. Drug Des. Devel. Ther., 2015, 9, 3989-4104. (39) He, M.W.; Lee, P. S.; Sweeney, Z. K. Promiscuity and the Conformational Rearrangement of Drug-Like Molecules: Insight from the Protein Data Bank. Chemmedchem, 2015, 10, 238-244. (40) Gaudreault, F.; Najmanovich, R. J. FlexAID: Revisiting Docking on Non-Native-Complex Structures. J. Chem. Inf. Model. 2015, 55, 1323-1336. (41) Dias, R.; Kolazckowski, B. Different combinations of atomic interactions predict protein-small molecule and protein-DNA/RNA affinities with similar accuracy. Proteins: Struct. Funct. Bioinfor., 2015, 83, 2100-2114. (42) Degac, J.; Winter, U.; Helms, V. Graph-Based Clustering of Predicted Ligand-Binding Pockets on Protein Surfaces. J. Chem. Inf. Model. 2015, 55, 1944-1952. (43) Cui, Y. H.; Chen, J.; Xu, T.; Tian, H. L. Structure-based grafting and identification of kinase-inhibitors to target mTOR signaling pathway as potential therapeutics for glioblastoma. Comput. Biol. Chem., 2015, 54, 57-65. (44) Chartier, M.; Najmanovich, R. Detection of Binding Site Molecular Interaction Field Similarities. J. Chem. Inf. Model. 2015, 55, 1600-1615. (45) Bai, F.; Liao, S.; Gu, J.; Jiang, H.; Wang, X.; Li, H. An Accurate Metalloprotein-Specific Scoring Function and Molecular Docking Program Devised by a Dynamic Sampling and Iteration Optimization Strategy. J. Chem. Inf. Model. 2015, 55, 833-847. (46) Ashtawy, H. M.; Mahapatra, N. R. BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. 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Xyloketal B, a marine compound, acts on a network of molecular proteins and regulates the activity and expression of rat cytochrome P450 3a: a bioinformatic and animal study. Drug Des. Devel. Ther., 2014, 8, 2555-2602. (55) Singh, K. D.; Naveena, Q.; Karthikeyan, M. Jak2 inhibitor - a jackpot for pharmaceutical industries: a comprehensive computational method in the discovery of new potent Jak2 inhibitors. Mol. Biosyst., 2014, 10, 2146-2159. (56) Reddy, A.S.; Tan, Z.; Zhang, S. X. Curation and Analysis of Multitargeting Agents for Polypharmacological Modeling. J. Chem. Inf. Model. 2014, 54, 2536-2543. (57) Perna, A. M.; Reisen, F.; Schmidt, T. P.; Geppert, T.; Pillong, M.; Weisel, M.; Hoy, B.; Simister, P. C.; Feller, S. M.; Wessler, S.; Schneider, G. Inhibiting Helicobacter pylori HtrA protease by addressing a computationally predicted allosteric ligand binding site. Chem. Sci., 2014, 5, 3583-3590. (58) Luo, J. S.; Guo, Y.; Zhong, Y.; Ma, D.; Li, W.; Li, M. A functional feature analysis on diverse protein-protein interactions: application for the prediction of binding affinity. J. Comput. Aided. Mol. Des. 2014, 28, 619-629. (59) Luo, H.; Zhang, P.; Huang, H.; Huang, J.; Kao, E.; Shi, L.; He, L.; Yang, L. DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic Acids Res. 2014, 42, W46-W52. (60) Lindblom, P. R.; Wu, G.; Liu, Z.; Jim, K. C.; Baldwin, J. J.; Gregg, R. E.; Claremon, D. A.; Singh, S. B. An electronic environment and contact direction sensitive scoring function for predicting affinities of protein-ligand complexes in Contour (R). J. Mol. Graph. Model., 2014, 53, 118-127. (61) Li, H. J.; Leung, K. S.; Wong, M. H.; Ballester, P. J. Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinformatics 2014, 15. (62) Li, H. J.; Leung, K. S.; Ballester, P. J.; Wong, M. H. istar: A Web Platform for Large-Scale Protein-Ligand Docking. Plos One, 2014, 9. (63) Kuenemann, M. A.; Bourbon, L. M.; Labb¨¦, C. M.; Villoutreix, B. O.; Sperandio, O. Which Three-Dimensional Characteristics Make Efficient Inhibitors of Protein-Protein Interactions? J. Chem. Inf. Model. 2014, 54, 3067-3079. (64) Kastritis, P. L.; Rodrigues, J. P.; Folkers, G. E.; Boelens, R.; Bonvin, A. M. Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface. J. Mol. Biol., 2014, 426, 2632-2652. (65) Hu, B. J.; Zhu, X.; Monroe, L.; Bures, M. G.; Kihara, D. PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions. Int. J. Mol. Sci., 2014, 15, 15122-15145. (66) Hu, B.J.; Lill, M. A. PharmDock: a pharmacophore-based docking program. J. Cheminform., 2014, 6. (67) Grinter, S. Z.; Zou, X. Q. A Bayesian Statistical Approach of Improving Knowledge-Based Scoring Functions for Protein-Ligand Interactions. 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