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Application of the PDBbind-CN Database
Applications of the PDBbind-CN database
Selected Applications of the PDBbind Database
(1) Maguire, J. B.; Haddox, H. K.; Strickland, D.; Halabiya, S. F.; Coventry, B.; Griffin, J. R.; Pulavarti, S.; Cummins, M.; Thieker, D. F.; Klavins, E.; Szyperski, T.; DiMaio, F.; Baker, D.; Kuhlman, B., Perturbing the energy landscape for improved packing during computational protein design. Proteins 2021, 89, 436-449. (PDBbind)
(2) Zhu, F.; Zhang, X.; Allen, J. E.; Jones, D.; Lightstone, F. C., Binding Affinity Prediction by Pairwise Function Based on Neural Network. J Chem Inf Model 2020, 60, 2766-2772. ( PDBbind 2018 refined set)
(3) Zheng, Z.; Borbulevych, O. Y.; Liu, H.; Deng, J.; Martin, R. I.; Westerhoff, L. M., MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment. J Chem Inf Model 2020, 60, 5437-5456. ( PDBbind 2019)
(4) Zhang, H.; Saravanan, K. M.; Lin, J.; Liao, L.; Ng, J. T.; Zhou, J.; Wei, Y., DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ 2020, 8, e8864. ( PDBbind 2017)
(5) Yuan, J. H.; Han, S. B.; Richter, S.; Wade, R. C.; Kokh, D. B., Druggability Assessment in TRAPP Using Machine Learning Approaches. J Chem Inf Model 2020, 60, 1685-1699. ( PDBbind 2017 refined set)
(6) Yoshidome, T.; Ikeguchi, M.; Ohta, M., Comprehensive 3D-RISM analysis of the hydration of small molecule binding sites in ligand-free protein structures. J Comput Chem 2020, 41, 2406-2419. ( PDBbind 2017 refined set)
(7) Yang, J.; Shen, C.; Huang, N., Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets. Front Pharmacol 2020, 11, 69. ( PDBbind 2015)
(8) Yang, J.; Kwon, S.; Bae, S. H.; Park, K. M.; Yoon, C.; Lee, J. H.; Seok, C., GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds. J Chem Inf Model 2020, 60, 3246-3254. ( PDBbind 2018)
(9) Xie, L.; Xu, L.; Kong, R.; Chang, S.; Xu, X., Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning. Frontiers in Pharmacology 2020, 11. (PDBbind)
(10) Xie, L.; Xu, L.; Chang, S.; Xu, X.; Meng, L., Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding. Chem Biol Drug Des 2020, 96, 973-983. (PDBbind)
(11) Willow, S. Y.; Xie, B.; Lawrence, J.; Eisenberg, R. S.; Minh, D. D. L., On the polarization of ligands by proteins. Phys Chem Chem Phys 2020, 22, 12044-12057. (PDBbind 2016 core set)
(12) Wierbowski, S. D.; Wingert, B. M.; Zheng, J.; Camacho, C. J., Cross-docking benchmark for automated pose and ranking prediction of ligand binding. Protein Sci 2020, 29, 298-305. (binding data)
(13) Wang, Y.; Hu, J.; Lai, J.; Li, Y.; Jin, H.; Zhang, L.; Zhang, L. R.; Liu, Z. M., TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network. J Chem Inf Model 2020, 60, 2754-2765. (PDBbind 2018 General Set)
(14) Wang, B.; Ng, H. L., Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4. J Comput Aided Mol Des 2020, 34, 201-217. (PDBbind 2017)
(15) Varela-Rial, A.; Majewski, M.; Cuzzolin, A.; Martinez-Rosell, G.; De Fabritiis, G., SkeleDock: A Web Application for Scaffold Docking in PlayMolecule. J Chem Inf Model 2020, 60, 2673-2677. (PDBbind 2018 refined set)
(16) Su, M.; Feng, G.; Liu, Z.; Li, Y.; Wang, R., Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? J Chem Inf Model 2020, 60, 1122-1136. (PDBbind 2016 refined set)
(17) Sriramulu, D. K.; Wu, S.; Lee, S.-G., Effect of ligand torsion number on the AutoDock mediated prediction of protein-ligand binding affinity. Journal of Industrial and Engineering Chemistry 2020, 83, 359-365. (PDBbind)
(18) Sriramulu, D. K.; Lee, S. G., Combinatorial Effect of Ligand and Ligand-Binding Site Hydrophobicities on Binding Affinity. J Chem Inf Model 2020, 60, 1678-1684. (PDBbind)
(19) Soni, A.; Bhat, R.; Jayaram, B., Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method. J Comput Aided Mol Des 2020, 34, 817-830. (PDBbind 2007 2013 2016 2017)
(20) Šlachtová, V.; Šebela, M.; Torfs, E.; Oorts, L.; Cappoen, D.; Berka, K.; Bazgier, V.; Brulíková, L., Novel thiazolidinedione-hydroxamates as inhibitors of Mycobacterium tuberculosis virulence factor Zmp1. European Journal of Medicinal Chemistry 2020, 185, 111812. (binding data)
(21) Singh, N.; Decroly, E.; Khatib, A. M.; Villoutreix, B. O., Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages. Eur J Pharm Sci 2020, 153, 105495. (PDBbind)
(22) Pinzi, L.; Rastelli, G., Identification of Target Associations for Polypharmacology from Analysis of Crystallographic Ligands of the Protein Data Bank. J Chem Inf Model 2020, 60, 372-390. (PDBbind)
(23) Penner, P.; Martiny, V.; Gohier, A.; Gastreich, M.; Ducrot, P.; Brown, D.; Rarey, M., Shape-Based Descriptors for Efficient Structure-Based Fragment Growing. J Chem Inf Model 2020, 60, 6269-6281. (PDBbind 2019)
(24) Na, G. S.; Kim, H. W.; Chang, H., Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis. J Chem Inf Model 2020, 60, 1137-1145. (PDBbind)
(25) Meli, R.; Biggin, P. C., spyrmsd: symmetry-corrected RMSD calculations in Python. J Cheminform 2020, 12, 49. (PDBbind refined set)
(26) Marchand, J. R.; Knehans, T.; Caflisch, A.; Vitalis, A., An ABSINTH-Based Protocol for Predicting Binding Affinities between Proteins and Small Molecules. J Chem Inf Model 2020, 60, 5188-5202. (PDBbind)
(27) Macari, G.; Toti, D.; Pasquadibisceglie, A.; Polticelli, F., DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina. Int J Mol Sci 2020, 21. (PDBbind 2013 2016 2018)
(28) Liu, Y.; Grimm, M.; Dai, W. T.; Hou, M. C.; Xiao, Z. X.; Cao, Y., CB-Dock: a web server for cavity detection-guided protein-ligand blind docking. Acta Pharmacol Sin 2020, 41, 138-144. (PDBbind 2018)
(29) Li, Y.; Gao, Y.; Holloway, M. K.; Wang, R., Prediction of the Favorable Hydration Sites in a Protein Binding Pocket and Its Application to Scoring Function Formulation. J Chem Inf Model 2020, 60, 4359-4375. (PDBbind 2016 core set)
(30) Li, S.; Wan, F.; Shu, H.; Jiang, T.; Zhao, D.; Zeng, J., MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities. Cell Systems 2020, 10, 308-322.e11. (PDBbind 2018)
(31) Li, J.; Song, Y.; Li, F.; Zhang, H.; Liu, W., FWAVina: A novel optimization algorithm for protein-ligand docking based on the fireworks algorithm. Comput Biol Chem 2020, 88, 107363. (PDBbind 2012)
(32) Li, G.; Su, Y.; Yan, Y. H.; Peng, J. Y.; Dai, Q. Q.; Ning, X. L.; Zhu, C. L.; Fu, C.; McDonough, M. A.; Schofield, C. J.; Huang, C.; Li, G. B., MeLAD: an integrated resource for metalloenzyme-ligand associations. Bioinformatics 2020, 36, 904-909. (PDBbind)
(33) Laufkotter, O.; Laufer, S.; Bajorath, J., Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome. Data Brief 2020, 32, 106189. (PDBbind)
(34) Kwon, Y.; Shin, W. H.; Ko, J.; Lee, J., AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks. Int J Mol Sci 2020, 21(22), 8424. (PDBbind 2016 2018)
(35) Katigbak, J.; Li, H.; Rooklin, D.; Zhang, Y., AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using beta-Clusters. J Chem Inf Model 2020, 60, 1494-1508. (PDBbind 2016 refined set)
(36) Karlov, D. S.; Sosnin, S.; Fedorov, M. V.; Popov, P., graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein-Ligand Complexes. ACS Omega 2020, 5, 5150-5159. (PDBbind 2007 2018)
(37) Kadukova, M.; Chupin, V.; Grudinin, S., Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. J Comput Aided Mol Des 2020, 34, 191-200. (PDBbind)
(38) Jimenez-Luna, J.; Cuzzolin, A.; Bolcato, G.; Sturlese, M.; Moro, S., A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules 2020, 25(11), 2487. (PDBbind 2017)
(39) Jiang, H.; Fan, M.; Wang, J.; Sarma, A.; Mohanty, S.; Dokholyan, N. V.; Mahdavi, M.; Kandemir, M. T., Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks. J Chem Inf Model 2020, 60, 4594-4602.(PDBbind 2016)
(40) Huang, K.; Luo, S.; Cong, Y.; Zhong, S.; Zhang, J. Z. H.; Duan, L., An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity. Nanoscale 2020, 12, 10737-10750. (PDBbind)
(41) Holderbach, S.; Adam, L.; Jayaram, B.; Wade, R. C.; Mukherjee, G., RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features. Front Mol Biosci 2020, 7, 601065. (PDBbind 2018 refined set)
(42) Hassan-Harrirou, H.; Zhang, C.; Lemmin, T., RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks. J Chem Inf Model 2020, 60, 2791-2802. (PDBbind 2016 2018)
(43) Ghanbarpour, A.; Mahmoud, A. H.; Lill, M. A., Instantaneous generation of protein hydration properties from static structures. Communications Chemistry 2020, 3. (PDBbind 2016 refined set)
(44) Gao, K.; Nguyen, D. D.; Sresht, V.; Mathiowetz, A. M.; Tu, M.; Wei, G. W., Are 2D fingerprints still valuable for drug discovery? Phys Chem Chem Phys 2020, 22, 8373-8390. (PDBbind 2015 2016)
(45) Gao, K.; Nguyen, D. D.; Chen, J.; Wang, R.; Wei, G. W., Repositioning of 8565 Existing Drugs for COVID-19. J Phys Chem Lett 2020, 11, 5373-5382. (PDBbind 2019)
(46) Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M. M.; Correia, B. E., Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods 2020, 17, 184-192. (PDBbind)
(47) Francoeur, P. G.; Masuda, T.; Sunseri, J.; Jia, A.; Iovanisci, R. B.; Snyder, I.; Koes, D. R., Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design. J Chem Inf Model 2020, 60, 4200-4215. (PDBbind 2016)
(48) Flachsenberg, F.; Meyder, A.; Sommer, K.; Penner, P.; Rarey, M., A Consistent Scheme for Gradient-Based Optimization of Protein-Ligand Poses. J Chem Inf Model 2020, 60, 6502-6522. (PDBbind 2016)
(49) Fine, J.; Muhoberac, M.; Fraux, G.; Chopra, G., DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening. J Chem Inf Model 2020, 60, 4137-4143. (PDBbind 2016)
(50) Fine, J.; Konc, J.; Samudrala, R.; Chopra, G., CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials. J Chem Inf Model 2020, 60, 1509-1527. (PDBbind 2016 core set)
(51) Feldmann, C.; Bajorath, J., Biological Activity Profiles of Multitarget Ligands from X-ray Structures. Molecules 2020, 25(4), 794. (PDBbind 2018)
(52) Feldmann, C.; Bajorath, J., X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes. Int J Mol Sci 2020, 21(11), 3782. (PDBbind 2019)
(53) Durai, P.; Ko, Y. J.; Pan, C. H.; Park, K., Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening. BMC Bioinformatics 2020, 21, 309. (PDBbind)
(54) Copoiu, L.; Torres, P. H. M.; Ascher, D. B.; Blundell, T. L.; Malhotra, S., ProCarbDB: a database of carbohydrate-binding proteins. Nucleic Acids Res 2020, 48, D368-D375. (PDBbind)
(55) Cinaroglu, S. S.; Timucin, E., Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets. Brief Bioinform 2020, 21, 2112-2125. (PDBbind refined set)
(56) Cho, H.; Lee, E. K.; Choi, I. S., Layer-wise relevance propagation of InteractionNet explains protein-ligand interactions at the atom level. Sci Rep 2020, 10, 21155. (PDBbind 2018)
(57) Cang, Z.; Wei, G. W., Persistent Cohomology for Data With Multicomponent Heterogeneous Information. SIAM J Math Data Sci 2020, 2, 396-418. (PDBbind 2007 2013 2015 2016)
(58) Boyles, F.; Deane, C. M.; Morris, G. M., Learning from the ligand: using ligand-based features to improve binding affinity prediction. Bioinformatics 2020, 36, 758-764. (PDBbind 2007 2013 2016 2018)
(59) Bonanni, D.; Lolli, M. L.; Bajorath, J., Computational Method for Structure-Based Analysis of SAR Transfer. J Med Chem 2020, 63, 1388-1396. (PDBbind)
(60) Bao, J.; He, X.; Zhang, J. Z. H., Development of a New Scoring Function for Virtual Screening: APBScore. J Chem Inf Model 2020, 60, 6355-6365. (PDBbind 2016 2018)
(61) Yang, Y.; Lu, J.; Yang, C.; Zhang, Y., Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S. J Comput Aided Mol Des 2019, 33, 1095-1105.(PDBbind 2016)
(62) Yang, W.; Sun, X.; Zhang, C.; Lai, L., Discovery of novel helix binding sites at protein-protein interfaces. Comput Struct Biotechnol J 2019, 17, 1396-1403. (PDBbind 2014)
(63) Wang, F.; Wu, F. X.; Li, C. Z.; Jia, C. Y.; Su, S. W.; Hao, G. F.; Yang, G. F., ACID: a free tool for drug repurposing using consensus inverse docking strategy. J Cheminform 2019, 11, 73. (PDBbind)
(64) Vangone, A.; Schaarschmidt, J.; Koukos, P.; Geng, C.; Citro, N.; Trellet, M. E.; Xue, L. C.; Bonvin, A., Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server. Bioinformatics 2019, 35, 1585-1587. (PDBbind 2013 core set)
(65) Moman, E.; Grishina, M. A.; Potemkin, V. A., Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions. J Comput Aided Mol Des 2019, 33, 943-953. (PDBbind 2018)
(66) Lu, J.; Hou, X.; Wang, C.; Zhang, Y., Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions. J Chem Inf Model 2019, 59, 4540-4549. (PDBbind 2016)
(67) Li, Y. J.; Rezaei, M. A.; Li, C. L.; Li, X. L. DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction. In 2019 Ieee International Conference on Bioinformatics and Biomedicine, Yoo, I. H.; Bi, J. B.; Hu, X., Eds.; 2019, pp 303-310. (PDBbind 2016 2018)
(68) Jimenez-Luna, J.; Perez-Benito, L.; Martinez-Rosell, G.; Sciabola, S.; Torella, R.; Tresadern, G.; De Fabritiis, G., DeltaDelta neural networks for lead optimization of small molecule potency. Chem Sci 2019, 10, 10911-10918. (PDBbind 2016)
(69) Ivanov, S. M.; Dimitrov, I.; Doytchinova, I. A., Bridging solvent molecules mediate RNase A - Ligand binding. PLoS One 2019, 14, e0224271. (PDBbind 2018 refined set)
(70) Ribeiro, J.; Rios-Vera, C.; Melo, F.; Schuller, A. Calculation of accurate interatomic contact surface areas for the quantitative analysis of non-bonded molecular interactions. Bioinformatics 2019, 35, 3499-3501. (PDBbind 2016)
(71) Lim, J.; Ryu, S.; Park, K.; Choe, Y. J.; Ham, J.; Kim, W. Y. Predicting drug-target interaction using a novel graph neural network with 3D structure-embeded graph representation. J. Chem. Inf. Model. 2019, 59, 3981-3988. (PDBbind 2018)
(72) Yang, J.;Baek, M.; Seok, C. GalaxyDock3: protein-ligand docking that considers the full ligand conformational flexibility. J. Comput. Chem. 2019, 40, 2739-2748. (PDBbind 2016)
(73) Zheng, L.; Fan, J.; Mu, Y.; OnionNet: a multiple-layer intermolecular-contact-based convolutional neural network for protein-ligand binding affinity prediction. ACS Omega 2019, 4, 15956-15965. (PDBbind 2016)
(74) Berishvili, V. P.; Perkin, V. O.; Voronkov, A. E.; Radchenko, E. V.; Syed, R.; Reddy, C. V. R.; Pillay, V.; Kumar, P.; Choonara, Y. E.; Kamal, A.; Palyulin, V. A. Time-domain analysis of molecular dynamics trajectories using deep neural networks: application to activity ranking of tankyrase inhibitors. J. Chem. Inf. Model. 2019, 59, 3519-3532. (PDBbind 2017 refined set)
(75) Wang, J.; Dokholyan, N. V. MedusaDock2.0: efficient and accurate protein-ligand docking with constraints. J. Chem. Inf. Model. 2019, 59, 2509-2515. (PDBbind 2017 refined set)
(76) Weng, G.; Wang, E.; Chen, F.; Sun, H.; Wang, Z.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes. Phys. Chem. Chem. Phys. 2019, 21, 10135-10145. (PDBbind 2017, peptide)
(77) Zhang, H.; Liao, L.; Saravanan, K. M.; Yin, P.; Wei, Y. DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity. PEERJ 2019, 7, e7362. (PDBbind 2018)
(78) Wojcikowski, M.; Kukielka, M.; Stepniewska-Dziubinska, M. M.; Siedlecki, P. Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions. Bioinformatics 2019, 35, 1334-1341. (PDBbind 2016)
(79) Trisciuzzi, D.; Nicolotti, O.; Miteva, M. A.; Villoutreix, B. O. Analysis of solvent-exposed and buried cocrystallized ligands: a case study to support the design of novel protein-protein interaction inhibitors. Drug Discovery Today 2019, 24, 551-559. (PDBbind 2017)
(80) Sunseri, J.; King, J. E.; Francoeur, P. G.; Koes, D. R. Convolutional neural network scoring and minimization in the D3R 2017 community challenge. J. Comput.-Aided Mol. Des. 2019, 33, 19-34. (PDBbind 2016 refined set)
(81) Nguyen, D. D.; Cang, Z.; Wu, K.; Wang, M.; Cao, Y.; Wei, G. W. Mathematical deep learning for pose and binding affinity prediction and ranking in D3R grand challenges. J. Comput.-Aided Mol. Des. 2019, 33, 71-82. (PDBbind for training set)
(82) Macari, G.; Toti, D.; Polticelli, F. Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies. J. Comput.-Aided Mol. Des. 2019, 33, 887-903.
(83) Preto, J.; Gentile, F.; Assessing and improving the performance of consensus docking strategies using the DockBox package. J. Comput.-Aided Mol. Des. 2019, 33, 817-829. (PDBbind 2013 core set)
(84) Jiang, M.; Li, Z.; Bian, Y.; Wei, Z. A novel protein descriptor for the prediction of drug binding sites. BMC Bioinformatics 2019, 20: 478. (PDBbind for test)
(85) Devaurs, D.; Antunes, D. A.; Hall-Swan, S.; Mitchell, N.; Moll, M.; Lizee, G.; Kavraki, L. E. Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. BMC Bioinformatics 2019, 20: 42. (PDBbind)
(86) Torres, P. H. M.; Sodero, A. C. R.; Jofily, P.; Silva, F. P. Key topics in molecular docking for drug design. Int. J. Mol. Sci. 2019, 20, 4574. (review, PDBbind project)
(87) Macari, G.; Toti, D.; Del Moro, C.; Polticelli, F. Fragment-based ligand-protein contact statistics: application to docking simulations. Int. J. Mol. Sci. 2019, 20, 2499. (PDBbind 2013 core set)
(88) Pei, J.; Zheng, Z.; Merz, K. M. Random forest refinement of the KECSA2 knowledge-based scoring function for protein decoy detection. J. Chem. Inf. Model. 2019, 59, 1919-1929. (PDBbind 2014)
(89) Zhu, M.; Song, X.; Chen, P.; Wang, W.; Wang, B. dbHDPLS: a database of human disease-related protein-ligand structures. Comput. Biol. Chem. 2019, 78, 353-358.
(90) Dittrich, J.; Schmidt, D.; Pfleger, C.; Gohlker, H. Converging a knowledge-based scoring function: DrugScore2018. J. Chem. Inf. Model. 2019, 59, 509-521. (PDBbind 2016)
(91) Wang, X.; Li, Z.; Jiang, M.; Wang, S.; Zhang, S.; Wei, Z. Molecule property prediction based on spatial graph embedding. J. Chem. Inf. Model. 2019, 59, 3817-3828.
(92) Cinaroglu, S. S.; Timucin, E. Comparative assessment of seven docking programs on a nonredundant metalloprotein subset of the PDBbind refined. J. Chem. Inf. Model. 2019, 59, 3846-3859. (PDBbind 2017 refined set)
(93) Bolcato, G.; Cuzzolin, A.; Bissaro, M.; Moro, S.; Sturlese, M. Can we still trust docking results? An extension of the applicability of DockBench on PDBbind database. Int. J. Mol. Sci. 2019, 20, 3558. (PDBbind refined set)
(94) Ozawa, S.-I.; Takahashi, M.; Yamaotsu, N.; Hirono, S. Structure-based virtual screening for novel chymase inhibitors by in silico fragment mapping. J. Mol. Graph. Model. 2019, 89, 102-108. (PDBbind 2013 core set)
(95) Ropp, P. J.; Spiegel, J. O.; Walker, J. L.; Green, H.; Morales, G. A.; Milliken, K. A.; Ringe, J. J.; Durrant, J. D. Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening. J. Cheminform. 2019, 11, 34. (PDBbind refined set)
(96) Chen, D. L.; Li, Y. B.; Guo, W.; Li, Y. D.; Savidge, T.; Li, X.; Fan, X. The shielding effect of metal complexes on the binding affinities of ligands to metalloproteins. Phys. Chem. Chem. Phys. 2019, 21, 205-216.
(97) Da, C.; Zhang, D.; Stashko, M.; Vasileiadi, E.; Parker, R. E.; Minson, K. A.; Huey, M. G.; Huelse, J. M.; Hunter, D.; Gilbert, T. S. K.; Norris-Drouin, J.; Miley, M.; Herring, L. E.; Graves, L. M.; DeRyckere, D.; Earp, H. S.; Graham, D. K.; Frye, S. V.; Wang, X. Data-driven construction of antitumor agents with controlled polypharmacology. J. Am. Chem. Soc. 2019, 141, 15700-15709. (for cross search)
(98) Zhang, H.; Liao, L.; Cai, Y.; Hu, Y.; Wang, H. IVS2vec: a tool of inverse virtual screening based on word2vec and deep learning techniques. Methods 2019, 166, 57-65. (PDBbind 2017)
(99) da Silva, A. D.; Bitencourt-Ferreira, G.; de Azevedo, W. F. Taba: a tool to analyze the binding affinity. J. Comput. Chem. 2019, 41, 69-73. (binding data)
(100) Chen, P.; Ke, Y.; Lu, Y.; Du, Y.; Li, J.; Yan, H.; Zhao, H.; Zhou, Y.; Yang, Y. DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state. J. Cheminform. 2019, 11: 52. (PDBbind 2016 refined set)
(101) Han, M.; Sun, D.; Rational creation and systematic analysis of cervical cancer kinase-inhibitor binding profile. J. Comput.-Aided Mol. Des. 2019, 33, 689-698. (binding data for Kinase inhibitors)
(102) Gilberg, E.; Bajorath, J. Recent progress in structure-based evaluation of compound promiscuity. ACS Omega 2019, 4, 2758-2765. (binding data)
(103) Gilberg, E.; Gutschow, M.; Bajorath, J. Promiscuous ligands from experimentally determined structures, binding conformations, and protein family-dependent interaction hotspots. ACS Omega 2019, 4, 1729-1737. (binding data)
(104) Miljkovic, F.; Bajorath, J. Computational analysis of kinase inhibitors identifies promiscuity cliffs across the human kinome. ACS Omega 2018, 3, 17295-17308. (for kinase complexes)
(105) Feinberg, E. N.; Sur, D.; Wu, Z.; Husic, B. E.; Mai, H.; Li, Y.; Sun, S.; Yang, J.; Ramsundar, B.; Pande, V. S. PotentialNet for molecular property prediction. ACS Cent. Sci. 2018, 4, 1520-1530. (PDBbind 2007)
(106) Stepniewska-Dziubinska, M. M.; Zielenkiewicz, P.; Siedlecki, P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 2018, 34, 3666-3674. (PDBbind 2016)
(107) Chen, F.; Sun, H.; Wang, J.; Zhu, F.; Liu, H.; Wang, Z.; Lei, T.; Li, Y.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA 2018, 24, 1183-1194. (PDBbind for RNA complex)
(108) Han, K.; Zhang, L.; Wang, M.; Zhang, R.; Wang, C.; Zhang, C. Prediction methods of Herbal compounds in Chinese medicinal herbs. Molecules 2018, 23, 2303. (review, citation of PDBbind project)
(109) Perez, A.; Martinez-Rosell G.; De Fabritiis, G. Simulations meet machine learning in structural biology. Curr. Opin. Struct. Biol. 2018, 49, 139-144. (PDBbind)
(110) Zheng, Z.; Pei, J.; Bansal, N.; Liu, H.; Song, L. F.; Merz, K. M. Generation of pairwise potentials using multidimensional data mining. J. Chem. Theory Comput. 2018, 14, 5045-5067. (PDBbind 2015)
(111) Yamaotsu, N.; Hirono, S. In silico fragment-mapping method: a new tool for fragment-based/structure-based drug discovery. J. Comput.-Aided Mol. Des. 2018, 32, 1229-1245.
(112) Zhao, R.; Cang, Z.; Tong, Y.; Wei, G. Protein pocket detection via convex hull surface evolution and associated Reeb graph. Bioinformatics 2018, 34, i830-i837. (PDBbind refined set 2007, 2013, 2015, 2016)
(113) de Avila, M. B.; de Azevedo, W. F. Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem. Biol. Drug Des. 2018, 92, 1468-1474. (binding data)
(114) Bitencourt-Ferreira, G.; Azevedo, W. F. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys. Chem. 2018, 240, 63-69. (binding data)
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(385) Chen, J. and L.H. Lai, Pocket v.2: Further developments on receptor-based pharmacophore modeling. Journal of Chemical Information and Modeling, 2006. 46(6): p. 2684-2691.
(386) Coleman, R.G. and K.A. Sharp, Travel depth, a new shape descriptor for macromolecules: Application to ligand binding. Journal of Molecular Biology, 2006. 362(3): p. 441-458.
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(388) Han, L.Y., et al., PEARLS: Program for energetic analysis of receptor-ligand system. Journal of Chemical Information and Modeling, 2006. 46(1): p. 445-450.
(389) Huang, N., B.K. Shoichet, and J.J. Irwin, Benchmarking sets for molecular docking. Journal of Medicinal Chemistry, 2006. 49(23): p. 6789-6801.
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If you had to, could you trace all your products in two hours?
The food and beverage (F&B) industry is experiencing an increase in the frequency of product recalls and other food safety related threats. Even the best and the biggest in the business are not impervious to quality failures in their production supply chains. Faced with market forces of diminishing consumer loyalty, focus on brand power and stringent regulations traceability is of practical importance today, more than ever before.


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Friday Favorites! What is your favorite character topiary at the Epcot International Flower & Garden Festival?

                <time class="post-date" datetime="2016-03-04">04 March 2016</time>  on Friday Favorites,  Disney World,  Epcot

The weather is getting warmer, the days are getting longer, and the trees and flowers are coming to life. That can only mean one thing: It's time for the Epcot International Flower & Garden Festival! One of our favorite things about the Flower & Garden Festival is all of the topiaries spread around Epcot that resemble Disney characters. This year, the festival runs for 3 months and there are over 3 dozen topiaries, including a brand new Ranger Mickey topiary to celebrate the centennial anniversary of the National Parks Service!

So this week, we're asking you which of the Flower & Garden topiaries is your favorite? Do you whistle while you walk past Snow White and the Seven Dwarfs topiaries in the Germany Pavilion? Do the Lady and the Tramp topiaries in the Italy Pavilion make it a bella notte? Do the Cinderella and Prince Charming topiaries in the France Pavilion make you dance?

Leave us a comment and let us know which topiary is your favorite? If you've never been to Epcot during the International Flower & Garden Festival, then search the web for photos of the topiaries (there are lots, including on our Instagram) and pick your favorite. And don't garden alone: Invite your friends, family, neighbors, horticulturist, everyone at the local nursery, and any lawn maintenance crew you see to come check out MouseGuests and join in on the conversation!

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Home India Haryana After Uttar Pradesh, now journalist booked in Haryana for exposing corruption in...
After Uttar Pradesh, now journalist booked in Haryana for exposing corruption in government
A journalist working for a TV channel in Haryana's Hisar district has alleged that the local police charged him with defamation and illegal trespassing after he exposed the mishandling of grains at a government storage facility through his reporting.
An FIR was filed against Anoop Kundu after his report exposed serious irregularities the State Food Civil Supplies and Consumer Affairs Department in Uklana. Assistant Food and Supplies Officer Sandeep Chahal, who originally filed a complaint against Kundu, alleged that the journalist used fake video to defame the government.
Kundu has defended his journalist adding that the state food and civil supplies minister Karan Dev Kamboj even praised his work when he appeared on the channel to give his reaction on the story. "He said that I've done a courageous reporting and promised to act against the erring officials," Kundu added.
DSP Hisar, Ashok Kumar, said, "I can assure you that nothing wrong will be done to anyone. If what he reported is true then rest assured no wrong will be done to him."
The latest episode comes just days after the Uttar Pradesh government, also ruled by the BJP, booked and arrested a journalist after his video showed how students at a school in Mirzapur were forced to eat salt and roti as part of the flagship government's mid-day meal scheme. Curiously, the UP Police too had booked journalist Pawan Jaiswal for defaming the state government.
Press freedom in India has been a matter of huge concern across the globe. India has consistently been slipping on a global press freedom index with the country's current ranking being at 140. This was 136 only two years ago.
Anoop Kundu
journalist booked
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Data drives the digital world. Much has been written about the pervasiveness of technology in the world and the promise of big data. We've all heard the mantra.
All speculation aside, the growing volume of data is a fact, and one that can't be ignored. International Data Corporation (IDC) estimates the amount of data in the world will reach 163 trillion gigabytes by 2025. Every industry—from transportation to manufacturing, healthcare to consumer products, financial services to research and development and all the others—is looking for new ways to harness and to use the growing volume of data.
Scientists understand that data is the fuel that powers insight, discovery and innovation. The Institute of Cancer Research (ICR), for example, says big data analytics plays an important role in the discovery of cancer drugs. Scientists are analyzing vast amounts of data—from patient samples, genomic sequencing, medical images, lab results, experimental data, pharmacological data, and many other sources—to help in their efforts.
could eventually drop to as little as $100.
proteomic, and other bioinformatic research.


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WEST INDIES ODI CAPTAIN (DWAYNE BRAVO) SUSPENDED
by ESPNcricinfo staff
Dwayne Bravo, the West Indies captain, has been suspended for one match after being fined a second time within 12 months for maintaining a slow over-rate. He will miss the crucial tri-series match against Sri Lanka on Sunday.
West Indies were ruled one over short of the target at the end of match against India in Port-of-Spain for which the match referee, Jeff Crowe, fined Bravo 20% of his match fee and his team members, 10% of their match fees.
"The captains were informed of their respective positions relating to over-rate breaches before the start of the series," Crowe said. "On Friday, the umpires had kept Dwayne [Bravo] informed throughout the innings about the position regarding over-rates, and more so when his side fell behind."
The charge was laid by on-field umpires Nigel Long and Peter Nero, as well as third umpire Nigel Duguid and fourth umpire Joel Wilson, with Bravo pleading guilty.
It was the second such offence for Bravo after he was fined for a slow over-rate during the Champions Trophy match against South Africa on June 14.
Virat Kohli was also slapped with a similar fine after India's match against Sri Lanka in Kingston.
Source: http://www.espncricinfo.com/tri-nation-west-indies-2013/content/story/648245.html
Posted by MANAGING EDITOR at 8:38 PM
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TROPICAL STORM "CHANTAL" FORMS OVER THE ATLANTIC
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A Nemzeti Közlekedési Hatóság (rövidítve: NKH) a közlekedésért felelős miniszter irányítása alá tartozó, központi hivatalként működő központi költségvetési szerv volt. A Hatóság 2007-ben jött létre a Közlekedési Főfelügyelet, a Központi Közlekedési Felügyelet, a megyei (fővárosi) közlekedési felügyeletek és a Polgári Légiközlekedési Hatóság jogutódjaként, melybe később beolvadt a Katonai Légügyi Hivatal és a Magyar Vasúti Hivatal is.

Az NKH-tól később leváltak a korábbi Közlekedési Felügyeletek, amelyek új névként Kormányhivatal Közlekedési Felügyelőségekként folytatták tovább tevékenységüket.

Az NKH Központ, a Közúti Gépjármű-közlekedési Hivatal, az Útügyi, Vasúti és Hajózási Hivatal, a Légügyi Hivatal, továbbá a megyei/fővárosi kormányhivatalok Műszaki Engedélyezési és Fogyasztóvédelmi Főosztályai látták el Magyarországon a közlekedéshez kapcsolódó engedélyezési, ellenőrzési, nyilvántartási  tevékenységet. E hatóságok nem végeztek közlekedés-igazgatási feladatokat, ez a KekKH feladata volt.

A közlekedésért felelős miniszter (Nemzeti Fejlesztési Minisztérium, NFM) szakmai irányítási és ellenőrzési feladatainak ellátásában a Központ közreműködött, melyet a gyakorlatban NKH első fokú hivatalai végeztek. 2016. december 31-én a jogutódlással megszűnt, a Nemzeti Fejlesztési Minisztériumba olvadt.

Jogállása és szervezete
A közlekedési hatóságok tevékenységét (hatáskörét és illetékességét) a 263/2006. (XII. 20.) kormányrendelet szabályozza. A Központ és a Hatóság első fokú szervei országos illetékességgel járnak el, székhelyük Budapest. A hatóság élén elnök áll, az elsőfokú hivatalokat elnökhelyettesek irányítják.

Elsőfokú szervei
 a Közúti Gépjármű-közlekedési Hivatal,
 az Útügyi, Vasúti és Hajózási Hivatal,
 a Légügyi Hivatal.

Források
378/2016. (XII. 2.) Korm. rendelet egyes központi hivatalok és költségvetési szervi formában működő minisztériumi háttérintézmények felülvizsgálatával összefüggő jogutódlásáról, valamint egyes közfeladatok átvételéről

Jegyzetek

További információk
 nkh.gov.hu  – hivatalos honlap
 23/2014. (V. 30.) NFM utasítás a Nemzeti Közlekedési Hatóság Szervezeti és Működési Szabályzatáról

Magyarországi állami szervezetek

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Nancy Pelosi sacrificed abortion protections to get health care passed. Peter Beinart argues the tradeoff saves the bill and the party, Amy Siskind says Obama let women down again, and Dana Goldstein asks whether this new wedge issue will tear Dems apart.
House Democrats were right to sacrifice abortion protections to get health care passed. Peter Beinart on why that strategic tradeoff can save the bill—and the party's future.
The heath-care abortion mess is just the latest example of how Barack Obama took women's votes—then let the country's majority constituency down.
Many Democrats cheered when the health bill passed the House, but pro-choice groups denounced a last-minute amendment to ban coverage of abortions. Now the issue is tearing apart health-reform supporters in the Senate.


Anjali was waiting for Vivek on Versova beach and behind the rocks were Atul and Alex, fixing their cameras on her and they too waiting for Vivek to arrive. After some time Vivek also came. They saw some sort of interaction between Anjali and Vivek, as they were not able to hear but were able to see through the focusing camera lenses. They started taking snaps one after the other. Within some span of time Anjali and Vivek were comfortably mixed with each other and were walking along the beach holding each other's hands. Here behind the rocks Atul and Alex were also moving ahead taking there snaps.
It was getting dark and suddenly they saw Vivek pulled Anjali in her tight embrace and held her up. Here the speed of snaps increased. Bust still they were not satisfied, as they did't got their poses they were anticipating.
Anjali's car stood at Hotel Oberoi. The taxi following their car also stopped keeping some safe distance. Anjali got down out of the car and headed for the hotel and Vivek also followed her. Atul looked at Alex with a meaningful stance, smiled, and they also followed them taking care that they will not be noticed by them.
Anjali and Vivek reached the hotel suit and the suit door got closed. Atul and Alex, following them, hurried and went to their closed door. Alex tried the door to open, but it was latched from inside.
" Now we are going to stand her as their guard or what?" Alex murmured in annoyance.
"Don't worry we have a solution" Atul said, assuring him.
Alex waited for a while for Atul to remove his head from the keyhole, but he was not ready to miss the scene going on inside. Lastly Alex forcefully removed his head from the keyhole and said, " My dear friend ... in this way we are not going to earn our bread and butter"
Atul, who was lost in the scene inside, came to his conscious.
" But how you are going to photograph them?" Alex asked the question of real concern.
"Don't worry we are equipped with technology" Atul reassured him and removed a cable like thing from his pocket. He attached one end of it to the camera and inserted the other end inside the room through keyhole.
" This is special camera my dear" Atul said and started to take snaps inside the room.


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Ernest Butcher
Edward Ernest Butcher (7 April 1885 – 8 June 1965) was a British actor, on stage from 1935, and with many film and TV appearances.[1][2][3] He was the second husband of the actress Muriel George, and stepfather to her son, the critic John Davenport.[4][5]
1936 Spotlight photo
Edward Ernest Butcher
(1885-04-07)7 April 1885
Burnley, Lancashire, England
8 June 1965(1965-06-08) (aged 80)
Muriel George
He appeared in the original production of J.B. Priestley's play When We Are Married at St. Martin's Theatre London, in 1938;[6] and reprised his performance in the film version, in 1943.[1]
Key to Harmony (1935)
The Small Man (1936)
Talking Feet (1937)
The Song of the Road (1937)
Stepping Toes (1938)
Me and My Pal (1939)
Black Eyes (1939)
Pack Up Your Troubles (1940)
Freedom Radio (1941)
Old Mother Riley in Business (1941)
'Pimpernel' Smith (1941)
When We Are Married (1943)
Candles at Nine (1944)
It's in the Bag (1944)
Tawny Pipit (1944)
It Happened One Sunday (1944)
The Years Between (1946)
Easy Money (1948)
My Brother Jonathan (1948)
My Brother's Keeper (1948)
For Them That Trespass (1949)
Diamond City (1949)
Meet Simon Cherry (1949)
Night and the City (1950)
No Trace (1950)
Blackout (1950)
Highly Dangerous (1950)
The Happy Family (1952)
Time Bomb (1953)
Background (1953)
The Desperate Man (1959)
"Ernest Butcher". Archived from the original on 2009-09-04. Retrieved 2011-03-23.
McFarlane, Brian (16 May 2016). "The Encyclopedia of British Film: Fourth edition". Oxford University Press – via Google Books.
"Ernest Butcher". www.aveleyman.com.
The New Review, vol. 3, issue 31, 1976, pg 69
"Production of When We Are Married - Theatricalia". theatricalia.com.
appeared in Terror on a Train, 1953, with Glenn Ford as seen on cable station TCM at10:45 - 12:15pm EST on 8/22/2017
Ernest Butcher on IMDb


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Annie Williams started her GFWC journey in 2011 and she immediately fell in love with her club and the women working alongside her.