Energy-optimized pharmacophore coupled virtual screening in the discovery of quorum sensing inhibitors of LasR protein of Pseudomonas aeruginosa

References

• Ahumedo, M., Velásquez, M., & Vivas Reyes, R. (2017). Virtual screening: Identification of compounds with possible quorum sensing agonistic activity in Pseudomonas aeruginosa. Revista Vitae, 24 (2), 89–101. doi:10.17533/udea.vitae.v24n2a02
   Google Scholar
• Algburi, A., Zehm, S., Netrebov, V., Bren, A. B., Chistyakov, V., & Chikindas, M. L. (2017). Subtilosin prevents biofilm formation by inhibiting bacterial quorum sensing. Probiotics and Antimicrobial Proteins, 9 (1), 81–90. doi:10.1007/s12602-016-9242-x
   PubMed Web of Science ®Google Scholar
• Allewell, N. M. (2016). Introduction to biofilms thematic minireview series. Journal of Biological Chemistry, 291 (24), 12527–12528. doi:10.1074/jbc.R116.734103
   PubMed Web of Science ®Google Scholar
• Azam, S. S., Uddin, R., & Wadood, A. (2012). Structure and dynamics of alpha-glucosidase through molecular dynamics simulation studies. Journal of Molecular Liquids, 174, 58–62. doi:10.1016/j.molliq.2012.07.003
   Web of Science ®Google Scholar
• Berman, H. M. (2000). The protein data bank. Nucleic Acids Research, 28 (1), 235–242. doi:10.1093/nar/28.1.235
   PubMed Web of Science ®Google Scholar
• Bodey, G. P., Bolivar, R., Fainstein, V., & Jadeja, L. (1983). Infections caused by Pseudomonas aeruginosa. Clinical Infectious Diseases, 5 (2), 279–313. doi:10.1093/clinids/5.2.279
   Google Scholar
• Boehm, H. J., Boehringer, M., Bur, D., Gmuender, H., Huber, W., Klaus, W., … Mueller, F. (2000). Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. Journal of Medicinal Chemistry, 43 (14), 2664–2674. doi:10.1021/jm000017s
   PubMed Web of Science ®Google Scholar
• Borkotoky, S., Meena, C. K., & Murali, A. (2016). Interaction analysis of T7 RNA polymerase with heparin and its low molecular weight derivatives—an in silico approach. Bioinformatics and Biology Insights, 10, 155–166. doi:10.4137/BBI.S40427
   PubMed Web of Science ®Google Scholar
• Chen, Z., Tian, G., Wang, Z., Jiang, H., Shen, J., & Zhu, W. (2010). Multiple pharmacophore models combined with molecular docking: A reliable way for efficiently identifying novel PDE4 inhibitors with high structural diversity. Journal of Chemical Information and Modeling, 50 (4), 615–625. doi:10.1021/ci9004173
   PubMed Web of Science ®Google Scholar
• Congreve, M., Carr, R., Murray, C., & Jhoti, H. (2003). A “rule of three” for fragment-based lead discovery? Drug Discovery Today, 19 (8), 876–877. doi:10.1016/S1359-6446(03)02831-9
   Google Scholar
• Das, D., Koh, Y., Tojo, Y., Ghosh, A. K., & Mitsuya, H. (2009). Prediction of potency of protease inhibitors using free energy simulations with polarizable quantum mechanics-based ligand charges and a hybrid water model. Journal of Chemical Information and Modeling, 49 (12), 2851–2862. doi:10.1021/ci900320p
   PubMed Web of Science ®Google Scholar
• Dhasmana, A., Raza, S., Jahan, R., Lohani, M., & Arif, J. M. (2019). High-throughput virtual screening (HTVS) of natural compounds and exploration of their biomolecular mechanisms. In New look to phytomedicine (pp. 523–548). Cambridge, MA: Academic Press. doi:10.1016/b978-0-12-814619-4.00020-3
   Google Scholar
• Dickson, C. J., Madej, B. D., Skjevik, Å. A., Betz, R. M., Teigen, K., Gould, I. R., & Walker, R. C. (2014). Lipid14: The amber lipid force field. Journal of Chemical Theory and Computation, 10 (2), 865–879. doi:10.1021/ct4010307
   PubMed Web of Science ®Google Scholar
• Dixon, S. L., Smondyrev, A. M., & Rao, S. N. (2006). PHASE: A novel approach to pharmacophore modeling and 3D database searching. Chemical Biology & Drug Design, 67 (5), 370–372. doi:10.1111/j.1747-0285.2006.00384.x
   PubMed Web of Science ®Google Scholar
• Doman, T. N., McGovern, S. L., Witherbee, B. J., Kasten, T. P., Kurumbail, R., Stallings, W. C., … Shoichet, B. K. (2002). Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. Journal of Medicinal Chemistry, 45 (11), 2213–2221. doi:10.1021/jm010548w
   PubMed Web of Science ®Google Scholar
• Dror, O., Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2009). Novel approach for efficient pharmacophore-based virtual screening: Method and applications. Journal of Chemical Information and Modeling, 49 (10), 2333–2343. doi:10.1021/ci900263d
   PubMed Web of Science ®Google Scholar
• Empereur-Mot, C., Guillemain, H., Latouche, A., Zagury, J. F., Viallon, V., & Montes, M. (2015). Predictiveness curves in virtual screening. Journal of Cheminformatics, 7 (1), 52. doi:10.1186/s13321-015-0100-8
   PubMedGoogle Scholar
• Fazzeli, H., Akbar, R., Moghim, S., Narimani, T., Arabestani, M. R., & Ghoddousi, A. R. (2012). Pseudomonas aeruginosa infections in patients, hospital means, and personnel’s specimens. Journal of Research in Medical Sciences, 17, 332–337.
   PubMed Web of Science ®Google Scholar
• Ferreira De Freitas, R., & Schapira, M. (2017). A systematic analysis of atomic protein-ligand interactions in the PDB. MedChemComm, 8 (10), 1970–1981. doi:10.1039/C7MD00381A
   PubMed Web of Science ®Google Scholar
• Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., … Shenkin, P. S. (2004). Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47 (7), 1739–1749. doi:10.1021/jm0306430
   PubMed Web of Science ®Google Scholar
• Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., … Mainz, D. T. (2006). Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. Journal of Medicinal Chemistry, 49 (21), 6177–6196. doi:10.1021/jm051256o
   PubMed Web of Science ®Google Scholar
• Ghosh, S., Nie, A., An, J., & Huang, Z. (2006). Structure-based virtual screening of chemical libraries for drug discovery. Current Opinion in Chemical Biology, 10 (3), 194–202. doi:10.1016/j.cbpa.2006.04.002
   PubMed Web of Science ®Google Scholar
• Grüneberg, S., Wendt, B., & Klebe, G. (2001). Subnanomolar inhibitors from computer screening: A model study using human carbonic anhydrase II. Angewandte Chemie International Edition, 40 (2), 389–393. doi:10.1002/1521-3773(20010119)40:2<389::AID-ANIE389>3.0.CO;2-#
   PubMed Web of Science ®Google Scholar
• Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004). Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry, 47 (7), 1750–1759. doi:10.1021/jm030644s
   PubMed Web of Science ®Google Scholar
• Hentzer, M., & Givskov, M. (2003). Pharmacological inhibition of quorum sensing for the treatment of chronic bacterial infections. Journal of Clinical Investigation, 112 (9), 1300–1307. doi:10.1172/JCI20074
   PubMed Web of Science ®Google Scholar
• Høiby, N., Bjarnsholt, T., Givskov, M., Molin, S., & Ciofu, O. (2010). Antibiotic resistance of bacterial biofilms. International Journal of Antimicrobial Agents, 35 (4), 322–332. doi:10.1016/j.ijantimicag.2009.12.011
   PubMed Web of Science ®Google Scholar
• Hou, T., Wang, J., Li, Y., & Wang, W. (2011). Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of Chemical Information and Modeling, 51 (1), 69–82. doi:10.1021/ci100275a
   PubMed Web of Science ®Google Scholar
• Hubbard, R. E., & Kamran Haider, M. (2010). Hydrogen bonds in proteins: Role and strength. In Encyclopedia of life sciences. Chichester, UK: Wiley. doi:10.1002/9780470015902.a0003011.pub2
   Google Scholar
• Hymavati Kumar, V., & Elizabeth Sobhia, M. (2012). Implication of crystal water molecules in inhibitor binding at ALR2 active site. Computational and Mathematical Methods in Medicine, 2012, 1–11. doi:10.1155/2012/541594
   Google Scholar
• Ioakimidis, L., Thoukydidis, L., Mirza, A., Naeem, S., & Reynisson, J. (2008). Benchmarking the reliability of QikProp correlation between experimental and predicted values. QSAR & Combinatorial Science, 27 (4), 445–456. doi:10.1002/qsar.200730051
   Google Scholar
• Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., & Coleman, R. G. (2012). ZINC: A free tool to discover chemistry for biology. Journal of Chemical Information and Modeling, 52(7), 1757. doi:10.1021/ci3001277
   PubMed Web of Science ®Google Scholar
• Jakobsen, T. H., Van Gennip, M., Phipps, R. K., Shanmugham, M. S., Christensen, L. D., Alhede, M., … Givskov, M. (2012). Ajoene, a sulfur-rich molecule from garlic, inhibits genes controlled by quorum sensing. Antimicrobial Agents and Chemotherapy, 56 (5), 2314–2325. doi:10.1128/AAC.05919-11
   PubMed Web of Science ®Google Scholar
• Jayashree, S., Murugavel, P., Sowdhamini, R., & Srinivasan, N. (2019). Interface residues of transient protein-protein complexes have extensive intra-protein interactions apart from inter-protein interactions. Biology Direct, 14(1), 1. doi:10.1186/s13062-019-0232-2
   PubMedGoogle Scholar
• Jiang, Q., Chen, J., Yang, C., Yin, Y., Yao, K., & Song, D. (2019). Quorum sensing: A prospective therapeutic target for bacterial diseases. Biomed Research International, 2019, 1. doi:10.1155/2019/2015978
   Web of Science ®Google Scholar
• Kalia, M., Singh, P. K., Yadav, V. K., Yadav, B. S., Sharma, D., Narvi, S. S., … Agarwal, V. (2017). Structure based virtual screening for identification of potential quorum sensing inhibitors against LasR master regulator in Pseudomonas aeruginosa. Microbial Pathogenesis, 107, 136–143. doi:10.1016/j.micpath.2017.03.026
   PubMed Web of Science ®Google Scholar
• Kaminski, G. A., Friesner, R. A., Tirado-Rives, J., & Jorgensen, W. L. (2001). Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. The Journal of Physical Chemistry B, 105 (28), 6474–6487. doi:10.1021/jp003919d
   Web of Science ®Google Scholar
• Kandakatla, N., & Ramakrishnan, G. (2014). Ligand based pharmacophore modeling and virtual screening studies to design novel HDAC2 inhibitors. Advances in Bioinformatics, 2014, 1. doi:10.1155/2014/812148
   Google Scholar
• Karthikeyan, M., & Vyas, R. (2014). Practical chemoinformatics. New Delhi: Springer. doi:10.1007/978-81-322-1780-0
   Google Scholar
• Kaserer, T., Beck, K. R., Akram, M., Odermatt, A., Schuster, D., & Willett, P. (2015). Pharmacophore models and pharmacophore-based virtual screening: Concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules, 20(12), 22799. doi:10.3390/molecules201219880
   PubMed Web of Science ®Google Scholar
• Kievit, T. R., Kakai, Y., Register, J. K., Pesci, E. C., & Iglewski, B. H. (2002). Role of the Pseudomonas aeruginosa las and rhl quorum-sensing systems in rhlI regulation. FEMS Microbiology Letters, 212 (1), 101–106. doi:10.1111/j.1574-6968.2002.tb11251.x
   PubMed Web of Science ®Google Scholar
• Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., … Bryant, S. H. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44 (D1), D1202–D1213. doi:10.1093/nar/gkv951
   PubMed Web of Science ®Google Scholar
• Kiratisin, P., Tucker, K. D., & Passador, L. (2002). LasR, a transcriptional activator of Pseudomonas aeruginosa virulence genes, functions as a multimer. Journal of Bacteriology, 184 (17), 4912–4919. doi:10.1128/JB.184.17.4912-4919.2002
   PubMed Web of Science ®Google Scholar
• Koh, C.-L., Sam, C.-K., Yin, W.-F., Tan, L. Y., Krishnan, T., Chong, Y. M., & Chan, K.-G. (2013). Plant-derived natural products as sources of anti-quorum sensing compounds. Sensors, 13 (5), 6217–6228. doi:10.3390/s130506217
   PubMed Web of Science ®Google Scholar
• Krieger, E., Darden, T., Nabuurs, S. B., Finkelstein, A., & Vriend, G. (2004). Making optimal use of empirical energy functions: Force-field parameterization in crystal space. Proteins: Structure, Function, and Bioinformatics, 57 (4), 678–683. doi:10.1002/prot.20251
   PubMed Web of Science ®Google Scholar
• Lila, G., Mulliqi, G., Raka, L., Kurti, A., Bajrami, R., & Azizi, E. (2018). Molecular epidemiology of Pseudomonas aeruginosa in university clinical center of kosovo. Infection and Drug Resistance, 11, 2039–2046. doi:10.2147/IDR.S174940
   PubMed Web of Science ®Google Scholar
• Lionta, E., Spyrou, G., Vassilatis, D., & Cournia, Z. (2014). Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Current Topics in Medicinal Chemistry, 14 (16), 1923–1938. doi:10.2174/1568026614666140929124445
   PubMed Web of Science ®Google Scholar
• Lipinski, C. A., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23 (1–3), 3–25.
   Web of Science ®Google Scholar
• Liu, K., & Kokubo, H. (2017). Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations: A cross-docking study. Journal of Chemical Information and Modeling, 57 (10), 2514–2522. doi:10.1021/acs.jcim.7b00412
   PubMed Web of Science ®Google Scholar
• Lobanov, M. Y., Bogatyreva, N. S., & Galzitskaya, O. V. (2008). Radius of gyration as an indicator of protein structure compactness. Molecular Biology, 42(4), 623–628. doi:10.1134/S0026893308040195
   Web of Science ®Google Scholar
• Luise, C., & Robaa, D. (2018). Application of virtual screening approaches for the identification of small molecule inhibitors of the methyllysine reader protein spindlin1. In Rational drug design, (pp. 347–370). New York, NY: Humana Press. doi:10.1007/978-1-4939-8630-9_21.
   Google Scholar
• Lyne, P. D., Lamb, M. L., & Saeh, J. C. (2006). Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. Journal of Medicinal Chemistry, 49 (16), 4805–4808. doi:10.1021/jm060522a
   PubMed Web of Science ®Google Scholar
• Muthusamy, K., Singh, K. D., Chinnasamy, S., Nagamani, S., Krishnasamy, G., Thiyagarajan, C., … Anusuyadevi, M. (2013). High throughput virtual screening and E-pharmacophore filtering in the discovery of new BACE-1 inhibitors. Interdisciplinary Sciences: Computational Life Sciences, 5 (2), 119–126. doi:10.1007/s12539-013-0157-x
   PubMed Web of Science ®Google Scholar
• Natarajan, P., Priyadarshini, V., Pradhan, D., Manne, M., Swargam, S., Kanipakam, H., … Amineni, U. (2016). E-pharmacophore-based virtual screening to identify GSK-3β inhibitors. Journal of Receptors and Signal Transduction, 36 (5), 445–458. doi:10.3109/10799893.2015.1122043
   PubMed Web of Science ®Google Scholar
• O’Loughlin, C. T., Miller, L. C., Siryaporn, A., Drescher, K., Semmelhack, M. F., & Bassler, B. L. (2013). A quorum-sensing inhibitor blocks Pseudomonas aeruginosa virulence and biofilm formation. Proceedings of the National Academy of Sciences, 110, 17981–17986. doi:10.1073/pnas.1316981110
   PubMed Web of Science ®Google Scholar
• Palakurti, R., Sriram, D., Yogeeswari, P., & Vadrevu, R. (2013). Multiple e-pharmacophore modeling combined with high-throughput virtual screening and docking to identify potential inhibitors of β-secretase(BACE1). Molecular Informatics, 32 (4), 385–398. doi:10.1002/minf.201200169
   PubMed Web of Science ®Google Scholar
• Patil, R., Das, S., Stanley, A., Yadav, L., Sudhakar, A., & Varma, A. K. (2010). Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. PLoS One, 5 (8), e12029. doi:10.1371/journal.pone.0012029
   PubMed Web of Science ®Google Scholar
• Pearson, J. P., Pesci, E. C., & Iglewski, B. H. (1997). Roles of Pseudomonas aeruginosa las and rhl quorum-sensing systems in control of elastase and rhamnolipid biosynthesis genes. Journal of Bacteriology, 179(18), 5756. doi:10.1128/jb.179.18.5756-5767.1997
   PubMed Web of Science ®Google Scholar
• Qiu, S., Azofra, L. M., MacFarlane, D. R., & Sun, C. (2018). Hydrogen bonding effect between active site and protein environment on catalysis performance in H2-producing [NiFe] hydrogenases. Physical Chemistry Chemical Physics, 20 (9), 6735–6743. doi:10.1039/C7CP07685A
   PubMed Web of Science ®Google Scholar
• Rasmussen, T. B., Skindersoe, M. E., Bjarnsholt, T., Phipps, R. K., Christensen, K. B., Jensen, P. O., … Givskov, M. (2005). Identity and effects of quorum-sensing inhibitors produced by penicillium species. Microbiology, 151 (5), 1325–1340. doi:10.1099/mic.0.27715-0
   PubMedGoogle Scholar
• Rémy, B., Mion, S., Plener, L., Elias, M., Chabrière, E., & Daudé, D. (2018). Interference in bacterial quorum sensing: A biopharmaceutical perspective. Frontiers in Pharmacology, 9, 203. doi:10.3389/fphar.2018.00203
   PubMed Web of Science ®Google Scholar
• Rishton, G. M. (1997). Reactive compounds and in vitro false positives in HTS. Drug Discovery Today, 2 (9), 382–384. doi:10.1016/S1359-6446(97)01083-0
   Web of Science ®Google Scholar
• Rumbaugh, K. P. (2014). Genomic complexity and plasticity ensure pseudomonas success. FEMS Microbiology Letters, 356 (2), 141–143. doi:10.1111/1574-6968.12517
   PubMed Web of Science ®Google Scholar
• Rutherford, S. T., & Bassler, B. L. (2012). Bacterial quorum sensing: Its role in virulence and possibilities for its control. Cold Spring Harbor Perspectives in Medicine, 2 (11), a012427–a012427. doi:10.1101/cshperspect.a012427
   PubMed Web of Science ®Google Scholar
• Salam, N. K., Nuti, R., & Sherman, W. (2009). Novel method for generating structure-based pharmacophores using energetic analysis. Journal of Chemical Information and Modeling, 49 (10), 2356–2368. doi:10.1021/ci900212v
   PubMed Web of Science ®Google Scholar
• Sarabhai, S., Harjai, K., Sharma, P., & Capalash, N. (2015). Ellagic acid derivatives from Terminalia chebula Retz increase the susceptibility of Pseudomonas aeruginosa to stress by inhibiting polyphosphate kinase. Journal of Applied Microbiology, 118 (4), 817–825. doi:10.1111/jam.12733
   PubMed Web of Science ®Google Scholar
• Saxena, S., Banerjee, G., Garg, R., & Singh, M. (2014). Comparative study of biofilm formation in Pseudomonas aeruginosa isolates from patients of lower respiratory tract infection. Journal of Clinical and Diagnostic Research, 8, 9–11. doi:10.7860/JCDR/2014/7808.4330
   Google Scholar
• Saxena, S., Durgam, L., & Guruprasad, L. (2019). Multiple e-pharmacophore modelling pooled with high-throughput virtual screening, docking and molecular dynamics simulations to discover potential inhibitors of plasmodium falciparum lactate dehydrogenase (PfLDH). Journal of Biomolecular Structure and Dynamics, 37 (7), 1783–1799. doi:10.1080/07391102.2018.1471417
   PubMed Web of Science ®Google Scholar
• Shelburne, C. E., An, F. Y., Dholpe, V., Ramamoorthy, A., Lopatin, D. E., & Lantz, M. S. (2007). The spectrum of antimicrobial activity of the bacteriocin subtilosin A. Journal of Antimicrobial Chemotherapy, 59 (2), 297–300. doi:10.1093/jac/dkl495
   PubMed Web of Science ®Google Scholar
• Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007). Epik: A software program for pK (a) prediction and protonation state generation for drug-like molecules. Journal of Computer-Aided Molecular Design, 21 (12), 681–691. doi:10.1007/s10822-007-9133-z
   PubMed Web of Science ®Google Scholar
• Sheridan, R. P., Singh, S. B., Fluder, E. M., & Kearsley, S. K. (2001). Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. Journal of Chemical Information and Computer Sciences, 41 (5), 1395–1406. doi:10.1021/ci0100144
   PubMedGoogle Scholar
• Shivakumar, D., Williams, J., Wu, Y., Damm, W., Shelley, J., & Sherman, W. (2010). Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the opls force field. Journal of Chemical Theory and Computation, 6 (5), 1509–1519. doi:10.1021/ct900587b
   PubMed Web of Science ®Google Scholar
• Sigala, P. A., Tsuchida, M. A., & Herschlag, D. (2009). Hydrogen bond dynamics in the active site of photoactive yellow protein. Proceedings of the National Academy of Sciences, 106 (23), 9232–9237. doi:10.1073/pnas.0900168106
   PubMed Web of Science ®Google Scholar
• Subramaniam, S., Mehrotra, M., & Gupta, D. (2008). Virtual high throughput screening (vHTS)–a perspective. Bioinformation, 3 (1), 14–17. doi:10.6026/97320630003014
   PubMedGoogle Scholar
• Tan, S. Y. Y., Chua, S. L., Chen, Y., Rice, S. A., Kjelleberg, S., Nielsen, T. E., … Givskov, M. (2013). Identification of five structurally unrelated quorum-sensing inhibitors of Pseudomonas aeruginosa from a natural-derivative database. Antimicrobial Agents and Chemotherapy, 57 (11), 5629–5641. doi:10.1128/AAC.00955-13
   PubMed Web of Science ®Google Scholar
• Toledo Warshaviak, D., Golan, G., Borrelli, K. W., Zhu, K., & Kalid, O. (2014). Structure-based virtual screening approach for discovery of covalently bound ligands. Journal of Chemical Information and Modeling, 54 (7), 1941–1950. doi:10.1021/ci500175r
   PubMed Web of Science ®Google Scholar
• Triballeau, N., Acher, F., Brabet, I., Pin, J. P., & Bertrand, H. O. (2005). Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. Journal of Medicinal Chemistry, 48 (7), 2534–2547. doi:10.1021/jm049092j
   PubMed Web of Science ®Google Scholar
• Tripathi, A. C., Sonar, P. K., Rathore, R., & Saraf, S. K. (2016). Structural insights into the molecular design of HER2 inhibitors. Open Pharmaceutical Sciences Journal, 3 (1), 164–181. doi:10.2174/1874844901603010164
   Google Scholar
• Truchon, J. F., & Bayly, C. I. (2007). Evaluating virtual screening methods: Good and bad metrics for the “early recognition” problem. Journal of Chemical Information and Modeling, 47 (2), 488–508. doi:10.1021/ci600426e
   PubMed Web of Science ®Google Scholar
• Turkina, M. V., & Vikström, E. (2019). Bacteria-host crosstalk: Sensing of the quorum in the context of Pseudomonas aeruginosa infections. Journal of Innate Immunity, 11 (3), 263–279. doi:10.1159/000494069
   PubMed Web of Science ®Google Scholar
• Valot, B., Guyeux, C., Rolland, J. Y., Mazouzi, K., Bertrand, X., & Hocquet, D. (2015). What it takes to be a Pseudomonas aeruginosa? The core genome of the opportunistic pathogen updated. PLoS One, 10 (5), e0126468. doi:10.1371/journal.pone.0126468
   PubMed Web of Science ®Google Scholar
• Veeramachaneni, G. K., Raj, K. K., Chalasani, L. M., Bondili, J. S., & Talluri, V. R. (2015). High-throughput virtual screening with e-pharmacophore and molecular simulations study in the designing of pancreatic lipase inhibitors. Drug Design, Development and Therapy, 9, 4397–4412. doi:10.2147/DDDT.S84052
   PubMed Web of Science ®Google Scholar
• Ventola, C. L. (2015). The antibiotic resistance crisis: Part 1: Causes and threats. Pharmacy and Therapeutics, 40 (4), 277–283.
   Google Scholar
• Vijayakumar, B., Parasuraman, S., Raveendran, R., & Velmurugan, D. (2014). Identification of natural inhibitors against angiotensin I converting enzyme for cardiac safety using induced fit docking and MM-GBSA studies. Pharmacognosy Magazine, 10, S639–S644. doi:10.4103/0973-1296.139809
   PubMed Web of Science ®Google Scholar
• Waszkowycz, B. (2002). Structure-based approaches to drug design and virtual screening. Current Opinion in Drug Discovery & Development, 5 (3), 407–413.
   PubMedGoogle Scholar
• Yang, L., Rybtke, M. T., Jakobsen, T. H., Hentzer, M., Bjarnsholt, T., Givskov, M., & Tolker-Nielsen, T. (2009). Computer-aided identification of recognized drugs as Pseudomonas aeruginosa quorum-sensing inhibitors. Antimicrobial Agents and Chemotherapy, 53(6), 2432–2443. doi:10.1128/AAC.01283-08
   PubMed Web of Science ®Google Scholar