Structure Elucidation and Identification of Novel Lead Molecules against
Sulfur Import Protein cysA of Mycobacterium tuberculosis

Mounika      Badineni; Vasavi      Malkhed; Lavanya      Rumandla; Ramesh      Malikanti; Rajender      Vadija; Kiran   Kumar   Mustyala

doi:10.2174/1389203724666230713124339

Abstract

Aims: The present work considers the Sulphate import ABC transporter protein (cysA) as a potential drug target for the identification of inhibitors for the protein.

Background: The ABC (ATP binding cassette) transporters play a crucial role in the survival and virulence of Mycobacterium tuberculosis by the acquisition of micronutrients from host tissue.

Objectives: The 3D structural features of the cysA protein are built. Molecular scaffolds are identified by implementing active site identification, ADME properties, Virtual Screening, and a few other computational techniques.

Methods: The theoretical model of cysA is predicted using homology modeling protocols, and the structure is validated by various validation methods. The prediction of partial dimer formation through protein-protein docking methods gave insight into the conformational changes taking place in the cysA protein. The natural substrate ATP is docked with cysA protein that confirms the ATP binding site. To find the drug-like compounds, virtual screening studies were carried out around the active site by several ligand databases.

Results: The findings demonstrate the significance of residues SER41, GLY42, ARG50, GLN85, HIS86, LYS91, ARG142, and ASP161 in drug-target interactions. The docking studies of existing TB drugs against cysA were also performed. The result analysis shows that none of the existing drugs inhibits the ATP active site, which confirms cysA as a promising drug target. Using in-silico methods, the ADME parameters of a few chosen ligand molecules are predicted and contrasted with the ADME characteristics of the available TB medications.

Conclusion: The results revealed the values of ADME parameters of selected ligand molecules are more permissible than existing TB drugs, which emphasizes the drug-like activity of ligand molecules by inhibition of cysA proteins. The structural data, active site information, and selected ligand molecules help in the identification of new therapeutic scaffolds for Tuberculosis.

« Previous Next »

Graphical Abstract

[1]

Global tuberculosis report 2022; World Health Organization: Geneva, 2022, pp. 88-100.

[2]

Global tuberculosis report 2021; World Health Organization: Geneva, 2021. 

[3]
Braibant, M.; Gilot, P.; Content, J. The ATP binding cassette (ABC) transport systems of Mycobacterium tuberculosis. FEMS Microbiol. Rev.,  2000, 24(4), 449-467.
 [http://dx.doi.org/10.1111/j.1574-6976.2000.tb00550.x] [PMID: 10978546]

[4]
Soni, D.K.; Dubey, S.K.; Bhatnagar, R. ATP-binding cassette (ABC) import systems of Mycobacterium tuberculosis : Target for drug and vaccine development. Emerg. Microbes Infect.,  2020, 9(1), 207-220.
 [http://dx.doi.org/10.1080/22221751.2020.1714488] [PMID: 31985348]

[5]
Cassio Barreto de Oliveira, M.; Balan, A. The ATP-binding cassette (Abc) transport systems in Mycobacterium tuberculosis: Structure, function, and possible targets for therapeutics. Biology,  2020, 9(12), 443.
 [http://dx.doi.org/10.3390/biology9120443] [PMID: 33291531]

[6]
Wooff, E.; Michell, S.L.; Gordon, S.V.; Chambers, M.A.; Bardarov, S.; Jacobs, W.R., Jr; Hewinson, R.G.; Wheeler, P.R. Functional genomics reveals the sole sulphate transporter of the Mycobacterium tuberculosis complex and its relevance to the acquisition of sulphur in vivo. Mol. Microbiol.,  2002, 43(3), 653-663.
 [http://dx.doi.org/10.1046/j.1365-2958.2002.02771.x] [PMID: 11929522]

[7]
Hatzios, S.K.; Bertozzi, C.R. The regulation of sulfur metabolism in Mycobacterium tuberculosis. PLoS Pathog.,  2011, 7(7), e1002036.
 [http://dx.doi.org/10.1371/journal.ppat.1002036] [PMID: 21811406]

[8]
Sabe, V.T.; Ntombela, T.; Jhamba, L.A.; Maguire, G.E.M.; Govender, T.; Naicker, T.; Kruger, H.G. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur. J. Med. Chem.,  2021, 224, 113705.
 [http://dx.doi.org/10.1016/j.ejmech.2021.113705] [PMID: 34303871]

[9]
Boutet, E.; Lieberherr, D.; Tognolli, M.; Schneider, M.; Bansal, P.; Bridge, A.J.; Poux, S.; Bougueleret, L.; Xenarios, I. UniProtKB/swiss-prot, the manually annotated section of the uniprot knowledgeBase: How to use the entry view. Methods Mol. Biol.,  2016, 1374, 23-54.
 [http://dx.doi.org/10.1007/978-1-4939-3167-5_2] [PMID: 26519399]

[10]
McGinnis, S.; Madden, T.L. BLAST: At the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res.,  2004, 32(Web Server issue), W20-5.
 [http://dx.doi.org/10.1093/nar/gkh435.] [PMID: 15215342]

[11]
Cole, C.; Barber, J.D.; Barton, G.J. The Jpred 3 secondary structure prediction server. Nucleic Acids Res.,  2008, 36(Web Server issue), W197-201.
 [http://dx.doi.org/10.1093/nar/gkn238] [PMID: 18463136]

[12]
Kelley, L.A.; Mezulis, S.; Yates, C.M.; Wass, M.N.; Sternberg, M.J.E. Europe PMC Funders Group The Phyre2 web portal for protein modelling, prediction and analysis. Nat. Protoc.,  2015, 10(6), 845-858.
 [http://dx.doi.org/10.1038/nprot.2015.053] [PMID: 25950237]

[13]
Thompson, J.D.; Higgins, D.G.; Gibson, T.J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res.,  1994, 22(22), 4673-4680.
 [http://dx.doi.org/10.1093/nar/22.22.4673] [PMID: 7984417]

[14]
Tsuboyama, N.; Szczepanski, A.P.; Zhao, Z.; Wang, L. MBD5 and MBD6 stabilize the BAP1 complex and promote BAP1-dependent cancer. Genome Biol.,  2022, 23(1), 206.
 [http://dx.doi.org/10.1186/s13059-022-02776-x] [PMID: 34980209]

[15]
Fiser, A.; Šali, A. Modeller: Generation and refinement of homology-based protein structure models. Methods Enzymol.,  2003, 374, 461-491.
 [http://dx.doi.org/10.1016/S0076-6879(03)74020-8] [PMID: 14696385]

[16]
Gore, M.; Desai, N.S. Computer-aided drug designing. Methods Mol. Biol.,  2014, 1168, 313-321.
 [http://dx.doi.org/10.1007/978-1-4939-0847-9_18] [PMID: 24870144]

[17]
Sloterdijk, P.; Voelker, S. Helping the world across the street; Voelker, S.; Sloterdijk, P., Eds.; Brill publisher, 2019, pp. 27-49.

[18]
Vanajothi, R.; Bhavaniramya, S.; Vijayakumar, R.; Alothaim, A.S.; Alqurashi, Y.E.; Vishnupriya, S. In silico and in vitro analysis of nigella sativa bioactives against chorismate synthase of listeria monocytogenes: A target protein for biofilm inhibition. Appl. Biochem. Biotechnol.,  2023, 195(1), 519-33.
 [http://dx.doi.org/10.1007/s12010-022-04157-3] [PMID: 36098931]

[19]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature,  2021, 596(7873), 583-589.
 [http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]

[20]
Bertoline, L. M. F.; Lima, A. N.; Krieger, J. E.; Teixeira, S. K. Before and after AlphaFold2: An overview of protein structure prediction. Front Bioinform.,  2023, 3, 1120370.
 [http://dx.doi.org/10.3389/fbinf.2023.1120370.] [PMID: 36926275]

[21]
Bryant, P.; Pozzati, G.; Elofsson, A. Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun.,  2022, 13(1), 1265.
 [http://dx.doi.org/10.1038/s41467-022-28865-w] [PMID: 35273146]

[22]
Dighe, S.N.; Deora, G.S.; De la Mora, E.; Nachon, F.; Chan, S.; Parat, M.O.; Brazzolotto, X.; Ross, B.P. Discovery and structure-activity relationships of a highly selective butyrylcholinesterase inhibitor by Structure-based virtual screening. J. Med. Chem.,  2016, 59(16), 7683-7689.
 [http://dx.doi.org/10.1021/acs.jmedchem.6b00356] [PMID: 27405689]

[23]
Modi, V.R.L.D; Chase, F. Assessment of refinement of template-based models in CASP11. Proteins: Structure, function, and bioinformatics. HHS Public Access,  2017, 84(S1), 260-81.

[24]
Heo, L.; Feig, M. PREFMD: A web server for protein structure refinement via molecular dynamics simulations. Bioinformatics,  2018, 34(6), 1063-1065.
 [http://dx.doi.org/10.1093/bioinformatics/btx726] [PMID: 29126101]

[25]
Feig, M.; Mirjalili, V. Protein structure refinement via molecular-dynamics simulations: What works and what does not? Proteins,  2016, 84(S1), 282-292.
 [http://dx.doi.org/10.1002/prot.24871] [PMID: 26234208]

[26]
Ponder, J.W.; Case, D.A. Force fields for protein simulations. Adv. Protein Chem.,  2003, 66, 27-85.
 [http://dx.doi.org/10.1016/S0065-3233(03)66002-X] [PMID: 14631816]

[27]
Nakkala, S.; Modak, C.; Bathula, R.; Lanka, G.; Somadi, G.; Sreekanth, S.; Jain, A.; Potlapally, S.R. Identification of new anti-cancer agents against CENTERIN: Structure-based virtual screening, AutoDock and binding free energy studies. J. Mol. Struct.,  2022, 1270, 133952.
 [http://dx.doi.org/10.1016/j.molstruc.2022.133952]

[28]
Kufareva, I.; Abagyan, R. Methods of protein structure comparison. Methods Mol. Biol.,  2011, 857, 231-257.
 [http://dx.doi.org/10.1007/978-1-61779-588-6_10] [PMID: 22323224]

[29]
Laskowski, R.A.; Jabłońska, J.; Pravda, L.; Vařeková, R.S.; Thornton, J.M. PDBsum: Structural summaries of PDB entries. Protein Sci.,  2018, 27(1), 129-134.
 [http://dx.doi.org/10.1002/pro.3289] [PMID: 28875543]

[30]
Laskowski, R.A.; Thornton, J.M. PDBSUM extras: SARS‐COV ‐2 and ALPHAFOLD models. Protein Sci.,  2022, 31(1), 283-289.
 [http://dx.doi.org/10.1002/pro.4238] [PMID: 34779073]

[31]
Priyadarsinee, L.; Sarma, H.; Sastry, G.N. Glycoprotein attachment with host cell surface receptor ephrin B2 and B3 in mediating entry of nipah and hendra virus: A computational investigation. J. Chem. Sci.,  2022, 134(4), 114.
 [http://dx.doi.org/10.1007/s12039-022-02110-9] [PMID: 36465097]

[32]
Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res.,  2007, 35(Web Server (S2)), W407-W410.
 [http://dx.doi.org/10.1093/nar/gkm290] [PMID: 17517781]

[33]
Ghesmati, Z.; Mokhtari, S.; Parvanak, M.; Siahkouhi, H.; Taheri-Anganeh, M.; Ahmadi, K.; Zarezade, V.; Vahedi, F.; Shajirat, Z.; Nezafat, N.; Movahedpour, A. Designing a humanized immunotoxin based on DELTA-stichotoxin-Hmg2a toxin: An in silico study. J. Mol. Model.,  2022, 28(12), 392.
 [http://dx.doi.org/10.1007/s00894-022-05389-0] [PMID: 36400988]

[34]
Ashik, M.A.; Islam, T.; Fujii, M.; Alam, M.M.; Hossain, M.N. Interaction pattern of aldose reductase with β-glucogallin: Active site exploration and multiple docking analyses. Inform. Med. Unlocked,  2022, 30, 100938.
 [http://dx.doi.org/10.1016/j.imu.2022.100938]

[35]
Connolly, M.L. Analytical molecular surface calculation. J. Appl. Cryst.,  1983, 16(5), 548-558.
 [http://dx.doi.org/10.1107/S0021889883010985]

[36]
Halgren, T.A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model.,  2009, 49(2), 377-389.
 [http://dx.doi.org/10.1021/ci800324m] [PMID: 19434839]

[37]
Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H.J. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res.,  2005, 33, W363-W367.
 [http://dx.doi.org/10.1093/nar/gki481] [PMID: 15980490]

[38]
Kumar, V.; Mishra, A.; Singh, A. Identification of promising nutraceuticals against filarial immune-modulatory proteins: Insights from in silico and ex vivo studies. RSC Advances,  2022, 12(35), 22542-22554.
 [http://dx.doi.org/10.1039/D2RA03287B] [PMID: 36105981]

[39]
Theodoulou, F.L.; Kerr, I.D. ABC transporter research: Going strong 40 years on. Biochem. Soc. Trans.,  2015, 43(5), 1033-1040.
 [http://dx.doi.org/10.1042/BST20150139] [PMID: 26517919]

[40]
Davidson, A.L.; Dassa, E.; Orelle, C.; Chen, J. Structure, function, and evolution of bacterial ATP-binding cassette systems. Microbiol. Mol. Biol. Rev.,  2008, 72(2), 317-364.
 [http://dx.doi.org/10.1128/MMBR.00031-07] [PMID: 18535149]

[41]
Jones, P.M.; George, A.M. Subunit interactions in ABC transporters: Towards a functional architecture. FEMS Microbiol. Lett.,  1999, 179(2), 187-202.
 [http://dx.doi.org/10.1111/j.1574-6968.1999.tb08727.x] [PMID: 10518715]

[42]
Hohl, M.; Briand, C.; Grütter, M.G.; Seeger, M.A. Crystal structure of a heterodimeric ABC transporter in its inward-facing conformation. Nat. Struct. Mol. Biol.,  2012, 19(4), 395-402.
 [http://dx.doi.org/10.1038/nsmb.2267] [PMID: 22447242]

[43]
Chen, J.; Lu, G.; Lin, J.; Davidson, A.L.; Quiocho, F.A. A tweezers-like motion of the ATP-binding cassette dimer in an ABC transport cycle. Mol. Cell,  2003, 12(3), 651-661.
 [http://dx.doi.org/10.1016/j.molcel.2003.08.004] [PMID: 14527411]

[44]
Agrawal, P.; Singh, H.; Srivastava, H.K.; Singh, S.; Kishore, G.; Raghava, G.P.S. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics,  2019, 19(S13), 426.
 [http://dx.doi.org/10.1186/s12859-018-2449-y] [PMID: 30717654]

[45]
Schneider, E.; Hunke, S. ATP-binding-cassette (ABC) transport systems: Functional and structural aspects of the ATP-hydrolyzing subunits/domains. FEMS Microbiol. Rev.,  1998, 22(1), 1-20.
 [http://dx.doi.org/10.1111/j.1574-6976.1998.tb00358.x] [PMID: 9640644]

[46]
Shivakumar, D.; Harder, E.; Damm, W.; Friesner, R.A.; Sherman, W. Improving the prediction of absolute solvation free energies using the next generation OPLS force field. J. Chem. Theory Comput.,  2012, 8(8), 2553-2558.
 [http://dx.doi.org/10.1021/ct300203w] [PMID: 26592101]

[47]
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem.,  2004, 47(7), 1739-1749.
 [http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]

[48]
kumar, B.H.; Manandhar, S.; Mehta, C.H.; Nayak, U.Y.; Pai, K.S.R. Structure-based docking, pharmacokinetic evaluation, and molecular dynamics-guided evaluation of traditional formulation against SARS-CoV-2 spike protein receptor bind domain and ACE2 receptor complex. Chem. Zvesti,  2022, 76(2), 1063-1083.
 [http://dx.doi.org/10.1007/s11696-021-01917-z] [PMID: 34690412]

[49]
Tamilvanan, T.; Hopper, W. High-throughput virtual screening and docking studies of matrix protein vp40 of ebola virus. Bioinformation,  2013, 9(6), 286-292.
 [http://dx.doi.org/10.6026/97320630009286] [PMID: 23559747]

[50]
Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem.,  2006, 49(21), 6177-6196.
 [http://dx.doi.org/10.1021/jm051256o] [PMID: 17034125]

[51]
Tiwari, V.; Viswanath, S. Identification of potential modulators of IFITM3 by in-silico modeling and virtual screening. Sci. Rep.,  2022, 12(1), 15952.
 [http://dx.doi.org/10.1038/s41598-022-20259-8] [PMID: 34992227]

[52]
Wang, E.; Sun, H.; Wang, J.; Wang, Z.; Liu, H.; Zhang, J.Z.H.; Hou, T. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chem. Rev.,  2019, 119(16), 9478-9508.
 [http://dx.doi.org/10.1021/acs.chemrev.9b00055] [PMID: 31244000]

[53]
Ashida, T.; Kikuchi, T. Overview of binding free energy calculation techniques for elucidation of biological processes and for drug discovery. Med. Chem.,  2015, 11(3), 248-253.
 [http://dx.doi.org/10.2174/1573406411666141229164157] [PMID: 25548929]

[54]
Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov.,  2015, 10(5), 449-461.
 [http://dx.doi.org/10.1517/17460441.2015.1032936] [PMID: 25835573]

[55]
Kollman, P.A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D.A.; Cheatham, T.E., III Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Acc. Chem. Res.,  2000, 33(12), 889-897.
 [http://dx.doi.org/10.1021/ar000033j] [PMID: 11123888]

[56]
Srinivasan, J.; Cheatham, T.E.; Cieplak, P.; Kollman, P.A.; Case, D.A. Continuum solvent studies of the stability of DNA, RNA, and phosphoramidite-DNA helices. J. Am. Chem. Soc.,  1998, 120(37), 9401-9409.
 [http://dx.doi.org/10.1021/ja981844+]

[57]
Srinivasan, J.; Miller, J.; Kollman, P.A.; Case, D.A. Continuum solvent studies of the stability of RNA hairpin loops and helices. J. Biomol. Struct. Dyn.,  1998, 16(3), 671-682.
 [http://dx.doi.org/10.1080/07391102.1998.10508279] [PMID: 10052623]

[58]
Thirunavukkarasu, M.K.; Veerappapillai, S.; Karuppasamy, R. Computational biophysics approach towards the discovery of multi-kinase blockers for the management of MAPK pathway dysregulation. Mol. Divers.,  2022, 1-8.
 [http://dx.doi.org/10.1007/s11030-022-10545-y] [PMID: 36260173]

[59]
Lucas, A.J.; Sproston, J.L.; Barton, P.; Riley, R.J. Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery. Expert Opin. Drug Discov.,  2019, 14(12), 1313-1327.
 [http://dx.doi.org/10.1080/17460441.2019.1660642] [PMID: 31538500]

[60]
Adelusi, T.I.; Oyedele, A.Q.K.; Boyenle, I.D.; Ogunlana, A.T.; Adeyemi, R.O.; Ukachi, C.D.; Idris, M.O.; Olaoba, O.T.; Adedotun, I.O.; Kolawole, O.E.; Xiaoxing, Y.; Abdul-Hammed, M. Molecular modeling in drug discovery. Inform. Med. Unlocked,  2022, 29, 100880.
 [http://dx.doi.org/10.1016/j.imu.2022.100880]

[61]
Wang, J.; Yang, B.; Revote, J.; Leier, A.; Marquez-Lago, T.T.; Webb, G.; Song, J.; Chou, K.C.; Lithgow, T. POSSUM: A bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics,  2017, 33(17), 2756-2758.
 [http://dx.doi.org/10.1093/bioinformatics/btx302] [PMID: 28903538]

[62]
Kerfeld, C.A.; Scott, K.M. Using BLAST to teach “E-value-tionary” concepts. PLoS Biol.,  2011, 9(2), e1001014.
 [http://dx.doi.org/10.1371/journal.pbio.1001014] [PMID: 21304918]

[63]
Malkhed, V.; Gudlur, B.; Kondagari, B.; Dulapalli, R.; Vuruputuri, U. Study of interactions between Mycobacterium tuberculosis proteins: SigK and anti-SigK. J. Mol. Model.,  2011, 17(5), 1109-1119.
 [http://dx.doi.org/10.1007/s00894-010-0792-7] [PMID: 20676709]

[64]
Vadija, R.; Mustyala, K.K.; Nambigari, N.; Dulapalli, R.; Dumpati, R.K.; Ramatenki, V.; Vellanki, S.P.; Vuruputuri, U. Homology modeling and virtual screening studies of FGF-7 protein—a structure-based approach to design new molecules against tumor angiogenesis. J. Chem. Biol.,  2016, 9(3), 69-78.
 [http://dx.doi.org/10.1007/s12154-016-0152-x] [PMID: 27493695]

[65]
Casadevall, G.; Duran, C.; Osuna, S. AlphaFold2 and deep learning for elucidating enzyme conformational flexibility and its application for design. JACS Au,  2023, 3(6), 1554-62.
 [http://dx.doi.org/10.1021/jacsau.3c00188]

[66]
Carugo, O.; Pongor, S. A normalized root-mean-spuare distance for comparing protein three-dimensional structures. Protein Sci.,  2001, 10(7), 1470-1473.
 [http://dx.doi.org/10.1110/ps.690101] [PMID: 11420449]

[67]
Stein, D.L. A model of protein conformational substates. Proc. Natl. Acad. Sci.,  1985, 82(11), 3670-3672.
 [http://dx.doi.org/10.1073/pnas.82.11.3670] [PMID: 16593568]

[68]
Ha, J.H.; Loh, S.N. Protein conformational switches: From nature to design. Chemistry,  2012, 18(26), 7984-7999.
 [http://dx.doi.org/10.1002/chem.201200348] [PMID: 22688954]

[69]
Lee, C.; Su, B.H.; Tseng, Y.J. Comparative studies of AlphaFold, RoseTTAFold and Modeller: A case study involving the use of G-protein-coupled receptors. Brief. Bioinform.,  2022, 23(5), bbac308.
 [http://dx.doi.org/10.1093/bib/bbac308] [PMID: 35945035]

[70]
Malikanti, R.; Vadija, R.; Veeravarapu, H.; Mustyala, K.K.; Malkhed, V.; Vuruputuri, U. Identification of small molecular ligands as potent inhibitors of fatty acid metabolism in Mycobacterium tuberculosis. J. Mol. Struct.,  2017, 1150, 227-241.
 [http://dx.doi.org/10.1016/j.molstruc.2017.08.090]

[71]
Deber, C.M.; Ng, D.P. Helix-helix interactions: Is the medium the message? Structure,  2015, 23(3), 437-438.
 [http://dx.doi.org/10.1016/j.str.2015.02.004] [PMID: 25738383]

[72]
Dumpati, R.; Dulapalli, R.; Kondagari, B.; Ramatenki, V.; Vellanki, S.; Vadija, R.; Vuruputuri, U. Suppressor of cytokine signalling-3 as a drug target for Type 2 diabetes mellitus: A structure-guided approach. ChemistrySelect,  2016, 1(10), 2502-2514.
 [http://dx.doi.org/10.1002/slct.201600640]

[73]
Pollastri, M.P. Overview on the rule of five. Curr. Protocols Pharmacol.,  2010, Chapter 9, 12.
 [PMID: 22294375]

[74]
Jorgensen, W.L. The many roles of computation in drug discovery. Science,  2004, 303(5665), 1813-1818.
 [http://dx.doi.org/10.1126/science.1096361] [PMID: 15031495]

[75]
Malkhed, V.; Mustyala, K.K.; Potlapally, S.R.; Vuruputuri, U. Modeling of alternate RNA polymerase sigma d factor and identification of novel inhibitors by virtual screening. Cell. Mol. Bioeng.,  2012, 5(4), 363-374.
 [http://dx.doi.org/10.1007/s12195-012-0238-7]

[76]
Fischer, A.; Smieško, M.; Sellner, M.; Lill, M.A. Decision making in structure-based drug discovery: Visual inspection of docking results. J. Med. Chem.,  2021, 64(5), 2489-2500.
 [http://dx.doi.org/10.1021/acs.jmedchem.0c02227] [PMID: 33617246]

[77]
Kolb, P.; Rosenbaum, D.M.; Irwin, J.J.; Fung, J.J.; Kobilka, B.K.; Shoichet, B.K. Structure-based discovery of β 2 -adrenergic receptor ligands. Proc. Natl. Acad. Sci.,  2009, 106(16), 6843-6848.
 [http://dx.doi.org/10.1073/pnas.0812657106] [PMID: 19342484]

Rights & Permissions Print Cite

Journal Information

For Authors

For Editors

For Reviewers

Explore Articles

Open Access

Open Access Articles

For Visitors

DOI https://dx.doi.org/10.2174/1389203724666230713124339	Print ISSN 1389-2037
Publisher Name Bentham Science Publisher	Online ISSN 1875-5550

Current Protein & Peptide Science

Structure Elucidation and Identification of Novel Lead Molecules against Sulfur Import Protein cysA of Mycobacterium tuberculosis

Abstract Play Pause

Graphical Abstract

Related Journals

Related Books

Abstract