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ISSN (Online): 1875-5607

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Review Article

The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review

Author(s): Saravanan Vijayakumar, Lukkani Laxman Kumar, Subhomoi Borkotoky and Ayaluru Murali*

Volume 24, Issue 11, 2024

Published on: 25 September, 2023

Page: [1089 - 1111] Pages: 23

DOI: 10.2174/1389557523666230901105231

Price: $65

Abstract

Drug discovery, vaccine design, and protein interaction studies are rapidly moving toward the routine use of molecular dynamics simulations (MDS) and related methods. As a result of MDS, it is possible to gain insights into the dynamics and function of identified drug targets, antibody-antigen interactions, potential vaccine candidates, intrinsically disordered proteins, and essential proteins. The MDS appears to be used in all possible ways in combating diseases such as cancer, however, it has not been well documented as to how effectively it is applied to infectious diseases such as Leishmaniasis. As a result, this review aims to survey the application of MDS in combating leishmaniasis. We have systematically collected articles that illustrate the implementation of MDS in drug discovery, vaccine development, and structural studies related to Leishmaniasis.

Of all the articles reviewed, we identified that only a limited number of studies focused on the development of vaccines against Leishmaniasis through MDS. Also, the PCA and FEL studies were not carried out in most of the studies. These two were globally accepted utilities to understand the conformational changes and hence it is recommended that this analysis should be taken up in similar approaches in the future.

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[1]
Baker, R.E.; Mahmud, A.S.; Miller, I.F.; Rajeev, M.; Rasambainarivo, F.; Rice, B.L.; Takahashi, S.; Tatem, A.J.; Wagner, C.E.; Wang, L.F.; Wesolowski, A.; Metcalf, C.J.E. Infectious disease in an era of global change. Nat. Rev. Microbiol., 2022, 20(4), 193-205.
[http://dx.doi.org/10.1038/s41579-021-00639-z] [PMID: 34646006]
[2]
Alvar, J.; Yactayo, S.; Bern, C. Leishmaniasis and poverty. Trends Parasitol., 2006, 22(12), 552-557.
[http://dx.doi.org/10.1016/j.pt.2006.09.004] [PMID: 17023215]
[3]
Boelaert, M.; Meheus, F.; Sanchez, A.; Singh, S.P.; Vanlerberghe, V.; Picado, A.; Meessen, B.; Sundar, S. The poorest of the poor: A poverty appraisal of households affected by visceral leishmaniasis in bihar, India. Tropical Med. Int. Health, 2009, 14(6), 639-644.
[4]
Serafim, T.D.; Coutinho-Abreu, I.V.; Dey, R.; Kissinger, R.; Valenzuela, J.G.; Oliveira, F.; Kamhawi, S. Leishmaniasis: The act of transmission. Trends Parasitol., 2021, 37(11), 976-987.
[http://dx.doi.org/10.1016/j.pt.2021.07.003] [PMID: 34389215]
[5]
McGwire, B.S.; Satoskar, A.R. Leishmaniasis: Clinical syndromes and treatment. QJM, 2014, 107(1), 7-14.
[http://dx.doi.org/10.1093/qjmed/hct116] [PMID: 23744570]
[6]
Martins-Melo, F.R.; Lima, M.S.; Ramos, A.N., Jr; Alencar, C.H.; Heukelbach, J. Mortality and case fatality due to visceral leishmaniasis in Brazil: A nationwide analysis of epidemiology, trends and spatial patterns. PLoS One, 2014, 9(4), e93770.
[http://dx.doi.org/10.1371/journal.pone.0093770] [PMID: 24699517]
[7]
El Hajj, R.; El Hajj, H.; Khalifeh, I. Fatal visceral leishmaniasis caused by Leishmania infantum, lebanon. Emerg. Infect. Dis., 2018, 24(5), 906-907.
[http://dx.doi.org/10.3201/eid2405.180019] [PMID: 29664382]
[8]
Evans, K.J.; Kedzierski, L. Development of vaccines against visceral leishmaniasis. J. Trop. Med., 2012, 2012, 1-14.
[http://dx.doi.org/10.1155/2012/892817] [PMID: 21912561]
[9]
Alvar, J.; Croft, S.L.; Kaye, P.; Khamesipour, A.; Sundar, S.; Reed, S.G. Case study for a vaccine against leishmaniasis. Vaccine, 2013, 31(Suppl. 2), B244-B249.
[http://dx.doi.org/10.1016/j.vaccine.2012.11.080] [PMID: 23598489]
[10]
Croft, S.L.; Olliaro, P. Leishmaniasis chemotherapy-challenges and opportunities. Clin. Microbiol. Infect., 2011, 17(10), 1478-1483.
[http://dx.doi.org/10.1111/j.1469-0691.2011.03630.x] [PMID: 21933306]
[11]
Mazire, P.; Agarwal, V.; Roy, A. Road map of pre clinical treatment for visceral leishmaniasis. Drug Dev. Res., 2022, 83(2), 317-327.
[http://dx.doi.org/10.1002/ddr.21907] [PMID: 34962315]
[12]
Kumari, S.; Kumar, V.; Tiwari, R.K.; Ravidas, V.; Pandey, K.; Kumar, A.; Amphotericin, B. A drug of choice for visceral leishmaniasis. Acta Trop., 2022, 235, 106661.
[http://dx.doi.org/10.1016/j.actatropica.2022.106661] [PMID: 35998680]
[13]
Pokharel, P.; Ghimire, R.; Lamichhane, P. Efficacy and safety of paromomycin for visceral leishmaniasis: A systematic review. J. Trop. Med., 2021, 2021, 8629039.
[http://dx.doi.org/10.1155/2021/8629039]
[14]
Sundar, S.; Chakravarty, J. Liposomal amphotericin B and leishmaniasis: Dose and response. J. Glob. Infect. Dis., 2010, 2(2), 159-166.
[http://dx.doi.org/10.4103/0974-777X.62886] [PMID: 20606972]
[15]
Verma, G.; Khan, M. F.; Akhtar, W.; Shaquiquzzaman, M.; Akhter, M.; Marella, A.; Naved, T.; Parvez, S.; Kumar, H.; Tauhid, S. Leishmaniasis: A menace. Int. J. Pharmaceut. Biol. Arch., 2017, 8(3), 01-07.
[16]
Zulfiqar, B.; Shelper, T.B.; Avery, V.M. Leishmaniasis drug discovery: Recent progress and challenges in assay development. Drug Discov. Today, 2017, 22(10), 1516-1531.
[http://dx.doi.org/10.1016/j.drudis.2017.06.004] [PMID: 28647378]
[17]
Prava, J.; Pan, A. In silico analysis of Leishmania proteomes and protein-protein interaction network: Prioritizing therapeutic targets and drugs for repurposing to treat leishmaniasis. Acta Trop., 2022, 229, 106337.
[http://dx.doi.org/10.1016/j.actatropica.2022.106337] [PMID: 35134348]
[18]
Vijayakumar, S.; Das, P. Recent progress in drug targets and inhibitors towards combating leishmaniasis. Acta Trop., 2018, 181, 95-104.
[http://dx.doi.org/10.1016/j.actatropica.2018.02.010] [PMID: 29452111]
[19]
Berdigaliyev, N.; Aljofan, M. An overview of drug discovery and development. Future Med. Chem., 2020, 12(10), 939-947.
[http://dx.doi.org/10.4155/fmc-2019-0307] [PMID: 32270704]
[20]
Voet, D.; Voet, J.G. Fundamental of Biochemistry: Life at the Molecular Level; John Wiley & Sons, 2016.
[21]
Burley, S.K.; Berman, H.M.; Bhikadiya, C.; Bi, C.; Chen, L.; Di Costanzo, L.; Christie, C.; Dalenberg, K.; Duarte, J.M.; Dutta, S.; Feng, Z.; Ghosh, S.; Goodsell, D.S.; Green, R.K. Guranović, V.; Guzenko, D.; Hudson, B.P.; Kalro, T.; Liang, Y.; Lowe, R.; Namkoong, H.; Peisach, E.; Periskova, I.; Prlić, A.; Randle, C.; Rose, A.; Rose, P.; Sala, R.; Sekharan, M.; Shao, C.; Tan, L.; Tao, Y.P.; Valasatava, Y.; Voigt, M.; Westbrook, J.; Woo, J.; Yang, H.; Young, J.; Zhuravleva, M.; Zardecki, C. RCSB Protein Data Bank: Biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res., 2019, 47(D1), D464-D474.
[http://dx.doi.org/10.1093/nar/gky1004] [PMID: 30357411]
[22]
Carpenter, E.P.; Beis, K.; Cameron, A.D.; Iwata, S. Overcoming the challenges of membrane protein crystallography. Curr. Opin. Struct. Biol., 2008, 18(5), 581-586.
[http://dx.doi.org/10.1016/j.sbi.2008.07.001] [PMID: 18674618]
[23]
Bongirwar, V.; Mokhade, A.S. Different methods, techniques and their limitations in protein structure prediction: A review. Prog. Biophys. Mol. Biol., 2022, 173(May), 72-82.
[http://dx.doi.org/10.1016/j.pbiomolbio.2022.05.002] [PMID: 35588858]
[24]
Paiva, V.A.; Gomes, I.S.; Monteiro, C.R.; Mendonça, M.V.; Martins, P.M.; Santana, C.A.; Gonçalves-Almeida, V.; Izidoro, S.C.; Melo-Minardi, R.C.; Silveira, S.A. Protein structural bioinformatics: An overview. Comput. Biol. Med., 2022, 147(147), 105695.
[http://dx.doi.org/10.1016/j.compbiomed.2022.105695] [PMID: 35785665]
[25]
Hameduh, T.; Haddad, Y.; Adam, V.; Heger, Z. Homology modeling in the time of collective and artificial intelligence. Comput. Struct. Biotechnol. J., 2020, 18, 3494-3506.
[http://dx.doi.org/10.1016/j.csbj.2020.11.007] [PMID: 33304450]
[26]
Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F.T.; de Beer, T.A.P.; Rempfer, C.; Bordoli, L.; Lepore, R.; Schwede, T. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res., 2018, 46(W1), W296-W303.
[http://dx.doi.org/10.1093/nar/gky427] [PMID: 29788355]
[27]
Yang, J.; Zhang, Y. Protein structure and function prediction using I-TASSER. Curr. Protoc. Bioinformatics, 2015, 52, 581-515.
[http://dx.doi.org/10.1002/0471250953.bi0508s52]
[28]
Kim, D. E. ChiVian, D.; Baker, D. Protein structure prediction and analysis using the robetta server. Nucleic Acids Res., 2004, 32(Web Server Iss.), 526-531.
[http://dx.doi.org/10.1093/nar/gkh468]
[29]
Kelley, L.A.; Mezulis, S.; Yates, C.M.; Wass, M.N.; Sternberg, M.J.E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc., 2015, 10(6), 845-858.
[http://dx.doi.org/10.1038/nprot.2015.053] [PMID: 25950237]
[30]
Ko, J.; Park, H.; Heo, L.; Seok, C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res., 2012, 40(Web Server issue), W294-7.
[http://dx.doi.org/10.1093/nar/gks493]
[31]
Källberg, M.; Wang, H.; Wang, S.; Peng, J.; Wang, Z.; Lu, H.; Xu, J. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc., 2012, 7(8), 1511-1522.
[http://dx.doi.org/10.1038/nprot.2012.085] [PMID: 22814390]
[32]
Eswar, N.; Webb, B.; Marti-Renom, M.A.; Madhusudhan, M.S.; Eramian, D.; Shen, M.Y.; Pieper, U.; Sali, A. Comparative protein structure modeling using modeller. Curr. Protoc. Bioinformatics, 2006. Chapter 5, Unit-5.6.
[http://dx.doi.org/10.1002/0471250953.bi0506s15]
[33]
Ó. Conchúir, S. Barlow, K.A.; Pache, R.A.; Ollikainen, N.; Kundert, K.; O’Meara, M.J.; Smith, C.A.; Kortemme, T. A web resource for standardized benchmark datasets, metrics, and rosetta protocols for macromolecular modeling and design. PLoS One, 2015, 10(9), e0130433.
[http://dx.doi.org/10.1371/journal.pone.0130433] [PMID: 26335248]
[34]
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]
[35]
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. Inf. Med. Unlocked, 2022, 29(29), 100880.
[http://dx.doi.org/10.1016/j.imu.2022.100880]
[36]
Sinha, S.; Tam, B.; Wang, S.M. Applications of molecular dynamics simulation in protein study. Membranes, 2022, 12(9), 844.
[http://dx.doi.org/10.3390/membranes12090844] [PMID: 36135863]
[37]
Ghasemi, J.B.; Abdolmaleki, A.; Shiri, F. Molecular docking challenges and limitations.In: Pharmaceutical sciences: Breakthroughs in research and practice; IGI Global, 2017, pp. 770-794.
[http://dx.doi.org/10.4018/978-1-5225-1762-7.ch030]
[38]
Gondelaud, F.; Schramm, A.; Brocca, S.; Natalello, A.; Grandori, R.; Santambrogio, C.; Longhi, S. Chapter 6 Methods for measuring structural disorder in proteins. in structure and intrinsic disorder in enzymology.In: Foundations and Frontiers in Enzymology; Gupta, M.N.; Uversky, V.N; Academic Press, 2023, pp. 149-198.
[39]
Toukach, F.V.; Ananikov, V.P. Recent advances in computational predictions of NMR parameters for the structure elucidation of carbohydrates: Methods and limitations. Chem. Soc. Rev., 2013, 42(21), 8376-8415.
[http://dx.doi.org/10.1039/c3cs60073d] [PMID: 23887200]
[40]
Piston, D.W.; Kremers, G. J. Fluorescent protein FRET: The good, the bad and the ugly. Trends Biochem. Sci., 2007, 32(9), 407-414.
[http://dx.doi.org/10.1016/j.tibs.2007.08.003] [PMID: 17764955]
[41]
Shukla, R.; Tripathi, T. Molecular dynamics simulation of protein and protein-ligand complexes.In: Computer-aided drug design; Springer: Singapore, 2020, pp. 133-161.
[http://dx.doi.org/10.1007/978-981-15-6815-2_7]
[42]
Filipe, H.A.L.; Loura, L.M.S. Molecular dynamics simulations: Advances and applications. Molecules, 2022, 27(7), 2105.
[http://dx.doi.org/10.3390/molecules27072105] [PMID: 35408504]
[43]
Metropolis, N.; Rosenbluth, A.W.; Rosenbluth, M.N.; Teller, A.H.; Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys., 1953, 21(6), 1087-1092.
[http://dx.doi.org/10.1063/1.1699114]
[44]
McCammon, J.A.; Gelin, B.R.; Karplus, M. Dynamics of folded proteins. Nature, 1977, 267(5612), 585-590.
[45]
Burley, S.K.; Berman, H.M.; Duarte, J.M.; Feng, Z.; Flatt, J.W.; Hudson, B.P.; Lowe, R.; Peisach, E.; Piehl, D.W.; Rose, Y.; Sali, A.; Sekharan, M.; Shao, C.; Vallat, B.; Voigt, M.; Westbrook, J.D.; Young, J.Y.; Zardecki, C. Protein data bank: A comprehensive review of 3D structure holdings and worldwide utilization by researchers, educators, and students. Biomolecules, 2022, 12(10), 1425.
[http://dx.doi.org/10.3390/biom12101425] [PMID: 36291635]
[46]
Jones, D.; Allen, J.E.; Yang, Y.; Drew Bennett, W.F.; Gokhale, M.; Moshiri, N.; Rosing, T.S. Accelerators for classical molecular dynamics simulations of biomolecules. J. Chem. Theory Comput., 2022, 18(7), 4047-4069.
[http://dx.doi.org/10.1021/acs.jctc.1c01214] [PMID: 35710099]
[47]
Kutzner, C.; Kniep, C.; Cherian, A.; Nordstrom, L.; Grubmüller, H.; de Groot, B.L.; Gapsys, V. GROMACS in the cloud: A Global supercomputer to speed up alchemical drug design. J. Chem. Inf. Model., 2022, 62(7), 1691-1711.
[http://dx.doi.org/10.1021/acs.jcim.2c00044] [PMID: 35353508]
[48]
van Gunsteren, W.F.; Berendsen, H.J.C. Computer simulation of molecular dynamics: Methodology, applications, and perspectives in chemistry. Angew. Chem. Int. Ed. Engl., 1990, 29(9), 992-1023.
[http://dx.doi.org/10.1002/anie.199009921]
[49]
Case, D.A.; Aktulga, H.M.; Belfon, K.; Ben-Shalom, I.; Brozell, S.R.; Cerutti, D.S.; Cheatham, T.E., III; Cruzeiro, V.W.D.; Darden, T.A.; Duke, R.E. Amber 2021 reference manual. 2021. Available from: https://ambermd.org/doc12/Amber21.pdf
[50]
Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem., 2005, 26(16), 1781-1802.
[http://dx.doi.org/10.1002/jcc.20289] [PMID: 16222654]
[51]
Thompson, A.P.; Aktulga, H.M.; Berger, R.; Bolintineanu, D.S.; Brown, W.M.; Crozier, P.S. in ’t Veld, P.J.; Kohlmeyer, A.; Moore, S.G.; Nguyen, T.D.; Shan, R.; Stevens, M.J.; Tranchida, J.; Trott, C.; Plimpton, S.J. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun., 2022, 271, 108171.
[http://dx.doi.org/10.1016/j.cpc.2021.108171]
[52]
Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; Mackerell, A.D. Jr CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem., 2010, 31(4), 671-690.
[PMID: 19575467]
[53]
Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem., 2004, 25(9), 1157-1174.
[http://dx.doi.org/10.1002/jcc.20035] [PMID: 15116359]
[54]
Vennelakanti, V.; Nazemi, A. Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins. In: Current Opinion in Structural Biology; Elsevier; , 2022; 72, pp. 9-17.
[55]
Martínez-Archundia, M.; Hernández Mojica, T.G.; Correa-Basurto, J.; Montaño, S.; Camacho-Molina, A. Molecular dynamics simulations reveal structural differences among wild-type NPC1 protein and its mutant forms. J. Biomol. Struct. Dyn., 2020, 38(12), 3527-3532.
[http://dx.doi.org/10.1080/07391102.2019.1664324] [PMID: 31506030]
[56]
Pedersen, K.B.; Flores-Canales, J.C.; Schiøtt, B. Predicting molecular properties of $α$-synuclein using force fields for intrinsically disordered proteins. Proteins, 2022, 91(1), 47-61.
[57]
Rafi, S.; Yasmin, S.; Uddin, R. A molecular dynamic simulation approach: Development of dengue virus vaccine by affinity improvement techniques. J. Biomol. Struct. Dyn., 2022, 40(1), 61-76.
[http://dx.doi.org/10.1080/07391102.2020.1811771] [PMID: 32865135]
[58]
Mahtarin, R.; Islam, S.; Islam, M.J.; Ullah, M.O.; Ali, M.A.; Halim, M.A. Structure and dynamics of membrane protein in SARS-CoV-2. J. Biomol. Struct. Dyn., 2022, 40(10), 4725-4738.
[http://dx.doi.org/10.1080/07391102.2020.1861983] [PMID: 33353499]
[59]
Rollins, Z.A.; Faller, R.; George, S.C. Using molecular dynamics simulations to interrogate T cell receptor non-equilibrium kinetics. Comput. Struct. Biotechnol. J., 2022, 20, 2124-2133.
[http://dx.doi.org/10.1016/j.csbj.2022.04.018] [PMID: 35832631]
[60]
Friedman, R.; Boye, K.; Flatmark, K. Molecular modelling and simulations in cancer research. Biochim. Biophys. Acta Rev. Cancer, 2013, 1836(1), 1-14.
[http://dx.doi.org/10.1016/j.bbcan.2013.02.001] [PMID: 23416097]
[61]
Vyas, V.K.; Ukawala, R.D.; Chintha, C.; Ghate, M. Homology modeling a fast tool for drug discovery: Current perspectives. Indian J. Pharm. Sci., 2012, 74(1), 1-17.
[http://dx.doi.org/10.4103/0250-474X.102537] [PMID: 23204616]
[62]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2014, 66(1), 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236]
[63]
v M, V.; Dubey, V.K.; Ponnuraj, K. Identification of two natural compound inhibitors of Leishmania donovani Spermidine Synthase (SpdS) through molecular docking and dynamic studies. J. Biomol. Struct. Dyn., 2018, 36(10), 2678-2693.
[http://dx.doi.org/10.1080/07391102.2017.1366947] [PMID: 28797195]
[64]
Panigrahi, G. C.; Qureshi, R.; Jakkula, P.; Kumar, K. A.; Khan, N.; Qureshi, I. A. Leishmanial aspartyl-TRNA synthetase: Biochemical, biophysical and structural insights. Int. J. Biol. Macromol., 2020, 165(Pt B), 2869-2885.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.10.140]
[65]
Bhowmik, D.; Jagadeesan, R.; Rai, P.; Nandi, R.; Gugan, K.; Kumar, D. Evaluation of potential drugs against leishmaniasis targeting catalytic subunit of Leishmania donovani nuclear DNA primase using ligand based virtual screening, docking and molecular dynamics approaches. J. Biomol. Struct. Dyn., 2021, 39(5), 1838-1852.
[http://dx.doi.org/10.1080/07391102.2020.1739557] [PMID: 32141397]
[66]
Sinha, M.; Jagadeesan, R.; Kumar, N.; Saha, S.; Kothandan, G.; Kumar, D. In-silico studies on Myo inositol-1-phosphate synthase of Leishmania donovani in search of anti-leishmaniasis. J. Biomol. Struct. Dyn., 2022, 40(8), 3371-3384.
[http://dx.doi.org/10.1080/07391102.2020.1847194] [PMID: 33200690]
[67]
Grover, A.; Katiyar, S.P.; Singh, S.K.; Dubey, V.K.; Sundar, D. A leishmaniasis study: Structure-based screening and molecular dynamics mechanistic analysis for discovering potent inhibitors of spermidine synthase. Biochim. Biophys. Acta. Proteins Proteomics, 2012, 1824(12), 1476-1483.
[http://dx.doi.org/10.1016/j.bbapap.2012.05.016] [PMID: 22684087]
[68]
Khatoon, S.; Aroosh, A.; Islam, A.; Kalsoom, S.; Ahmad, F.; Hameed, S.; Abbasi, S.W.; Yasinzai, M.; Naseer, M.M. Novel coumarin-isatin hybrids as potent antileishmanial agents: Synthesis, In silico and in vitro evaluations. Bioorg. Chem., 2021, 110, 104816.
[http://dx.doi.org/10.1016/j.bioorg.2021.104816] [PMID: 33799180]
[69]
Méndez-Cuesta, C.A.; Méndez-Lucio, O.; Castillo, R. Homology modeling, docking and molecular dynamics of the Leishmania mexicana arginase: A description of the catalytic site useful for drug design. J. Mol. Graph. Model., 2012, 38, 50-59.
[http://dx.doi.org/10.1016/j.jmgm.2012.08.003] [PMID: 23085157]
[70]
Grover, A.; Katiyar, S.P.; Jeyakanthan, J.; Dubey, V.K.; Sundar, D. Mechanistic insights into the dual inhibition strategy for checking Leishmaniasis. J. Biomol. Struct. Dyn., 2012, 30(4), 474-487.
[http://dx.doi.org/10.1080/07391102.2012.682212] [PMID: 22694167]
[71]
Grover, A.; Katiyar, S.P.; Jeyakanthan, J.; Dubey, V.K.; Sundar, D. Blocking protein kinase C signaling pathway: Mechanistic insights into the anti-leishmanial activity of prospective herbal drugs from withania somnifera. BMC Genomics, 2012, 13(S7), S20.
[http://dx.doi.org/10.1186/1471-2164-13-S7-S20]
[72]
Yadav, S.; Mandal, H.; Saravanan, V.; Das, P.; Singh, S.K. In vitro and In silico analysis of L. donovani enoyl acyl carrier protein reductase - A possible drug target. J. Biomol. Struct. Dyn., 2021, 39(16), 6056-6069.
[http://dx.doi.org/10.1080/07391102.2020.1802337] [PMID: 32762412]
[73]
Souza, B.C.; Lacerda, P.S.; Pita, S.S.R.; Kato, R.B.; Leite, F.H.A. Identification of potential Leishmania chagasi superoxide dismutase allosteric modulators by structure-based computational approaches: Homology modelling, molecular dynamics and pharmacophore-based virtual screening. J. Biomol. Struct. Dyn., 2021, 39(18), 7000-7016.
[http://dx.doi.org/10.1080/07391102.2020.1804453] [PMID: 32794433]
[74]
Maganti, L.; Manoharan, P.; Ghoshal, N. Probing the structure of Leishmania donovani chagasi DHFR-TS: Comparative protein modeling and protein-ligand interaction studies. J. Mol. Model., 2010, 16(9), 1539-1547.
[http://dx.doi.org/10.1007/s00894-010-0649-0] [PMID: 20174846]
[75]
Shinde, S.; Mol, M.; Jamdar, V.; Singh, S. Molecular modeling and molecular dynamics simulations of GPI 14 in Leishmania major: Insight into the catalytic site for active site directed drug design. J. Theor. Biol., 2014, 351, 37-46.
[http://dx.doi.org/10.1016/j.jtbi.2014.02.017] [PMID: 24583312]
[76]
Patel, B.; Patel, D.; Parmar, K.; Chauhan, R.; Singh, D.D.; Pappachan, A.L. donovani XPRT: Molecular characterization and evaluation of inhibitors. Biochim. Biophys. Acta. Proteins Proteomics, 2018, 1866(3), 426-441.
[http://dx.doi.org/10.1016/j.bbapap.2017.12.002] [PMID: 29233758]
[77]
Mandal, H.; Vijayakumar, S.; Yadav, S.; Kumar Singh, S.; Das, P. Validation of NAD synthase inhibitors for inhibiting the cell Viability of Leishmania donovani: In silico and in vitro approach. J. Biomol. Struct. Dyn., 2019, 37(17), 4481-4493.
[http://dx.doi.org/10.1080/07391102.2018.1552199] [PMID: 30526395]
[78]
Colovos, C.; Yeates, T.O. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci., 1993, 2(9), 1511-1519.
[http://dx.doi.org/10.1002/pro.5560020916] [PMID: 8401235]
[79]
Eisenberg, D.; Lüthy, R.; Bowie, J.U. VERIFY3D: Assessment of protein models with three-dimensional profiles. In: In: Methods in Enzymology; Academic Press; , 1997; 277, pp. 396-404.
[80]
Laskowski, R.A.; MacArthur, M.W.; Moss, D.S.; Thornton, J.M. PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Cryst., 1993, 26(2), 283-291.
[http://dx.doi.org/10.1107/S0021889892009944]
[81]
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 issue), 407-410.
[http://dx.doi.org/10.1093/nar/gkm290]
[82]
Shen, M.; Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci., 2006, 15(11), 2507-2524.
[http://dx.doi.org/10.1110/ps.062416606] [PMID: 17075131]
[83]
Dhumal, T.T.; Kumar, R.; Paul, A.; Roy, P.K.; Garg, P.; Singh, S. Molecular explorations of the Leishmania donovani 6-phosphogluconolactonase enzyme, a key player in the pentose phosphate pathway. Biochimie, 2022, 202, 212-225.
[http://dx.doi.org/10.1016/j.biochi.2022.08.006] [PMID: 36037881]
[84]
Mohammadi-Ghalehbin, B.; Shiran, J.A.; Gholizadeh, N.; Razzaghi-Asl, N. Synthesis, antileishmanial activity and molecular modeling of new 1-aryl/alkyl-3-benzoyl/cyclopropanoyl thiourea derivatives. Mol. Divers., 2022.
[http://dx.doi.org/10.1007/s11030-022-10508-3] [PMID: 36001225]
[85]
Katiki, M.; Sharma, M.; Neetu, N.; Rentala, M.; Kumar, P. Biophysical and modeling-based approach for the identification of inhibitors against DOHH from Leishmania donovani. Brief. Funct. Genomics, 2022, 22(2), 217-226.
[http://dx.doi.org/10.1093/bfgp/elac014] [PMID: 35809341]
[86]
Sasidharan, S.; Saudagar, P. Flavones reversibly inhibit Leishmania donovani tyrosine aminotransferase by binding to the catalytic pocket: An integrated In silico-in vitro approach. Int. J. Biol. Macromol., 2020, 164, 2987-3004.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.08.107] [PMID: 32798546]
[87]
Vadloori, B.; Sharath, A.K.; Prabhu, N.P.; Maurya, R. Homology modelling, molecular docking, and molecular dynamics simulations reveal the inhibition of Leishmania donovani dihydrofolate reductase-thymidylate synthase enzyme by Withaferin-A. BMC Res. Notes, 2018, 11(1), 246.
[http://dx.doi.org/10.1186/s13104-018-3354-1] [PMID: 29661206]
[88]
Sarfraz, M.; Wang, C.; Sultana, N.; Ellahi, H.; Rehman, M.F.; Jameel, M.; Akhter, S.; Kanwal, F.; Tariq, M.I.; Xue, S. 2,3-Dihydroquinazolin-4(1H)-one as a new class of anti-leishmanial agents: A combined experimental and computational study. Crystals, 2021, 12(1), 44.
[http://dx.doi.org/10.3390/cryst12010044]
[89]
Kutzner, C.; Páll, S.; Fechner, M.; Esztermann, A.; de Groot, B.L.; Grubmüller, H. Best Bang for Your Buck: GPU Nodes for GROMACS Biomolecular Simulations; Wiley Online Library, 2015.
[90]
Mijiddorj, B.; Kaneda, S.; Sato, H.; Kitahashi, Y.; Javkhlantugs, N.; Naito, A.; Ueda, K.; Kawamura, I. The role of d - allo -isoleucine in the deposition of the anti- Leishmania peptide bombinin H4 as revealed by 31 P solid-state NMR, VCD spectroscopy, and MD simulation. Biochim. Biophys. Acta. Proteins Proteomics, 2018, 1866(7), 789-798.
[http://dx.doi.org/10.1016/j.bbapap.2018.01.005] [PMID: 29337209]
[91]
Schuler, L.D.; Daura, X.; van Gunsteren, W.F. An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. J. Comput. Chem., 2001, 22(11), 1205-1218.
[http://dx.doi.org/10.1002/jcc.1078]
[92]
Reif, M.M.; Winger, M.; Oostenbrink, C. Testing of the gromos force-field parameter set 54A8: Structural properties of electrolyte solutions, lipid bilayers, and proteins. J. Chem. Theory Comput., 2013, 9(2), 1247-1264.
[http://dx.doi.org/10.1021/ct300874c] [PMID: 23418406]
[93]
Cao, Z.; Liu, L.; Wang, J. Why the OPLS-AA force field cannot produce the β-hairpin structure of H1 peptide in solution when comparing with the GROMOS 43A1 force field? J. Biomol. Struct. Dyn., 2011, 29(3), 527-539.
[http://dx.doi.org/10.1080/07391102.2011.10507403] [PMID: 22066538]
[94]
Schüttelkopf, A.W.; van Aalten, D.M.F. PRODRG: A tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr. D Biol. Crystallogr., 2004, 60(8), 1355-1363.
[http://dx.doi.org/10.1107/S0907444904011679] [PMID: 15272157]
[95]
Malde, A.K.; Zuo, L.; Breeze, M.; Stroet, M.; Poger, D.; Nair, P.C.; Oostenbrink, C.; Mark, A.E. An automated force field topology builder (ATB) and repository: Version 1.0. J. Chem. Theory Comput., 2011, 7(12), 4026-4037.
[http://dx.doi.org/10.1021/ct200196m] [PMID: 26598349]
[96]
Martin, R.; Heider, D. ContraDRG: Automatic partial charge prediction by machine learning. Front. Genet., 2019, 10, 990.
[http://dx.doi.org/10.3389/fgene.2019.00990] [PMID: 31737032]
[97]
Wang, C.; Greene, D.A.; Xiao, L.; Qi, R.; Luo, R. Recent developments and applications of the MMPBSA method. Front. Mol. Biosci., 2018, 4, 87.
[http://dx.doi.org/10.3389/fmolb.2017.00087] [PMID: 29367919]
[98]
Borkotoky, S.; Banerjee, M.; Modi, G.P.; Dubey, V.K. Identification of high affinity and low molecular alternatives of boceprevir against SARS-CoV-2 main protease: A virtual screening approach. Chem. Phys. Lett., 2021, 770, 138446.
[http://dx.doi.org/10.1016/j.cplett.2021.138446] [PMID: 33623170]
[99]
Prakash, A.; Borkotoky, S.; Dubey, V.K. Targeting two potential sites of SARS-CoV-2 main protease through computational drug repurposing. J. Biomol. Struct. Dyn., 2022, 41(7), 1-11.
[http://dx.doi.org/10.1080/07391102.2022.2044907] [PMID: 35266856]
[100]
Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model., 2011, 51(1), 69-82.
[http://dx.doi.org/10.1021/ci100275a] [PMID: 21117705]
[101]
Poli, G.; Granchi, C.; Rizzolio, F.; Tuccinardi, T. Application of MM-PBSA methods in virtual screening. Molecules, 2020, 25(8), 1971.
[http://dx.doi.org/10.3390/molecules25081971] [PMID: 32340232]
[102]
Valdés-Tresanco, M.S.; Valdés-Tresanco, M.E.; Valiente, P.A.; Moreno, E. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS. J. Chem. Theory Comput., 2021, 17(10), 6281-6291.
[http://dx.doi.org/10.1021/acs.jctc.1c00645] [PMID: 34586825]
[103]
Senior, A.W.; Evans, R.; Jumper, J.; Kirkpatrick, J.; Sifre, L.; Green, T.; Qin, C.; Žídek, A.; Nelson, A.W.R.; Bridgland, A.; Penedones, H.; Petersen, S.; Simonyan, K.; Crossan, S.; Kohli, P.; Jones, D.T.; Silver, D.; Kavukcuoglu, K.; Hassabis, D. Improved protein structure prediction using potentials from deep learning. Nature, 2020, 577(7792), 706-710.
[http://dx.doi.org/10.1038/s41586-019-1923-7] [PMID: 31942072]
[104]
Bateman, A.; Martin, M-J.; Orchard, S.; Magrane, M.; Ahmad, S.; Alpi, E.; Bowler-Barnett, E.H.; Britto, R.; Bye-A-Jee, H.; Cukura, A.; Denny, P.; Dogan, T.; Ebenezer, T.G.; Fan, J.; Garmiri, P.; da Costa Gonzales, L.J.; Hatton-Ellis, E.; Hussein, A.; Ignatchenko, A.; Insana, G.; Ishtiaq, R.; Joshi, V.; Jyothi, D.; Kandasaamy, S.; Lock, A.; Luciani, A.; Lugaric, M.; Luo, J.; Lussi, Y.; MacDougall, A.; Madeira, F.; Mahmoudy, M.; Mishra, A.; Moulang, K.; Nightingale, A.; Pundir, S.; Qi, G.; Raj, S.; Raposo, P.; Rice, D.L.; Saidi, R.; Santos, R.; Speretta, E.; Stephenson, J.; Totoo, P.; Turner, E.; Tyagi, N.; Vasudev, P.; Warner, K.; Watkins, X.; Zaru, R.; Zellner, H.; Bridge, A.J.; Aimo, L.; Argoud-Puy, G.; Auchincloss, A.H.; Axelsen, K.B.; Bansal, P.; Baratin, D.; Batista Neto, T.M.; Blatter, M-C.; Bolleman, J.T.; Boutet, E.; Breuza, L.; Gil, B.C.; Casals-Casas, C.; Echioukh, K.C.; Coudert, E.; Cuche, B.; de Castro, E.; Estreicher, A.; Famiglietti, M.L.; Feuermann, M.; Gasteiger, E.; Gaudet, P.; Gehant, S.; Gerritsen, V.; Gos, A.; Gruaz, N.; Hulo, C.; Hyka-Nouspikel, N.; Jungo, F.; Kerhornou, A.; Le Mercier, P.; Lieberherr, D.; Masson, P.; Morgat, A.; Muthukrishnan, V.; Paesano, S.; Pedruzzi, I.; Pilbout, S.; Pourcel, L.; Poux, S.; Pozzato, M.; Pruess, M.; Redaschi, N.; Rivoire, C.; Sigrist, C.J.A.; Sonesson, K.; Sundaram, S.; Wu, C.H.; Arighi, C.N.; Arminski, L.; Chen, C.; Chen, Y.; Huang, H.; Laiho, K.; McGarvey, P.; Natale, D.A.; Ross, K.; Vinayaka, C.R.; Wang, Q.; Wang, Y.; Zhang, J. UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Res., 2023, 51(D1), D523-D531.
[http://dx.doi.org/10.1093/nar/gkac1052] [PMID: 36408920]
[105]
Kant, V.; Vijayakumar, S.; Sahoo, G.C.; Chaudhery, S.S.; Das, P. In-silico screening and validation of high-affinity tetra-peptide inhibitor of Leishmania donovani O-acetyl serine sulfhydrylase (OASS). J. Biomol. Struct. Dyn., 2018, 37(2), 481-492.
[http://dx.doi.org/10.1080/07391102.2018.1429315] [PMID: 29415627]
[106]
Sharma, V.K.; Kathuria, D.; Bharatam, P.V. Identification of selective LdDHFR inhibitors using quantum chemical and molecular modeling approach. J. Biomol. Struct. Dyn., 2021, 40(19), 8687-8695.
[http://dx.doi.org/10.1080/07391102.2021.1915182] [PMID: 33904374]
[107]
Sharma, V.K.; Bharatam, P.V. Identification of selective inhibitors of Ld DHFR enzyme using pharmacoinformatic methods. J. Comput. Biol., 2021, 28(1), 43-59.
[http://dx.doi.org/10.1089/cmb.2019.0332] [PMID: 32207987]
[108]
Meshram, R.J.; Bagul, K.T.; Aouti, S.U.; Shirsath, A.M.; Duggal, H.; Gacche, R.N. Modeling and simulation study to identify threonine synthase as possible drug target in Leishmania major. Mol. Divers., 2021, 25(3), 1679-1700.
[http://dx.doi.org/10.1007/s11030-020-10129-8] [PMID: 32737682]
[109]
Pandey, R.K.; Sharma, D.; Bhatt, T.K.; Sundar, S.; Prajapati, V.K. Developing imidazole analogues as potential inhibitor for Leishmania donovani trypanothione reductase: Virtual screening, molecular docking, dynamics and ADMET approach. J. Biomol. Struct. Dyn., 2015, 33(12), 2541-2553.
[http://dx.doi.org/10.1080/07391102.2015.1085904] [PMID: 26305585]
[110]
Ochoa, R.; Watowich, S.J.; Flórez, A.; Mesa, C.V.; Robledo, S.M.; Muskus, C. Drug search for leishmaniasis: A virtual screening approach by grid computing. J. Comput. Aided Mol. Des., 2016, 30(7), 541-552.
[http://dx.doi.org/10.1007/s10822-016-9921-4] [PMID: 27438595]
[111]
Holanda, V.N.; Silva, W.V.; Nascimento, P.H.; Silva, S.R.B.; Cabral Filho, P.E.; Assis, S.P.O.; Silva, C.A.; Oliveira, R.N.; Figueiredo, R.C.B.Q.; Lima, V.L.M. Antileishmanial activity of 4-phenyl-1-[2-(phthalimido-2-yl)ethyl]-1H-1,2,3-triazole (PT4) derivative on Leishmania amazonensis and Leishmania braziliensis: In silico ADMET, in vitro activity, docking and molecular dynamic simulations. Bioorg. Chem., 2020, 105(July), 104437.
[http://dx.doi.org/10.1016/j.bioorg.2020.104437] [PMID: 33339081]
[112]
Rode, N.; Tantray, A.; Shelar, A.; Patil, R.; Terdale, S. Amino acid ionic liquid-catalyzed synthesis, anti-Leishmania activity, molecular docking, molecular dynamic simulation, and ADME study of 3,4-dihydropyrimidin-2(1 H)-(thio)ones. Synth. Commun., 2022, 52(2), 190-204.
[http://dx.doi.org/10.1080/00397911.2021.2010757]
[113]
Patel, V.M.; Patel, N.B.; Chan-Bacab, M.J.; Rivera, G. N -Mannich bases of benzimidazole as a potent antitubercular and antiprotozoal agents: Their synthesis and computational studies. Synth. Commun., 2020, 50(6), 858-878.
[http://dx.doi.org/10.1080/00397911.2020.1725057]
[114]
Gangalum, P.R.; de Castro, W.; Vieira, L.Q.; Dey, R.; Rivas, L.; Singh, S.; Majumdar, S.; Saha, B. Platelet-activating factor receptor contributes to antileishmanial function of miltefosine. J. Immunol., 2015, 194(12), 5961-5967.
[http://dx.doi.org/10.4049/jimmunol.1401890] [PMID: 25980013]
[115]
Raj, S.; Sasidharan, S.; Dubey, V.K.; Saudagar, P. Identification of lead molecules against potential drug target protein MAPK4 from L. donovani: An in-silico approach using docking, molecular dynamics and binding free energy calculation. PLoS One, 2019, 14(8), e0221331.
[http://dx.doi.org/10.1371/journal.pone.0221331] [PMID: 31425543]
[116]
Rodrigues, M.H.C.; da Costa, A.P.L.; Molfetta, F.A. Design of new compounds for glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) enzyme of Leishmania mexicana from molecular modeling. Revista Virtual de Quimica, 2020, 12(2), 536-552.
[http://dx.doi.org/10.21577/1984-6835.20200042]
[117]
Ansari, M.Y.; Ahsan, M.J.; Yasmin, S.; Sahoo, G.C.; Saini, V.; Das, P. In silico identification of novel antagonists and binding insights by structural and functional analyses of guanylate kinase of Leishmania donovani and interaction with inhibitors. Gene Rep., 2017, 8(July), 134-143.
[http://dx.doi.org/10.1016/j.genrep.2017.07.003]
[118]
Guhe, V.; Anjum, F.; Shafie, A.; Hassan, M.I.; Pasupuleti, V.R.; Singh, S. Infection dynamics of ATG8 in leishmania: Balancing autophagy for therapeutics. Molecules, 2022, 27(10), 3142.
[http://dx.doi.org/10.3390/molecules27103142] [PMID: 35630618]
[119]
Kant, V. kumar, P.; Ranjan, R.; Kumar, P.; Mandal, D.; Vijayakumar, S. In silico screening, molecular dynamic simulations, and in vitro activity of selected natural compounds as an inhibitor of Leishmania donovani 3-mercaptopyruvate sulfurtransferase. Parasitol. Res., 2022, 121(7), 2093-2109.
[http://dx.doi.org/10.1007/s00436-022-07532-5] [PMID: 35536513]
[120]
Vidhya, V.M.; Lakshmi, B.S.; Ponnuraj, K. In silico prediction of a new lead compound targeting enolase of trypanosomatids through structure-based virtual screening and molecular dynamic studies. J. Mol. Model., 2020, 26(2), 23.
[http://dx.doi.org/10.1007/s00894-019-4284-0] [PMID: 31912304]
[121]
Pandey, R.K.; Verma, P.; Sharma, D.; Bhatt, T.K.; Sundar, S.; Prajapati, V.K. High-throughput virtual screening and quantum mechanics approach to develop imipramine analogues as leads against trypanothione reductase of leishmania. Biomed. Pharmacother., 2016, 83, 141-152.
[http://dx.doi.org/10.1016/j.biopha.2016.06.010] [PMID: 27470561]
[122]
Kolte, B.S.; Londhe, S.R.; Solanki, B.R.; Gacche, R.N.; Meshram, R.J. FilTer BaSe: A web accessible chemical database for small compound libraries. J. Mol. Graph. Model., 2018, 80, 95-103.
[http://dx.doi.org/10.1016/j.jmgm.2017.12.020] [PMID: 29328995]
[123]
Gupta, Y.; Goicoechea, S.; Romero, J.G.; Mathur, R.; Caulfield, T.R.; Becker, D.P.; Durvasula, R.; Kempaiah, P. Repurposing Lansoprazole and Posaconazole to treat Leishmaniasis: Integration of in vitro testing, pharmacological corroboration, and mechanisms of action. Yao Wu Shi Pin Fen Xi, 2022, 30(1), 129-150.
[http://dx.doi.org/10.38212/2224-6614.3394] [PMID: 35647721]
[124]
Adeniyi, A.; Ajibade, P. Comparing the suitability of autodock, gold and glide for the docking and predicting the possible targets of Ru(II)-based complexes as anticancer agents. Molecules, 2013, 18(4), 3760-3778.
[http://dx.doi.org/10.3390/molecules18043760] [PMID: 23529035]
[125]
Shirts, M.R.; Klein, C.; Swails, J.M.; Yin, J.; Gilson, M.K.; Mobley, D.L.; Case, D.A.; Zhong, E.D. Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset. J. Comput. Aided Mol. Des., 2017, 31(1), 147-161.
[http://dx.doi.org/10.1007/s10822-016-9977-1] [PMID: 27787702]
[126]
Piana, S.; Klepeis, J.L.; Shaw, D.E. Assessing the accuracy of physical models used in protein-folding simulations: Quantitative evidence from long molecular dynamics simulations. Curr. Opin. Struct. Biol., 2014, 24, 98-105.
[http://dx.doi.org/10.1016/j.sbi.2013.12.006] [PMID: 24463371]
[127]
Bernardi, R.C.; Melo, M.C.R.; Schulten, K. Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochim. Biophys. Acta, Gen. Subj., 2015, 1850(5), 872-877.
[http://dx.doi.org/10.1016/j.bbagen.2014.10.019] [PMID: 25450171]
[128]
Zwier, M.C.; Chong, L.T. Reaching biological timescales with all-atom molecular dynamics simulations. Curr. Opin. Pharmacol., 2010, 10(6), 745-752.
[http://dx.doi.org/10.1016/j.coph.2010.09.008] [PMID: 20934381]
[129]
Ahmad, K.; Rizzi, A.; Capelli, R.; Mandelli, D.; Lyu, W.; Carloni, P. Enhanced-sampling simulations for the estimation of ligand binding kinetics: Current status and perspective. Front. Mol. Biosci., 2022, 9, 899805.
[http://dx.doi.org/10.3389/fmolb.2022.899805] [PMID: 35755817]
[130]
Ngo, S.T.; Vu, K.B.; Bui, L.M.; Vu, V.V. Effective estimation of ligand-binding affinity using biased sampling method. ACS Omega, 2019, 4(2), 3887-3893.
[http://dx.doi.org/10.1021/acsomega.8b03258] [PMID: 31459599]
[131]
Marrink, S. Protein-ligand binding with Martini 3. Biophys. J., 2023, 122(3), 325a.
[http://dx.doi.org/10.1016/j.bpj.2022.11.1817]
[132]
Mendoza-Martínez, C.; Galindo-Sevilla, N.; Correa-Basurto, J.; Ugalde-Saldivar, V.M.; Rodríguez-Delgado, R.G.; Hernández-Pineda, J.; Padierna-Mota, C.; Flores-Alamo, M.; Hernández-Luis, F. Antileishmanial activity of quinazoline derivatives: Synthesis, docking screens, molecular dynamic simulations and electrochemical studies. Eur. J. Med. Chem., 2015, 92, 314-331.
[http://dx.doi.org/10.1016/j.ejmech.2014.12.051] [PMID: 25576738]
[133]
Guerra, M.E.R.; Fadel, V.; Maltarollo, V.G.; Baldissera, G.; Honorio, K.M.; Ruggiero, J.R.; dos Santos Cabrera, M.P. MD simulations and multivariate studies for modeling the antileishmanial activity of peptides. Chem. Biol. Drug Des., 2017, 90(4), 501-510.
[http://dx.doi.org/10.1111/cbdd.12970] [PMID: 28267894]
[134]
Tabrez, S.; Rahman, F.; Ali, R.; Alouffi, A.S.; Akand, S.K.; Alshehri, B.M.; Alshammari, F.A.; Alam, A.; Alaidarous, M.A.; Banawas, S.; Dukhyil, A.A.B.; Rub, A. Cynaroside inhibits Leishmania donovani UDP-galactopyranose mutase and induces reactive oxygen species to exert antileishmanial response. Biosci. Rep., 2021, 41(1), BSR20203857.
[http://dx.doi.org/10.1042/BSR20203857] [PMID: 33367614]
[135]
Razzaghi-Asl, N.; Hashemi, N. Identification of potential antileishmanial agents via structure-based molecular simulations. J. Mol. Graph. Model., 2022, 110(110), 108039.
[http://dx.doi.org/10.1016/j.jmgm.2021.108039] [PMID: 34736055]
[136]
Singh, S.; Prajapati, V. K. Exploring actinomycetes natural products to identify potential multi-target inhibitors against leishmania donovani. 3 Biotech, 2022, 12(9), 1-22.
[http://dx.doi.org/10.1007/s13205-022-03304-1]
[137]
Qureshi, K.A.; Imtiaz, M.; Al Nasr, I.; Koko, W.S.; Khan, T.A.; Jaremko, M.; Mahmood, S.; Fatmi, M.Q. Antiprotozoal activity of thymoquinone (2-Isopropyl-5-methyl-1,4-benzoquinone) for the treatment of leishmania major-induced leishmaniasis: In silico and in vitro studies. Antibiotics, 2022, 11(9), 1206.
[http://dx.doi.org/10.3390/antibiotics11091206] [PMID: 36139985]
[138]
Jakkula, P.; Narsimulu, B.; Qureshi, I.A. Biochemical and structural insights into 6-phosphogluconate dehydrogenase from Leishmania donovani. Appl. Microbiol. Biotechnol., 2021, 105(13), 5471-5489.
[http://dx.doi.org/10.1007/s00253-021-11434-4] [PMID: 34250571]
[139]
Kaur, P.K.; Tripathi, N.; Desale, J.; Neelagiri, S.; Yadav, S.; Bharatam, P.V.; Singh, S. Mutational and structural analysis of conserved residues in ribose-5-phosphate isomerase B from leishmania donovani: Role in substrate recognition and conformational stability. PLoS One, 2016, 11(3), e0150764.
[http://dx.doi.org/10.1371/journal.pone.0150764] [PMID: 26953696]
[140]
Agnihotri, P.; Singh, S.P.; Shakya, A.K.; Pratap, J.V. Biochemical and biophysical characterization of Leishmania donovani gamma-glutamylcysteine synthetase. Biochem. Biophys. Rep., 2016, 8, 127-138.
[http://dx.doi.org/10.1016/j.bbrep.2016.08.016] [PMID: 28955948]
[141]
Bora, N.; Nath Jha, A. An integrative approach using systems biology, mutational analysis with molecular dynamics simulation to challenge the functionality of a target protein. Chem. Biol. Drug Des., 2019, 93(6), cbdd.13502.
[http://dx.doi.org/10.1111/cbdd.13502] [PMID: 30891955]
[142]
Sasidharan, S.; Saudagar, P. Biochemical and structural characterization of tyrosine aminotransferase suggests broad substrate specificity and a two state folding mechanism in Leishmania donovani. FEBS Open Bio, 2019, 9(10), 1769-1783.
[http://dx.doi.org/10.1002/2211-5463.12715] [PMID: 31393078]
[143]
Sasidharan, S.; Saudagar, P. Concerted motion of structure and active site charge is required for tyrosine aminotransferase activity in Leishmania parasite. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2020, 232, 118133.
[http://dx.doi.org/10.1016/j.saa.2020.118133] [PMID: 32086045]
[144]
Khan, S.; Alonso, L.; Roduit, C.; Bandyopadhyay, S.; Singh, S.; Saha, S.; Tacchini-Cottier, F.; Roy, S.; Dietler, G.; Kasas, S.; Das, P.; Krishnasastry, M.V.; Saha, B. Differential peptide binding to CD40 evokes counteractive responses. Hum. Immunol., 2012, 73(5), 465-469.
[http://dx.doi.org/10.1016/j.humimm.2012.02.019] [PMID: 22406255]
[145]
Roy, S.; Basu, S.; Datta, A.K.; Bhattacharyya, D.; Banerjee, R.; Dasgupta, D. Equilibrium unfolding of cyclophilin from Leishmania donovani: Characterization of intermediate states. Int. J. Biol. Macromol., 2014, 69, 353-360.
[http://dx.doi.org/10.1016/j.ijbiomac.2014.05.063] [PMID: 24887548]
[146]
Roy, S.; Basu, S.; Dasgupta, D.; Bhattacharyya, D.; Banerjee, R. The unfolding MD simulations of cyclophilin: Analyzed by surface contact networks and their associated metrics. PLoS One, 2015, 10(11), e0142173.
[http://dx.doi.org/10.1371/journal.pone.0142173] [PMID: 26545107]
[147]
Roy, K.; Mandloi, S.; Chakrabarti, S.; Roy, S. Cholesterol corrects altered conformation of MHC-II protein in leishmania donovani infected macrophages: Implication in therapy. PLoS Negl. Trop. Dis., 2016, 10(5), e0004710.
[http://dx.doi.org/10.1371/journal.pntd.0004710] [PMID: 27214205]
[148]
Hamza, A.; Kébaïer, C.; Vasilescu, D.; Sarma, M.H.; Sarma, R.H. Molecular modeling of a Leishmania antigen eIF-4A: Identification of a potential epitope implicated in the adjuvant effect. J. Biomol. Struct. Dyn., 2003, 21(1), 43-53.
[http://dx.doi.org/10.1080/07391102.2003.10506904] [PMID: 12854958]
[149]
Eslami, G.; Frikha, F.; Salehi, R.; Khamesipour, A.; Hejazi, H.; Nilforoushzadeh, M.A. Cloning, expression and dynamic simulation of TRYP6 from Leishmania major (MRHO/IR/75/ER). Mol. Biol. Rep., 2011, 38(6), 3765-3776.
[http://dx.doi.org/10.1007/s11033-010-0492-5] [PMID: 21120619]
[150]
Bianchini, G.; Bocedi, A.; Ascenzi, P.; Gavuzzo, E.; Mazza, F.; Aschi, M. Molecular dynamics simulation of Leishmania major surface metalloprotease GP63 (Leishmanolysin). Proteins, 2006, 64(2), 385-390.
[http://dx.doi.org/10.1002/prot.21009] [PMID: 16708363]
[151]
Singh, S.; Mandlik, V. Structure based investigation on the binding interaction of transport proteins in leishmaniasis: insights from molecular simulation. Mol. Biosyst., 2015, 11(5), 1251-1259.
[http://dx.doi.org/10.1039/C4MB00713A] [PMID: 25761976]
[152]
Bejugam, P.R.; Singh, S. Computing molecular devices in L.major through transcriptome analysis: Structured simulation approach. PLoS One, 2016, 11(2), e0148909.
[http://dx.doi.org/10.1371/journal.pone.0148909] [PMID: 26901858]
[153]
Hollingsworth, S.A.; Nguyen, B.D.; Chreifi, G.; Arce, A.P.; Poulos, T.L. Insights into the dynamics and dissociation mechanism of a protein redox complex using molecular dynamics. J. Chem. Inf. Model., 2017, 57(9), 2344-2350.
[http://dx.doi.org/10.1021/acs.jcim.7b00421] [PMID: 28841378]
[154]
Kumar, S.; Suguna, K. Crystal structure of the retroviral protease like domain of a protozoal DNA damage inducible 1 protein. FEBS Open Bio, 2018, 8(9), 1379-1394.
[http://dx.doi.org/10.1002/2211-5463.12491] [PMID: 30186740]
[155]
Biswas, G.; Ghosh, S.; Raghuraman, H.; Banerjee, R. Probing conformational transitions of PIN1 from L. major during chemical and thermal denaturation. Int. J. Biol. Macromol., 2020, 154, 904-915.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.03.166] [PMID: 32209371]
[156]
Hernández-Alvarez, L.; Oliveira, A.B., Jr; Hernández-González, J.E.; Chahine, J.; Pascutti, P.G.; de Araujo, A.S.; de Souza, F.P. Computational study on the allosteric mechanism of Leishmania major IF4E-1 by 4E-interacting protein-1: Unravelling the determinants of m7GTP cap recognition. Comput. Struct. Biotechnol. J., 2021, 19, 2027-2044.
[http://dx.doi.org/10.1016/j.csbj.2021.03.036] [PMID: 33995900]
[157]
Costa, C.H.S.; Bichara, T.W.; Gomes, G.C.; dos Santos, A.M.; da Costa, K.S.; Lima, A.H.L.; Alves, C.N.; Lameira, J. Unraveling the conformational dynamics of glycerol 3-phosphate dehydrogenase, a nicotinamide adenine dinucleotide-dependent enzyme of Leishmania mexicana. J. Biomol. Struct. Dyn., 2021, 39(6), 2044-2055.
[http://dx.doi.org/10.1080/07391102.2020.1742206] [PMID: 32174264]
[158]
Naithani, A.; Taylor, P.; Erman, B.; Walkinshaw, M.D. A molecular dynamics study of allosteric transitions in leishmania mexicana pyruvate kinase. Biophys. J., 2015, 109(6), 1149-1156.
[http://dx.doi.org/10.1016/j.bpj.2015.05.040] [PMID: 26210208]
[159]
Vijayakumar, S.; Das, P. Structural, molecular motions, and free-energy landscape of Leishmania sterol-14α-demethylase wild type and drug resistant mutant: a comparative molecular dynamics study. J. Biomol. Struct. Dyn., 2019, 37(6), 1477-1493.
[http://dx.doi.org/10.1080/07391102.2018.1461135] [PMID: 29620481]
[160]
da Costa, K.S.; Galúcio, J.M.P.; Leonardo, E.S.; Cardoso, G.; Leal, É.; Conde, G.; Lameira, J. Structural and evolutionary analysis of Leishmania Alba proteins. Mol. Biochem. Parasitol., 2017, 217(July), 23-31.
[http://dx.doi.org/10.1016/j.molbiopara.2017.08.006] [PMID: 28847609]
[161]
Pérez-Montoto, L.G.; Dea-Ayuela, M.A.; Prado-Prado, F.J.; Bolas-Fernández, F.; Ubeira, F.M.; González-Díaz, H. Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks. Polymer, 2009, 50(15), 3857-3870.
[http://dx.doi.org/10.1016/j.polymer.2009.05.055] [PMID: 32287404]
[162]
González-Díaz, H.; Dea-Ayuela, M.A.; Pérez-Montoto, L.G.; Prado-Prado, F.J.; Agüero-Chapín, G.; Bolas-Fernández, F.; Vazquez-Padrón, R.I.; Ubeira, F.M. QSAR for RNases and theoretic–experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein. Mol. Divers., 2010, 14(2), 349-369.
[http://dx.doi.org/10.1007/s11030-009-9178-0] [PMID: 19578942]
[163]
Santos, D.A.; de Souza Costa, M.G.; Alves, C.R.; Caffarena, E.R. Structural and dynamic insights into the C-terminal extension of cysteine proteinase B from Leishmania amazonensis. J. Mol. Graph. Model., 2016, 70, 30-39.
[http://dx.doi.org/10.1016/j.jmgm.2016.08.007] [PMID: 27649549]
[164]
Sutter, A.; Antunes, D.; Silva-Almeida, M.; Costa, M.G.S.; Caffarena, E.R. Structural insights into leishmanolysins encoded on chromosome 10 of Leishmania (Viannia) braziliensis. Mem. Inst. Oswaldo Cruz, 2017, 112(9), 617-625.
[http://dx.doi.org/10.1590/0074-02760160522] [PMID: 28902287]
[165]
Fozongari, F.; Dalimi, A.; Arab, S.S.; Behmanesh, M.; Khammari, A. Trypanothione reductase gene mutations in meglumine anti-moniate resistant isolates from cutaneous leishmaniasis pa-tients using molecular dynamics method. Iran. J. Parasitol., 2020, 15(4), 511-520.
[http://dx.doi.org/10.18502/ijpa.v15i4.4856] [PMID: 33884008]
[166]
De Souza, O.N.; Goodfellow, J.M. The intrinsic curvature of a 51 bp K-DNA fragment of Leishmania tarentolae: A molecular model. J. Biomol. Struct. Dyn., 1998, 15(5), 905-930.
[http://dx.doi.org/10.1080/07391102.1998.10508212] [PMID: 9619513]
[167]
Abdellahi, L.; Iraji, F. Vaccination in leishmaniasis: A review article. Iran. Biomed. J., 2022, 26(1), 1-35.
[168]
Kashif, M.; Hira, S.K.; Manna, P.P. Immunoinformatics based design and prediction of proteome-wide killer cell epitopes of leishmania donovani: Potential application in vaccine development. J. Biomol. Struct. Dyn., 2021, 40(21), 10578-10591.
[http://dx.doi.org/10.1080/07391102.2021.1945495] [PMID: 34219625]
[169]
Vakili, B.; Eslami, M.; Hatam, G.R.; Zare, B.; Erfani, N.; Nezafat, N.; Ghasemi, Y. Immunoinformatics-aided design of a potential multi-epitope peptide vaccine against Leishmania infantum. Int. J. Biol. Macromol., 2018, 120(Pt A), 1127-1139.
[http://dx.doi.org/10.1016/j.ijbiomac.2018.08.125] [PMID: 30172806]
[170]
Rabienia, M.; Roudbari, Z.; Ghanbariasad, A.; Abdollahi, A.; Mohammadi, E.; Mortazavidehkordi, N.; Farjadfar, A. Exploring membrane proteins of Leishmania major to design a new multi-epitope vaccine using immunoinformatics approach. Eur. J. Pharm. Sci., 2020, 152, 105423.
[http://dx.doi.org/10.1016/j.ejps.2020.105423] [PMID: 32534195]
[171]
Ojha, R.; Pandey, R.K.; Prajapati, V.K. Vaccinomics strategy to concoct a promising subunit vaccine for visceral leishmaniasis targeting sandfly and leishmania antigens. Int. J. Biol. Macromol., 2020, 156, 548-557.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.04.097] [PMID: 32311400]
[172]
Yadav, S.; Prakash, J.; Shukla, H.; Das, K.C.; Tripathi, T.; Dubey, V.K. Design of a multi-epitope subunit vaccine for immune-protection against Leishmania parasite. Pathog. Glob. Health, 2020, 114(8), 471-481.
[http://dx.doi.org/10.1080/20477724.2020.1842976] [PMID: 33161887]
[173]
Lari, A.; Lari, N.; Biabangard, A. Immunoinformatics approach to design a novel subunit vaccine against visceral leishmaniasis. Int. J. Pept. Res. Ther., 2022, 28(1), 34.
[http://dx.doi.org/10.1007/s10989-021-10344-3] [PMID: 34931120]
[174]
Zhang, J.; Li, J.; Hu, K.; Zhou, Q.; Chen, X.; He, J.; Yin, S.; Chi, Y.; Liao, X.; Xiao, Y.; Qin, H.; Zheng, Z.; Chen, J. Screening novel vaccine candidates for Leishmania donovani by combining differential proteomics and immunoinformatics analysis. Front. Immunol., 2022, 13(June), 902066.
[http://dx.doi.org/10.3389/fimmu.2022.902066] [PMID: 35812381]
[175]
Pawar, S.; Chem, S.R. Review on discovery studio: An important tool for molecular docking. Asian J. Research Chem, 2021, 14(1), 1.
[176]
Kozakov, D.; Hall, D.; Xia, B.; Porter, K. The clusPro web server for protein-protein docking. Nat. Protoc., 2017, 12(2), 255-278.
[177]
Schneidman-Duhovny, D. PatchDock and symmdock: Servers for rigid and symmetric docking. Nucleic Acids Res., 2005, 33(Web Server issue), W363-7.
[178]
Guvench, O.; MacKerell, A.D., Jr Comparison of protein force fields for molecular dynamics simulations. Methods Mol. Biol., 2008, 443, 63-88.
[http://dx.doi.org/10.1007/978-1-59745-177-2_4] [PMID: 18446282]
[179]
Sellis, D.; Vlachakis, D.; Vlassi, M. Gromita: A fully integrated graphical user interface to gromacs 4. Bioinform. Biol. Insights, 2009, 3, BBI.S3207.
[http://dx.doi.org/10.4137/BBI.S3207] [PMID: 20140074]
[180]
Garberoglio, G. OBGMX: A web-based generator of GROMACS topologies for molecular and periodic systems using the universal force field. J. Comput. Chem., 2012, 33(27), 2204-2208.
[http://dx.doi.org/10.1002/jcc.23049]
[181]
Briones, R.; Blau, C.; Kutzner, C. GROmaρs: A GROMACS-based toolset to analyze density maps derived from molecular dynamics simulations. Biophy. J., 2019, 116(1), 4-11.
[182]
Lukat, G.; Krüger, J.; Sommer, B. APL@Voro: A Voronoi-based membrane analysis tool for GROMACS trajectories. J. Chem. Inf. Model., 2013, 53(11), 2908-2925.
[http://dx.doi.org/10.1021/ci400172g] [PMID: 24175728]
[183]
Rawat, R.; Kant, K.; Kumar, A.; Bhati, K.; Verma, S.M. HeroMDAnalysis: An automagical tool for GROMACS-based molecular dynamics simulation analysis. Future Med. Chem., 2021, 13(5), 447-456.
[http://dx.doi.org/10.4155/fmc-2020-0191] [PMID: 33496197]

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