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Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Research Article

Computer-Aided Structure Prediction of Bluetongue Virus Coat Protein VP2 Assisted by Optimized Potential for Liquid Simulations (OPLS)

Author(s): Leena Prajapati, Ravina Khandelwal, Kadapakkam Nandabalan Yogalakshmi, Anjana Munshi* and Anuraj Nayarisseri*

Volume 20, Issue 19, 2020

Page: [1720 - 1732] Pages: 13

DOI: 10.2174/1568026620666200516153753

Price: $65

Abstract

Background: The capsid coated protein of Bluetongue virus (BTV) VP2 is responsible for BTV transmission by the Culicoides vector to vertebrate hosts. Besides, VP2 is responsible for BTV entry into permissive cells and hence plays a major role in disease progression. However, its mechanism of action is still unknown.

Objective: The present investigation aimed to predict the 3D structure of Viral Protein 2 of the bluetongue virus assisted by Optimized Potential for Liquid Simulations (OPLS), structure validation, and an active site prediction.

Methods: The 3D structure of the VP2 protein was built using a Python-based Computational algorithm. The templates were identified using Smith waterman’s Local alignment. The VP2 protein structure validated using PROCHECK. Molecular Dynamics Simulation (MDS) studies were performed using an academic software Desmond, Schrodinger dynamics, for determining the stability of a model protein. The Ligand-Binding site was predicted by structure comparison using homology search and proteinprotein network analysis to reveal their stability and inhibition mechanism, followed by the active site identification.

Results: The secondary structure of the VP2 reveals that the protein contains 220 alpha helix atoms, 40 310 helix, 151 beta sheets, 134 coils and 424 turns, whereas the 3D structure of Viral Protein 2 of BTV has been found to have 15774 total atoms in the structure. However, 961 amino acids were found in the final model. The dynamical cross-correlation matrix (DCCM) analysis tool identifies putative protein domains and also confirms the stability of the predicted model and their dynamical behavior difference with the correlative fluctuations in motion.

Conclusion: The biological interpretation of the Viral Protein 2 was carried out. DCCM maps were calculated, using a different coordinate reference frame, through which, protein domain boundaries and protein domain residue constituents were identified. The obtained model shows good reliability. Moreover, we anticipated that this research should play a promising role in the identification of novel candidates with the target protein to inhibit their functional significance.

Keywords: Virus coat protein, VP2, Bluetongue virus, Protein Modeling, Threading, Homology Modeling, MD simulation.

Graphical Abstract
[1]
Cox, H.R. Bluetongue. Bacteriol. Rev., 1954, 18(4), 239-253. [http://dx.doi.org/10.1128/MMBR.18.4.239-253.1954]. [PMID: 13219048].
[2]
Meiswinkel, R.; van Rijn, P.; Leijs, P.; Goffredo, M. Potential new Culicoides vector of bluetongue virus in northern Europe. Vet. Rec., 2007, 161(16), 564-565. [http://dx.doi.org/10.1136/vr.161.16.564]. [PMID: 17951565].
[3]
Tabachnick, W.J. Culicoides and the global epidemiology of bluetongue virus infection. Vet. Ital., 2004, 40(3), 144-150. [PMID: 20419653].
[4]
Meiswinkel, R.; Paweska, J.T. Evidence for a new field Culicoides vector of African horse sickness in South Africa. Prev. Vet. Med., 2003, 60(3), 243-253. [http://dx.doi.org/10.1016/S0167-5877(02)00231-3]. [PMID: 12900162].
[5]
Gubbins, S.; Carpenter, S.; Baylis, M.; Wood, J. L.; Mellor, P. S. Assessing the risk of bluetongue to UK livestock: uncertainty and sensitivity analyses of a temperature-dependent model for the basic reproduction number. J. R. Soc. Interface, 2008, 5(20), 363–371.
[6]
Cebra, C.; Cebra, M. Diseases of the hematologic, immunologic, and lymphatic systems (multisystem diseases). in:Sheep and goat medicine; WB Saunders: Philadelphia, 2012, 405–438, . [http://dx.doi.org/10.1016/B978-1-4377-2353-3.10016-2]
[7]
Matsuo, E.; Celma, C.C.; Boyce, M.; Viarouge, C.; Sailleau, C.; Dubois, E.; Bréard, E.; Thiéry, R.; Zientara, S.; Roy, P. Generation of replication-defective virus-based vaccines that confer full protection in sheep against virulent bluetongue virus challenge. J. Virol., 2011, 85(19), 10213-10221. [http://dx.doi.org/10.1128/JVI.05412-11]. [PMID: 21795358].
[8]
He, C.Q.; Ding, N.Z.; He, M.; Li, S.N.; Wang, X.M.; He, H.B.; Liu, X.F.; Guo, H.S. Intragenic recombination as a mechanism of genetic diversity in bluetongue virus. J. Virol., 2010, 84(21), 11487-11495. [http://dx.doi.org/10.1128/JVI.00889-10]. [PMID: 20702614].
[9]
Hassan, S.H.; Wirblich, C.; Forzan, M.; Roy, P. Expression and functional characterization of bluetongue virus VP5 protein: role in cellular permeabilization. J. Virol., 2001, 75(18), 8356-8367. [http://dx.doi.org/10.1128/JVI.75.18.8356-8367.2001]. [PMID: 11507181].
[10]
Mertens, P.P.C.; Burroughs, J.N.; Anderson, J. Purification and properties of virus particles, infectious subviral particles, and cores of bluetongue virus serotypes 1 and 4. Virology, 1987, 157(2), 375-386. [http://dx.doi.org/10.1016/0042-6822(87)90280-7]. [PMID: 3029978].
[11]
Mertens, P.P.C.; Burroughs, J.N.; Walton, A.; Wellby, M.P.; Fu, H.; O’Hara, R.S.; Brookes, S.M.; Mellor, P.S. Enhanced infectivity of modified bluetongue virus particles for two insect cell lines and for two Culicoides vector species. Virology, 1996, 217(2), 582-593. [http://dx.doi.org/10.1006/viro.1996.0153]. [PMID: 8610450].
[12]
Urakawa, T.; Ritter, D.G.; Roy, P. Expression of largest RNA segment and synthesis of VP1 protein of bluetongue virus in insect cells by recombinant baculovirus: association of VP1 protein with RNA polymerase activity. Nucleic Acids Res., 1989, 17(18), 7395-7401. [http://dx.doi.org/10.1093/nar/17.18.7395]. [PMID: 2552409].
[13]
Stäuber, N.; Martinez-Costas, J.; Sutton, G.; Monastyrskaya, K.; Roy, P. Bluetongue virus VP6 protein binds ATP and exhibits an RNA-dependent ATPase function and a helicase activity that catalyze the unwinding of double-stranded RNA substrates. J. Virol., 1997, 71(10), 7220-7226. [http://dx.doi.org/10.1128/JVI.71.10.7220-7226.1997]. [PMID: 9311795].
[14]
Monaco, F.; Cammà, C.; Serini, S.; Savini, G. Differentiation between field and vaccine strain of bluetongue virus serotype 16. Vet. Microbiol., 2006, 116(1-3), 45-52. [http://dx.doi.org/10.1016/j.vetmic.2006.03.024]. [PMID: 16713688].
[15]
Hewat, E.A.; Booth, T.F.; Roy, P. Structure of bluetongue virus particles by cryoelectron microscopy. J. Struct. Biol., 1992, 109(1), 61-69. [http://dx.doi.org/10.1016/1047-8477(92)90068-L]. [PMID: 1337461].
[16]
Zhang, X.; Boyce, M.; Bhattacharya, B.; Zhang, X.; Schein, S.; Roy, P.; Zhou, Z.H. Bluetongue virus coat protein VP2 contains sialic acid-binding domains, and VP5 resembles enveloped virus fusion proteins. Proc. Natl. Acad. Sci. USA, 2010, 107(14), 6292-6297. [http://dx.doi.org/10.1073/pnas.0913403107] [PMID: 20332209].
[17]
Zhang, X.; Patel, A.; Celma, C.C.; Yu, X.; Roy, P.; Zhou, Z.H. Atomic model of a nonenveloped virus reveals pH sensors for a coordinated process of cell entry. Nat. Struct. Mol. Biol., 2016, 23(1), 74-80. [http://dx.doi.org/10.1038/nsmb.3134]. [PMID: 26641711].
[18]
Ebert, D.H.; Deussing, J.; Peters, C.; Dermody, T.S. Cathepsin L and cathepsin B mediate reovirus disassembly in murine fibroblast cells. J. Biol. Chem., 2002, 277(27), 24609-24617. [http://dx.doi.org/10.1074/jbc.M201107200]. [PMID: 11986312].
[19]
Limaye, A.; Sweta, J.; Madhavi, M.; Mudgal, U.; Mukherjee, S.; Sharma, S.; Hussain, T.; Nayarisseri, A.; Singh, S.K. In silico insights on GD2: A potential target for pediatric neuroblastoma. Curr. Top. Med. Chem., 2019, 19(30), 2766-2781. [http://dx.doi.org/10.2174/1568026619666191112115333]. [PMID: 31721713].
[20]
Nayarisseri, A. Prospects of utilizing computational techniques for the treatment of human diseases. Curr. Top. Med. Chem., 2019, 19(13), 1071-1074. [http://dx.doi.org/10.2174/156802661913190827102426]. [PMID: 31490742].
[21]
Bandaru, S.; Sumithnath, T.G.; Sharda, S.; Lakhotia, S.; Sharma, A.; Jain, A.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Helix-coil transition signatures b-raf v600e mutation and virtual screening for inhibitors directed against mutant b-raf. Curr. Drug Metab., 2017, 18(6), 527-534. [http://dx.doi.org/10.2174/1389200218666170503114611]. [PMID: 28472910].
[22]
Nasr, A.B.; Ponnala, D.; Sagurthi, S.R.; Kattamuri, R.K.; Marri, V.K.; Gudala, S.; Lakkaraju, C.; Bandaru, S.; Nayarisseri, A. Molecular Docking studies of FKBP12-mTOR inhibitors using binding predictions. Bioinformation, 2015, 11(6), 307-315. [http://dx.doi.org/10.6026/97320630011307]. [PMID: 26229292].
[23]
Dunna, N.R.; Kandula, V.; Girdhar, A.; Pudutha, A.; Hussain, T.; Bandaru, S.; Nayarisseri, A. High affinity pharmacological profiling of dual inhibitors targeting RET and VEGFR2 in inhibition of kinase and angiogeneis events in medullary thyroid carcinoma. Asian Pac. J. Cancer Prev., 2015, 16(16), 7089-7095. [http://dx.doi.org/10.7314/APJCP.2015.16.16.7089]. [PMID: 26514495].
[24]
Sinha, C.; Nischal, A.; Bandaru, S.; Kasera, P.; Rajput, A.; Nayarisseri, A.; Khattri, S. An in silico approach for identification of novel inhibitors as a potential therapeutics targeting HIV-1 viral infectivity factor. Curr. Top. Med. Chem., 2015, 15(1), 65-72. [http://dx.doi.org/10.2174/1568026615666150112114337]. [PMID: 25579575].
[25]
Sinha, C.; Nischal, A.; Pant, K.K.; Bandaru, S.; Nayarisseri, A.; Khattri, S. Molecular docking analysis of RN18 and VEC5 in A3G-Vif inhibition. Bioinformation, 2014, 10(10), 611-616. [http://dx.doi.org/10.6026/97320630010611]. [PMID: 25489169].
[26]
Bandaru, S.; Marri, V.K.; Kasera, P.; Kovuri, P.; Girdhar, A.; Mittal, D.R.; Ikram, S.; Gv, R.; Nayarisseri, A. Structure based virtual screening of ligands to identify cysteinyl leukotriene receptor 1 antagonist. Bioinformation, 2014, 10(10), 652-657. [http://dx.doi.org/10.6026/97320630010652]. [PMID: 25489175].
[27]
Dunna, N.R.; Bandaru, S.; Akare, U.R.; Rajadhyax, S.; Gutlapalli, V.R.; Yadav, M.; Nayarisseri, A. Multiclass comparative virtual screening to identify novel Hsp90 inhibitors: a therapeutic breast cancer drug target. Curr. Top. Med. Chem., 2015, 15(1), 57-64. [http://dx.doi.org/10.2174/1568026615666150112113627]. [PMID: 25579569].
[28]
Bandaru, S.; Ponnala, D.; Lakkaraju, C.; Bhukya, C.K.; Shaheen, U.; Nayarisseri, A. Identification of high affinity non-peptidic small molecule inhibitors of MDM2-p53 interactions through structure-based virtual screening strategies. Asian Pac. J. Cancer Prev., 2015, 16(9), 3759-3765. [http://dx.doi.org/10.7314/APJCP.2015.16.9.3759]. [PMID: 25987034].
[29]
Akare, U.R.; Bandaru, S.; Shaheen, U.; Singh, P.K.; Tiwari, G.; Singare, P.; Nayarisseri, A.; Banerjee, T. Molecular docking approaches in identification of High affinity inhibitors of Human SMO receptor. Bioinformation, 2014, 10(12), 737-742. [http://dx.doi.org/10.6026/97320630010737]. [PMID: 25670876].
[30]
Bandaru, S.; Alvala, M.; Akka, J.; Sagurthi, S.R.; Nayarisseri, A.; Singh, S.K.; Mundluru, H.P. Identification of small molecule as a high affinity β2 agonist promiscuously targeting wild and mutated (Thr164Ile) β 2 adrenergic receptor in the treatment of bronchial asthma. Curr. Pharm. Des., 2016, 22(34), 5221-5233. [http://dx.doi.org/10.2174/1381612822666160513145721]. [PMID: 27174812].
[31]
Ali, M.A.; Vuree, S.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Identification of high-affinity small molecules targeting gamma secretase for the treatment of Alzheimer’s disease. Curr. Top. Med. Chem., 2019, 19(13), 1173-1187. [http://dx.doi.org/10.2174/1568026619666190617155326]. [PMID: 31244427].
[32]
Pyde, A.N.; Rao, P.N.; Jain, A.; Soni, D.; Saket, S.; Ahmed, S.; Vuree, S.; Nayarisseri, A. Identification and characterization of foodborne pathogen Listeria monocytogenes strain Pyde1 and Pyde2 using 16S rRNA gene sequencing. J. Pharm. Res., 2013, 6(7), 736-741. [http://dx.doi.org/10.1016/j.jopr.2013.07.009].
[33]
Nayarisseri, A.; Yadav, M.; Bhatia, M.; Pandey, A.; Elkunchwar, A.; Paul, N.; Sharma, D.; Kumar, G. Impact of Next-Generation Whole-Exome sequencing in molecular diagnostics. Drug Invention Today, 2013, 5(4), 327-334. [http://dx.doi.org/10.1016/j.dit.2013.07.005].
[34]
Wishard, R.; Jaiswal, M.; Parveda, M.; Amareshwari, P.; Bhadoriya, S.S.; Rathore, P.; Yadav, M.; Nayarisseri, A.; Nair, A.S. Identification and characterization of alkaline protease producing Bacillus firmus species EMBS023 by 16S rRNA gene sequencing. Interdiscip. Sci., 2014, 6(4), 271-278. [http://dx.doi.org/10.1007/s12539-014-0187-z]. [PMID: 25118655].
[35]
Nayarisseri, A.; Singh, P.; Singh, S.K. Screening, isolation and characterization of biosurfactant-producing Bacillus tequilensis strain ANSKLAB04 from brackish river water. Int. J. Environ. Sci. Technol., 2019, 16(11), 7103-7112. [http://dx.doi.org/10.1007/s13762-018-2089-9].
[36]
Nayarisseri, A.; Singh, P.; Singh, S.K. Screening, isolation and characterization of biosurfactant producing Bacillus subtilis strain ANSKLAB03. Bioinformation, 2018, 14(6), 304-314. [http://dx.doi.org/10.6026/97320630014304]. [PMID: 30237676].
[37]
Amareshwari, P.; Bhatia, M.; Venkatesh, K.; Roja Rani, A.; Ravi, G.V.; Bhakt, P.; Bandaru, S.; Yadav, M.; Nayarisseri, A.; Nair, A.S. Isolation and characterization of a novel chlorpyrifos degrading flavobacterium species EMBS0145 by 16S rRNA gene sequencing. Interdiscip. Sci., 2015, 7(1), 1-6. [http://dx.doi.org/10.1007/s12539-012-0207-9]. [PMID: 25248957].
[38]
Chandok, H.; Shah, P.; Akare, U.R.; Hindala, M.; Bhadoriya, S.S.; Ravi, G.V.; Sharma, V.; Bandaru, S.; Rathore, P.; Nayarisseri, A. Screening, isolation and identification of Probiotic producing lactobacillus acidophilus strains EMBS081 & EMBS082 by 16S rRNA gene sequencing. Interdiscip. Sci., 2015, 7(3), 242-248. [http://dx.doi.org/10.1007/s12539-015-0002-5]. [PMID: 26199209].
[39]
Nayarisseri, A.; Suppahia, A.; Nadh, A.G.; Nair, A.S. Identification and characterization of a pesticide degrading flavobacterium species EMBS0145 by 16S rRNA gene sequencing. Interdiscip. Sci., 2015, 7(2), 93-99. [http://dx.doi.org/10.1007/s12539-015-0016-z]. [PMID: 26202942].
[40]
Nadh, G. Identification of azo dye degrading Sphingomonas strain EMBS022 and EMBS023 using 16S rRNA gene sequencing. Curr. Bioinform., 2015, 10(5), 599-605. [http://dx.doi.org/10.2174/1574893610666151008012312].
[41]
Bandaru, S.; Prasad, M.H.; Jyothy, A.; Nayarisseri, A.; Yadav, M. Binding modes and pharmacophoric features of muscarinic antagonism and β2 agonism (MABA) conjugates. Curr. Top. Med. Chem., 2013, 13(14), 1650-1655. [http://dx.doi.org/10.2174/15680266113139990115]. [PMID: 23889054].
[42]
Nayarisseri, A.; Moghni, S.M.; Yadav, M.; Kharate, J.; Sharma, P.; Chandok, K.H.; Shah, K.P. In silico investigations on HSP90 and its inhibition for the therapeutic prevention of breast cancer. J. Pharm. Res., 2013, 7(2), 150-156. [http://dx.doi.org/10.1016/j.jopr.2013.02.020].
[43]
Shaheen, U.; Akka, J.; Hinore, J.S.; Girdhar, A.; Bandaru, S.; Sumithnath, T.G.; Nayarisseri, A.; Munshi, A. Computer aided identification of sodium channel blockers in the clinical treatment of epilepsy using molecular docking tools. Bioinformation, 2015, 11(3), 131-137. [http://dx.doi.org/10.6026/97320630011131]. [PMID: 25914447].
[44]
Gudala, S.; Khan, U.; Kanungo, N.; Bandaru, S.; Hussain, T.; Parihar, M.; Nayarisseri, A.; Mundluru, H.P. Identification and pharmacological analysis of high efficacy small molecule inhibitors of EGF-EGFR interactions in clinical treatment of non-small cell lung carcinoma: A computational approach. Asian Pac. J. Cancer Prev., 2015, 16(18), 8191-8196. [http://dx.doi.org/10.7314/APJCP.2015.16.18.8191]. [PMID: 26745059].
[45]
Sinha, K.; Majhi, M.; Thakur, G.; Patidar, K.; Sweta, J.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Computer-aided drug designing for the identification of high-affinity small molecule targeting cd20 for the clinical treatment of chronic lymphocytic leukemia (CLL). Curr. Top. Med. Chem., 2018, 18(29), 2527-2542. [http://dx.doi.org/10.2174/1568026619666181210150044]. [PMID: 30526461].
[46]
Babitha, P.P.; Sahila, M.M.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Molecular docking and pharmacological investigations of rivastigmine-fluoxetine and coumarin-tacrine hybrids against acetyl choline esterase. Bioinformation, 2015, 11(8), 378-386. [http://dx.doi.org/10.6026/97320630011378]. [PMID: 26420918].
[47]
Natchimuthu, V.; Bandaru, S.; Nayarisseri, A.; Ravi, S. Design, synthesis and computational evaluation of a novel intermediate salt of N-cyclohexyl-N-(cyclohexylcarbamoyl)-4-(trifluoromethyl) ben-zamide as potential potassium channel blocker in epileptic paroxysmal seizures. Comput. Biol. Chem., 2016, 64, 64-73. [http://dx.doi.org/10.1016/j.compbiolchem.2016.05.003]. [PMID: 27266485].
[48]
Patidar, K.; Deshmukh, A.; Bandaru, S.; Lakkaraju, C.; Girdhar, A.; Vr, G.; Banerjee, T.; Nayarisseri, A.; Singh, S.K. Virtual screening approaches in identification of bioactive compounds akin to delphinidin as potential HER2 inhibitors for the treatment of breast cancer. Asian Pac. J. Cancer Prev., 2016, 17(4), 2291-2295. [http://dx.doi.org/10.7314/APJCP.2016.17.4.2291]. [PMID: 27221932].
[49]
Sahila, M.M.; Babitha, P.P.; Bandaru, S.; Nayarisseri, A.; Doss, V.A. Molecular docking based screening of GABA (A) receptor inhibitors from plant derivatives. Bioinformation, 2015, 11(6), 280-289. [http://dx.doi.org/10.6026/97320630011280]. [PMID: 26229288].
[50]
Bandaru, S.; Tarigopula, P.; Akka, J.; Marri, V.K.; Kattamuri, R.K.; Nayarisseri, A.; Mangalarapu, M.; Vinukonda, S.; Mundluru, H.P.; Sagurthi, S.R. Association of Beta 2 adrenergic receptor (Thr164Ile) polymorphism with Salbutamol refractoriness in severe asthmatics from Indian population. Gene, 2016, 592(1), 15-22. [http://dx.doi.org/10.1016/j.gene.2016.07.043]. [PMID: 27450915].
[51]
Khandekar, N.; Singh, S.; Shukla, R.; Tirumalaraju, S.; Bandaru, S.; Banerjee, T.; Nayarisseri, A. Structural basis for the in vitro known acyl-depsipeptide 2 (ADEP2) inhibition to Clp 2 protease from Mycobacterium tuberculosis. Bioinformation, 2016, 12(3), 92-97. [http://dx.doi.org/10.6026/97320630012092]. [PMID: 28149041].
[52]
Bandaru, S.; Alvala, M.; Nayarisseri, A.; Sharda, S.; Goud, H.; Mundluru, H.P.; Singh, S.K. Molecular dynamic simulations reveal suboptimal binding of salbutamol in T164I variant of β2 adrenergic receptor. PLoS One, 2017, 12(10)e0186666 [http://dx.doi.org/10.1371/journal.pone.0186666]. [PMID: 29053759].
[53]
Sharda, S.; Sarmandal, P.; Cherukommu, S.; Dindhoria, K.; Yadav, M.; Bandaru, S.; Sharma, A.; Sakhi, A.; Vyas, T.; Hussain, T.; Nayarisseri, A.; Singh, S.K. A virtual screening approach for the identification of high affinity small molecules targeting bcr-abl1 inhibitors for the treatment of chronic myeloid leukemia. Curr. Top. Med. Chem., 2017, 17(26), 2989-2996. [http://dx.doi.org/10.2174/1568026617666170821124512]. [PMID: 28828991].
[54]
Jain, D.; Udhwani, T.; Sharma, S.; Gandhe, A.; Reddy, P.B.; Nayarisseri, A.; Singh, S.K. Design of novel JAK3 inhibitors towards rheumatoid arthritis using molecular docking analysis. Bioinformation, 2019, 15(2), 68-78. [http://dx.doi.org/10.6026/97320630015068]. [PMID: 31435152].
[55]
Mendonça-Junior, F.J.B.; Scotti, M.T.; Nayarisseri, A.; Zondegoumba, E.N.T.; Scotti, L. Natural bioactive products with antioxidant properties useful in neurodegenerative diseases. Oxid. Med. Cell. Longev., 2019, 20197151780 [http://dx.doi.org/10.1155/2019/7151780]. [PMID: 31210847].
[56]
Nayarisseri, A.; Hood, E.A. Advancement in microbial cheminformatics. Curr. Top. Med. Chem., 2018, 18(29), 2459-2461. [http://dx.doi.org/10.2174/1568026619666181120121528]. [PMID: 30457050].
[57]
Gokhale, P.; Chauhan, A.P.S.; Arora, A.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. Bioinformation, 2019, 15(2), 104-115. [http://dx.doi.org/10.6026/97320630015104]. [PMID: 31435156].
[58]
Shukla, P.; Khandelwal, R.; Sharma, D.; Dhar, A.; Nayarisseri, A.; Singh, S.K. Virtual screening of il-6 inhibitors for idiopathic arthritis. Bioinformation, 2019, 15(2), 121-130. [http://dx.doi.org/10.6026/97320630015121]. [PMID: 31435158].
[59]
Udhwani, T.; Mukherjee, S.; Sharma, K.; Sweta, J.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. Design of PD-L1 inhibitors for lung cancer. Bioinformation, 2019, 15(2), 139-150. [http://dx.doi.org/10.6026/97320630015139]. [PMID: 31435160].
[60]
Rao, D.M.; Nayarisseri, A.; Yadav, M.; Patel, D. Comparative modeling of methylentetrahydrofolate reductase (MTHFR) enzyme and its mutational assessment: in silico approach. Int. J. Bioinformatics Res., 2010, 2(1), 5-9. [http://dx.doi.org/10.9735/0975-3087.2.1.5-9].
[61]
Kelotra, S.; Jain, M.; Kelotra, A.; Jain, I.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. An in silico appraisal to identify high affinity anti-apoptotic synthetic tetrapeptide inhibitors targeting the mammalian caspase 3 enzyme. Asian Pac. J. Cancer Prev., 2014, 15(23), 10137-10142. [http://dx.doi.org/10.7314/APJCP.2014.15.23.10137]. [PMID: 25556438].
[62]
Sweta, J.; Khandelwal, R.; Srinitha, S.; Pancholi, R.; Adhikary, R.; Ali, M.A.; Nayarisseri, A.; Vuree, S.; Singh, S.K. Identification of high-affinity small molecule targeting idh2 for the clinical treatment of acute myeloid leukemia. Asian Pac. J. Cancer Prev., 2019, 20(8), 2287-2297. [http://dx.doi.org/10.31557/APJCP.2019.20.8.2287]. [PMID: 31450897].
[63]
Gutlapalli, V.R.; Sykam, A.; Nayarisseri, A.; Suneetha, S.; Suneetha, L.M. Insights from the predicted epitope similarity between Mycobacterium tuberculosis virulent factors and its human homologs. Bioinformation, 2015, 11(12), 517-524. [http://dx.doi.org/10.6026/97320630011517]. [PMID: 26770024].
[64]
Nayarisseri, A.; Yadav, M.; Wishard, R. Computational evaluation of new homologous down regulators of Translationally Controlled Tumor Protein (TCTP) targeted for tumor reversion. Interdiscip. Sci., 2013, 5(4), 274-279. [http://dx.doi.org/10.1007/s12539-013-0183-8]. [PMID: 24402820].
[65]
Praseetha, S.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Pharmacological analysis of vorinostat analogues as potential anti-tumor agents targeting human histone deacetylases: an epigenetic treatment stratagem for cancers. Asian Pac. J. Cancer Prev., 2016, 17(3), 1571-1576. [http://dx.doi.org/10.7314/APJCP.2016.17.3.1571]. [PMID: 27039807].
[66]
Majhi, M.; Ali, M.A.; Limaye, A.; Sinha, K.; Bairagi, P.; Chouksey, M.; Shukla, R.; Kanwar, N.; Hussain, T.; Nayarisseri, A.; Singh, S.K. An in silico investigation of potential egfr inhibitors for the clinical treatment of colorectal cancer. Curr. Top. Med. Chem., 2018, 18(27), 2355-2366. [http://dx.doi.org/10.2174/1568026619666181129144107]. [PMID: 30499396].
[67]
Sharma, K.; Patidar, K.; Ali, M.A.; Patil, P.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening for the identification of high affinity compounds as potent vegfr2 inhibitors for the treatment of renal cell carcinoma. Curr. Top. Med. Chem., 2018, 18(25), 2174-2185. [http://dx.doi.org/10.2174/1568026619666181130142237]. [PMID: 30499413].
[68]
Shameer, K.; Nayarisseri, A.; Romero Duran, F.X.; González-Díaz, H. Improving neuropharmacology using big data, machine learning and computational algorithms. Curr. Neuropharmacol., 2017, 15(8), 1058-1061. [http://dx.doi.org/10.2174/1570159X1508171114113425]. [PMID: 29199918].
[69]
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial (Thematic issue: chemoinformatics models for pharmaceutical design, part 2). Curr. Pharm. Des., 2016, 22(34), 5177-5178. [http://dx.doi.org/10.2174/138161282234161110222751]. [PMID: 27852211].
[70]
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial (Thematic Issue: chemoinformatics models for pharmaceutical design, part 1). Curr. Pharm. Des., 2016, 22(33), 5041-5042. [http://dx.doi.org/10.2174/138161282233161109224932]. [PMID: 27852204].
[71]
Kelotra, A.; Gokhale, S.M.; Kelotra, S.; Mukadam, V.; Nagwanshi, K.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. Alkyloxy carbonyl modified hexapeptides as a high affinity compounds for Wnt5A protein in the treatment of psoriasis. Bioinformation, 2014, 10(12), 743-749. [http://dx.doi.org/10.6026/97320630010743]. [PMID: 25670877].
[72]
Chandrakar, B.; Jain, A.; Roy, S.; Gutlapalli, V.R.; Saraf, S.; Suppahia, A.; Verma, A.; Tiwari, A.; Yadav, M.; Nayarisseri, A. Molecular modeling of Acetyl-CoA carboxylase (ACC) fromJatropha curcas and virtual screening for identification of inhibitors journal of pharmacy research 2013, 6(9), 913-918.
[73]
Khandelwal, R.; Chauhan, A.P.S.; Bilawat, S.; Gandhe, A.; Hussain, T.; Hood, E.A.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening for the identification of high affinity small molecule towards STAT3 for the clinical treatment of Osteosarcoma. Curr. Top. Med. Chem., 2018, 18(29), 2511-2526. [http://dx.doi.org/10.2174/1568026618666181115092001]. [PMID: 30430945].
[74]
Nayarisseri, A.; Singh, S.K. Functional inhibition of VEGF and EGFR suppressors in cancer treatment. Curr. Top. Med. Chem., 2019, 19(3), 178-179. [http://dx.doi.org/10.2174/156802661903190328155731]. [PMID: 30950335].
[75]
Monteiro, A.F.M.; Viana, J.O.; Nayarisseri, A.; Zondegoumba, E.N.; Mendonça Junior, F.J.B.; Scotti, M.T.; Scotti, L. Computational studies applied to flavonoids against alzheimer’s and parkinson’s diseases. Oxid. Med. Cell. Longev., 2018, 2018 7912765. [http://dx.doi.org/10.1155/2018/7912765][PMID: 30693065].
[76]
Patidar, K.; Panwar, U.; Vuree, S.; Sweta, J.; Sandhu, M.K.; Nayarisseri, A.; Singh, S.K. An in silico approach to identify high affinity small molecule targeting m-tor inhibitors for the clinical treatment of breast cancer. Asian Pac. J. Cancer Prev., 2019, 20(4), 1229-1241. [http://dx.doi.org/10.31557/APJCP.2019.20.4.1229]. [PMID: 31030499].
[77]
Sharda, S.; Khandelwal, R.; Adhikary, R.; Sharma, D.; Majhi, M.; Hussain, T. A computer-aided drug designing for pharmacological inhibition of ALK inhibitors induces apoptosis and differentiation in Non-small cell lung cancer. curr. top. med. chem, 2019, 19(13), 1129-1144. [http://dx.doi.org/10.2174/1568026619666190521084941] [PMID:31109278].

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