Biophysical Chemistry of Macromolecules Research Group at the State University of Maringá
DOI:
https://doi.org/10.5753/jidm.2024.2607Keywords:
Bioinformatics, Machine learning, Molecular docking, Molecular dynamics, Virtual screeningAbstract
The interdisciplinary field of Biophysical Chemistry, which applies concepts from Physical Chemistry to describe biological phenomena, is essential for modern molecular biology advancements. This approach enables the description of biological systems in terms of their constituent parts, such as atoms and molecules, facilitating a structural understanding of their characteristics. Nonetheless, to describe such large systems, computational methods are needed. The Biophysical Chemistry of Macromolecules research group at the State University of Maringá is dedicated to investigating such systems, mainly protein-ligand complexes, through bioinformatics approaches combined with experimental techniques to validate in silico results. The main purpose of the research projects is to develop applications for drug discovery in the context of antimicrobial, antiviral, antifungal, and antihyperglycemic agents, with the aim of advancing the field of bioinformatics in Brazil.
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Seixas F. A., Silva M. R. da, Murakami M. T., Tosqui P. and Colombo M. F. The water effect on the kinetic of the bovine liver catalase. Protein & Peptide Letters 18 (9): 879–885, 2011.
Homem, D. P., Flores Jr., R., Tosqui, P., Rozada, T. C., Basso, E. A., Gasparotto Junior, A., and Seixas, F. A. V. Homology modelling of dihydrofolate reductase from T. gondii bonded to antagonists: molecular docking and molecular dynamics simulations. Molecular BioSystems 9 (6): 1308–1315, 2013.
Fan, J., Fu, A., and Zhang, L. Progress in molecular docking. Quantitative Biology 7 (2): 83–89, 2019.
Morris, G. M., Goodsell, D. S., Pique, M. E., Lindstrom, W. L., Huey, R., Forli, S., Hart, W. E., Halliday, S., Belew, R., and Olson, A. J. AutoDock Version 4.2. User guide: 1–66, 2012.
Trott, O., and Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31 (2): 455–461, 2010.
Thomsen, R., and Christensen, M. H. MolDock: A New Technique for High-Accuracy Molecular Docking. Journal of Medicinal Chemistry 49 (11): 3315–3321, 2006.
Verdonk M. L., Cole J. C., Hartshorn M. J., Murray C. W., and Taylor R. D. Improved protein-ligand docking using GOLD. Proteins 52 (4): 609–623, 2003.
Burley, S. K., Berman, H. M., Christie, C., Duarte, J. M., Feng, Z., Westbrook, J., Young, J., and Zardecki, C. RCSB Protein Data Bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education. Protein Science 27 (1): 316–330, 2018.
Phillips, J. C., Braun, R.,Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., and Schulten, K. Scalable molecular dynamics with NAMD. Journal of Computational Chemistry 26 (16): 1781–1802, 2005.
Brooks, B. R., Brooks III, C. L., Mackerell Jr., A. D., Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A. R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R. W., Post, C. B., Pu, J. Z., Schaefer, M., Tidor, B., Venable, R. M., Woodcock, H. L., Wu, X., Yang, W., York, D. M., and Karplus, M. CHARMM: The biomolecular simulation program. Journal of Computational Chemistry 30 (10): 1545–1614, 2005.
Zoete, V., Cuendet, M. A., Grosdidier, A., and Michielin, O. SwissParam: A fast force field generation tool for small organic molecules. Journal of Computational Chemistry 32 (11): 2359–2368, 2011.
Neese, F. The ORCA program system. WIREs Computational Molecular Science 2 (1): 73–78, 2012.
Duarte Junior, F. F., Bueno, P. S. A., Pedersen, S. L., Rando, F. S., Pattaro Júnior, J. R., Caligari, D., Ramos, A. C., Polizelli, L. G., Lima, A. F. dos S., Lima Neto, Q. A. de, Krude, T, Seixas, F. A. V., and Fernandez, M. A. Identification and characterization of stem-bulge RNAs in Drosophila melanogaster. RNA Biology 16 (3): 330–339, 2019.
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., and Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1-2: 19–25, 2015.
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A., and Case, D. A. Development and testing of a general amber force field. Journal of Computational Chemistry 25 (9): 1157–1174, 2004.
Genheden, S., and Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery 10 (5): 449–461, 2015.
Kato, C. G., Gonçalves, G. de A., Peralta, R. A., Seixas, F. A. V., de Sá-Nakanishi, A. B., Bracht, L., Comar, J. F., Bracht, A., and Peralta, R. M. Inhibition of α-Amylases by Condensed and Hydrolysable Tannins: Focus on Kinetics and Hypoglycemic Actions. Enzyme Research 2017: 2090, 2017.
Bueno, P. S. A., Kato-Schwartz, C. G., de Souza Lima, D., Bracht, A., Peralta, R. M., and Seixas, F. A. V. In silico evaluation of condensed and hydrolysable tannins as inhibitors of pancreatic α-amylase. Journal of Molecular Modeling 25 (9): 275, 2019.
Kato-Schwartz, C. G., Corrêa, R. C. G., de Souza Lima, D., de Sá-Nakanishi, A. B., de Almeida Gonçalves, G., Seixas, F. A. V., Haminiuk, C. W. I., Barros, L., Ferreira, I. C. F. R., Bracht, A., and Peralta, R. M. Potential anti-diabetic properties of Merlot grape pomace extract: An in vitro, in silico and in vivo study of α-amylase and α-glucosidase inhibition. Food Research International 137: 109462, 2020.
Kato-Schwartz, C. G., de Sá-Nakanishi, A. B., Guidi, A. C., Gonçalves, G. de A., Bueno, F. G., Zani, B. P. M., de Mello, J. C. P., Bueno, P. S. A., Seixas, F. A. V., Bracht, A., and Peralta, R. M. Carbohydrate digestive enzymes are inhibited by Poincianella pluviosa stem bark extract: relevance on type 2 diabetes treatment. Clinical Phytoscience 6 (1): 31, 2020.
da Silva, T. B. V., Castilho, P. A., de Sá-Nakanishi, A. B., Seixas, F. A. V., Dias, M. I., Barros, L., Ferreira, I. C. F. R., Bracht, A., and Peralta, R. M. The inhibitory action of purple tea on in vivo starch digestion compared to other Camellia sinensis teas. Food Research International 150: 110781, 2021.
Anand, P., and Singh, B. A review on cholinesterase inhibitors for Alzheimer’s disease. Archives of Pharmacal Research 36 (4): 375–399, 2013.
Cerbai, F., Giovannini, M. G., Melani, C., Enz, A., and Pepeu, G. N1phenethyl-norcymserine, a selective butyrylcholinesterase inhibitor, increases acetylcholine release in rat cerebral cortex: a comparison with donepezil and rivastigmine. European Journal Pharmacology 572 (2-3): 142–150, 2007.
Johnson, G., and Moore, S. W. Why has butyrylcholinesterase been retained? Structural and functional diversification in a duplicated gene. Neurochemistry Internacional 61 (5): 783–797, 2012.
Yamazaki, D. A. S., Rozada, A. M. F., Baréa, P., Reis, E. C., Basso, E. A., Sarragiotto, M. H., Seixas, F. A. V., and Gauze, G. F. Novel arylcarbamate-N-acylhydrazones derivatives as promising BuChE inhibitors: Design, synthesis, molecular modeling and biological evaluation. Bioorganic Medicinal Chemistry 32: 115991, 2021.
Moreira, T. F. M., Pessoa, L. G. A., Seixas, F. A. V., Ineu, R. P., Gonçalves, O. H., Leimann, F. V., and Ribeiro, R. P. Chemometric evaluation of enzymatic hydrolysis in the production of fish protein hydrolysates with acetylcholinesterase inhibitory activity. Food Chemistry 367: 130728, 2022.
Shikanai-Yasuda, M. A., Mendes, R. P., Colombo, A. L., Queiroz-Telles, F. de, Kono, A. S. G., Paniago, A. M. M., Nathan, A., Valle, A. C. F. do, Bagagli, E., Benard, G., Ferreira, M. S., Teixeira, M. de M., Silva-Vergara, M. L., Pereira, R. M., Cavalcante, R. de S., Hahn, R., Durlacher, R. R., Khoury, Z., Camargo, Z. P. de, Moretti, M. L., and Martinez, R. Brazilian guidelines for the clinical management of paracoccidioidomycosis. Revista da Sociedade Brasileira de Medicina Tropical 50 (5): 715–740, 2017.
Bueno, P. S. A., Rodrigues, F. A. V., Santos, J. L., Canduri, F., Biavatti, D. C., Pimentel, A. L., Bagatin, M. C., Kioshima, E. S., Gauze, G. de F., and Seixas, F. A. V. New inhibitors of homoserine dehydrogenase from Paracoccidioides brasiliensis presenting antifungal activity. Journal of Molecular Modeling 25 (325), 2019.
Bagatin, M. C., Rozada, A. M. F., Rodrigues, F. A. V., Bueno, P. S. A., Santos, J. L., Canduri, F., Kioshima, E. S., Seixas, F. A. V., Basso, E. A., and Gauze, G. F. New 4-methoxy-naphthalene derivatives as promisor antifungal agents for paracoccidioidomycosis treatment. Future Microbiology 14 (3): 235–245, 2019.
Rodrigues-Vendramini, F. A. V., Marschalk, C., Toplak, M., Macheroux, P., Bonfim-Mendonça, P. de S., Svidzinski, T. I. E., Seixas, F. A. V., and Kioshima, E. S. Promising New Antifungal Treatment Targeting Chorismate Synthase from Paracoccidioides brasiliensis. Antimicrobial Agents and Chemotherapy 63 (1), 2019.
Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., and Chong, J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Research 44 (D1): D1045–D1053, 2016.
Bueno, P. S. A., Rodrigues-Vendramini, F. A. V., Toplak, M., Macheroux, P., Kioshima, E. S., and Seixas, F. A. V. New inhibitors of chorismate synthase present antifungal activity against Paracoccidioides brasiliensis. Future Microbiology 14 (11): 969–980, 2019.
Abubakar, I., Lipman, M., McHugh, T. D., and Fletcher, H. Uniting to end the TB epidemic: advances in disease control from prevention to better diagnosis and treatment. BMC Medicine 14 (47), 2016. World Health Organization, World Health Organization Global Tuberculosis Report 2014. Geneva: World Health Organization, 2014. [link], 2018.
Johnsson, K., Froland, W. A., and Schultz, P. G. Overexpression, purification, and characterization of the catalase-peroxidase KatG from Mycobacterium tuberculosis. Journal of Biological Chemistry 272 (5): 2834–2840, 1997.
Singh, R., Wiseman, B., Deemagarn, T., Jha, V., Switala, J., and Loewen, P. C. Comparative study of catalase-peroxidases (KatGs). Archives of Biochemistry and Biophysics 471 (2): 207–214, 2008.
Sherman, D. R., Mdluli, K., Hickey, M. J., Arain, T. M., Morris, S. L., Barry, C. E., and Stover, C. K. Compensatory ahpC Gene Expression in Isoniazid-Resistant Mycobacterium tuberculosis. Science 272 (5268): 1641–1643, 1996.
Ng, V. H., Cox, J. S., Sousa, A. O., MacMicking, J. D., and McKinney, J. D. Role of KatG catalase-peroxidase in mycobacterial pathogenesis: countering the phagocytes oxidative burst. Molecular Microbiology 52 (5): 1291–1302, 2004.
Cardoso, R. F., Cooksey, R. C., Morlock, G. P., Barco, P., Cecon, L., Forestiero, F., Leite, C. Q., Sato, D. N., Shikama, M. de L., Mamizuka, E. M., Hirata, R. D., and Hirata, M. H. Screening and characterization of mutations in isoniazid-resistant Mycobacterium tuberculosis isolates obtained in Brazil. Antimicrobial Agents and Chemotherapy 48 (9): 3373–3381, 2004.
Pimentel, A. L., de Lima Scodro, R. B., Caleffi-Ferracioli, K. R., Siqueira, V. L., Campanerut-Sá, P. A., Lopes, L. D., de Almeida, A. L., Cardoso, R. F., and Seixas, F. A. V. Mutations in catalase-peroxidase KatG from isoniazid resistant Mycobacterium tuberculosis clinical isolates: insights from molecular dynamics simulations. Journal of Molecular Modeling 23 (4): 121, 2017.
Trevisan, D. A. C., da Silva, P. V., Farias, A. B. P., Campanerut-Sá, P. A. Z., Ribeiro, T. D. V. R., Faria, D. R., de Mendonça, P. S. B., de Mello, J. C. P., Seixas, F. A. V., and Mikcha, J. M. G. Antibacterial activity of Barbatimão (Stryphnodendron adstringens) against Staphylococcus aureus: in vitro and in silico studies. Letters in Applied Microbiology 71 (3): 259–271, 2020.
Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., and Shoichet, B. K. Relating protein pharmacology by ligand chemistry. Nature Biotechnology 25: 197–206, 2007.
Giacomina, A. M., Garcia Jr., J. B., Zonattic, W. F., Silva-Santos, M. C., Laktima, M. C., and Baruque-Ramos, J. Brazilian silk production: economic and sustainability aspects. Procedia Engineering 200: 89–95, 2017.
Ribeiro, L. de F. C., Brancalhão, R. M. C., Torquato, E. F. B., and Fernandez, M. A. Susceptibility of the Bombyx mori cardia cells to Nucleopolyhedrovirus, multiple subgroup, BmMNPV. Journal of Invertebrate Pathology 100 (3): 195–198, 2009.
Hom, L. G., and Volkman, L. E. Autographa californica M nucleopolyhedrovirus chiA is required for processing of V-CATH. Virology 277 (1): 178–183, 2000.
Bueno, P. S. A., Biavatti, D. C., Chiarello, A. S., Fassina, V. A., Fernandez, M. A., and Seixas, F. A. V. The structure of viral cathepsin from Bombyx mori Nuclear Polyhedrosis Virus as a target against grasserie: docking and molecular dynamics simulations. Journal of Biomolecular Structure and Dynamics 37 (14): 3607–3615, 2019.
Esteller, M. Non-coding RNAs in human disease. Nature Review Genetics 12: 861–874, 2011.
De Souza Lima, D., Amichi, L. J. A., Constantino, A. A., Fernandez, M. A., and Seixas, F. A. V. NCYPred: A Bidirectional LSTM Network with Attention for Y RNA and short non-coding RNA classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
Kalvari, I., Nawrocki, E. P., Ontiveros-Palacios, N., Argasinska, J., Lamkiewicz, K., Marz, M., Griffiths-Jones, S., Toffano-Nioche, C., Gautheret, D., Weinberg, Z., Rivas, E., Eddy, S. R., Finn, R. D., Bateman, A., and Petrov, A. I. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research 49 (D1): D192–D200, 2021.
Boria, I., Gruber, A. R., Tanzer, A., Bernhart, S. H., Lorenz, R., Mueller, M. M., Hofacker, I. L., and Stadler, P. F. Nematode sbRNAs: homologs of vertebrate Y RNAs. Journal of Molecular Evolution 70 (4): 346–358, 2010.
Wheeler, T. J., and Eddy, S. R. nhmmer: DNA homology search with profile HMMs. Bioinformatics 29 (19): 2487– 2489, 2013.
Langdon, W. B., Petke, J., and Lorenz, R. Evolving Better RNAfold Structure Prediction. Lecture Notes in Computer Science, vol. 10781. Springer, pp. 220–236, 2018.
Purzycka, K. J., Popenda, M., Szachniuk, M., Antczak, M., Lukasiak, P., Blazewicz, J., and Adamiak, R. W. Automated 3D RNA Structure Prediction Using the RNAComposer Method for Riboswitches. Methods in Enzymology 553: 3–34, 2015.
