Bioinformatics and Computational Biology Research at the Computer Science Department at UFMG

Authors

DOI:

https://doi.org/10.5753/jidm.2024.2661

Keywords:

Artificial Intelligence, Computational Biology, Data Mining, Data Visualization, Machine Learning, Research Group, Structural Bioinformatics

Abstract

Bioinformatics is an emerging research field that encompasses the use of computational methods, algorithms, and tools to solve life science problems. At the Laboratory of Bioinformatics and Systems (LBS), our research lines include the use of graph-based algorithms to improve the prediction of the structure and function of macromolecules, the detection of molecular recognition patterns, the application of mathematical models and artificial intelligence techniques to assist enzyme engineering, and development of models, algorithms, and tools. Additionally, the group has played a role in scientific outreach and spreading bioinformatics in Brazil. In this article, we summarize the 20 years of Bioinformatics and Computational Biology research conducted by our group at LBS in the Department of Computer Science at the Universidade Federal de Minas Gerais (DCC-UFMG).

Downloads

Download data is not yet available.

References

Barroso, J. R. M., Mariano, D., Dias, S. R., Rocha, R. E., Santos, L. H., Nagem, R. A., and de Melo-Minardi, R. C. (2020). Proteus: an algorithm for proposing stabilizing mutation pairs based on interactions observed in known protein 3d structures. BMC Bioinformatics, 21(1):1–21.

Bastard, K., Smith, A. T., Vergne-Vaxelaire, C., Perret, A., Zaparucha, A., de Melo-Minardi, R., Mariage, A., Boutard, M., Debard, A., Lechaplais, C., et al. (2014). Revealing the hidden functional diversity of an enzyme family. Nature Chemical Biology, 10(1):42–49.

Boari de Lima, E., Meira, W., and de Melo-Minardi, R. C. (2016). Isofunctional protein subfamily detection using data integration and spectral clustering. PLoS Computational biology, 12(6):e1005001.

Costa, L. S. C., Mariano, D. C. B., Rocha, R. E. O., Kraml, J., Silveira, C. H. d., Liedl, K. R., de Melo-Minardi, R. C., and Lima, L. H. F. d. (2019). Molecular dynamics gives new insights into the glucose tolerance and inhibition mechanisms on β-glucosidases. Molecules, 24(18):3215.

de Giuseppe, P. O., Souza, T. d. A., Souza, F. H. M., Zanphorlin, L. M., Machado, C. B., Ward, R. J., Jorge, J. A., Furriel, R. d. P. M., and Murakami, M. T. (2014). Structural basis for glucose tolerance in gh1 β-glucosidases. Acta Crystallographica Section D: Biological Crystallography, 70(6):1631–1639.

de Melo, R., Lopes, C., Jr., F. F., da Silveira, C., Santoro, M., Carceroni, R., Jr., W. M., and Araújo, A. (2006). A contact map matching approach to protein structure similarity analysis. Genet. Mol. Res., 5(2):284–308.

de Melo, R., Ribeiro, C., Murray, C., Veloso, C., da Silveira, S., Neshich, G., Jr., W. M., Carceroni, R., and Santoro, M. (2007). Finding protein-protein interaction patterns by contact map matching. Genet. Mol. Res., 6:1–10.

de Melo-Minardi, R. C. and Bastos, L. L. (2021). Ex-pandindo as paredes da sala de aula: aprendizados com o ensino a distância e ensino remoto emergencial. Revista da Universidade Federal de Minas Gerais, 28(1):106–125.

Dos Santos, V. P., Rodrigues, A., Dutra, G., Bastos, L., Mariano, D., Mendonça, J. G., Lobo, Y. J. G., Mendes, E., Maia, G., dos Santos Machado, K., et al. (2022). Evolve: understanding the impact of mutations in sars-cov-2 variants spike protein on antibodies and ace2 affinity through patterns of chemical interactions at protein interfaces. PeerJ, 10:e13099.

Fassio, A. V., Martins, P. M., Guimarães, S. d. S., Junior, S. S., Ribeiro, V. S., de Melo-Minardi, R. C., and Silveira, S. d. A. (2017). Vermont: a multi-perspective visual interactive platform for mutational analysis. BMC Bioinformatics, 18(10):403.

Fassio, A. V., Santos, L. H., Silveira, S. A., Ferreira, R. S., and de Melo-Minardi, R. C. (2019). nAPOLI: a graph-based strategy to detect and visualize conserved proteinligand interactions in large-scale. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17:1317–1328.

Gadelha Campelo, J. A. F., Rodrigues Monteiro, C., da Silveira, C. H., de Azevedo Silveira, S., and Cardoso de Melo-Minardi, R. (2019). Protein structural signatures revisited: Geometric linearity of main chains are more relevant to classification performance than packing of residues. In Rojas, I., Valenzuela, O., Rojas, F., and Ortuño, F., editors, Bioinformatics and Biomedical Engineering, pages 391–402, Cham. Springer International Publishing.

Goncalves, W. R., Goncalves-Almeida, V. M., Arruda, A. L., Meira Jr, W., da Silveira, C. H., Pires, D. E., and de Melo-Minardi, R. C. (2015). Pdbest: a user–friendly platform for manipulating and enhancing protein structures. Bioinformatics, 31(17):2894–2896.

Liborio, L. and Resende, V. (2021). Revista Brasileira de Bioinformática, volume 1 of 1, chapter Introdução aos bancos de dados biológicos. Alfahelix, Lagoa Santa, first edition.

Lima, L. H. F. d., Fernandez-Quintéro, M. L., Rocha, R. E. O., Mariano, D. C. B., de Melo-Minardi, R. C., and Liedl, K. R. (2021). Conformational flexibility correlates with glucose tolerance for point mutations in β-glucosidases–a computational study. Journal of Biomolecular Structure and Dynamics, 39(5):1621–1634.

Mariano, D., Da Fonseca Júnior, N. J., Santos, L. H., and Minardi, R. C. d. M. (2023). Bioinformatics in the age of data science: algorithms, methods, and tools applied from omics to structural data. Frontiers in Bioinformatics, 3:1246859.

Mariano, D. and de Melo Minardi, R. C. (2016). Introdução à Programação Para Bioinformática Com Perl, volume 1 of 1. CreateSpace Independent Publishing Platform, first edition.

Mariano, D. and de Melo Minardi, R. C. (2017). Introdução à Programação Web para Bioinformática: HTML, CSS, PHP and JavaScript., volume 1 of 1. CreateSpace Independent Publishing Platform, first edition.

Mariano, D. and et al (2015). Introdução à Programação para Bioinformática com Biopython., volume 1 of 1. CreateSpace Independent Publishing Platform, first edition.

Mariano, D., Ferreira, M., Sousa, B. L., Santos, L. H., and de Melo-Minardi, R. C. (2020a). A brief history of bioinformatics told by data visualization. In Advances in Bioinformatics and Computational Biology: 13th Brazilian Symposium on Bioinformatics, BSB 2020, São Paulo, Brazil, November 23–27, 2020, Proceedings 13, pages 235–246. Springer.

Mariano, D., Leite, C., Santos, L., Marins, L., Machado, K., Werhli, A., Lima, L., and de Melo-Minardi, R. (2017a). Characterization of glucose-tolerant β-glucosidases used in biofuel production under the bioinformatics perspective: A systematic review. Genet Mol Res, 16(3):10–4238.

Mariano, D., Martins, P., Santos, L., and de Melo-Minardi, R. C. (2019a). Introducing programming skills for life science students. Biochemistry and Molecular Biology Education, 0(0).

Mariano, D., Pantuza, N., Santos, L. H., Rocha, R. E., de Lima, L. H., Bleicher, L., and de Melo-Minardi, R. C. (2020b). Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases. BMC Molecular and Cell Biology, 21(1):1–15.

Mariano, D. and Santos, L. (2021). Manual de Escrita Científica: Teoria e Prática Aplicadas à Bioinformática. Alfahelix.

Mariano, D., Santos, L. H., Meleiro, L. P., Henrique, L., de Lima, F., Marins, L. F., and de Melo-Minardi, R. C. (2022). Using computers to improve biofuel production. Frontiers Young Minds. DOI: 10.3389/frym.2022.751195.

Mariano, D. C., Leite, C., Santos, L. H., Rocha, R. E., and de Melo-Minardi, R. C. (2017b). A guide to performing systematic literature reviews in bioinformatics.

Mariano, D. C. B., Santos, L. H., Machado, K. d. S., Werhli, A. V., de Lima, L. H. F., and de Melo-Minardi, R. C. (2019b). A computational method to propose mutations in enzymes based on structural signature variation (ssv). International journal of molecular sciences, 20(2):333.

Martins, P., Mariano, D., Carvalho, F. C., Bastos, L. L., Moraes, L., Paixão, V., and Cardoso de Melo-Minardi, R. (2023). Propedia v2. 3: A novel representation approach for the peptide-protein interaction database using graph-based structural signatures. Frontiers in Bioinformatics, 3:1103103. DOI: 10.3389/fbinf.2023.1103103.

Martins, P. M., Mayrink, V. D., de A. Silveira, S., da Silveira, C. H., de Lima, L. H. F., and de Melo-Minardi, R. C. (2018). How to compute protein residue contacts more accurately? In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC ’18, page 60–67, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3167132.3167136.

Martins, P. M., Santos, L. H., Mariano, D., Queiroz, F. C., Bastos, L. L., de S. Gomes, I., Fischer, P. H. C., Rocha, R. E. O., Silveira, S. A., de Lima, L. H. F., de Magalhães, M. T. Q., Oliveira, M. G. A., and de Melo-Minardi, R. C. (2021). Propedia: a database for protein–peptide identification based on a hybrid clustering algorithm. BMC Bioinformatics, 22(1). DOI: 10.1186/s12859-020-03881-z.

Medina, S. G., Fassio, A. V., de A. Silveira, S., da Silveira, C. H., and de Melo-Minardi, R. C. (2017). CALI: A Novel Visual Model for Frequent Pattern Mining in Protein-Ligand Graphs. In 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), pages 352–358. IEEE. ISSN: 2471-7819. DOI: 10.1109/BIBE.2017.00-29.

Myung, Y., Pires, D. E., and Ascher, D. B. (2022). Csmab: Graph-based antibody–antigen binding affinity prediction and docking scoring function. Bioinformatics, 38(4):1141–1143.

Paixão, V., Puelles, A., de Abreu, A., Bastos, L., dos Santos, L., Carvalho, F., Mariano, D., and de Melo Minardi, R. (2023). LBS Tech - Desenvolvimento web. LBS Tech. Alfahelix Publicações.

Paixão, V. M. and Melo-Minardi, R. C. (2022). Computational methodology for discovery of potential inhibitory peptides. In Advances in Bioinformatics and Computational Biology, pages 91–96. Springer Nature.

Pimentel, V., Mariano, D., Cantão, L., Bastos, L., Fischer, P., de Lima, L., and Fassio, A. (2021). Melo-minardi rcd (2021) vtr: A web tool for identifying analogous contacts on protein structures and their complexes. Front. Bioinformatics, 1:1–10. DOI: 10.3389/fbinf.2021.730350.

Pires, D. E. and Ascher, D. B. (2016). Csm-lig: a web server for assessing and comparing protein–small molecule affinities. Nucleic acids research, 44(W1):W557–W561.

Pires, D. E., Ascher, D. B., and Blundell, T. L. (2014). mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics, 30(3):335–342.

Pires, D. E., de Melo-Minardi, R. C., da Silveira, C. H., Campos, F. F., and Meira Jr, W. (2013). aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction. Bioinformatics, 29(7):855–861.

Pires, D. E., de Melo-Minardi, R. C., dos Santos, M. A., da Silveira, C. H., Santoro, M. M., and Meira, W. (2011). Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns. In BMC Genomics, volume 12, page S12. Springer.

Ribeiro, V. S., Santana, C. A., Fassio, A. V., Cerqueira, F. R., da Silveira, C. H., Romanelli, J. P., Patarroyo-Vargas, A., Oliveira, M. G., Gonçalves-Almeida, V., Izidoro, S. C., de Melo-Minardi, R. C., et al. (2020). visGReMLIN: graph mining-based detection and visualization of con- served motifs at 3D protein-ligand interface at the atomic level. BMC Bioinformatics, 21(2):1–12.

Rocha, R. E. O., Mariano, D. C. B., Almeida, T. S., Cor- rêaCosta, L. S., Fischer, P. H. C., Santos, L. H., Caffarena, E. R., da Silveira, C. H., Lamp, L. M., Fernandez-Quintero, M. L., et al. (2023). Thermostabilizing mechanisms of canonical single amino acid substitutions at a gh1 β-glucosidase probed by multiple md and computational approaches. Proteins: Structure, Function, and Bioinformatics, 91(2):218–236.

Rodrigues, L. M. (2017). Mutagraph: modelos e algoritmos para predição na afinidade de complexos proteicos através de Graph Kernel e métricas de redes complexes. PhD thesis, Universidade Federal de Minas Gerais.

Santana, C. A., Cerqueira, F. R., Da Silveira, C. H., Fassio, A. V., de Melo-Minardi, R. C., and Silveira, S. d. A. (2016). GReMLIN: A graph mining strategy to infer protein-ligand interaction patterns. In 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), pages 28–35, Taichung, Taiwan. IEEE, IEEE.

Santos, L. M. and Melo-Minardi, R. C. (2022). Identifying large scale conformational changes in proteins through distance maps and convolutional networks. In Advances in Bioinformatics and Computational Biology, pages 56–67. Springer Nature.

Santos, V., Martins, P., and Mariano, D. (2021). Revista Brasileira de Bioinformática, volume 1 of 1, chapter Alinhamentos estruturais: métodos de sobreposição de proteínas e outras moléculas. Alfahelix, Lagoa Santa, first edition.

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K., and Hassabis, D. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577:706–710. DOI: 10.1038/s41586-019-1923-7.

Silva, A. L. D. and et al (2021). From in-person to the online world: Insights into organizing events in bioinformatics. Frontiers in Bioinformatics, 1.

Silva, M. F., Martins, P. M., Mariano, D. C., Santos, L. H., Pastorini, I., Pantuza, N., Nobre, C. N., and de Melo-Minardi, R. C. (2019). Proteingo: motivation, user experience, and learning of molecular interactions in biological complexes. Entertainment Computing, 29:31–42.

Silveira, C., Pires, D., de Melo, R., Habesch, R., Ribeiro, C., Veloso, C., Lopes, J., Neshich, G., Meira Jr., W., and Santoro, M. (2009). Protein cutoff scanning: a comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins. Proteins, 74(3):727–743.

Silveira, S., de Melo-Minardi, R., Silveira, C., Santoro, M., and Meira Jr., W. (2014a). Enzymap: Exploiting protein annotation for modeling and predicting ec number changes in uniprot/swiss-prot. PloS One, 9(2):e89162.

Silveira, S., Rodrigues, A., de Melo-Minardi, R., da Silveira, C., and Meira Jr., W. (2012). ADVISe: Visualizing the dynamics of enzyme annotations in uniprot/swiss-prot. In Biological Data Visualization (BioVis), 2012 IEEE Symposium on, pages 49–56, Seattle. IEEE, IEEE.

Silveira, S. A., Fassio, A. V., Gonçalves-Almeida, V. M., de Lima, E. B., Barcelos, Y. T., Aburjaile, F. F., Rodrigues, L. M., Meira Jr, W., and de Melo-Minardi, R. C. (2014b). VERMONT: Visualizing mutations and their effects on protein physicochemical and topological property conservation. In BMC Proceedings, volume 8, page S4. BMC Proceedings. DOI: 10.1186/1753-6561-8-S2-S4.

Webb, B. and Sali, A. (2016). Comparative protein structure modeling using modeller. Current protocols in bioinformatics, 54:5.6.1–5.6.37.

Xavier, L., Bastos, L. L., and Santos, L. H. (2021). Revista Brasileira de Bioinformática, volume 1 of 1, chapter Modelagem computacional de proteínas. Alfahelix, Lagoa Santa, first edition.

Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., and Baker, D. (2020). Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences, 117(3):1496–1503.

Yang, Y., Zhang, X., Yin, Q., Fang, W., Fang, Z., Wang, X., Zhang, X., and Xiao, Y. (2015). A mechanism of glucose tolerance and stimulation of gh1 β-glucosidases. Scientific reports, 5(1):1–12.

Downloads

Published

2024-02-16

How to Cite

Mariano, D., Chaves Carvalho, F., Bastos, L. L., Moraes dos Santos, L., Morais Paixão, V., & C. de Melo-Minardi, R. (2024). Bioinformatics and Computational Biology Research at the Computer Science Department at UFMG. Journal of Information and Data Management, 15(1), 35–44. https://doi.org/10.5753/jidm.2024.2661

Issue

Section

Brazilian Bioinformatics Research Groups