Computational Biology Laboratory - Combi-Lab


  • Karina dos Santos Machado Universidade Federal do Rio Grande
  • Adriano Velasque Werhli Universidade Federal do Rio Grande



Research Group, Computational Biology, Bioinformatics, History


This article presents the Computational Biology - Combi-Lab research group at the Universidade Federal do Rio Grande (FURG) which started its activities in 2011. The main objective of the group is to bring together researchers and students who are interested in all aspects of Computational Biology. Specifically, the group aims to develop, improve and use sophisticated statistical, computational, and mathematical methods to contribute to the advancement of this research area. This article provides an overview of the Combi-Lab timeline from its founding to the actual days, highlighting various articles and discussing about the future of the group. More importantly, joint projects and collaborators are presented, and their contribution to the development of the Bioinformatics is explained. In conclusion, as we look to the past and face the challenges of the future, we hold fast to our goal of becoming a solid and leading reference in Computational Biology at our university and community, and giving back to the society the maximum that we can.


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Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., and Lindahl, E. (2015). Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1:19–25.

Agostinho, N., Machado, K. S., and Werhli, A. V. (2015). Inference of regulatory networks with a convergence improved MCMC sampler. BMC Bioinformatics, 16:306. DOI: 10.1186/s12859-015-0734-6.

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Barreto, N. M., Machado, K. S., and Werhli, A. V. (2017). Inference of regulatory networks with mcmc sampler guided by mutual information. In Proceedings of the Symposium on Applied Computing, SAC ’17, pages 18–23, New York, NY, USA. ACM. DOI: 10.1145/3019612.3022189.

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Camargo, A. D. (2017). EN-MUTATE : predição do impacto de mutações pontuais em proteínas utilizando Ensemble Learning. Master’s thesis, PPGComp, Engenharia de Computação, Universidade Federal do Rio Grande (FURG), Rio Grande, Brasil.

da Silva, R. S., Marins, L. F., Almeida, D. V., dos Santos Machado, K., and Werhli, A. V. (2019). A comparison of classifiers for predicting the class color of fluorescent proteins. Computational Biology and Chemistry, 83:107089. DOI:

do Carmo Guimarães, J. d. L., von Groll, A., Unis, G., Dalla-Costa, E. R., Rossetti, M. L. R., Vianna, J. S., Ramos, D. F., Reis, A. J., Halicki, P. C. B., Scaini, J. L. R., et al. (2021). Whole-genome sequencing as a tool for studying the microevolution of drug-resistant serial mycobacterium tuberculosis isolates. Tuberculosis, 131:102137.

Dos Santos, M. C., Scaini, J. L. R., Lopes, M. V. C., Rodrigues, B. G., Silva, N. O., Borges, C. R. L., Dos Santos, S. C., dos Santos Machado, K., Werhli, A. V., da Silva, P. E. A., et al. (2021). Mefloquine synergism with antituberculosis drugs and correlation to membrane effects: Biologic, spectroscopic and molecular dynamics simulations studies. Bioorganic Chemistry, 110:104786.

dos Santos Machado, K. and Grudinin, S. (2020). On recent methods for incorporating receptor flexibility in molecular docking. In Proceedings of the XXI Congrès do Groupe de Graphisme et Modélisation Moléculaire (GGMM),, pages 65–65. ACM.

Durruthy, M. G., Monserrat, J. M., de Oliveira, P. V., Fagan, S. B., Werhli, A. V., Machado, K., Melo, A., González-Díaz, H., Concu, R., and Cordeiro, M. N. D. D. S. (2019). Computational mitotarget-scanning based on topological vacancies of single-walled carbon nanotubes with human mitochondrial hvdac1 channel. Chemical Research in Toxicology, 32(4):566–577.

e Silva, E. F., Figueira, F., Cañedo, A., Machado, K., Salgado, M., Silva, T., Wagner, E., Mattozo, F., Lima, É., Sales-Neto, J., et al. (2018). C-phycocyanin to overcome the multidrug resistance phenotype in human erythroleukemias with or without interaction with abc transporters. Biomedicine & Pharmacotherapy, 106:532–542.

Eberhardt, J., Santos-Martins, D., Tillack, A. F., and Forli, S. (2021). Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings. Journal of Chemical Information and Modeling, 61(8):3891–3898.

Figueiredo, D. F., Antunes, D. A., Rigo, M. M., Mendes, M. F., Silva, J. P., Mayer, F. Q., Matte, U., Giugliani, R., Vieira, G. F., and Sinigaglia, M. (2014). Lessons from molecular modeling human α-l-iduronidase. Journal of Molecular Graphics and Modelling, 54:107–113.

Foldvari, M. and Bagonluri, M. (2008). Carbon nanotubes as functional excipients for nanomedicines: Ii. drug delivery and biocompatibility issues. Nanomedicine: Nanotechnology, Biology and Medicine, 4(3):183–200.

Freitas, E. K. H. d., Camargo, A. D., Balboni, M., Werhli, A. V., and Santos Machado, K. d. (2021). Ensemble of protein stability upon point mutation predictors. In Brazilian Conference on Intelligent Systems, pages 73–88. Springer.

Gonzalez-Durruthy, M., Werhli, A. V., Cornetet, L., Machado, K. S., Gonzalez-Diaz, H., Wasiliesky, W., Ruas, C. P., Gelesky, M. A., and Monserrat, J. M. (2016). Predicting the binding properties of single walled carbon nanotubes (SWCNT) with an ADP/ATP mitochondrial carrier using molecular docking, chemoinformatics, and nano-QSBR perturbation theory. RSC Adv., 6:58680–58693. DOI: 10.1039/C6RA08883J.

González-Durruthy, M., Werhli, A. V., Seus, V., Machado, K. S., Pazos, A., Munteanu, C. R., González-Díaz, H., and Monserrat, J. M. (2017). Decrypting strong and weak single-walled carbon nanotubes interactions with mitochondrial voltage-dependent anion channels using molecular docking and perturbation theory. Scientific reports, 7(1):1–19.

Guidony, N. S., Scaini, J. L. R., Oliveira, M. W. B., Machado, K. S., Bastos, C., Escarrone, A. L., and Souza, M. M. (2021). Abc proteins activity and cytotoxicity in zebrafish hepatocytes exposed to triclosan. Environmental Pollution, 271:116368.

Irwin, J., Sterling, T., Mysinger, M., Bolstad, E., and Coleman, R. (2012). ZINC: A free tool to discovery chemistry for biology. Journal of Computational Chemistry, pages 1757–1768.

Josende, M. E., Nunes, S. M., de Oliveira Lobato, R., González-Durruthy, M., Kist, L. W., Bogo, M. R., Wasielesky, W., Sahoo, S., Nascimento, J. P., Furtado, C. A., et al. (2020). Graphene oxide and gst-omega enzyme: An interaction that affects arsenic metabolism in the shrimp litopenaeus vannamei. Science of The Total Environment, 716:136893.

Kadukova, M., Machado, K. d. S., Chacón, P., and Grudinin, S. (2021). Korp-pl: a coarse-grained knowledge-based scoring function for protein–ligand interactions. Bioinformatics, 37(7):943–950.

Kuntz, I. D. (1992). Structure-based Strategies for Drug Design and Discovery. Science, 257:1078–1082.

Lettnin, A. P., Wagner, E. F., Carrett-Dias, M., dos Santos Machado, K., Werhli, A., Cañedo, A. D., Trindade, G. S., and de Souza Votto, A. P. (2019). Silencing the oct4-pg1 pseudogene reduces oct-4 protein levels and changes characteristics of the multidrug resistance phenotype in chronic myeloid leukemia. Molecular biology reports, 46(2):1873–1884.

Lindorff-Larsen, K., Trbovic, N., Maragakis, P., Piana, S., and Shaw, D. E. (2012). Structure and dynamics of an unfolded protein examined by molecular dynamics simulation. Journal of the American Chemical Society, 134(8):3787–3791.

Lopes, P. P. (2021). Uma abordagem Ensemble Learning para aprimorar a predição de energia livre de ligação entre complexos proteína-ligante. Master’s thesis, PPGComp, Engenharia de Computação, Universidade Federal do Rio Grande (FURG), Rio Grande, Brasil.

Lybrand, T. (1995). Ligand-Protein Docking and Rational Drug Design. Curr. Opin. Struct. Biol., 5:224–228.

Machado, K. S. (2011). Seleção eficiente de conformações de receptor flexível em simulações de docagem molecular. Tese de Doutorado. Programa de Pós Graduação em Ciência da Computação PPGCC. Pontifícia Universidade Católica do Rio Grande do Sul.

Mariano, D., Leite, C., Santos, L., Marins, L., Machado, K.S. ; Werhli, A., Lima, L., and De Melo-Minardi, R. (2017). Characterization of glucose-tolerant beta-glucosidases used in biofuel production under the bioinformatics perspective: a systematic review. GENETICS AND MOLECULAR RESEARCH, 16:1–10.

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. (2019). A computational method to propose mutations in enzymes based on structural signature variation (ssv). International journal of molecular sciences, 20(2):333.

Meng, X. Y. Y., Zhang, H. X. X., and Cui, M. (2011). Molecular docking: a powerful approach for structure-based drug discovery. Current computer-aided drug design, 7:146–157.

Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., and Olson, A. J. (2009). Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. Computational Chemistry, 16:85–91.

Páll, S., Abraham, M. J., Kutzner, C., Hess, B., and Lindahl, E. (2014). Tackling exascale software challenges in molecular dynamics simulations with gromacs. In International conference on exascale applications and software, pages 3–27. Springer.

Perazzo, G. X., Winck, A. T., and Machado, K. S. (2013). A data warehouse as an infrastructure to mine molecular descriptors for virtual screening. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, pages 1335–1336, Coimbra, Portugal.

Piana, S., Lindorff-Larsen, K., and Shaw, D. E. (2011). How robust are protein folding simulations with respect to force field parameterization? Biophysical journal, 100(9):L47–L49.

Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P. Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., Van Der Spoel, D., et al. (2013). Gromacs 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7):845–854.

Ramos, P., Schmitz, M., Gama, S., Portantiolo, A., Durruthy, M. G., de Souza Votto, A. P., Cornetet, L. R., dos Santos Machado, K., Werhli, A., Tonel, M. Z., et al. (2018). Cytoprotection of lipoic acid against toxicity induced by saxitoxin in hippocampal cell line ht-22 through in silico modeling and in vitro assays. Toxicology, 393:171–184.

Ramos, P. B., Colombo, G. M., Schmitz, M. J., Simião, C. S., dos Santos Machado, K., Werhli, A. V., Costa, L. D. F., Yunes, J. S., Prentice, C., Wasielesky, W., et al. (2022). Chemoprotection mediated by açaí berry (euterpe oleracea) in white shrimp litopenaeus vannamei exposed to

the cyanotoxin saxitoxin analyzed by in vivo assays and docking modeling. Aquatic Toxicology, 246:106148.

Recamonde-Mendoza, M., Werhli, A. V., and Biolo, A. (2019). Systems biology approach identifies key regulators and the interplay between mirnas and transcription factors for pathological cardiac hypertrophy. Gene, 698:157–169.

Rocha, R. E., Chaves, E. J., Fischer, P. H., Costa, L. S., Grillo, I. B., da Cruz, L. E., Guedes, F. C., da Silveira, C. H., Scotti, M. T., Camargo, A. D., et al. (2021). A higher flexibility at the sars-cov-2 main protease active site compared to sars-cov and its potentialities for new inhibitor virtual screening targeting multi-conformers. Journal of Biomolecular Structure and Dynamics, pages 1–21.

Rodriguez-Bussey, I. G., Doshi, U., and Hamelberg, D. (2016). Enhanced molecular dynamics sampling of drug target conformations. Biopolymers, 105(1):35–42.

Salgado, M. T. S. F., Lopes, A. C., e Silva, E. F., Cardoso, J. Q., Vidal, R. S., Cavalcante-Silva, L. H. A., Carvalho, D. C. M., dos Santos Machado, K., Rodrigues-Mascarenhas, S., Rumjanek, V. M., et al. (2021). Relation between abcb1 overexpression and cox2 and alox5 genes in human erythroleukemia cell lines. Prostaglandins & other lipid mediators, 155:106553.

Santa-Helena, E., da Costa Cabrera, D., D’Oca, M. G. M., Scaini, J. L. R., de Oliveira, M. W. B., Werhli, A. V., dos Santos Machado, K., Gonçalves, C. A. N., and Nery, L. E. M. (2020). Long-chain fatty dihydropyridines: Docking calcium channel studies and antihypertensive activity. Life Sciences, 259:118210.

Scaini, J. L. R., Camargo, A. D., Seus, V. R., von Groll, A., Werhli, A. V., da Silva, P. E. A., and dos Santos Machado, K. (2019). Molecular modelling and competitive inhibition of a mycobacterium tuberculosis multidrug-resistance efflux pump. Journal of Molecular Graphics and Modelling, 87:98–108.

Seus, V. R., Perazzo, G. X., Winck, A. T., Werhli, A. V., and Machado, K. S. (2014). An infrastructure to mine molecular descriptors for ligand selection on virtual screening. BioMed Research International, 2014.

Seus, V. R., Silva, Jr., L., Gomes, J., da Silva, P. E. A., Werhli, A. V., Prates, N., Zanatta, N., and Machado, K. S. (2016). A framework for virtual screening. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, SAC ’16, pages 31–36, New York, NY, USA. ACM.

Shen, C., Ding, J., Wang, Z., Cao, D., Ding, X., and Hou, T. (2020). From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. Wiley Interdisciplinary Reviews: Computational Molecular Science, 10(1):e1429.

Silva, L., Carrion, L. L., von Groll, A., Costa, S. S., Junqueira, E., Ramos, D. F., Cantos, J., Seus, V. R., Couto, I., da Silva Fernandes, L., et al. (2017). In vitro and in silico analysis of the efficiency of tetrahydropyridines as drug efflux inhibitors in escherichia coli. International journal of antimicrobial agents, 49(3):308–314.

Su, M., Yang, Q., Du, Y., Feng, G., Liu, Z., Li, Y., and Wang, R. (2018). Comparative assessment of scoring functions: the casf-2016 update. Journal of chemical information and modeling, 59(2):895–913.

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How to Cite

dos Santos Machado, K., & Velasque Werhli, A. (2024). Computational Biology Laboratory - Combi-Lab. Journal of Information and Data Management, 15(1), 23–34.



Brazilian Bioinformatics Research Groups