The Barroso Research lab: biomolecular interactions, computing, and data-driven science to understand and engineer biological and pharmaceutical systems in a global academic partnership




antibody, biomolecular interactions, complexation, high-performance computing, molecular simulation, pH effects, virus


Biomolecular interactions, high throughput computing, and data-driven science have been the central research foundations of the Barroso Research laboratory. We have been developing and applying innovative computational technology, offering a rational computational-based approach to the investigation of protein systems, and discovering key disease-related protein mechanisms, therapeutic agents, biomarkers, and proteins for specific applications and their controlled release. Born in 2001 at the School of Pharmaceutical Sciences at Ribeirão Preto with the genes of transdisciplinary and internationalization, the laboratory has always been well integrated with research groups in Europe, the US, and Latin America. Students from different fields and places have been forged in this environment at the crossroads of Structural Bioinformatics, Molecular Biophysics, Biological Physics, Physical Chemistry, Engineering, Medicine, Food, and Pharma. The more than 50 scientific papers published in high-impact journals, book chapters, and conference talks reflect our contributions to expanding knowledge and advancing Bioinformatics as an important tool to understand nature and guide innovations.


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Abbott, A. (2022). Could drugs prevent Alzheimer’s? These trials aim to find out. Nature, 603(7900):216–219.

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Alrosan, M., Tan, T.-C., Easa, A. M., Gammoh, S., and Alu’datt, M. H. (2021). Molecular forces governing protein-protein interaction: Structure-function relationship of complexes protein in the food industry. Critical Reviews in Food Science and Nutrition, 0(0):1–17.

Autreto, P. A. S., Figueiredo, F. V., Nonato, M. C., and Barroso da Silva, F. L. (2003). Application of the Poisson-Boltzmann approach on structural biology: An initial study of the complex trypsin-bpti. Braz. J. Pharm. Sci., supl.2:39:203.

Bahar, I., Jernigan, R. L., and Dill, K. A. (2017). Protein Actions – Principles and Modeling. Garland Science, New York.

Barneaud-Rocca, D., Etchebest, C., and Guizouarn, H. (2013). Structural model of the anion exchanger 1 (SLC4A1) and identification of transmembrane segments forming the transport site. J Biol Chem, 288(37):26372–26384.

Barroso da Silva, F. L. (1999). Statistical Mechanical Studies of Aqueous solutions and Biomolecular Systems. Reproenheten SLU Alnarp, Lund University, Sweden.

Barroso da Silva, F. L. (2013). Peculiaridades nos mecanismos moleculares de proteínas em solução aquosa: Exemplo da importância do equilíbrio ácido-base para aplicações em biotecnologia. Química, 131(oct-dez):43–48.

Barroso da Silva, F. L. (2024). Constant-pH simulation methods for biomolecular systems. In Yáñez, M. and Boyd, R. J., editors, Comprehensive Computational Chemistry (First Edition), pages 942–963. Elsevier, Oxford, first edition edition. DOI:

Barroso da Silva, F. L., Bogren, D., Söderman, O., and Jönsson, B. (2002). Titration of fatty acids solubilized in catonic, nonionic and anionic micelles: Theory and experiment. J. Phys. Chem. B, 106:3515–3522.

Barroso da Silva, F. L., Boström, M., and Persson, C. (2014). Effect of Charge Regulation and Ion–Dipole Interactions on the Selectivity of Protein–Nanoparticle Binding. Langmuir, 30(14):4078–4083.

Barroso da Silva, F. L., Carloni, P., Cheung, D., Cottone, G., Donnini, S., Foegeding, E. A., Gulzar, M., Jacquier, J. C., Lobaskin, V., MacKernan, D., Mohammad Hosseini Naveh, Z., Radhakrishnan, R., and Santiso, E. E. (2020). Understanding and controlling food protein structure and function in foods: Perspectives from experiments and computer simulations. Annual Review of Food Science and Technology, 11(1):365–387.

Barroso da Silva, F. L., Derreumaux, P., and Pasquali, S. (2017). Fast coarse-grained model for RNA titration. J. Chem. Phys., 146(3):035101+.

Barroso da Silva, F. L., Derreumaux, P., and Pasquali, S. (2018). Protein-RNA complexation driven by the charge regulation mechanism. Biochemical and Biophysical Research Communications, 498(2):264–273. DOI: 10.1016/j.bbrc.2017.07.027.

Barroso da Silva, F. L. and Dias, L. G. (2017). Development of constant-pH simulation methods in implicit solvent and applications in biomolecular systems. Biophys Rev, 9(5):699–728.

Barroso da Silva, F. L., Giron, C. C., and Laaksonen, A. (2022). Electrostatic features for the receptor binding domain of SARS-CoV-2 wildtype and its variants. compass to the severity of the future variants with the charge-rule. 126(36):6835–6852. DOI: 10.1021/acs.jpcb.2c04225.

Barroso Da Silva, F. L. and Jönsson, B. (2009). Polyelectrolyte-protein complexation driven by charge regulation. Soft Matter, 5(15):2862–2868.

Barroso da Silva, F. L., Jönsson, B., and Penfold, R. (2001a). A critical investigation of the Tanford-Kirkwood scheme by means of Monte Carlo simulations. Prot. Sci., 10:1415–1425.

Barroso da Silva, F. L., Linse, S., and Jönsson, B. (2005). Binding of charged ligands to macromolecules. anomalous salt dependence. J. Phys. Chem. B, 109:2007–2013.

Barroso da Silva, F. L., Lund, M., Jönsson, B., and Åkesson, T. (2006). On the Complexation of Proteins and Polyelectrolytes. J. Phys. Chem. B, 110(9):4459–4464.

Barroso da Silva, F. L. and MacKernan, D. (2017). Benchmarking a Fast Proton Titration Scheme in Implicit Solvent for Biomolecular Simulations. J. Chem. Theory Comput., 13(6):2915–2929.

Barroso da Silva, F. L., Olivares-Rivas, W., and Colmenares, P. J. (2007). Basic statistics and variational concepts behind the reverse Monte Carlo technique. Molecular Simulation, 33(8):639–647.

Barroso da Silva, F. L., Olivares-Rivas, W., Degrève, L., and Åkesson, T. (2001b). Application of a new reverse Monte Carlo algorithm to polyatomic molecular systems. I. liquid water. The Journal of Chemical Physics, 114(2):907–914.

Barroso da Silva, F. L., Pasquali, S., Derreumaux, P., and Dias, L. G. (2016). Electrostatics analysis of the mutational and pH effects of the N-terminal domain self-association of the Major Ampullate Spidroin. Soft Matter, 12(25):5600–5612.

Barroso da Silva, F. L., Sterpone, F., and Derreumaux, P. (2019). OPEP6: A New Constant-pH Molecular Dynamics Simulation Scheme with OPEP Coarse-Grained Force Field. J. Chem. Theory Comput., 15(6):3875–3888.

Barroso da Silva, F. L., Svensson, B., Åkesson, T., and Jönsson, B. (1998). A new algorithm for reverse Monte Carlo simulations. J. Chem. Phys., 109:2624–2629.

Barroso da Silva, F. L., Svensson, B., Åkesson, T., and Jönsson, B. (1999). Response to Comments on A new algorithm for Reverse Monte Carlo simulations. J. Chem. Phys., 111:5622–5623.

Bello, E. A. and Schwinn, D. A. (1996). Molecular Biology and Medicine: A Primer for the Clinician. Anesthesiology, 85(6):1462–1478.

Berger, M., Shankar, V., and Vafai, A. (2002). Therapeutic applications of monoclonal antibodies. The American journal of the medical sciences, 324(1):14–30.

Bergethon, P. R. (1998). The Physical Basis of Biochemistry - The Foundations of Molecular Biophysics. Springer-Verlag New York Inc., New York.

Borkovec, M., Jonsson, B., and Koper, G. J. M. (2001). Surface and Colloid Science, chapter Ionization Processes and Proton Binding in Polyprotic Systems: Small Molecules, Proteins, Interfaces, and Polyelectrolytes, pages 99–339. Springer, Boston.

Bornot, A., Etchebest, C., and de Brevern, A. G. (2010). Predicting protein flexibility through the prediction of local structures. Proteins, 79(3):839–852.

Brasil, C. R., Delbem, A. C., and Barroso da Silva, F. L. (2013). Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction. J. Comput. Chem., 34(20):1719–1734.

Braun, P. and Gingras, A.-C. (2012). History of protein–protein interactions: From egg-white to complex networks. PROTEOMICS, 12(10):1478–1498.

Bromberg, L. (2008). Polymeric micelles in oral chemotherapy. Journal of controlled release : official journal of the Controlled Release Society, 128(2):99–112.

Bryant, P., Pozzati, G., and Elofsson, A. (2022). Improved prediction of protein-protein interactions using alphafold2. Nature Communications, 13:1265.

Buyya, R., Vecchiola, C., and Selvi, S. T. (2013). Chapter 7 - high-throughput computing: Task programming. In Buyya, R., Vecchiola, C., and Selvi, S. T., editors, Mastering Cloud Computing, pages 211–252. Morgan Kaufmann, Boston.

Calixto, T. M. R. (2010). Análises de propriedades eletrostáticas e estruturais de complexos de proteínas para o desenvolvimento de preditores de complexação em larga escala, master thesis, faculdade de ciências farmacêuticas de ribeirão preto.

Carlsson, F., Linse, P., and Malmsten, M. (2001). Monte carlo simulations of polyelectrolyte- protein complexation. J. Phys. Chem. B, 105(38):9040–9049.

Carlsson, F., Malmsten, M., and Linse, P. (2003). Protein-polyelectrolyte cluster formation and redissolution: a monte carlo study. Journal of the American Chemical Society, 125(10):3140–3149.

Carvaillo, J.-C., Tubiana, T., Detchanamourtty, S., Bressanelli, S., da Silva, F. L. B., and Boulard, Y. (2024). Studying self-assembly of norovirus capsid by a combination of in silico methods. bioRxiv. DOI: 10.1101/2024.01.21.575142.

Chadwick, K., Chen, J., Santiso, E. E., and Trout, B. L. (2019). Molecular Modeling Applications in Crystallization, page 136–171. Cambridge University Press, 3 edition.

Clark, J. A. (2021). Bridging the Gap in Modeling Polymer Behavior using Simulation Methods on Multiple Scales. PhD thesis, North Carolina State University, Raleigh (NCSU), USA.

Creighton, T. E. (1983). Proteins – Structures and Molecular Principles. W. E. Freeman and Company, New York.

Cretin, G., Galochkina, T., de Brevern, A. G., and Gelly, J.-C. (2021). Pythia: Deep learning approach for local protein conformation prediction. International Journal of Molecular Sciences, 22(16):8831.

de Brevern, A. G., Autin, L., Colin, Y., Bertrand, O., and Etchebest, C. (2009). In silico studies on DARC. Infect Disord Drug Targets, 9(3):289–303.

de Brevern, A. G., Wong, H., Tournamille, C., Colin, Y., Le Van Kim, C., and Etchebest, C. (2005). A structural model of a seven-transmembrane helix receptor: the duffy antigen/receptor for chemokine (DARC). Biochim Biophys Acta, 1724(3):288–306.

de Carvalho, S. J., Fenley, M. O., and Barroso da Silva, F. L. (2008). Protein-Ion binding process on finite macromolecular concentration. a Poisson-Boltzmann and Monte Carlo study. J. Phys. Chem. B, 112(51):16766–16776.

de Carvalho, S. J., Ghiotto, R. C. T., and Barroso da Silva, F. L. (2006). Monte Carlo and modified Tanford-Kirkwood results for macromolecular electrostatics calculations. J. Phys. Chem. B, 110:8832–8839.

de Kruif, C. G., Weinbreck, F., and de Vries, R. (2004). Complex coacervation of proteins and anionic polysaccharides. Current Opinion in Colloid & Interface Science, 9(5):340–349.

De Las Rivas, J. and Fontanillo, C. (2010). Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks. PLoS Comput Biol, 6(6):e1000807.

de Lima, T. W., Caliri, A., Barroso da Silva, F. L., Tinos, R., Travieso, G., da Silva, I. N., de Souza, P. S. L., Marques, E., Delbem, A. C. B., Bonatto, V., Faccioli, R., Brasil, C. R. S., Gabriel, P. H. R., do O, V. T., and Bonetti, D. R. F. (2009). Some modeling issues for protein structure prediction using evolutionary algorithms. In dos Santos, W. P., editor, Evolutionary Computation, chapter 9, pages 153–178. IntechOpen, Rijeka.

De Vries, R. (2004). Monte carlo simulations of flexible polyanions complexing with whey proteins at their isoelectric point. The Journal of chemical physics, 120(7):3475–3481.

de Vries, R. and Cohen Stuart, M. (2006). Theory and simulations of macroion complexation. Current Opinion in Colloid & Interface Science, 11(5):295–301.

Degrève, L. and Barroso da Silva, F. L. (1999a). Detailed study of 1M aqueous NaCl solution by computer simulations. J. Chem. Phys., 111:5150–5156.

Degrève, L. and Barroso da Silva, F. L. (1999b). Structure of concentrated aqueous NaCl solution: a Monte Carlo study. J. Chem. Phys., 110(6):3070–3078.

Degrève, L. and Barroso da Silva, F. L. (2000). Detailed microscopic study of 1M aqueous NaCl solution by computer simulations. J. Mol. Liquids, 87:217–232.

Degrève, L., Barroso da Silva, F. L., Quintale Jr, C., and de Souza, A. R. (1995). Application of the Reverse Monte Carlo simulations to diatomic molecules. I: The noncomplete radial distribution functions. J. Molec. Struct. (Theochem), 335:89–96.

Delboni, L. and Barroso da Silva, F. L. (2016). On the complexation of whey proteins. Food Hydrocolloids, 55:89–99.

Dill, K. A. (1999). Strengthening biomedicine’s roots. Nature, 400:309–310.

Dobaño, C., Santano, R., Jiménez, A., Vidal, M., Chi, J., Rodrigo Melero, N., Popovic, M., López-Aladid, R., Fernández-Barat, L., Tortajada, M., Carmona-Torre, F., Reina, G., Torres, A., Mayor, A., Carolis, C., García-Basteiro, A. L., Aguilar, R., Moncunill, G., and Izquierdo, L. (2021). Immunogenicity and crossreactivity of antibodies to the nucleocapsid protein of SARS-CoV-2: utility and limitations in seroprevalence and immunity studies. Transl. Res., 232:60–74.

Doublier, J. L., Garnier, C., Renard, D., and Sanchez, C. (2000). Protein–polysaccharide interactions. Current Opinion in Colloid & Interface Science, 5(3-4):202–214.

Eghbal, N. and Choudhary, R. (2018). Complex coacervation: Encapsulation and controlled release of active agents in food systems. LWT, 90:254–264.

Etchebest, C. and Debret, G. (2010). Critical review of general guidelines for membrane proteins model building and analysis. Methods Mol Biol, 654:363–385.

Fogha, J., Bayry, J., Diharce, J., and de Brevern, A. G. (2021). Structural and evolutionary exploration of the IL-3 family and its alpha subunit receptors. Amino Acids, 53(8):1211–1227.

Frigori, R. B., Barroso da Silva, F. L., Carvalho, P. P. D., and Alves, N. A. (2020). Occurrence of biased conformations as precursors of assembly states in fibril elongation of amyloid-β fibril variants: An in silico study. J. Phys. Chem. B, 124(14):2798–2805.

Galochkina, T., Ng Fuk Chong, M., Challali, L., Abbar, S., and Etchebest, C. (2019). New insights into glut1 mechanics during glucose transfer. Scientific Reports, 9(1):998.

Garcia-Moreno, B. (1995). Probing structural and physical basis of protein energetics linked to protons and salt. Methods in Enzymnology, 259:512–538.

Giron, C. C., Laaksonen, A., and Barroso da Silva, F. L. (2021). Up State of the SARS-COV-2 Spike Homotrimer Favors an Increased Virulence for New Variants. Front. Med. Technol., 3:694347.

Giron, C. C., Laaksonen, A., and Barroso da Silva, F. L. (2022). Differences between omicron SARS-CoV-2 rbd and other variants in their ability to interact with cell receptors and monoclonal antibodies. Journal of Biomolecular Structure and Dynamics, 0(0):1–21. DOI: 10.1080/07391102.2022.2095305.

Giron, C. G., Laaksonen, A., and Barroso da Silva, F. L. (2020). On the interactions of the receptor-binding domain of SARS-CoV-1 and SARS-CoV-2 spike proteins with monoclonal antibodies and the receptor ACE2. Virus Research, 285:198021.

Gray, C. G., Gubbins, K. E., and Joslin, C. G. (2011). Theory of Molecular Fluids: Applications. Oxford, London.

Grymonpré, K. R., Staggemeier, B. A., Dubin, P. L., and Mattison, K. W. (2001). Identification by integrated computer modeling and light scattering studies of an electrostatic serum albumin-hyaluronic acid binding site. Biomacromolecules, 2:422–429.

Gu, J. and Bourne, P. E. (2009). Structural Bioinformatics. Wiley-Blackwell, New Jersey.

Hinsen, K., Vaitinadapoule, A., Ostuni, M. A., Etchebest, C., and Lacapere, J.-J. (2015). Construction and validation of an atomic model for bacterial tspo from electron microscopy density, evolutionary constraints, and biochemical and biophysical data. Biochimica et Biophysica Acta (BBA) - Biomembranes, 1848(2):568–580.

Hoarau, M., Badieyan, S., and Marsh, E. N. G. (2017). Immobilized enzymes: understanding enzyme – surface interactions at the molecular level. Org. Biomol. Chem., 15(45):9539–9551.

Hull, R. (2014). Chapter 10 - movement of viruses within plants. In Hull, R., editor, Plant Virology (Fifth Edition), pages 531–603. Academic Press, Boston, fifth edition edition.

Ishivatari, L. H. U., de Oliveira, L. L., Barroso da Silva, F. L., and Tinós, R. (2011). Algoritmos genéticos com função de avaliação dinâmica para o problema de predição de estruturas de proteínas. In 10th Brazilian Congress on Computational Intelligence (CBIC2011), pages 1–8, Fortaleza, CE. Brazilian Society on Computational Intelligence (SBIC).

Jönsson, B., Lund, M., and Barroso da Silva, F. L. (2007). Electrostatics in Macromolecular Solution. In Dickinson, E. and Leser, M. E., editors, Food Colloids: Self-Assembly and Material Science, pages 129–154. Royal Society of Chemistry, Londres.

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., and Hassabis, D. (2021). Highly accurate protein structure prediction with alphafold. Nature, 596(7873):583–589.

Kirkwood, J. G. and Shumaker, J. B. (1952). Forces Between Protein Molecules in Solution Arising from Fluctuations in Proton Charge and Configuration. Proc. Natl. Acad. Sci. USA, 38:863–871.

Kiyoshi, M., Caaveiro, J. M. M., Miura, E., Nagatoishi, S., Nakakido, M., Soga, S., Shirai, H., Kawabata, S., and Tsumoto, K. (2014). Affinity improvement of a therapeutic antibody by structure-based computational design: Generation of electrostatic interactions in the transition state stabilizes the antibody-antigen complex. PLoS ONE, 9(1):1–9.

Kotlyar, M., Pastrello, C., Rossos, A. E. M., and Jurisica, I. (2019). Protein–Protein Interaction Databases. In Ranganathan, S., Gribskov, M., Nakai, K., and Schönbach, C., editors, Encyclopedia of Bioinformatics and Computational Biology, pages 988–996. Academic Press, Oxford.

Krisko, A. and Etchebest, C. (2007). Theoretical model of human apolipoprotein B100 tertiary structure. Proteins, 66(2):342–358.

Lacapère, J.-J., Pebay-Peyroula, E., Neumann, J.-M., and Etchebest, C. (2007). Determining membrane protein structures: still a challenge! Trends Biochem Sci, 32(6):259–270.

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Lipkowitz, K. B. and Boyd, D. B. (1990-2022). Reviews in Computational Chemistry, volume 1–32 (serie). VCH Publishers, inc., New York.

Lousa, D., Soares, C. M., and Barroso da Silva, F. L. (2022). Computational Approaches to Foster Innovation in the Treatment and Diagnosis of Infectious Diseases. Front Med Technol, 4:841088.

Lu, R.-M., Hwang, Y.-C., Liu, I.-J., Lee, C.-C., Tsai, H.-Z., Li, H.-J., and Wu, H.-C. (2020). Development of therapeutic antibodies for the treatment of diseases. Journal of Biomedical Science, 27(1):1.

Lunkad, R., Barroso da Silva, F. L., and Košovan, P. (2022). Both Charge-Regulation and Charge-Patch Distribution Can Drive Adsorption on the Wrong Side of the Isoelectric Point. J. Am. Chem. Soc., 144(4):1813–1825.

Luscombe, N. M., Greenbaum, D., and Gerstein, M. (2001). What is bioinformatics? a proposed definition and overview of the field. Methods Inf Med., 40(4):3460–58.

Maljković, M. M., Mitić, N. S., and de Brevern, A. G. (2022). Prediction of structural alphabet protein blocks using data mining. Biochimie, 197:74–85.

Melarkode Vattekatte, A., Shinada, N. K., Narwani, T. J., Noël, F., Bertrand, O., Meyniel, J.-P., Malpertuy, A., Gelly, J.-C., Cadet, F., and de Brevern, A. G. (2020). Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction. PeerJ, 8:e8408.

Mendonça, D. C., Macedo, J. N., Guimarães, S. L., Barroso da Silva, F. L., Cassago, A., Garratt, R. C., Portugal, R. V., and Araujo, A. P. U. (2019). A revised order of subunits in mammalian septin complexes. Cytoskeleton, 76(9-10):457–466.

Miernyk, J. A. and Thelen, J. J. (2008). Biochemical approaches for discovering protein–protein interactions. The Plant Journal, 53(4):597–609.

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Montellano Duran, N., Spelzini, D., Wayllace, N., Boeris, V., and Barroso da Silva, F. L. (2018). A combined experimental and molecular simulation study of factors influencing interaction of quinoa proteins–carrageenan. International Journal of Biological Macromolecules, 107:949–956.

Neamtu, A., Mocci, F., Laaksonen, A., and Barroso da Silva, F. L. (2022a). Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants. Technical report, bioRxiv.

Neamtu, A., Mocci, F., Laaksonen, A., and da Silva, F. L. B. (2022b). Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of sars-cov-2 spike proteins from different variants. Colloids and Surfaces B: Biointerfaces, page 112986.

O’Kennedy, R., Fitzgerald, S., and Murphy, C. (2017). Don’t blame it all on antibodies – The need for exhaustive characterisation, appropriate handling, and addressing the issues that affect specificity. TrAC Trends in Analytical Chemistry, 89:53–59.

Pasquali, S., Frezza, E., and Barroso da Silva, F. L. (2019). Coarse-grained dynamic RNA titration simulations. Interface Focus, 9(3):20180066.

Perutz, M. F. (1978). Electrostatic effects in proteins. Science, 201:1187–1191.

Phan, G., Benabdelhak, H., Lascombe, M.-B., Benas, P., Rety, S., Picard, M., Ducruix, A., Etchebest, C., and Broutin, I. (2010). Structural and dynamical insights into the opening mechanism of P. aeruginosa OprM channel. Structure, 18(4):507–517.

Postic, G., Ghouzam, Y., Etchebest, C., and Gelly, J.-C. (2017). TMPL: a database of experimental and theoretical transmembrane protein models positioned in the lipid bilayer. Database (Oxford), 2017(1):1–7.

Poveda-Cuevas, S., Etchebest, C., and Silva, F. (2018). Insights into the ZIKV NS1 Virology from Different Strains through a Fine Analysis of Physicochemical Properties. ACS Omega, 3(11):16212–16229. DOI: 10.1021/acsomega.8b02081.

Poveda-Cuevas, S. A., Barroso da Silva, F. L., and Etchebest, C. (2021). How the Strain Origin of Zika Virus NS1 Protein Impacts Its Dynamics and Implications to Their Differential Virulence. J. Chem. Inf. Model., 61(3):1516–1530.

Poveda-Cuevas, S. A., Barroso da Silva, F. L., and Etchebest, C. (2023). NS1 from two Zika virus strains differently interact with a membrane: Insights to understand their differential virulence. Journal of Chemical Information and Modeling, 63(4):1386–1400. PMID: 36780300. DOI: 10.1021/acs.jcim.2c01461.

Poveda-Cuevas, S. A., Etchebest, C., and Barroso da Silva, F. L. (2020). Identification of Electrostatic Epitopes in Flavivirus by Computer Simulations: The PROCEEDpKa Method. J. Chem. Inf. Model., 60(2):944–963. DOI: 10.1021/acs.jcim.9b00895.

Poveda-Cuevas, S. A., Etchebest, C., and Barroso da Silva, F. L. (2022). Self-association features of NS1 proteins from different flaviviruses. Virus Research, 318:198838. DOI:

Prates-Syed, W. A., Chaves, L. C. S., Crema, K. P., Vuitika, L., Lira, A., Côrtes, N., Kersten, V., Guimarães, F. E. G., Sadraeian, M., Barroso da Silva, F. L., Cabral-Marques, O., Barbuto, J. A. M., Russo, M., Câmara, N. O. S., and Cabral-Miranda, G. (2021). VLP-Based COVID-19 Vaccines: An Adaptable Technology against the Threat of New Variants. Vaccines, 9(12):1409.

Prudkin-Silva, C., Pérez, O. E., Martínez, K. D., and Barroso da Silva, F. L. (2020). Combined experimental and molecular simulation study of insulin–chitosan complexation driven by electrostatic interactions. Journal of Chemical Information and Modeling, 60(2):854–865.

Ramaraj, T., Angel, T., Dratz, E. A., Jesaitis, A. J., and Mumey, B. (2012). Antigen–antibody interface properties: composition, residue interactions, and features of 53 non-redundant structures. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1824(3):520–532.

Rao, R., Diharce, J., Dugué, B., Ostuni, M. A., Cadet, F., and Etchebest, C. (2020). Versatile dimerisation process of translocator protein (TSPO) revealed by an extensive sampling based on a Coarse-Grained dynamics study. J Chem Inf Model, 60(8):3944–3957.

Regenmortel, M. (2014). Specificity, polyspecificity, and heterospecificity of antibody-antigen recognition. Journal of Molecular Recognition, 27:627–639.

Ross, A. M. and Lahann, J. (2015). Current Trends and Challenges in Biointerfaces Science and Engineering. Annu Rev Chem Biomol Eng., 6(1):161–186.

Samish, I., Bourne, P. E., and Najmanovich, R. J. (2014). Achievements and challenges in structural bioinformatics and computational biophysics. Bioinformatics, 31(1):146–150.

Santiso, E. E. (2014). Understanding the effect of adsorption on activated processes using molecular theory and simulation. Molecular Simulation, 40(7-9):664–677.

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How to Cite

Barroso da Silva, F. L., Etchebest, C., E. Santiso, E., Corrêa Giron, C., Grandguillaume, I., Borges Marques, R., & Poveda-Cuevas, S. A. (2024). The Barroso Research lab: biomolecular interactions, computing, and data-driven science to understand and engineer biological and pharmaceutical systems in a global academic partnership. Journal of Information and Data Management, 15(1), 242–254.



Brazilian Bioinformatics Research Groups