1. Barroso da Silva, F.L.; Giron, C. C.; Laaksonen, A. Electrostatic Features for the Receptor Binding Domain of SARSCOV‑2 Wildtype and Its Variants. Compass to the Severity of the Future Variants with the Charge-Rule. Journal of Physical Chemistry B, DOI:10.1021/acs.jpcb.2c04225, 2022.
  2. Giron, C. C.; Laaksonen, A.; Barroso da Silva, F.L. 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, DOI: 10.1080/07391102.2022.2095305, 2022.
  3. Poveda-Cuevas, S. A.; Etchebest, C.; Barroso da Silva, F.L. Self-association features of NS1 proteins from different flaviviruses, Virus Research, v. 318, 198838, 2022.
  4. Lousa, D.; Soares, C.M.; Barroso da Silva, F.L. Computational approaches to foster innovation in the treatment and diagnosis of infectious diseases, Front. Med. Technol., v. 4, article 841088, 2022.
  5. Lunkad, R.; Barroso da Silva, F.L.; Košovan, P.  Both Charge-Regulation and Charge-Patch Distribution Can Drive Adsorption on the Wrong Side of the Isoelectric Point, J. American Chemical Society, 144 (4), 1813-1825, 2022.
  6. Prates-Syed, W.; Chaves, L. C. S.; Crema, L.P.; Vuitika, L.; Lira, A.; Cortes, 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.; Cabral-Miranda, G.  VLP-Based COVID-19 Vaccines: An Adaptable Technology against the Threat of New Variants, Vaccines, v. 9(12), 1409, 2021.
  7. Giron, C. C.; Laaksonen, A.; Barroso da Silva, F.L Up State of the SARS-COV-2 Spike Homotrimer Favors an Increased Virulence for New Variants, Front. Med. Technol., v. 3, article 694347, 2021.
  8. Poveda-Cuevas, S. A.; Barroso da Silva, F.L; Etchebest, C. How the Strain Origin of Zika Virus NS1 Protein Impacts Its Dynamics and Implications to Their Differential Virulence, J. Chemical Information and Modeling, v. 61, pp. 1516-1530, 2021.
  9. Smith, R.J.; Fabiani, T.; Wang, S.; Ramesh, S.; Khan, S.; Santiso, E.; Barroso da Silva, F.L; Gorman, C.; Menegatti, S. Exploring the physicochemical and morphological properties of peptide-hybridized dendrimers (DendriPeps) and their aggregates, Journal of Polymer Science 58 (16), 2234-2247, 2020.
  10. Giron, C. C.; Laaksonen, A.; Barroso da Silva, F.L 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, May 13; 198021, 2020.  
  11. Frigori, R.; Barroso da Silva, F.L; Carvalho, P.; Alves, N.  The 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, v. 124, pp. 2798-2805, 2020. 
  12. Prudkin da Silva, C.; Perez, O. E.; Martínez, K.D.; Barroso da Silva, F.L. A combined experimental and molecular simulation study of insulin-chitosan complexation driven by electrostatic interactions, J. Chemical Information and Modeling, v. 60, pp. 854-865, 2020. 
  13. Poveda-Cuevas, S. A.; Etchebest, C.; Barroso da Silva, F.L. On the identification of electrostatic epitopes in flavivirus by computer simulations: The PROCEEDpKa Method, J. Chemical Information and Modeling, v. 60, pp. 944-963, 2020.
  14. 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.; Naveh, Z.M.H.; Radhakrishnan, R.; Santiso, E., Understanding and Controlling Food Protein Folding and Aggregation and Taste: perspectives from experiment and simulation, Annual Review of Food Science and Technology, v. 11, pp. 365-387, 2020.
  15. Mendonça, D.C.; Macedo, J.N.; Guimaraes, S.L.; Barroso da Silva, F.L.; Cassago, A.; Garratt, R.C.; Portugal, R.; Araujo, A.P.U. A revised order of subunits in mammalian septin complexes, Cytoskeleton, v. 76, pp. 457-466, 2019 [cover/paper of the year/top download in 2019]. 
  16. Barroso da Silva, F.L.; Sterpone, F.; Derreumaux, P. OPEP6: A new constant-pH Molecular Dynamics simulation scheme with OPEP coarse-grained force field, J. Chemical Theory and Computation, v. 15, pp. 3875-3888, 2019. 
  17. Pasquali, S.; Frezza, E.; Barroso da Silva, F.L. Coarse-grained dynamic RNA titration simulations. Interface Focus, 9:20180066, 2019. 
  18. Poveda-Cuevas, S. A.; Etchebest, C.; Barroso da Silva, F.L. Insights into the ZIKV NS1 Virology from Different Strains through a Fine Analysis of Physicochemical Properties. ACS Omega, Vol. 3, No. 11, pp. 16212-16229, 2018.
  19. Duran, N. M.; Spelzini, D.; Boeris, V.; Barroso da Silva, F.L. A Combined Experimental and Molecular Simulation Study of Factors Influencing interaction of Quinoa Proteins-Carrageenan. International Journal of Biological Macromolecules, v. 107, Part A, pp. 949-956, 2018.
  20. Barroso da Silva, FL; Derreumaux, P; Pasquali, S. Protein-RNA complexation driven by the charge regulation mechanism. Biochemical and Biophysical Research Communications, v. 498(4), 264-273, 2018.
  21. Morales-Álvarez, ED; Rivera-Hoyos, CM; Poveda-Cuevas, SA; Reyes-Guzmán, EA; Pedroza-Rodríguez, AM; Reyes-Montaño, EA; Poutou-Piñales, RA. Malachite Green and Crystal Violet Decolorization by Ganoderma lucidum and Pleurotus ostreatus Supernatant and by rGlLCC1 and rPOXA 1B Concentrates: Molecular Docking Analysis. Appl Biochem Biotechnol 184(3):794-805, 2018.
  22. Barroso da Silva, F.L.; Dias, L.G. Development of constant-pH simulation methods in implicit solvent and applications in biomolecular systems. Biophysical Reviews, v. 9(5), pp. 699–728, 2017.
  23. Srivastava, D.; Santiso, E.E.; Gubbins, K.E; Barroso da Silva, F.L. Computationally mapping pKa shifts due to the presence of a polyelectrolyte chain around whey proteins. Langmuir, v. 33(42), pp. 11417–11428, 2017.
  24. Barroso da Silva, F.L.; Derreumaux, P.; Pasquali, S. Fast coarse-grained model for RNA titration. J. Chem. Phys., v. 146, pp. 035101, 2017.
  25. Barroso da Silva, F.L.; MacKernan, D. Benchmarking a fast proton titration scheme in implicit solvent for biomolecular simulations. J. Chemical Theory and Computation, v. 13, pp. 2915-2929, 2017.
  26. Delboni, L.A; Barroso da Silva, F.L. On the complexation of whey proteins. Food Hydrocolloids, v. 55, pp. 89-99, 2016.
  27. Barroso da Silva, F.L.; Derreumaux, P.; Pasquali, S.; Dias, L.G. Electrostatics analysis of the mutational and pH effects of the N-terminal domain self-association of the Major Ampullate Spidroin. Soft Matter, v. 12, pp. 5600-5612, 2016.
  28. Rivera-Hoyos, CM; Morales-Álvarez, ED; Poveda-Cuevas, SA; Reyes-Guzmán, EA; Poutou-Piñales, RA; Reyes-Montaño, EA. Computational Analysis and Low-Scale Constitutive Expression of Laccases Synthetic Genes GlLCC1 from Ganoderma lucidum and POXA 1B from Pleurotus ostreatus in Pichia pastoris. PLoS ONE 10(1): e0116524, 2015.
  29. Barroso da Silva, F.L.; Boström, M.; Persson, C. The Effect of Charge Regulation and Ion-Dipole Interactions on the Selectivity of Protein-Nanoparticle Binding. Langmuir, v. 30, pp. 4078-4083, 2014.
  30. Brasil, C. R.; Delbem, A. C.; Barroso da Silva, F.L. Multi-objective Evolutionary Algorithm with Many Tables for purely-ab initio Protein Structure Prediction. J. Comp. Chemistry, v. 34, pp. 1719-1734, 2013 [cover].
  31. Barroso Da Silva, F.L. Peculiarities in the molecular mechanisms of proteins in aqueous solution with interest for industries, Biotechnology and Biosciences. Química, v. 131, pp. 43-48, 2013.
  32. Ishivatari, L.H.U. ; De Oliveira, L.L.; Barroso da Silva, F.L.; Tinós, R. Algoritmos Genéticos com Função de Avaliação Dinâmica para o Problema de Predição de Estruturas de Proteínas. Associação Brasileira de Inteligência Computacional, CBIC, pp. 1-8, 2011 (DOI:10.21528/CBIC2011-25.3).
  33. Teixeira, A.A..; Lund, M.; Barroso da Silva, F.L. Fast Proton Titration Scheme for Multiscale Modeling of Protein Solutions. J. Chemical Theory and Computation, v. 6, pp. 3259-3266, 2010.
  34. de Lima, Telma Woerle; Caliri, Antonio; Barroso Da Silva, Fernando Luís; Tinós, R.; Delbem, A. C. B.; Travieso, Gonzalo; Da Silva, I. N.; de Souza, Paulo Sergio Lopes; Marques, Eduardo; Bonatto, Vanderlei; Faccioli, Rodrigo. “Some Modeling Issues for Protein Structure Prediction using Evolutionary Algorithms”. In: Aleksandar Lazinica. (Org.). Evolutionary Computation. Viena, Austria: IN-TECH, 2009.
  35. Jönsson, B.; Lund, M.; Barroso Da Silva, Fernando Luís. “Electrostatics in macro-molecular solutions” In:  Food Colloids: Self-Assembly and Material Science. Dickinson, E. and Leser, M.E. (Eds) Royal Society of Chemistry, Cambridge, 2007, Cap. 9, pp. 129 (ISBN 0854042717).  
  36. Barroso da Silva, F.L.; Jönsson, B. Polyelectrolyte protein complexation driven by charge regulation. Soft Matter, v. 5, pp. 2862-2868, 2009. 
  37. De Carvalho, S.J.; Fenley, M.; Barroso da Silva, F.L. Protein-ion Binding Process on Finite Macromolecular Concentration. A Poisson-Boltzmann and Monte Carlo Study. Journal of Physical Chemistry B, v. 112, pp. 16766-16776, 2008.  
  38. Barroso da Silva, F.L.; Olivares, W.; Colmenares, P.J. Basic statistics and variational concepts behind the Reverse Monte Carlo technique. Molecular simulation, v. 33, pp. 639-647, 2007.
  39. Barroso da Silva, F.L.; Lund, M.; Jönsson, B; Åkesson, T. On the Complexation of Proteins and Polyelectrolytes. Journal of Physical Chemistry B, v. 110, n.9, pp. 4459-4464, 2006.
  40. De Carvalho, S.J.; Ghiotto, R.C.T.; Barroso da Silva, F.L. Monte Carlo and modified Tanford-Kirkwood results for macromolecular electrostatic calculations. Journal of Physical Chemistry B, v. 110, pp. 8832-8839, 2006.
  41. Barroso da Silva, F.L.; Linse, S.; Jönsson, B. The binding of charged ligands to macromolecules. Anomalous salt dependence. J. Phys. Chem. B, 109, pp. 2007-2013, 2005.
  42. Figueiredo, F.V.; Autreto da Silva, P.A.; Nonato, M.C.; Barroso da Silva, F.L. Application of Poisson-Boltzmann Approach on Structural Biology: An Initial Study of The Complex Trypsin-bpti. Revista Brasileira de Ciências Farmacêuticas, v. 39. pp. 193, 2003.
  43. Barroso da Silva, F.L.; Bogren, D.; Söderman, O.; Jönsson, B.; Åkesson, T. Titration of fatty acids solubilized in cationic, nonionic and anionic micelles. Theory and experiment. Journal of Physical Chemistry B, v. 106, n.13, pp. 3515-3522, 2002.
  44. Barroso da Silva, F.L.; Olivares, W.; Dègreve, L.; Åkesson, T. Application of a New Reverse Monte Carlo Algorithm to Polyatomic Molecular Systems I. Liquid water. Journal of Chemical Physics, v. 114, n.2, pp. 907-914, 2001.
  45. Barroso da Silva, F.L.; Jönsson, B.; Åkesson, T. A critical investigation of the Tanford-Kirkwood model by means of Monte Carlo simulations. Protein Science, v. 10, pp. 1415-1425, 2001.
  46. Dègreve, L.; Barroso da Silva, F.L. Detailed study of 1M aqueous NaCl solution by computer simulations. Journal of Molecular Liquids, v. 87, n.2-3, pp. 217-232, 2000.
  47. Dègreve, L.; Barroso da Silva, F.L. Structure of concentrated aqueous NaCl solution: a Monte Carlo study. Journal of Chemical Physics, v. 110, n.6, pp. 3070-3078, 1999.
  48. Dègreve, L.; Barroso da Silva, F.L. Large ionic clusters in concentrated aqueous NaCl solution. Journal of Chemical Physics, v. 111, n.11, pp. 5150-5156, 1999.
  49. Barroso da Silva, F.L.; Svensson, B.; Åkesson, T.; Jönsson, B. Response to comments on a new algorithm for Reverse Monte Carlo Simulations. Journal of Chemical Physics, v. 111, n.12, pp. 5622, 1999.
  50. Barroso da Silva, F.L.; Svensson, B.; Åkesson, T.; Jönsson, B. A new algorithm for Reverse Monte Carlo Simulations. Journal of Chemical Physics, v. 109, n.7, pp. 2624-2629, 1998.
  51. Dègreve, L.; Barroso da Silva, F.L.; Quintale Junior, C.; Souza, A.R. Application of the Reverse Monte Carlo simulations to diatomic molecules. Journal of Molecular Structure, v. 335, pp. 89-96, 1995.
  52. Skaf, M. S.; Barroso da Silva, F.L. Explorando as propriedades estruturais e dinâmicas de solventes por simulação computacional. Química Nova, Brasil, v. 17, n.6, pp. 507-512, 1994.