Dinler Amaral Antunes

Dinler Antunes is a Postdoctoral Research Associate at the Computer Science Department of Rice University (Houston, TX), interested in structural bioinformatics and immunology. He is working with structural prediction of peptide-MHC (pMHC) complexes, with applications on cellular immune response and cancer immunotherapy. He is developing new docking-based methods to predict binding modes of large peptides to MHC-I receptors, aiming to explore MHC allotypes with special biomedical interest.

He attained his BS degree in Biomedicine in 2008, at the Federal University of Rio Grande do Sul (UFRGS, Brazil), and received a MS in Genetics and Molecular Biology from the same University in 2011. During his PhD, concluded in 2014, he applied bioinformatics tools to identify molecular features responsible for complex immunological phenomena, such as heterologous immunity. His work has shown in silico predictions of T cell cross-reactivity among viral epitopes, which were later confirmed by in vitro experiments. He is now working in collaboration with a team from M.D. Anderson, developing structure-based methods that can be used to improve peptide-target selection in personalized cancer immunotherapy. He is also a monthly contributor of the Brazilian Society of Immunology Blog (SBlogI).

Contact: dinler@rice.edu

Curriculum and social media

Participation in the development of tools and databases

  • DINC: an incremental meta-docking approach for docking large ligands.
  • DockTope: an automated docking-based method for structural prediction of pMHC complexes.
  • CrossTope: a curate repository of pMHC structures, focused on immunogenicity and cross-reactivity.

Recent Publications

  1. J. R. Abella, D. A. Antunes, C. Clementi, and L. E. Kavraki, “APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations,” Molecules, vol. 24, no. 5, 2019.
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  2. D. A. Antunes, J. R. Abella, D. Devaurs, M. M. Rigo, and L. E. Kavraki, “Structure-based methods for binding mode and binding affinity prediction for peptide-MHC complexes,” Current Topics in Medicinal Chemistry, vol. 19, no. 1, 2019.
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  3. D. Devaurs, D. A. Antunes, and L. E. Kavraki, “Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data,” International Journal of Molecular Sciences, vol. 19, no. 11, p. 3406, 2018.
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  4. D. A. Antunes, D. Devaurs, M. Moll, G. Lizée, and L. E. Kavraki, “General prediction of peptide-MHC binding modes using incremental docking: A proof of concept,” Scientific Reports, vol. 8, p. 4327, 2018.
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  5. D. Devaurs, M. Papanastasiou, D. A. Antunes, J. R. Abella, M. Moll, D. Ricklin, J. D. Lambris, and L. E. Kavraki, “Native state of complement protein C3d analysed via hydrogen exchange and conformational sampling,” International Journal of Computational Biology and Drug Design, vol. 11, no. 1/2, pp. 90–113, 2018.
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  6. D. A. Antunes, M. Moll, D. Devaurs, K. R. Jackson, G. Lizée, and L. E. Kavraki, “DINC 2.0: a new protein-peptide docking webserver using an incremental approach,” Cancer Research, vol. 77, no. 21, pp. e55–57, Nov. 2017.
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  7. D. A. Antunes, M. M. Rigo, M. V. Freitas, M. M. FA, M. Sinigaglia, G. Lizée, L. E. Kavraki, L. K. Selin, M. Cornberg, and G. F. Vieira, “Interpreting T-cell cross-reactivity through structure: implications for TCR-based cancer immunotherapy,” Front. Immunol., vol. 8, no. 1210, 2017.
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  8. D. Devaurs, D. A. Antunes, M. Papanastasiou, M. Moll, D. Ricklin, J. D. Lambris, and L. E. Kavraki, “Coarse-grained conformational sampling of protein structure improves the fit to experimental hydrogen-exchange data,” Frontiers in Molecular Biosciences, vol. 4, no. 13, 2017.
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  9. D. A. Antunes, D. Devaurs, and L. E. Kavraki, “Understanding the challenges of protein flexibility in drug design,” Expert Opinion on Drug Discovery, vol. 10, no. 12, pp. 1301–1313, 2015.
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