Antunes Lab
Dinler Amaral Antunes, Ph.D.
Assistant Professor, Computational Biology
Department of Biology and Biochemistry
Office: SERC, 3007
Contact: dinler@uh.edu
Dr. Dinler Amaral Antunes’ research group uses structural bioinformatics methods, such as molecular modeling, molecular docking and molecular dynamics, to investigate protein-ligand interactions with relevant biomedical applications. Since they are constantly pushing the limits of what can be done with available tools, his group is also actively adapting and developing new computational methods to address specific biological problems.
In addition to collaborative projects involving broader biomedical applications (e.g., drug discovery), his lab has a particular focus on studying the mechanisms involved in cellular immunity. This type of adaptive immunity is mediated by T-cell lymphocytes and is key for immunological responses targeting both viruses and cancer cells. T-cells can recognize pieces of proteins (i.e., peptides) displayed at the surface of other cells by a family of receptors known as human leukocyte antigens (HLAs). The recognition of peptide-HLA complexes is mediated by the complementarity determining regions (CDRs) of the T-cell receptor (TCR), representing a central step for the activation of T-cells and the development of cellular immunity.
Understanding the molecular features driving the affinity and specificity of the TCR/pHLA interaction can create new opportunities for biomedical applications across different fields, including antiviral vaccine development, cancer immunotherapy, and the treatment of autoimmune diseases. Over the past decade, Dr. Antunes has developed several tools to enable the computational modeling and structural analysis of peptide-HLA complexes. Now, he wants to combine these computational methods with data from high throughput molecular biology and proteomics approaches, to improve the safety and the efficacy of future T-cell-based immunotherapies.
More at Antunes Lab Research

Dinler Amaral Antunes, Ph.D.
Assistant Professor, Computational Biology
Department of Biology and Biochemistry
University of Houston
Houston, Texas 77204-5001
Office: SERC, 3007
dinler@uh.edu

Finn Beruldsen
Graduate Student

Sae Hee Choi
Graduate Student
Azariah Harrison, Undergraduate Student
E’Myah Jones, Undergraduate Student
Aditya Nair, Undergraduate Student
Kirby Nguyen, Undergraduate Student
Isabel Nguyen, Undergraduate Student
Elnaz Parchini, Undergraduate Student
Former Lab Members

Pamella Borges
Graduate Student

Akash Borigi
Former Masters Graduate Student

Hoa Nhu Le
Former Doctoral Graduate Student

Jaila Lewis
Graduate Student
Julia F. P. De Almeida
Alfredo Calderón-Macedo
Undergraduate Student Researcher
Uyen Do
Undergraduate Student Researcher
André Luís Fonseca Faustino, Ph.D.
Former Postdoctoral Fellow
Martiela Vaz de Freitas
Former Postdoctoral Fellow
Gabriel Galvez
Hussain Kalavadwala
Former Undergraduate Student Researcher
Oscar Olbera
Undergraduate Student Researcher
Yasmina Rezai
San Tran
Former Undergraduate Student Researcher
Selected Publications
- Beruldsen F, de Freitas MV, and Antunes DA, “High resolution mapping of protein motions in time and space with RMSX and Flipbook,” Scientific Reports, 2026.
- Borges P, Vaz de Freitas M, Yoo J, Beruldsen F, Lewis J, Freitas de Sousa FJ, Choi SH, Nguyen DB, Zanatta G, Jang JH, Donadi E, Alachkar H, Wolf S, Rigo M, Jeon H, and Antunes DA, “Mapping the TCR landscape: computational tools empowering translational immunology and therapy design,” J Immunother Cancer, vol. 14, no. 6, p. e014184, 2026.
- Le HN, de Freitas MV, and Antunes DA, “Strengths and limitations of web servers for the modeling of TCRpMHC complexes,” Computational and Structural Biotechnology Journal, vol. 23, pp. 2938–2948, July 2024.
- Antunes DA, Baker BM, Cornberg M, and Selin LK, “Editorial: Quantification and prediction of T-cell cross-reactivity through experimental and computational methods,” Frontiers in Immunology, vol. 15, p. 1377259, Feb. 2024.
- Fonseca AF and Antunes DA, “CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases,” Frontiers in Immunology, vol. 14, p. 1142573, June 2023.