Abstract:
The peptide binding to Major Histocompatibility Complex (MHC) proteins is an important step in the antigen-presentation pathway. Thus, predicting the binding potential of peptides with MHC is essential for the design of peptide-based therapeutics. Most of the available machine learning-based models predict the peptide-MHC binding based on the sequence of amino acids alone. Given the importance of structural information in determining the stability of the complex, here we have utilized both the complex structure and the peptide sequence features to predict the binding affinity of peptides to human receptor HLA-A*02:01. To our knowledge, no such model has been developed for the human HLA receptor before that incorporates both structure and sequence-based features
Publication: Journal of Micromechanics and Molecular Physics
Publisher: worldscientific.com
Authors: Shikhar Saxena
Keywods: binding affinity,deep learning,structure,sequence, Peptide MHC Binding
Meet one of the Author:
Dr. Shikhar Saxena received Ph.D. in Biomedical Data Science and a strong background in applying quantitative methods to solve complex problems. Proficient in programming languages such as Python and R, and experienced in statistical modeling, machine learning, and data visualization. Adept at analyzing large-scale datasets and translating data-driven insights into actionable recommendations.
Affiliations:
Nanyang Technological University Singapore – A research-intensive public university, Nanyang Technological University, Singapore (NTU Singapore) has 33,000 undergraduate and postgraduate students in the Engineering, Business, Science, Medicine, Humanities, Arts, & Social Sciences, and Graduate colleges. The Nanyang Technological University is one of the world’s top universities ranking 26th in the QS World University Ranking 2023 and 36th in the Times Higher Education (THE) University Ranking 2023.
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