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October 14, 2024

Invited Talk - Sequence-based & structure-based machine learning methods for studying the adaptive immune response

The Institute of Informatics & Telecommunications hosted invited speaker Dr Romanos Fasoulis for a talk titled Sequence-based and structure-based machine learning methods for studying the adaptive immune response on Monday 21 October 2024 at 13.00pm EEST.

The talk took place physically at the Lecture Room of the Institute of Informatics & Telecommunications, while participation was also possible via zoom.

About the talk: The adaptive immune system comprises various biological mechanisms that, in unison, protect an organism against various threats, such as pathogens, viral infections, and tumor cells. One of such mechanisms involves the binding of intracellular protein fragments called peptides to class-I Major Histocompability Complexes (MHCs). The formed peptide-MHC (pMHC) complex is presented to the surface of the cell, where it interacts with the T-cell receptor, an interaction that can elicit an immune response. Knowing which peptides bind to MHCs, which peptides are presented to the surface of the cell, and which peptides elicit an immune response, is crucial for successful clinical applications and therapies. Due to the advent of mass spectrometry resulting in high-throughput generation of amino acid sequence-based pMHC binding data, amino acid sequence-based Machine Learning (ML) approaches have dominated the field, showing immense potential. At the same time however, it is known that the pMHC interaction is characterized by a strong structural component that is shown to be extremely important in fully explaining pMHC binding and peptide immunogenicity.

This talk will introduce ML methodologies that attend to both the amino acid sequence component and the structural component of the pMHC interaction. Focusing on the sequence component first, TLStab and TLImm, two ML-based tools that predict peptide kinetic stability and peptide immunogenicity respectively, will be introduced. Developed through adopting transfer learning methodologies, TLStab and TLImm outperform state-of-the-art approaches in their respective tasks. Next, focusing on the structural component of the pMHC interaction, RankMHC, a novel, Learning to Rank-based pMHC binding mode identification tool, will be presented. RankMHC outperforms both classical protein-ligand scoring functions and pMHC-specific scoring functions in predicting the most representative peptide conformation among an ensemble of conformations. Overall, these two tools demonstrate the successful use and potential of ML methodologies in both sequence-based and structural-based analyses.

Short Bio: Dr Romanos Fasoulis earned a doctorate in Computer Science at Rice University. He also completed an Electrical and Computer Engineering bachelor’s degree in the National Technical University of Athens (NTUA). His research interests mainly focus on immunoinformatics and machine learning applications on cell-mediated immunity and therapeutic peptide discovery.

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