Multitask Classification of Antimicrobial Peptides for Simultaneous Assessment of Antimicrobial Property and Structural Fold

Antimicrobial peptides (AMPs) play a significant role in guiding drug design, advancing targeted therapies, and cancer treatment research. The function of peptides is highly associated with their three-dimensional structure. AMPs particularly favor alpha-helical structures, or alpha-folds, due to their ability to disrupt the protective layers that surround cells effectively and their structural stability. Existing classifiers mainly identify AMPs but overlook their structural fold which can provide valuable insights into their function. To address this limitation, we introduce an innovative multitask classifier that recognizes AMPs and predicts their alphahelical folds simultaneously. Our approach employs k-mers and Transformer networks for efficient, accurate multitask classification. Results on the datasets indicate comparable performance compared to single-task methods in half the time and complexity.@INPROCEEDINGS{10446946, author={Zervou, Michaela Areti and Doutsi, Effrosyni and Pantazis, Yannis and Tsakalides, Panagiotis}, booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Multitask Classification of Antimicrobial Peptides for Simultaneous Assessment of Antimicrobial Property and Structural Fold}, year={2024}, volume={}, number={}, pages={1836-1840}, keywords={Training;Proteins;Peptides;Signal processing;Transformers;Real-time systems;Complexity theory;Antimicrobial peptides;Multitask classification;Transformers;k-mers}, doi={10.1109/ICASSP48485.2024.10446946}}