Predicting Protein Functions: A Deep Learning Approach to Unraveling Biological Complexity

This project addresses the critical challenge of predicting protein functions from amino acid sequences using machine learning approaches. With the exponential growth of genomic sequence data from various species, there is an urgent need for accurate computational methods to assign biological functions to proteins. Our work focuses on developing a predictive model that leverages both primary sequence data and complementary biological information to improve function prediction accuracy. The model will be trained on a comprehensive dataset of protein sequences with known functions, incorporating various features including amino acid composition, sequence patterns, and potentially other biological markers. This project addresses the critical challenge of predicting protein functions from amino acid sequences using a novel deep learning model. The model combines a transformer-based sequence encoder, an auxiliary feature integration layer, and a multi-label classification head to accurately predict multiple Gene Ontology (GO) terms for each input protein sequence. This research contributes to the broader field of functional genomics and has significant implications for understanding cellular mechanisms, disease pathways, and drug development. Success in this project could accelerate the annotation of newly discovered proteins and provide valuable insights for therapeutic interventions across various medical and agricultural applications.