LiteSeqCNN + MULTI-ATTENTION BASED FRAMEWORK FOR PROTEIN FUNCTION PREDICTION | IJCT Volume 13 – Issue 2 | IJCT-V13I2P16

This study establishes the significant benefits of integrating an attention mechanism with the Lite-SeqCNN architecture for protein function prediction, addressing key limitations of the original model. By leveraging attention, the enhanced model selectively focuses on biologically relevant regions within protein sequences, yielding substantial improvements in both predictive accuracy and interpretability. This advancement not only supports more precise annotations of protein functions but also provides insights into the underlying molecular mechanisms, fostering a deeper understanding of sequence-function relationships. The attention-enhanced Lite-SeqCNN demonstrates the importance of emphasizing functional motifs, which are often sparse but critical, enabling researchers to identify sequence regions that contribute most to the predictive outcomes. The interpretability offered by attention mechanisms bridges computational models and biological reasoning, making the predictions more transparent and actionable in bioinformatics applications. Despite the computational cost introduced by the attention mechanism, the improvements justify the trade-off, particularly when applied to large datasets such as Data2017. The approach sets a precedent for designing lightweight yet interpretable models in protein bioinformatics, paving the way for their use in high- throughput genomic and proteomic studies. Future directions include exploring advanced attention mechanisms, such as multi- head attention or scaled dot-product attention, to further enhance the model’s performance. Expanding the training dataset to include more diverse protein families could improve generalizability across various biological contexts. Additionally, applying the attention-enhanced Lite-SeqCNN to related bioinformatics challenges, such as predicting protein-protein interactions or identifying post-translational modification sites, could demonstrate the versatility and broader applicability of the model. Overall, this work provides a robust foundation for integrating attention mechanisms into deep learning frameworks for biological sequence analysis, addressing challenges posed by large-scale data while offering interpretable and accurate predictions that can drive future discoveries in the field.