[Proposal-ML]Protein Sequence Generation Model

pan jiang, Zeeshan Zulfiqar, Ivan Iazykov

Keywords: protein sequence generation, Mamba architecture, efficient modeling, protein language models, biological dynamics

TL;DR: This proposal explores implementing a Mamba architecture for efficient protein sequence generation, aiming to balance performance with reduced computational resource demands and making the architecture more suited to biological dynamics.

Abstract: This proposal outlines a novel approach for protein sequence generation, a key area in computational biology focused on generating sequences with specific functional and structural characteristics. Despite recent advances with deep learning models, challenges remain in handling long sequences and capturing biological dynamics. We propose implementing a Mamba architecture that leverages selective state-space updates to achieve efficient protein sequence generation. With linear computational complexity and effective handling of long-range dependencies, this architecture is designed to be more suited to biological dynamics while reducing resource requirements. A comprehensive evaluation on the UniRef50 dataset will demonstrate its potential to deliver competitive performance, providing a viable solution for research environments with limited computational resources.

Submission Number: 52