Integrating Functional Knowledge into Protein Design: A Novel Approach to Tokenization and Noise Injection for Function-Aware Protein Language Models
Name
tang-adrina77-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
1.9 MB
Format
Adobe PDF
Checksum (MD5)
02ab0967216a17fe82a7e3bdbe9e7a9a
Author(s)
Tang, Adrina
Advisor(s)
Berger, Bonnie
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Designing novel proteins with specific biological functions remains a fundamental challenge in computational biology. While recent advances in protein language models have enabled powerful sequence-based representations, most models, including state-of-the-art systems like ESM3, fall short in effectively encoding functional context during protein generation. In this work, we present a multimodal protein co-design framework that conditions sequence generation on fine-grained functional annotations, specifically leveraging residue-level Gene Ontology (GO) term labels on sequences from the UniRef100 database. By explicitly associating functional signals with residue elements of proteins, our model learns to generate function-conditioned protein sequences that are biologically plausible and semantically consistent. Unlike prior approaches, which treat function as a secondary feature or a classification task, our method focuses on joint reasoning over function and sequence during the design process. This closes a critical gap in the current landscape of protein design tools, offering a scalable and generalizable approach to co-designing protein sequences with user-specified functional profiles.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
