Using Co-evolutionary Information to Improve Protein Language Modelling
Name
Ram-soumyar-meng-eecs-2021-thesis.pdf
Description
Thesis PDF
Size
320.16 KB
Format
Adobe PDF
Checksum (MD5)
d4b77260789a839a6166aefd4a4e30ad
Author(s)
Ram, Soumya
Advisor(s)
Bepler, Tristan
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Protein engineering has the potential to solve complex global problems in medicine, clean energy, and manufacturing. However, current protein engineering efforts are hampered by a lack of supervised data. We help recitify this issue by developing supervised models that perform well in data-constrained settings by generalizing across protein engineering tasks and better incorporating coevolutionary and structural information. We also develop an unsupervised language model that conditions the target sequence on its multiple sequence alignment, allowing us to better model protein families.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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