ChaperoNet: Distillation of Language Model Semantics to Folded Three-Dimensional Protein Structures

Determining the structure of proteins has been a long-standing goal in biology. Lan- guage models have been recently deployed to capture the evolutionary semantics of protein sequences, and as an emergent property, were found to be structural learn- ers. Enriched with multiple sequence alignments (MSA), these transformer models were able to capture significant information about a protein’s tertiary structure. In this work, we show how such structural information can be recovered by processing language model embeddings, and introduce a two-stage folding pipeline to directly es- timate three-dimensional folded structures from protein sequences. We envision that this pipeline will provide a basis for efficient, end-to-end protein structure prediction through protein language modeling.

Name thesis:degree_name

Master

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• dos Santos Costa, Allan

Advisor dc:contributor.advisor

• Jacobson, Joseph M.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/142842

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/142842