2026-09-18T19:15:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1428422026-07-31T12:52:30Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jacobson, Joseph M.
Costa, Allan dos Santos (Allan S.)
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2022-05-31T13:32:10Z
2022-05-31T13:32:10Z
2021-09
2022-05-25T15:54:53.397Z
https://hdl.handle.net/1721.1/142842
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.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
ChaperoNet: Distillation of Language Model Semantics to Folded Three-Dimensional Protein Structures
Thesis
application/pdf
Master
Master of Science in Media Arts and Sciences
Publication
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<Title>ChaperoNet: Distillation of Language Model Semantics to Folded Three-Dimensional Protein Structures</Title>
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<Abstract>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.</Abstract>
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