Structure-Infused Protein Language Models
Penaherrera, D.; Koes, D. R.
Show abstract
Embeddings from protein language models (PLMs) capture intricate patterns for protein sequences, enabling more accurate and efficient prediction of protein properties. Incorporating protein structure information as direct input into PLMs results in an improvement on the predictive ability of protein embeddings on downstream tasks. In this work we demonstrate that indirectly infusing structure information into PLMs also leads to performance gains on structure related tasks. The key difference between this framework and others is that at inference time the model does not require access to structure to produce its embeddings.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- ProteinBERT: A universal deep-learning model of protein sequence and function 96%
- Pair-EGRET: enhancing the prediction of protein-proteininteraction sites through graph attention networks and protein language models 96%
- Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.