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Related Experiment Video

Updated: Jun 27, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Structure-Infused Protein Language Models.

Daniel Peñaherrera¹, David Ryan Koes¹

¹University of Pittsburgh.

Biorxiv : the Preprint Server for Biology

|May 7, 2024

Summary

This summary is machine-generated.

Protein language models (PLMs) benefit from structure information for better predictions. This study shows indirect structure infusion improves performance without needing structure at inference time.

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Last Updated: Jun 27, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational biology
Bioinformatics
Protein science

Background:

Protein language models (PLMs) excel at capturing sequence patterns for predicting protein properties.
Integrating protein structure data directly into PLMs enhances embedding predictive power for downstream tasks.

Approach:

This study explores indirectly infusing protein structure information into PLMs.
The developed framework improves performance on structure-related tasks.

Key Points:

Indirect structure infusion enhances PLM performance on structure-related tasks.
Crucially, the model does not require structure access during inference, making it more versatile.

Conclusions:

This approach offers a novel way to leverage structural insights within PLMs.
It provides a method for generating structure-aware protein embeddings without direct structural input at inference.