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Related Experiment Video

Updated: Jul 12, 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

Protein functional site annotation using local structure embeddings.

Alexander Derry¹, Russ B Altman², Russ B Altman^1,2,3

¹Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.

Biorxiv : the Preprint Server for Biology

|October 31, 2023

Summary

This summary is machine-generated.

We developed PARSE (Protein Annotation by Residue-Specific Enrichment), a new method to predict protein function and identify responsible residues. This approach enhances accuracy for enzyme function prediction and discovers novel proteins.

Keywords:

Explainability Functional site annotation Machine learning Protein function

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Last Updated: Jul 12, 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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Published on: November 3, 2011

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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Published on: July 16, 2017

Area of Science:

Proteomics
Structural Biology
Bioinformatics

Background:

Vast protein databases contain many proteins with unknown functions, creating an annotation gap.
Current machine learning methods struggle to link global protein function to specific responsible residues.

Conclusions:

PARSE offers a powerful tool for accurate, explainable protein function prediction at the residue level.
Its ability to perform one-shot predictions and handle rare functions expands its applicability.
The method highlights the utility of local structure for discovering novel protein functions and annotations.