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Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Related Experiment Video

Updated: Aug 17, 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

Deep embedding and alignment of protein sequences.

Felipe Llinares-López¹, Quentin Berthet¹, Mathieu Blondel¹

¹Brain Team, Google Research, Paris, France.

|December 15, 2022

Summary

This summary is machine-generated.

Deep learning advances enable DEDAL (deep embedding and differentiable alignment), a new model for protein sequence alignment. DEDAL significantly improves the accuracy of aligning divergent sequences and detecting homologous proteins.

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Last Updated: Aug 17, 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: August 16, 2017

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:

Bioinformatics
Computational Biology
Genomics

Background:

Protein sequence alignment is crucial for understanding protein structure and function.
Current alignment algorithms struggle with highly divergent sequences, leading to poor annotation.
Advances in deep learning offer new possibilities for improving sequence alignment.

Purpose of the Study:

To introduce DEDAL (deep embedding and differentiable alignment), a novel deep learning-based model for protein sequence alignment.
To enhance the accuracy of aligning highly divergent protein sequences.
To improve the detection of remote homologous proteins.

Main Methods:

Leveraging deep learning for language modeling and differentiable programming.
Training DEDAL on large datasets of raw protein sequences and known alignments.
Evaluating DEDAL's performance against existing sequence alignment methods.

Main Results:

DEDAL demonstrates a two- to threefold improvement in alignment correctness for remote homologs.
The model exhibits superior discrimination between remote homologs and unrelated sequences.
DEDAL's approach paves the way for enhanced downstream applications in structural and functional genomics.

Conclusions:

DEDAL represents a significant advancement in protein sequence alignment, particularly for divergent sequences.
The model's improved accuracy and discrimination capabilities will benefit numerous bioinformatics tasks.
This work highlights the potential of deep learning to address long-standing challenges in computational biology.