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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Detecting remote evolutionary relationships among proteins by large-scale semantic embedding.

Iain Melvin1, Jason Weston, William Stafford Noble

  • 1NEC Laboratories America, Princeton, New Jersey, United States of America.

Plos Computational Biology
|February 8, 2011
PubMed
Summary
This summary is machine-generated.

ProtEmbed, a new algorithm, maps protein sequences into a semantic space to detect evolutionary relationships. This method outperforms existing tools for remote homology detection and aids in understanding protein sequence space.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Protein and DNA sequence databases are crucial for identifying evolutionarily related sequences.
  • Pairwise sequence comparison methods are foundational for database searches but struggle with remote homology detection.
  • Local models like profile hidden Markov models are effective but may not fully capture complex relationships.

Purpose of the Study:

  • To develop a novel algorithm, ProtEmbed, for detecting remote evolutionary relationships in protein sequences.
  • To leverage global data structure, inspired by web search and natural language processing, for improved sequence analysis.
  • To create a low-dimensional semantic space for protein sequences where related proteins are positioned closely.

Main Methods:

  • ProtEmbed learns embeddings of protein sequences into a low-dimensional semantic space.
  • The algorithm incorporates additional evidence, such as 3D structural similarity and class labels, into the embedding process.
  • It exploits the global structure of the protein sequence data space.

Main Results:

  • ProtEmbed demonstrates superior accuracy in remote homology detection compared to PSI-BLAST and HHSearch.
  • The algorithm also outperforms the previous RankProp method, which uses protein similarity networks.
  • The learned embedding space is visualizable, offering insights into protein sequence space structure.

Conclusions:

  • ProtEmbed offers a powerful new approach for identifying distant evolutionary links between proteins.
  • The method's ability to incorporate diverse evidence and visualize sequence space enhances its utility.
  • This work advances computational methods for understanding protein evolution and function.