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Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading.

Sutanu Bhattacharya1, Rahmatullah Roche1, Md Hossain Shuvo1

  • 1Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States.

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|May 28, 2021
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Protein homology detection uses deep learning to analyze coevolutionary signals, improving protein structure prediction. New methods align predicted interaction maps for enhanced distant-homology threading.

Keywords:
homology modelinginter-residue interaction mapprotein homologyprotein structure predictionprotein threading

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in bioinformatics

Background:

  • Sequence-based protein homology detection is crucial for accurate protein structure prediction.
  • Detecting homology in weakly related proteins with divergent evolutionary profiles remains a significant challenge.
  • Deep neural networks show promise in extracting coevolutionary signals from multiple sequence alignments.

Purpose of the Study:

  • To summarize recent advancements in protein homology detection.
  • To highlight the role of inter-residue interaction map threading in this field.
  • To discuss current limitations and future directions for improving sensitivity.

Main Methods:

  • Utilizing deep neural network architectures to mine coevolutionary signals.
  • Estimating inter-residue interaction maps from multiple sequence alignments.
  • Threading based on the alignment of predicted interaction maps at various granularities.

Main Results:

  • Deep learning effectively extracts coevolutionary information for homology detection.
  • Predicted inter-residue interaction maps provide valuable data for structure prediction.
  • Alignment of interaction maps, from binary to finer-grained, enhances distant-homology threading.

Conclusions:

  • Inter-residue interaction map threading represents a significant development in protein homology detection.
  • Further research into map alignment granularities and combinations can improve sensitivity.
  • Addressing current limitations is key to advancing protein structure prediction accuracy.