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Scoring protein sequence alignments using deep learning.

Bikash Shrestha1, Badri Adhikari1

  • 1Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63132, USA.

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|April 6, 2022
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Summary
This summary is machine-generated.

Predicting the quality of protein sequence alignments (SAs) is crucial for accurate protein structure prediction. This study introduces a novel deep learning method to score and rank multiple SAs, improving downstream structure modeling.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Accurate protein structure prediction relies heavily on high-quality sequence alignments (SAs).
  • Existing methods lack a way to assess the quality of different SAs for a given protein sequence without building models.
  • Selecting the best SA is critical for improving the accuracy of predicted protein structures.

Purpose of the Study:

  • To develop a method for predicting the quality of protein sequence alignments (SAs).
  • To enable the selection of superior SAs for enhanced protein structure prediction.
  • To provide a computational tool for evaluating multiple SAs for a single protein sequence.

Main Methods:

  • Generated a dataset of diverse SAs for 1351 representative proteins.
  • Investigated deep learning architectures to predict local distance difference test (lDDT) scores from SAs.
  • Utilized lDDT scores as a proxy for SA quality.

Main Results:

  • Developed and validated a deep learning model capable of scoring and ranking SAs.
  • Demonstrated the method's effectiveness on independent CASP13 and CASP14 test datasets.
  • Showcased how SA selection using this method can lead to improved protein structure prediction accuracy.

Conclusions:

  • The developed method accurately predicts the quality of protein sequence alignments.
  • This approach facilitates the selection of optimal SAs, thereby enhancing protein structure prediction.
  • The tool offers a valuable solution for researchers dealing with multiple SA options.