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Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

Badri Adhikari1, Jie Hou2, Jianlin Cheng2

  • 1Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, Missouri.

Proteins
|October 20, 2017
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This study evaluates protein contact prediction methods using multiple sequence alignment and coevolution. Machine learning integration significantly improves accuracy, highlighting the importance of deep alignments for predicting residue contacts.

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CASPcoevolutiondeep learningmachine learningmultiple sequence alignmentprotein contact prediction

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Machine Learning in Protein Science

Background:

  • Accurate prediction of residue-residue contacts is crucial for protein structure determination.
  • Traditional methods rely on sequence profiles, secondary structure, and solvent accessibility.
  • Coevolutionary information from multiple sequence alignments offers a powerful complementary approach.

Purpose of the Study:

  • To evaluate the impact of multiple sequence alignment depth, coevolutionary features, and machine learning on protein contact prediction.
  • To compare three distinct prediction methods: baseline (traditional features), coevolution-based, and a consensus approach.
  • To assess performance on the CASP12 dataset, particularly for challenging free-modeling domains.

Main Methods:

  • Developed and applied three deep learning-based methods: MULTICOM-NOVEL (traditional features), MULTICOM-CONSTRUCT (coevolution features), and MULTICOM-CLUSTER (consensus).
  • Utilized a novel alignment algorithm to generate deep multiple sequence alignments for deriving coevolutionary signals.
  • Integrated traditional and coevolutionary features using neural networks for enhanced contact prediction.

Main Results:

  • Coevolution-based features alone improved average precision from 28.4% to 41.6% on the CASP12 dataset.
  • The consensus method (MULTICOM-CLUSTER) achieved the highest precision of 56.3% by integrating all features.
  • Performance was particularly strong on free-modeling domains, with up to 41.7% precision for long-range contacts (top L/5).

Conclusions:

  • The quality and depth of multiple sequence alignments are critical drivers of contact prediction accuracy.
  • Coevolutionary information and its integration via machine learning significantly enhance the prediction of residue-residue contacts.
  • The study demonstrates the effectiveness of combining diverse feature types for robust protein contact prediction.