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DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction

Farhan Quadir1, Raj S Roy1, Elham Soltanikazemi1

  • 1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.

Frontiers in Molecular Biosciences
|September 9, 2021
PubMed
Summary

DeepComplex predicts protein quaternary structures using deep learning for inter-chain contact prediction and distance-based modeling. This freely accessible web server aids protein function analysis, engineering, and drug design.

Keywords:
deep learningdistance-based modelinginter-chain contact predictionprotein complex structure predictionprotein interactionprotein quaternary structure prediction

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Area of Science:

  • Structural biology
  • Computational biology
  • Bioinformatics

Background:

  • Protein-protein interactions are crucial for biological functions.
  • Predicting protein complex structures aids in understanding protein function, engineering proteins, and drug design.
  • Existing tools for predicting quaternary structures often lack user-friendliness or advanced deep learning capabilities.

Purpose of the Study:

  • To develop a user-friendly web server, DeepComplex, for predicting the quaternary structures of dimeric protein complexes.
  • To leverage deep learning for accurate inter-chain contact prediction.
  • To integrate contact prediction with distance-based modeling for structure reconstruction.

Main Methods:

  • Deep learning models were employed to predict inter-chain residue-residue contacts in homodimers and heterodimers.
  • Predicted contacts were utilized in a distance-based modeling approach to construct the quaternary structure.
  • A web server was developed for easy input of protein tertiary structures and visualization of results.

Main Results:

  • DeepComplex accurately predicts inter-chain contacts for dimeric protein complexes.
  • The server successfully reconstructs quaternary structures based on predicted contacts.
  • Outputs include multiple sequence alignment, contact maps, and predicted dimer structures.

Conclusions:

  • DeepComplex provides a valuable, accessible tool for quaternary structure prediction of protein dimers.
  • The integration of deep learning and distance-based modeling offers an effective approach for structural analysis.
  • The web server facilitates research in protein function, engineering, and drug discovery.