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A Protocol for Computer-Based Protein Structure and Function Prediction
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Deep-learning contact-map guided protein structure prediction in CASP13.

Wei Zheng1, Yang Li1,2, Chengxin Zhang1

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.

Proteins
|August 1, 2019
PubMed
Summary

Automated protein structure prediction pipelines, Zhang-Server and QUARK, significantly improved accuracy in CASP13. New modules enhanced contact prediction, boosting performance for proteins lacking templates and demonstrating utility for template-based modeling.

Keywords:
CASP13ab initio foldingcontact predictiondeep convolutional neural networksdeep multiple sequence alignmentprotein structure prediction

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Structure Prediction

Background:

  • Accurate protein structure prediction is crucial for understanding biological function.
  • Automated methods are essential for high-throughput analysis.
  • CASP (Critical Assessment of protein Structure Prediction) provides a benchmark for evaluating prediction methods.

Purpose of the Study:

  • To evaluate the performance of two novel automated protein structure prediction pipelines, Zhang-Server and QUARK, in the CASP13 competition.
  • To assess the impact of new modules for multiple sequence alignment (MSA) generation, contact prediction (NeBcon, ResPRE), and structure assembly on prediction accuracy.
  • To compare the performance of the enhanced pipelines (C-I-TASSER, C-QUARK) against their predecessors (I-TASSER, QUARK).

Main Methods:

  • Development and implementation of two automated structure prediction pipelines: Zhang-Server (based on C-I-TASSER) and QUARK (based on C-QUARK).
  • Incorporation of three key modules: a novel MSA generation protocol, an improved meta-contact predictor (NeBcon) utilizing deep residual convolutional neural networks (ResPRE), and an optimized contact potential for structure assembly.
  • Evaluation of model accuracy using TM-scores on CASP13 free modeling (FM) and template-based modeling (TBM) domains.

Main Results:

  • C-I-TASSER and C-QUARK achieved significantly higher TM-scores compared to I-TASSER and QUARK for 50 CASP13 FM domains lacking homologous templates (28% and 56% improvement, respectively).
  • Contact-map predictions showed utility for TBM domains, with C-I-TASSER models yielding significantly higher TM-scores than I-TASSER models (P < .05).
  • Performance gains were attributed to improved accuracy of deep-learning-based contact maps and a balanced integration of restraints and potentials.

Conclusions:

  • The enhanced automated pipelines, C-I-TASSER and C-QUARK, represent a significant advancement in protein structure prediction accuracy, particularly for targets lacking templates.
  • Deep learning-based contact prediction is a key driver of success in modern structure prediction.
  • Challenges remain in predicting quaternary structures and accurately modeling terminal regions due to difficulties in domain partitioning, reassembly, and sparse MSAs.