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Predicting protein model correctness in Coot using machine learning.

Paul S Bond1, Keith S Wilson1, Kevin D Cowtan1

  • 1Department of Chemistry, University of York, York YO10 5DD, United Kingdom.

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|August 4, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network-based correctness score to automatically identify errors in protein models. This tool significantly improves the accuracy and efficiency of protein structure validation and refinement.

Keywords:
Cootmachine learningmodel buildingsoftwarestructure solutionvalidation

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Manual correction of errors in protein models is time-consuming.
  • Automated model-building software and validation tools can reduce this burden.
  • Accurate protein models are crucial for understanding biological function.

Purpose of the Study:

  • To develop a new, automated method for assessing the correctness of protein models.
  • To reduce the manual effort required for protein model validation and refinement.
  • To integrate an automated correctness scoring system into existing model-building pipelines.

Main Methods:

  • A neural network was trained to predict residue correctness using features from 639 automatically built protein models.
  • Features included map-to-model correlation, density, B factors, clashes, Ramachandran, rotamer scores, and resolution.
  • Two neural networks were developed: one for main-chain atoms and one for side chains.

Main Results:

  • The neural networks achieved high accuracy, correctly categorizing 92.3% of main-chain atoms and 87.6% of side chains.
  • A Coot ML Correctness script was developed to visualize scores and enable automatic pruning of incorrect residues.
  • Integration into the CCP4i2 Buccaneer pipeline significantly improved automated model building, especially for high-resolution structures.

Conclusions:

  • The developed neural network-based correctness score offers an effective automated solution for protein model validation.
  • This approach reduces the burden of manual error correction, enhancing efficiency in structural biology.
  • The tool has been successfully integrated into automated pipelines, improving the quality of final protein models.