TopQA: A topological representation for single-model protein quality assessment with machine learning

John Smith, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Dong Si, Miao Sun, Renzhi Cao

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Correctly predicting the complex three-dimensional structure of a protein from its sequence would allow for a superior understanding of the function of specific proteins with many applications. We propose a novel method aimed to tackle a crucial step in the protein prediction problem, assessing the quality of generated predictions. Unlike traditional methods, our method, to the best of our knowledge, is the first to analyse the topology of the predicted structure. We found that our new representation provided accurate information regarding the location of the protein's backbone. Using this information, we implemented a novel algorithm based on convolutional neural network (CNN) to predict GDT-TS score for given protein models. Our method has shown promising results - overall correlation of 0.41 on CASP12 dataset. Future work will aim to implement additional features into our representation. The software is freely available at GitHub: Https://github.com/caorenzhi/TopQA.

Original languageEnglish
Pages (from-to)144-153
Number of pages10
JournalInternational Journal of Computational Biology and Drug Design
Volume13
Issue number1
DOIs
StatePublished - 2020

Keywords

  • CNN
  • Convolutional neural network
  • Protein single-model quality assessment
  • Topological representation

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