Article: TopQA: a topological representation for single-model protein quality assessment with machine learning Journal: International Journal of Computational Biology and Drug Design (IJCBDD) 2020 Vol.13 No.1 pp.144 - 153 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: John Smith; Matthew Conover; Natalie Stephenson; Jesse Eickholt; Dong Si; Miao Sun; Renzhi Cao

Addresses: Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA ' Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA ' Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA ' Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA ' Department of Computer Science, University of Washington Bothell, Bothell, WA 98011, USA ' JingChi Inc., Sunnyvale, CA 94089, USA ' Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA

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.

Keywords: CNN; convolutional neural network; protein single-model quality assessment; topological representation.

DOI: 10.1504/IJCBDD.2020.105095

International Journal of Computational Biology and Drug Design, 2020 Vol.13 No.1, pp.144 - 153

Received: 28 Jul 2018
Accepted: 19 Sep 2018
Published online: 13 Feb 2020 *

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

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