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ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.
Hippe, Kyle; Lilley, Cade; William Berkenpas, Joshua; Chandana Pocha, Ciri; Kishaba, Kiyomi; Ding, Hui; Hou, Jie; Si, Dong; Cao, Renzhi.
  • Hippe K; Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA.
  • Lilley C; Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA.
  • William Berkenpas J; Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA.
  • Chandana Pocha C; Saint Louis University, USA.
  • Kishaba K; Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA.
  • Ding H; Center for Informational Biology at University of Electronic Science and Technology of China.
  • Hou J; Saint Louis University, USA.
  • Si D; University of Washington Bothell, USA.
  • Cao R; Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA.
Brief Bioinform ; 23(1)2022 01 17.
Article in English | MEDLINE | ID: covidwho-1434365
ABSTRACT
MOTIVATION The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius $r$ of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grade their placement within the protein as a whole. Moreover, we have shown the potential of ZoomQA to identify problematic regions of the SARS-CoV-2 protein complex.

RESULTS:

We benchmark ZoomQA on CASP14, and it outperforms other state-of-the-art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features and shows that our method is able to match the performance of other state-of-the-art methods without the use of homology searching against databases or PSSM matrices.

AVAILABILITY:

http//zoomQA.renzhitech.com.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Viral Proteins / Models, Molecular / Caspases / Machine Learning / SARS-CoV-2 / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Bib

Full text: Available Collection: International databases Database: MEDLINE Main subject: Viral Proteins / Models, Molecular / Caspases / Machine Learning / SARS-CoV-2 / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Bib