ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features

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, si...

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Bibliographic Details
Published in:Briefings in bioinformatics 2022-01, Vol.23 (1)
Main Authors: Hippe, Kyle, Lilley, Cade, William Berkenpas, Joshua, Chandana Pocha, Ciri, Kishaba, Kiyomi, Ding, Hui, Hou, Jie, Si, Dong, Cao, Renzhi
Format: Article
Language:English
Subjects:
Accuracy
Amino acids
Bioinformatics
Caspases - chemistry
Chemical properties
COVID-19
Homology
Humans
Learning algorithms
Machine Learning
Model accuracy
Models, Molecular
Problem Solving Protocol
Protein structure
Protein Structure, Quaternary
Protein Structure, Tertiary
Proteins
Residues
SARS-CoV-2 - chemistry
Sequence Analysis, Protein
Severe acute respiratory syndrome coronavirus 2
Viral Proteins - chemistry
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Summary: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
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbab384