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1.
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)
COVID-19 , Caspases/chemistry , Machine Learning , Models, Molecular , SARS-CoV-2/chemistry , Viral Proteins/chemistry , Humans , Protein Structure, Quaternary , Protein Structure, Tertiary , Sequence Analysis, Protein
2.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1364739

ABSTRACT

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.


Subject(s)
COVID-19 Vaccines/genetics , COVID-19/drug therapy , Drug Discovery , SARS-CoV-2/genetics , Artificial Intelligence , COVID-19/genetics , COVID-19/virology , COVID-19 Vaccines/chemistry , Drug Design , Humans , Machine Learning , Pandemics , SARS-CoV-2/chemistry , SARS-CoV-2/pathogenicity
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