S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures

This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.

Drugs swapping in coronavirus strains: a structural biology view.

Coronavirus belongs to the coronaviridae family, having a single-stranded RNA as genetic material of 26-42 kb in size. The first coronavirus infection emerged in 2002, caused by SARS-CoV1. Since then, genome sequences and three-dimensional structures of crucial proteins and enzymes of the virus have been studied in detail. The novel coronavirus (nCoV) outbreak has caused the COVID19 pandemic, which is responsible for the deaths of millions of people worldwide. The nCoV was later renamed as SARS-CoV2. The details of most of the COV proteins are available at the atomic and molecular levels. The entire genome is made up of 12 open reading frames that code for 27 different proteins. The spike surface glycoprotein, the envelope protein, the nucleocapsid protein, and the membrane protein are the four structural proteins which are required for virus attachment, entrance, assembly, and pathogenicity. The remaining proteins encoded are called non-structural (NSPs) and support the survival of the virus. Several non-structural proteins are also validated targets for drug development against coronavirus and are being used for drug design purposes. To perform a comparative study, sequences and three-dimensional structures of four crucial viral enzymes, Mpro, PLpro, RdRp, and EndoU from SARS-CoV1 and SARS-CoV2 variants were analyzed. The key structural elements and ligands recognizing amino acid residues were found to be similar in enzymes from both strains. The significant sequences and structural resemblance also suggest that a drug developed either for SARS-CoV1 or SARS-CoV2 using these enzymes may also have the potential to cross-react.Communicated by Ramaswamy H. Sarma.

S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures

This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.

Potential Autoimmunity Resulting from Molecular Mimicry between SARS-CoV-2 Spike and Human Proteins.

Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. The coronavirus SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. Thus, we computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative high autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins, such as PRKG1 involved in platelet activation and calcium regulation, and tropomyosin, which is linked to cardiac disease. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions.

RNA Secondary Structure Database, Analysis Tool-Set, and Case-Study Results on SARS-CoV-2

COVID-19 pandemic has brought immense attention to SARS-CoV-2 and related microbiology studies. To defeat this deadly virus, its RNA is being studied by many researchers around the globe. This study primarily aims to compile a large RNA dataset to analyze RNA secondary structure of SARS-CoV-2 efficiently. We propose improvements on database creation and maintenance, and structure analysis tools. As a continuation of our previous works, we automate the creation of RNA secondary structures database in a new format by converting data collected from publicly available online resources. We present new secondary structure analysis algorithms that improve performance of existing tools. Results of GPU-based implementation are also presented for RNA search operations. We also introduce tools with new objectives, which answer fundamental RNA secondary structure queries. Our tools on the current database have been tested with SARS-CoV-2 related RNA secondary structures. A novel RNA secondary structure search-based multiple RNA comparison is introduced and tested too. Structural-only and structure-with-nucleotide search results particularly related to SARS-CoV-2 are presented in details. As a successful case study, the framework presented here offers some unique capabilities and is shown as a useful exploratory tool for future RNA analysis studies. © 2021 IEEE.

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering, and structural motif identification and discovery of protein kinases, hydrolases, and SARS-CoV-2's protein via the application of amino acid grouping.

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.