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. (June 2021)

• Record Type: Journal Article
• Authors: Sarkar, Titli; Raghavan, Vijay V.; Chen, Feng; Riley, Andrew; Zhou, Sophia; Xu, Wu
• Is Part Of: Computational biology and chemistry. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Protein similarity -- 3-D structure -- Alignment-free -- Structural motif -- Structure comparison -- Amino acid grouping; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107479
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Exploring the effectiveness of the TSR-based method for protein clustering and structural motif identification via amino acid grouping. We have classified the keys into the different categories for better understanding of protein structure relations. Applying amino acid grouping to the TSR-based method modestly improves the accuracy of protein clustering in certain cases. Applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. TSR-based protein 3-D structural comparison method has its uniqueness in identification and discovery of structural motifs/binding sites. The substructures we defined for coronaviruses' nsp16 will help future antiviral drug design for improving therapeutic outcome. The substructures we have defined will also help to understand how nsp10 interacts with nsp16 to regulate function of nsp16. Abstract: 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.