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

Titli Sarkar; Vijay V Raghavan; Feng Chen; Andrew Riley; Sophia Zhou; Wu Xu

doi:10.1016/j.compbiolchem.2021.107479

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

Comput Biol Chem. 2021 Jun:92:107479. doi: 10.1016/j.compbiolchem.2021.107479. Epub 2021 Mar 29.

Authors

Titli Sarkar¹, Vijay V Raghavan¹, Feng Chen², Andrew Riley¹, Sophia Zhou³, Wu Xu⁴

Affiliations

¹ The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA.
² High Performance Computing, 329 Frey Computing Services Center, Louisiana State University, Baton Rouge, LA 70803, USA.
³ Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
⁴ Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA. Electronic address: wxx6941@louisiana.edu.

PMID: 33951604
DOI: 10.1016/j.compbiolchem.2021.107479

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.

Keywords: 3-D structure; Alignment-free; Amino acid grouping; Protein similarity; Structural motif; Structure comparison.

MeSH terms

Amino Acid Sequence
Antiviral Agents / chemistry
Binding Sites
COVID-19 Drug Treatment
Cluster Analysis
Computer Simulation*
Imaging, Three-Dimensional
Models, Chemical*
Models, Molecular
Protein Binding
Protein Conformation
SARS-CoV-2*
Viral Proteins / chemistry*

Substances

Antiviral Agents
Viral Proteins