Bioinformatics-based Characterization of Proteins Related to SARS-CoV- 2 Using the Polarity Index Method® (PIM®) and Intrinsic Disorder Predisposition

Document Type

Article

Publication Date

2022

Keywords

Severe Acute Respiratory Syndrome 2 Proteins, Antimicrobial Peptides, Structural Proteomics, Bioinformatics, Intrinsic Disorder Predisposition, PIM Profile

Digital Object Identifier (DOI)

http://dx.doi.org/10.2174/1570164618666210106114606

Abstract

Background: The global outbreak of the 2019 novel Coronavirus disease (COVID-19) caused by infection with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which appeared in China at the end of 2019, signifies a major public health issue at the current time.

Objective: The objective of the present study is to characterize the physicochemical properties of the SARS-CoV-2 proteins at a residues level, and to generate a “bioinformatics fingerprint” in the form of a “PIM profile” created for each sequence utilizing the Polarity Index Method (PIM), suitable for the identification of these proteins.

Methods: Two different bioinformatics approaches were used to analyze sequence characteristics of these proteins at the residues level, an in-house bioinformatics system PIM, and a set of the commonly used algorithms for the prediction of protein intrinsic disorder predisposition, such as PONDR VLXT, PONDR VL3, PONDR VSL2, PONDR FIT, IUPred_short and IUPred_long. The PIM profile was generated for four SARS-CoV-2 structural proteins and compared with the corresponding profiles of the SARS-CoV-2 non-structural proteins, SARS-CoV-2 putative proteins, SARS-- CoV proteins, MERS-CoV proteins, sets of bacterial, fungal, and viral proteins, cell-penetrating peptides, and a set of intrinsically disordered proteins. We also searched for the UniProt proteins with PIM profiles similar to those of SARS-CoV-2 structural, non-structural, and putative proteins.

Results: We show that SARS-CoV-2 structural, non-structural, and putative proteins are characterized by a unique PIM profile. A total of 1736 proteins were identified from the 562,253 “reviewed” proteins from the UniProt database, whose PIM profile was similar to that of the SARS-CoV-2 structural, non-structural, and putative proteins.

Conclusion: The PIM profile represents an important characteristic that might be useful for the identification of proteins similar to SARS-CoV-2 proteins.

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Citation / Publisher Attribution

Current Proteomics, v. 19, issue 1, p. 51-64

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