1
|
Polanco C, Uversky VN, Dayhoff GW, Huberman A, Buhse T, Márquez MF, Vargas-Alarcón G, Castañón-González JA, Andrés L, Dı́az-González JL, González-Bañales K. Bioinformatics-Based Characterization of Proteins Related to SARS-CoV- 2 Using the Polarity Index Method® (PIM®) and Intrinsic Disorder Predisposition. CURR PROTEOMICS 2022. [DOI: 10.2174/1570164618666210106114606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
The global outbreak of the 2019 novel Coronavirus Disease (COVID-19) caused by the 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 predic-tion 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.
Collapse
Affiliation(s)
- Carlos Polanco
- Department of Electromechanical Instrumentation, Instituto Nacional de Cardiología “Ignacio Chávez”, México City
14800, México
- Department of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México
City 04510, México
| | - Vladimir N. Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer\'s Research Institute, Morsani
College of Medicine, University of South Florida, Tampa, FL33647, USA
- Protein Research Group, Institute for
Biological Instrumentation of the Russian Academy of Sciences, Federal Research Center “Pushchino Scientific Center
for Biological Research of the Russian Academy of Sciences”, 142290 Pushchino, Moscow region, Russia
| | - Guy W. Dayhoff
- Department of Molecular Medicine and USF Health Byrd Alzheimer\'s Research Institute, Morsani
College of Medicine, University of South Florida, Tampa, FL33647, USA
| | - Alberto Huberman
- Department of Biochemistry, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, C.P. 14080 México City,
México
| | - Thomas Buhse
- Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Morelos, Cuernavaca Morelos
62209, México
| | - Manlio F. Márquez
- Subdirección de Investigación Clínica, Instituto Nacional de Cardiología “Ignacio Chávez”, México
City 14800, México
| | - Gilberto Vargas-Alarcón
- Dirección de Investigación, Instituto Nacional de Cardiología “Ignacio Chávez”, México City
14800, México
| | | | - Leire Andrés
- Department
of Pathology, Hospital de Cruces, 48903, Barakaldo, Spain
| | - Juan Luciano Dı́az-González
- Department of Computer Sciences, Instituto de
Ciencias Nucleares, Universidad Nacional Autónoma de México, México City 04510, México
| | - Karina González-Bañales
- Department of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México
City 04510, México
| |
Collapse
|
2
|
Prabakaran R, Rawat P, Thangakani AM, Kumar S, Gromiha MM. Protein aggregation: in silico algorithms and applications. Biophys Rev 2021; 13:71-89. [PMID: 33747245 PMCID: PMC7930180 DOI: 10.1007/s12551-021-00778-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/01/2021] [Indexed: 01/08/2023] Open
Abstract
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.
Collapse
Affiliation(s)
- R. Prabakaran
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Puneet Rawat
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - A. Mary Thangakani
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT USA
| | - M. Michael Gromiha
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
- School of Computing, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa Japan
| |
Collapse
|