Generic placeholder image

Current Proteomics


ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

Research Article

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

Author(s): Carlos Polanco*, Vladimir N. Uversky, Guy W. Dayhoff, Alberto Huberman, Thomas Buhse, Manlio F. Márquez, Gilberto Vargas-Alarcón, Jorge Alberto Castañón-González, Leire Andrés, Juan Luciano Dı́az-González and Karina González-Bañales

Volume 19, Issue 1, 2022

Published on: 06 January, 2021

Page: [51 - 64] Pages: 14

DOI: 10.2174/1570164618666210106114606

Price: $65


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.

Keywords: Severe acute respiratory syndrome 2 proteins, antimicrobial peptides, structural proteomics, bioinformatics, intrinsic disorder predisposition, PIM profile.

Graphical Abstract
Peiris, J.S.; Yuen, K.Y.; Osterhaus, A.D.; Stöhr, K. The severe acute respiratory syndrome. N. Engl. J. Med., 2003, 349(25), 2431-2441.
Guo, Y.R.; Cao, Q.D.; Hong, Z.S.; Tan, Y.Y.; Chen, S.D.; Jin, H.J.; Tan, K.S.; Wang, D.Y.; Yan, Y. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status. Mil. Med. Res., 2020, 7(1), 11.
Tripp, E.A.; Zhang, N.; Schneider, H.; Huang, Y.; Mueller, G.M.; Hu, Z.; Häggblom, M.; Bhattacharya, D. Reshaping Darwin’s Tree: Impact of the Symbiome. Trends Ecol. Evol., 2017, 32(8), 552-555.
a hub for protein information. Nucleic Acids Res., 2014, 43(Database issue), D204-D212.
Polanco, C. , 2016.
Romero, P.; Obradovic, Z.; Li, X.; Garner, E.C.; Brown, C.J.; Dunker, A.K. Sequence complexity of disordered protein. Proteins, 2001, 42, 38-48.
Obradovic, Z.; Peng, K.; Vucetic, S.; Radivojac, P.; Dunker, A.K. Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins, 2005, 61(Suppl. 7), 176-182.
Peng, K.; Vucetic, S.; Radivojac, P.; Brown, C.J.; Dunker, A.K.; Obradovic, Z. Optimizing long intrinsic disorder predictors with protein evolutionary information. J. Bioinform. Comput. Biol., 2005, 3, 35-60.
Dosztanyi, Z.; Csizmok, V.; Tompa, P.; Simon, I. IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics, 2005, 21, 3433-3434.
Xue, B.; Dunbrack, R.L.; Williams, R.W.; Dunker, A.K.; Uversky, V.N. PONDR FIT: a meta-predictor of intrinsically disordered amino acids. Biochim. Biophys. Acta, 2010, 1804, 996-1010.
Mahlapuu, M.; Håkansson, J.; Ringstad, L.; Björn, C. Antimicrobial Peptides: An Emerging Category of Therapeutic Agents. Front. Cell. Infect. Microbiol., 2016, 6, 194.
Agrawal, P.; Bhalla, S.; Usmani, S.S.; Singh, S.; Chaudhary, K.; Raghava, G.P.; Gautam, A. CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides. Nucleic Acids Res., 2015, 44(D1), D1098-D10103.
Oldfield, C.J.; Cheng, Y.; Cortese, M.S.; Brown, C.J.; Uversky, V.N.; Dunker, A.K. Comparing and combining predictors of mostly disordered proteins. Biochemistry, 2005, 44, 1989-2000.
Siegel, S. Estadística no paramétrica aplicada a las ciencias., (1st ed. ), 1st ed. 1985.
Uversky, V.N.; Gillespie, J.R.; Fink, A.L. Why are “natively unfolded” proteins unstructured under physiologic conditions? Proteins, 2000, 41, 415-427.
Dunker, A.K.; Lawson, J.D.; Brown, C.J.; Williams, R.M.; Romero, P.; Oh, J.S.; Oldfield, C.J.; Campen, A.M.; Ratliff, C.M.; Hipps, K.W.; Ausio, J.; Nissen, M.S.; Reeves, R.; Kang, C.; Kissinger, C.R.; Bailey, R.W.; Griswold, M.D.; Chiu, W.; Garner, E.C.; Obradovic, Z. Intrinsically disordered protein. J. Mol. Graph. Model., 2001, 19, 26-59.
Radivojac, P.; Iakoucheva, L.M.; Oldfield, C.J.; Obradovic, Z.; Uversky, V.N.; Dunker, A.K. Intrinsic disorder and functional proteomics. Biophys. J., 2007, 92, 1439-1456.
Vacic, V.; Uversky, V.N.; Dunker, A.K.; Lonardi, S. Composition Profiler: a tool for discovery and visualization of amino acid composition differences. BMC Bioinformatics, 2007, 8, 211.
He, B.; Wang, K.; Liu, Y.; Xue, B.; Uversky, V.N.; Dunker, A.K. Predicting intrinsic disorder in proteins: an overview. Cell Res., 2009, 19, 929-949.
Meng, F.; Uversky, V.N.; Kurgan, L. Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell. Mol. Life Sci., 2017, 74, 3069-3090.
Prilusky, J.; Felder, C.E.; Zeev-Ben-Mordehai, T.; Rydberg, E.H.; Man, O.; Beckmann, J.S.; Silman, I.; Sussman, J.L. FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics, 2005, 21, 3435-3438.
Campen, A.; Williams, R.M.; Brown, C.J.; Meng, J.; Uversky, V.N.; Dunker, A.K. TOP-IDP-scale: a new amino acid scale measuring propensity for intrinsic disorder. Protein Pept. Lett., 2008, 15, 956-963.
Walsh, I.; Giollo, M.; Di Domenico, T.; Ferrari, C.; Zimmermann, O.; Tosatto, S.C. Comprehensive large-scale assessment of intrinsic protein disorder. Bioinformatics, 2015, 31, 201-208.
Polanco, C. Samaniego- Mendoza, J.L.; Buhse, T.; Uversky, N.V.; Bañuelos Chao, I.P.; Tavera, F.M.; Tavera, D.M.; Falconi, M.; Ponce de León, A.V. On the regularities of the polar profiles of proteins related to ebola virus infection and their functional domains. Cell Biochem. Biophys., 2018, 76, 411-431.
Qu, X.X.; Hao, P.; Song, X.J.; Jiang, S.M.; Liu, Y.X.; Wang, P.G.; Rao, X.; Song, H.D.; Wang, S.Y.; Zuo, Y.; Zheng, A.H.; Luo, M.; Wang, H.L.; Deng, F.; Wang, H.Z.; Hu, Z.H.; Ding, M.X.; Zhao, G.P.; Deng, H.K. Identification of two critical amino acid residues of the severe acute respiratory syndrome coronavirus spike protein for its variation in zoonotic tropism transition via a double substitution strategy. J. Biol. Chem., 2005, 280(33), 29588-29595.
Nidhan, K. Biswas, Partha P Majumder. Analysis of RNA Sequences of 3636 SARS-CoV-2 Collected From 55 Countries Reveals Selective Sweep of One Virus Type. Indian J. Med. Res., 2020, •••
Gudbjartsson, D.F.; Helgason, A.; Jonsson, H.; Magnusson, O.T.; Melsted, P.; Norddahl, G.L.; Saemundsdottir, J.; Sigurdsson, A.; Sulem, P.; Agustsdottir, A.B.; Eiriksdottir, B.; Fridriksdottir, R.; Gardarsdottir, E.E.; Georgsson, G.; Gretarsdottir, O.S.; Gudmundsson, K.R.; Gunnarsdottir, T.R.; Gylfason, A.; Holm, H.; Jensson, B.O.; Jonasdottir, A.; Jonsson, F.; Josefsdottir, K.S.; Kristjansson, T.; Magnusdottir, D.N.; le Roux, L.; Sigmundsdottir, G.; Sveinbjornsson, G.; Sveinsdottir, K.E.; Sveinsdottir, M.; Thorarensen, E.A.; Thorbjornsson, B.; Löve, A.; Masson, G.; Jonsdottir, I.; Möller, A.D.; Gudnason, T.; Kristinsson, K.G.; Thorsteinsdottir, U.; Stefansson, K. Spread of SARS-CoV-2 in the Icelandic Population. N. Engl. J. Med., 2020, 382(24), 2302-2315.
Polanco, C.; Samaniego, J.L.; Uversky, V.N.; Castañón-González, J.A.; Buhse, T.; Leopold-Sordo, M.; Madero-Arteaga, A.; Morales-Reyes, A.; Tavera-Sierra, L.; González-Bernal, J.A.; Arias-Estrada, M. Identification of proteins associated with amyloidosis by polarity index method. Acta Biochim. Pol., 2015, 62(1), 41-55.

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy