Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Protein Stability Determination (PSD): A Tool for Proteomics Analysis

Author(s): Anindya Sundar Panja, Akash Nag, Bidyut Bandopadhyay and Smarajit Maiti*

Volume 14, Issue 1, 2019

Page: [70 - 77] Pages: 8

DOI: 10.2174/1574893613666180315121614

Price: $65

Abstract

Background: Protein Stability Determination (PSD) is a sequence-based bioinformatics tool which was developed by utilizing a large input of datasets of protein sequences in FASTA format. The PSD can be used to analyze the meta-proteomics data which will help to predict and design thermozyme and mesozyme for academic and industrial purposes. The PSD also can be utilized to analyze the protein sequence and to predict whether it will be stable in thermophilic or in the mesophilic environment.

Method and Results: This tool which is supported by any operating system is designed in Java and it provides a user-friendly graphical interface. It is a simple programme and can predict the thermostability nature of proteins with >90% accuracy. The PSD can also predict the nature of constituent amino acids i.e. acidic or basic and polar or nonpolar etc.

Conclusion: PSD is highly capable to determine the thermostability status of a protein of hypothetical or unknown peptides as well as meta-proteomics data from any established database. The utilities of the PSD driven analyses include predictions on the functional assignment to a protein. The PSD also helps in designing peptides having flexible combinations of amino acids for functional stability. PSD is freely available at https://sourceforge.net/projects/protein-sequence-determination.

Keywords: Proteomics, thermophilic or mesophilic proteins, protein stability determination, amino acid property.

« Previous Next »
Graphical Abstract

[1]
Xuhua X, Wen-Hsiung L. What amino acid properties affect protein evolution? J Mol Evol 1998; 47: 557-64.
[2]
Baginsky S, Hennig L, Zimmermann P, Gruissem W. Gene expression analysis, proteomics, and network discovery. Plant Physiol 2010; 152: 402-10.
[3]
VerBerkmoes NC, Denef VJ, Hettich RL, Banfield JF. Functional analysis of natural microbial consortia using community proteomics. Nat Rev Microbiol 2009; 7: 196-205.
[4]
Schneider T, Riedel K. Environmental proteomics: analysis of structure and function of microbial communities. Proteomics 2010; 10: 785-98.
[5]
Hettich RL, Sharma R, Chourey K, Giannone RJ. Microbial metapro-teomics: identifying the repertoire of proteins that microorganisms use to compete and cooperate in complex environmental communities. Curr Opin Microbiol 2012; 15: 373-80.
[6]
DeLong EF, Preston CM, Mincer T, Rich V, Hallam SJ, Frigaard NU. Community genomics among stratified microbial assemblages in the ocean′s interior. Science 2006; 311: 496-503.
[7]
Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 2004; 428: 37-43.
[8]
Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson SJ, Remington K. The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families. PLoS Biol 2007; 5: e16.
[9]
Magliery TJ. Protein stability: computation, sequence statistics, and new experimental methods. Curr Opin Struct Biol 2015; 33: 161-8.
[10]
Bahrami A, Shojaosadati S, Mahbeli G. Biodegradation of dibenzothiophene by thermophilic bacteria. Biotechnol Lett 2001; 23: 899-901.
[11]
Bruins ME, Janssen AE, Boom RM. Thermozymes and their applications: a review of recent literature and patents. Appl Biochem Biotechnol 2001; 90: 155-86.
[12]
Bauer M, Driskil L, Callen W, Snead M, Mathur E, Kelly R. An endoglucanase EglA, from the hyperthermophilic archaeon Pyrococcus furiosus hydrolyzes a-1,4 bonds in mixed linkage (1-3), (1-4)-b-D-glucans and cellulose. J Bacteriol 1999; 181: 284-90.
[13]
Antranikian G, Herzberg C, Gottschalk G. Production of thermostable a-amylase, pullulanase and a-glucosidase in continuous culture by a new Clostridium isolate. Appl Environ Microbiol 1987; 53: 1668-73.
[14]
Haki GD, Rakshit SK. Developments in industrially important thermostable enzymes: a review. Bioresour Technol 2003; 89: 17-34.
[15]
Mozhaev VV. Mechanism-based strategies for protein thermo-stabilization. Trends Biotechnol 1993; 11: 88-95.
[16]
Panja AS, Bandopadhyay B, Maiti S. Protein thermostability is owing to their preferences to non-polar smaller volume amino acids, variations in residual physico-chemical properties and more salt-bridges. PLoS One 2015; 10(7): e0131495.
[17]
Alberts B, Johnson A, Lewis J. Molecular Biology of the Cell. 4th ed. Garland Science: New York 2002.
[18]
Metpally RP, Reddy BV. Comparative proteome analysis of psychrophilic versus mesophilic bacterial species: Insights into the molecular basis of cold adaptation of proteins. BMC Genomics 2009; 10: 11.
[19]
Gromiha MM, Suresh MX. Discrimination of mesophilic and thermophilic proteins using machine learning algorithms. Proteins 2008; 70: 1274-9.
[20]
Jahandideh S, Abdolmaleki P, Jahandideh M, Barzegari Asadabadi E. Sequence and structural parameters enhancing adaptation of proteins to low temperatures. J Theor Biol 2007; 246: 159-66.
[21]
Kumar S, Tsai CJ, Nussinov R. Thermodynamic differences among homologous thermophilic and mesophilic proteins. Biochemistry 2001; 40: 14152-65.
[22]
Zhang G, Fang B. Application of amino acid distribution along the sequence for discriminating mesophilic and thermophilic proteins. Process Biochem 2006; 41: 1792-8.
[23]
Si J, Zhao R, Wu R. An overview of the prediction of protein DNA-binding sites. Int J Mol Sci 2015; 16: 5194-215.
[24]
Hoppe C, Schomburg D. Prediction of protein thermostability with a direction-and distance-dependent knowledge-based potential. Protein Sci 2005; 14: 2682-92.
[25]
Zhang G, Fang B. Support vector machine for discrimination of thermophilic and mesophilic proteins based on amino acid composition. Protein Pept Lett 2006; 13: 965-70.
[26]
Kumwenda B, Litthauer D, Bishop OT, Reva O. Analysis of protein thermostability enhancing factors in industrially important thermus bacteria species. Evol Bioinform Online 2013; 9: 327-42.
[27]
Zhang G, Fang B. LogitBoost classifier for discriminating thermophilic and mesophilic proteins. J Biotechnol 2007; 127: 417-24.
[28]
Lin H, Chen W. Prediction of thermophilic proteins using feature selection technique. J Microbiol 2011; 84(1): 67-70.
[29]
Zuo YC, Chen W, Fan GL, Li QZ. A similarity distance of diversity measure for discriminating mesophilic and thermophilic proteins. Amino Acids 2013; 44: 573-80.
[30]
Wang L, Li C. Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification. Biotechnol Lett 2014; 36: 1963-9.
[31]
Wachter J, Hill S. Positive Selection Pressure Drives Variation on the Surface-Exposed Variable Proteins of the Pathogenic Neisseria. PLoS One 2016; 11: e0161348.
[32]
Hwang JH, Park JY, Park HJ, et al. Ecological factors drive natural selection pressure of avian aryl hydrocarbon receptor 1 genotypes. Sci Rep 2016; 6: 27526.
[33]
Oz T, Guvenek A, Yildiz S, Karaboga E, et al. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol Biol Evol 2014; 31: 2387-401.
[34]
Moreno-Fenoll C, Cavaliere M, Martínez-García E, Poyatos JF. Eco-evolutionary feedbacks can rescue cooperation in microbial populations. Sci Rep 2017; 7: 42561.
[35]
Akashi H, Kliman RM, Eyre-Walker A. Mutation pressure, natural selection, and the evolution of base composition in Drosophila. Genetica 1998; 102-103(1-6): 49-60.
[36]
Paperin G, Green DG, Sadedin S. Dual-phase evolution in complex adaptive systems. J R Soc Interface 2011; 8: 609-29.

Rights & Permissions Print Cite
Article Metrics
39
2
© 2024 Bentham Science Publishers | Privacy Policy