Abstract
Low Complexity Regions (LCRs) are fragments of protein sequences that are characterized by a small diversity in amino acid composition. LCRs could play important roles in protein functions or they could be relevant to protein structure. However, for many years, low complexity regions were ignored by the scientific community which resulted in lack of algorithms and tools that could be used to analyze this specific type of protein sequences.
Recently, researchers became interested in the so-called dark proteome and studies on such kind of proteins revealed that a vast amount of them include LCRs. Therefore, there is an urgent need to adapt existing methods or develop new ones that could be useful for analysis of LCRs, especially in the context of their functional roles in protein sequences.
In this paper, we present LCR-BLAST which is a new modification of BLAST designed to search for similarities among LCRs. This modification consists of the following elements: turning on short sequence parameters, turning off compositional based statistics, applying our own version of identity scoring matrix and replacing E-value with mean-score statistics. In order to evaluate the performance of our new modification, we compare the number of similar pairs found by LCR-BLAST with performance of a standard BLAST tool and of BLAST with a specific set of parameters for compositionally biased and short sequences. We show that our new method provides a robust and balanced solution for searching for similarities among LCRs.
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References
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)
Andrade, M.A., Perez-Iratxeta, C., Ponting, C.P.: Protein repeats: structures, functions, and evolution. J. Struct. Biol. 134, 117–131 (2001)
Bhowmick, A., Brookes, D.H., Yost, S.R., Dyson, H.J., Forman-Kay, J.D., Gunter, D., Head-Gordon, M., Hura, G.L., Pande, V.S., Wemmer, D.E., Wright, P.E., Head-Gordon, T.: Finding our way in the dark proteome. J. Am. Chem. Soc. 138(31), 9730–9742 (2016)
Consortium, T.U.: Uniprot: a worldwide hub of protein knowledge. Nucleic Acids Res. 17, D506–D515 (2019)
Dayhoff, M.O., Schwartz, R.M.: Chapter 22: a model of evolutionary change in proteins. In: Atlas of Protein Sequence and Structure (1978)
Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89(22), 10915–10919 (1992)
Karlin, S., Altschul, S.: Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. U.S.A. 87(6), 2264–2268 (1990)
Kruger, R.: Illuminating the dark proteome. Cell (2016)
Marcotte, E.M., Pellegrini, M., Yeates, T.O., Eisenberg, D.: A census of protein repeats. J. Mol. Biol. 293(1), 151–160 (1999)
Mier, P., Andrade-Navarro, M.A.: Glutamine codon usage and polyq evolution in primates depend on the q stretch length. Genome Biol. Evol. 10, 816–825 (2018)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)
Wootton, J.C., Federhen, S.: Statistics of local complexity in amino acid sequences and sequence databases. Comput. Chem. 17(2), 149–163 (1993)
Acknowledgements
The research was supported by Statutory Research of Institute of Informatics, Silesian University of Technology, Gliwice, Poland grant no. BK-204/RAU2/2019 (AG) and co-financed by the European Union through the European Social Fund, grant POWR.03.02.00-00-I029 (PJ).
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Jarnot, P., Ziemska-Legięcka, J., Grynberg, M., Gruca, A. (2020). LCR-BLAST—A New Modification of BLAST to Search for Similar Low Complexity Regions in Protein Sequences. In: Gruca, A., Czachórski, T., Deorowicz, S., Harężlak, K., Piotrowska, A. (eds) Man-Machine Interactions 6. ICMMI 2019. Advances in Intelligent Systems and Computing, vol 1061 . Springer, Cham. https://doi.org/10.1007/978-3-030-31964-9_16
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DOI: https://doi.org/10.1007/978-3-030-31964-9_16
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