Unravelling the relationship between protein sequence and low-complexity regions entropies: Interactome implications

作者: F. Martins , R. Gonçalves , J. Oliveira , M. Cruz-Monteagudo , J.M. Nieto-Villar

DOI: 10.1016/J.JTBI.2015.06.049

关键词:

摘要: Low-complexity regions are sub-sequences of biased composition in a protein sequence. The influence these over evolution, specific functions and highly interactive capacities is well known. Although sequence entropy has been largely studied, its relationship with low-complexity the subsequent effects on function remains unclear. In this work we propose theoretical empirical model integrating local complexity parameters. Our results indicate that related length, entropies inside outside as their number average size. We found small but significant increment hubs proteins. agreement our model, dependent balance between length size regions. Finally, models proteins analysis provide evidence supporting modifications more relevant than changes

参考文章(42)
Francois-Xavier Theillet, Lajos Kalmar, Peter Tompa, Kyou-Hoon Han, Philipp Selenko, A. Keith Dunker, Gary W. Daughdrill, Vladimir N Uversky, The alphabet of intrinsic disorder: I. Act like a Pro: On the abundance and roles of proline residues in intrinsically disordered proteins. Intrinsically Disordered Proteins. ,vol. 1, ,(2013) , 10.4161/IDP.24360
B.J. Strait, T.G. Dewey, The Shannon information entropy of protein sequences. Biophysical Journal. ,vol. 71, pp. 148- 155 ,(1996) , 10.1016/S0006-3495(96)79210-X
S. Karlin, L. Brocchieri, A. Bergman, J. Mrazek, A. J. Gentles, Amino acid runs in eukaryotic proteomes and disease associations Proceedings of the National Academy of Sciences of the United States of America. ,vol. 99, pp. 333- 338 ,(2002) , 10.1073/PNAS.012608599
Cristian Robert Munteanu, Humberto González-Díaz, Alexandre L. Magalhães, Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices Journal of Theoretical Biology. ,vol. 254, pp. 476- 482 ,(2008) , 10.1016/J.JTBI.2008.06.003
Davide De Lucrezia, Debora Slanzi, Irene Poli, Fabio Polticelli, Giovanni Minervini, Do Natural Proteins Differ from Random Sequences Polypeptides? Natural vs. Random Proteins Classification Using an Evolutionary Neural Network PLoS ONE. ,vol. 7, pp. e36634- 10 ,(2012) , 10.1371/JOURNAL.PONE.0036634
Cristian Robert Munteanu, Humberto González-Díaz, Fernanda Borges, Alexandre Lopes de Magalhães, Natural/random protein classification models based on star network topological indices. Journal of Theoretical Biology. ,vol. 254, pp. 775- 783 ,(2008) , 10.1016/J.JTBI.2008.07.018
Humberto González-Dı́az, Reinaldo Molina, Eugenio Uriarte, Markov entropy backbone electrostatic descriptors for predicting proteins biological activity. Bioorganic & Medicinal Chemistry Letters. ,vol. 14, pp. 4691- 4695 ,(2004) , 10.1016/J.BMCL.2004.06.100
Benjamin Schuster-Böckler, Alex Bateman, Reuse of structural domain–domain interactions in protein networks BMC Bioinformatics. ,vol. 8, pp. 259- 259 ,(2007) , 10.1186/1471-2105-8-259
Edward A. Weathers, Michael E. Paulaitis, Thomas B. Woolf, Jan H. Hoh, Insights into protein structure and function from disorder-complexity space Proteins. ,vol. 66, pp. 16- 28 ,(2006) , 10.1002/PROT.21055