Computational approaches for evaluating the effect of sequence variations and the intrinsically disordered C-terminal region of the Helicobacter pylori CagA protein on the interaction with tyrosine kinase Src

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Author list: Delgado P, Penaranda N, Zamora MA, Delgado MD, Bohorquez E, Castro H, Barrios AFG, Jaramillo C
Publisher: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals
Publication year: 2014
Volume number: 20
Issue number: 8
Number of pages: 8
ISSN: 1610-2940
Languages: English-Great Britain (EN-GB)


Abstract

The Helicobacter pylori CagA protein was the first bacterial oncoprotein to be identified as important in the development of human malignancies such as gastric cancer. It is not clear how it is able to deregulate a set of cell control mechanisms to induce carcinogenesis following translocation into human gastric epithelial cells. It is likely, however, that structural variations in the CagA sequence alter its affinity with the host proteins inducing differences in the pathogenicity of different H. pylori strains. Using the recently elucidated N-terminal 3D structure of H. pylori CagA, information on the full cagA gene sequence, and intrinsically disordered protein structure predictions methods we evaluated the interaction of different CagA variants with the kinase Src. An automated docking followed by molecular dynamics simulations were performed to explore CagA interaction modes with Src, one of its cellular partners. The computational approach let us establish that even in the presence of the same number and type of EPIYA motifs, CagA protein can reveal different spatial distributions. Based on the lowest affinity energy and higher number of interactions it was established that the principal forces governing the CagA-Src interaction are electrostatic. Results showed that EPIYA-D models presents higher affinity with some host proteins than EPIYA-C. Thus, we highlight the importance and advantage of the use of computational tools in combining chemical and biological data with bioinformatics for modeling and prediction purposes in some cases where experimental techniques present limitations.


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Last updated on 2019-23-08 at 11:15