Author

S. Gourinath

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Jul 2026

Structural insight of the EhFP10-Rho2-Myosin-IB tricomplex reveals a novel signaling module for cytoskeleton remodeling in Entamoeba histolytica.

Entamoeba histolytica, a unicellular protozoan parasite, relies on phagocytosis as a key mechanism of pathogenesis. During this process, actin cytoskeleton remodeling occurs, often mediated by multiple signaling pathways, including the phosphoinositide-regulated Rho signaling cascade. EhFP10, a member of the Dbl homology GEF family, has been previously implicated in phagocytosis and pinocytosis through its interaction with EhMyosin-IB and its role in actin-myosin reorganization. In this study, we report the structural elucidation of the FP10_DH domain at 2.4 Å resolution. Despite low sequence similarity with known DH domains, FP10_DH shares significant structural similarity. Functional assays confirmed that the GDP-GTP exchange activity of FP10, mediated via Rho2, involves the DH (Dbl Homology) domain. Protein-protein interaction studies revealed that the FP10_DH domain directly interacts with Rho2. In contrast, the C-terminal region of FP10, previously known to bind the MyIB_SH3 domain, contributes to signal transmission. Furthermore, we demonstrate that Rho2 itself interacts with the MyIB_SH3 domain, forming a cross-connection. In silico studies of a dimer and tricomplex consisting of Rho2, FP10_DH, and MyIB_SH3-FP10_Cter peptide, suggesting a dynamic but functionally stable signaling module. This indicates two pathways by which FP10 might interact with MyIB: a direct mechanism via FP10_C-ter binding to the MyIB_SH3 domain, and an indirect pathway in which FP10 activates Rho2, which in turn binds to MyIB. Interactions between Rho and PAK4 suggest additional signal transduction, and PAK4 is hypothesized to phosphorylate Myosin-IB during actin--myosin reorganization. These findings propose a novel signaling pathway involving FP10 and Rho2 that facilitates myosin-mediated remodeling of the actin cytoskeleton.

Avinash Kumar Gautam, Preeti Umarao, S. Gourinath · The FEBS Journal · 0 citations

Open access Aug 2026

An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein

The Ca2+ binding sites of proteins are critical for their function, particularly in processes such as signal transduction, enzyme regulation, and structural stability. In this study, the calcium-binding sites of NtEhCaBP1 (Entamoeba histolytica calcium-binding protein). This paper proposes Statistical Ranking Deep Learning (SR-ML) to estimate the binding affinities of ten protein variants, The proposed SR-ML model computes the features in the proteins with the detection of sequences in the bindings. The classification of binding sites evaluated with the optimization of the features. With each predicted variant’s binding affinity correlates well with its experimental value with Kendall Tau (τ) values ranging from 0.78 to 0.95 and Spearman rank correlation (ρ) ranging from 0.75 to 0.94. Specifically, the Root Mean Square, Deviation (RMSD) shows protein flexibility in values of 0.95 to 1.50 angstrom and Root Mean Fluctuation (RMSF) values of 0.30 angstrom to 0.50 angstrom. The binding energy falls from negative 4.90 kcal/mol to negative 7.20 kcal/mol proposing differing levels of protein stability. Secondly, considering calcium coordination geometry we describe how there are octahedral, tetrahedral and trigonal bipyramidal structures in various proteins, with Kd values of 0.3 uM to 5.0 uM. The anti-AIDS bioactive example of mutagenesis validation is at a 120-folds to 600-folds increase from binding affinity for several mutations involving dynamic correlation with values of between 0.88 to 0.97. These outcomes reveal that the SR-ML model has certain predictive preciseness in terms of the Ca-binding sites and protein motions, which is valuable for Drug designing involving the Ca signalling Pathway.

P. Parwekar, S. Gourinath, Jaishree Jain et al. · PLoS ONE · 0 citations