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GHOSH, I.

Publications and source records attributed to GHOSH, I..

2 recordsLinked to original sources

Mechanism of phosphorylation dependent interactions of complete Retinoblastoma-AB pocket domain with its linker

Retinoblastoma protein (pRb) is a critical tumor suppressor, whose activity is regulated through phosphorylation of Serine/Threonine residues within unstructured linker regions. Due to the absence of high-resolution structures encompassing these linkers, many studies have indirectly examined the phosphorylation effects on pRb-protein interactions. Here, we present computational models of the full pRb pocket domain (pRbAB), incorporating the flexible linker (pRbPL) that connects boxes A and B. Three models, generated by ROBETTA and AlphaFold, were used to explore the mechanisms by which phosphorylation at Ser608 and Ser612 influence the interaction of pRbAB with E2F transactivation domain (E2FTA). All models revealed a short helix (residues 602-607), similar to that observed in a previously published crystal structure of pRbAB with shortened pRbPL linker and phosphomimetic mutation at Ser608, with E2FTA (PDB:4ELL), and proposed to competitively inhibit E2FTA binding. Interestingly, this helix formed irrespective of phosphorylation, indicating that additional factors influencing the interaction upon phosphorylation. In our models, the pRbPL linker displayed significant motion, especially near Ser608 and Ser612, facilitating phosphorylation, but without inducing large conformational changes in the pocket domains. Phosphorylation at Ser608 and Ser612 resulted in changes in multiple parameters including electrostatic properties, compactness and energy landscape of pRbAB constituting important mechanisms that promote E2F release. Additionally, recapitulating known R661W mutation in these models correctly showed reduced E2FTA binding, demonstrating the predictive power of these models to study the effects of missense mutations on the stability, conformation and interactions of pRbAB with E2FTA.

cell biology↗

A machine learning based method for the identification of functionally important genes

Deciphering complex mechanisms underlying communicable and non-communicable diseases require comprehensive analysis of genetic factors and interactions between them. Experimental identification of genes related to pathogens virulence and human diseases is effective but laborious, time consuming and costly. Consequently, many genetic factors associated with pathogenesis or disease still remain to be unknown. In present work, a normalized point wise mutual information (nPMI) driven computational method has been developed to identify the association between biological entities (genes) and physiological responses utilizing published literatures in PubMed. Association prediction models are then developed using machine learning approach for four different datasets including virulent genes from two bacterial species (E. coli and S. enterica) and stress responsive genes from two plant species (A. thaliana and O. sativa). This approach provides a generic platform for identifying association of genes in diverse biological systems (host and pathogen) and provide up-to-date estimate of association measures of the genes with stress, virulence. In future, the causal relation between them may be of research importance.

bioinformatics↗