Biology subjects

Goyal, R. K.

Publications and source records attributed to Goyal, R. K..

2 recordsLinked to original sources

bioRxiv2025

Time-Resolved Crystallography Reveals the Mechanisms of GTP hydrolysis for N-RAS and the Oncogenic Mutants G12C, G12V and Q61L

The RAS family of small GTPases are molecular switches that convey downstream signals regulating cell proliferation, differentiation, and apoptosis. The signaling competent GTP-bound RAS transitions to its inactive GDP-bound form through {gamma}-phosphate hydrolysis. Oncogenic RAS mutations hamper GTP hydrolysis and are present in up to 30% of all human cancers. Structural studies of RAS proteins bound to non-hydrolysable GTP analogs have revealed snapshots of the enzyme in its possibly active form. Yet, the mechanism of GTP hydrolysis has not been structurally resolved. To visualize this reaction in real time, we performed time-resolved crystallographic experiments employing a photolabile caged-GTP substrate. Fifty-seven distinctive reaction intermediates were captured during hydrolysis of a live GTP for N-RAS, the oncogenic mutants G12C, G12V and Q61L; and Y32R, a fast hydrolytic mutant. The reaction mechanisms and rates for the native and each of the mutants differed significantly; however, they shared common elements: an initially catalytically-defective open state, which transitions into the closed Michaelis complex state with solvent-assisted O3B-P{gamma} bond lengthening and breaking, followed by the release of the Mg2+ stabilized PO3-/PO4-3 species and unfolding of the switch loops. Given the conserved nature of GTP- and ATP-ases active sites, this structural work lays the basis to understand the universal mechanism of {gamma}-phosphate hydrolysis. Furthermore, search for cryptic binding sites during GTP hydrolysis in G12C, G12V, and Q61L mutants reveals the presence of distinctive state-dependent binding pockets that could be targets for structure-based drug discovery of experimentally resolved intermediates states.

cancer biology↗

bioRxiv2019

Differential expression feature extraction (DEFE) and its application in RNA-seq data analysis

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset to detect the association between such a group of genes and a phenotypic trait. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, up-, down- regulated, or unchanged. We propose here a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e. 1=up, 2=down, and 0=unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many datasets, of which we present in this study two sets of RNA-sequencing (RNA-seq) data on wheat challenged with stress related phytohormones or Fusarium graminearum, the causal agent of fusarium head blight (FHB), a devastating wheat disease to illustrate the algorithm. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB. DEFE enabled discovery of genes closely associated with defense related signaling molecules such as JAZ10, shikimate and chorismate biosynthesis pathway and groups of wheat genes with differential effects between more or less virulent strains of Fusarium graminearum.

genomics↗