Publications by authors named "Rishin Haldar"

2 Publications

  • Page 1 of 1

Effective utilisation of influence maximization technique for the identification of significant nodes in breast cancer gene networks.

Comput Biol Med 2021 Jun 18;133:104378. Epub 2021 Apr 18.

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. Electronic address:

Background: Identifying the most important genes in a cancer gene network is a crucial step in understanding the disease's functional characteristics and finding an effective drug.

Method: In this study, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally. The novel approach involved incorporating gene expression data and protein to protein interaction network to create a customized pruned and weighted gene network. This was then readily provided to the influence maximization procedure. The weighted gene network was also processed through a widely accepted framework that identified essential proteins to benchmark the proposed method.

Results: The proposed method's results had matched with the majority of the output from the benchmarked framework. The key takeaway from the experiment was that the influential genes identified by the proposed method, which did not match favorably with the widely accepted framework, were found to be very important by previous in-vivo studies on breast cancer.

Interpretation & Conclusion: The new findings generated from the proposed method give us a favorable reason to infer that influence maximization added a more diversified approach to define and identify important genes and could be incorporated with other popular computational techniques for more relevant results.
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June 2021

Comprehensive in silico screening and molecular dynamics studies of missense mutations in Sjogren-Larsson syndrome associated with the ALDH3A2 gene.

Adv Protein Chem Struct Biol 2020 4;120:349-377. Epub 2020 Feb 4.

Department of Biomedical Sciences, College of Health and Sciences, Qatar University, Doha, Qatar.

Sjögren-Larsson syndrome (SLS) is an autoimmune disorder inherited in an autosomal recessive pattern. To date, 80 missense mutations have been identified in association with the Aldehyde Dehydrogenase 3 Family Member A2 (ALDH3A2) gene causing SLS. Disruption of the function of ALDH3A2 leads to excessive accumulation of fat in the cells, which interferes with the normal function of protective membranes or materials that are necessary for the body to function normally. We retrieved 54 missense mutations in the ALDH3A2 from the OMIM, UniProt, dbSNP, and HGMD databases that are known to cause SLS. These mutations were examined with various in silico stability tools, which predicted that the mutations p.S308N and p.R423H that are located at the protein-protein interaction domains are the most destabilizing. Furthermore, to determine the atomistic-level differences within the protein-protein interactions owing to mutations, we performed macromolecular simulation (MMS) using GROMACS to validate the motion patterns and dynamic behavior of the biological system. We found that both mutations (p.S380N and p.R423H) had significant effects on the protein-protein interaction and disrupted the dimeric interactions. The computational pipeline provided in this study helps to elucidate the potential structural and functional differences between the ALDH3A2 native and mutant homodimeric proteins, and will pave the way for drug discovery against specific targets in the SLS patients.
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February 2021