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Parikesit, A. A.

Publications and source records attributed to Parikesit, A. A..

2 recordsLinked to original sources

In silico Comparison of Novel Proteins from Plasmodium Falciparum as Candidate Drug Targets against Malaria

Malaria is a parasitic disease with a persistent prevalence in multiple parts of the world. Plasmodium falciparum, the deadliest human-infecting strain, has been a rising concern as the multi-faceted life-cycle poses a challenge in developing anti-malarial treatments while the effectiveness of present drugs begins to decline. As such, various research has been done to identify new drug targets leveraging modern computational biology techniques, such as proteomics. The objective of this study is to evaluate potential drug targets by comparing novel proteins in P. falciparum as reported in recent literature (2018-2022) based on their structural analysis of their 3D models along with the druggability properties and protein-protein interactions. A list of 22 proteins were acquired from 48 literature, in which only 7 can be considered as a favorable candidate protein drug target due to their non-human homologous nature, ERRAT score, residues percentage in favourable spot in Ramachandran plot, and high druggability score. Following an examination of protein-protein interactions, it is found that among the evaluated proteins only PF3D7_1438600 exhibits a sufficiently robust network, highlighting it to be the most promising candidate for drug targeting. This study demonstrates the valuable role of proteomics in uncovering potential drug targets and highlights its significance in driving emerging research efforts.

bioinformatics↗

Large-Scale Evolutionary Patterns of Protein Domain Distributions in Eukaryotes

The genomic inventory of protein domains is an important indicator of an organisms regulatory and metabolic capabilities. Existing gene annotations, however, can be plagued by substantial ascertainment biases that make it difficult to obtain and compare quantitative domain data. We find that quantitative trends across the Eukarya can be investigated based on a combination of gene prediction and standard domain annotation pipelines. Species-specific training is required, however, to account for the genomic peculiarities in many lineages. In contrast to earlier studies we find wide-spread statistically significant avoidance of protein domains associated with distinct functional high-level gene-ontology terms.\n\n1998 ACM Subject Classification J.3 Life and Medical Sciences

bioinformatics↗