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Krishnamurthy, R.

Publications and source records attributed to Krishnamurthy, R..

2 recordsLinked to original sources

Depsipeptide nucleic acids: prebiotic formation, oligomerization, and self-assembly of a new candidate proto-nucleic acid

The mechanism by which genetic polymers spontaneously formed on the early Earth is currently unknown. The RNA World hypothesis implies that RNA oligomers were produced prebiotically, but the demonstration of this process has proven challenging. Alternatively, RNA may be the product of evolution and some, or all, of its chemical components may have been preceded by functionally analogous moieties that were more readily accessible under plausible early-Earth conditions. We report a new class of nucleic acid analog, depsipeptide nucleic acid, which displays several properties that make it an attractive candidate for the first informational polymer to arise on the Earth. The monomers of depsipeptide nucleic acids can form under plausibly prebiotic conditions. These monomers oligomerize spontaneously when dried from aqueous solutions to form nucleobase-functionalized depsipeptides. Once formed, these depsipeptide nucleic acid oligomers are capable of complementary self-assembly, and are resistant to hydrolysis in the assembled state. These results suggest that the initial formation of primitive, self-assembling, informational polymers may have been relatively facile.

biochemistry

deepMINE - Natural Language Processing based Automatic Literature Mining and Research Summarization for Early Stage Comprehension in Pandemic Situations specifically for COVID-19

The recent pandemic created due to Novel Coronavirus (nCOV-2019) from Wuhan, China demanding a large scale of a general health emergency. This demands novel research on the vaccine to fight against this pandemic situation, re-purposing of the existing drugs, phylogenetic analysis to identify the origin and determine the similarity with other known viruses, etc. The very preliminary task from the research community is to analyze the wide verities of existing related research articles, which is very much time-consuming in such situations where each minute counts for saving hundreds of human lives. The entire manual processing is even lower down the efficiency in mining the information. We have developed a complete automatic literature mining system that delivers efficient and fast mining from existing biomedical literature databases. With the help of modern-day deep learning algorithms, our system also delivers a summarization of important research articles that provides ease and fast comprehension of critical research articles. The system is currently scanning nearly 1,46,115,136 English words from 29,315 research articles in not greater than 1.5 seconds with multiple search keywords. Our research article presents the criticality of literature mining, especially in pandemic situations with the implementation and online deployment of the system.

bioinformatics