Biology subjects

KP, S.

Publications and source records attributed to KP, S..

4 recordsLinked to original sources

bioRxiv2025

Genomic Dissection of Metabolism, Prophage Elements, and Ecological Adaptations in Candidatus Liberibacter asiaticus

Candidatus Liberibacter asiaticus (CLas) is an obligate intracellular pathogen that causes citrus greening disease. Prolonged host association has driven reductive evolution and functional adaptation, leaving central carbon metabolism disrupted by the absence of phosphoglucose isomerase (pgi) in glycolysis and transaldolase in the pentose phosphate pathway (PPP). Here, we characterized the bifunctional enzyme, pyrophosphate-fructose-6-phosphate 1-phosphotransferase (PFP), that connects glycolysis and PPP. PFP primarily converts D-fructose 6-phosphate (F6P) to D-fructose 1,6-bisphosphate (FBP). The proposed alternative function of PFP is the conversion of D-sedoheptulose 7-phosphate (S7P) to D-sedoheptulose 1,7-bisphosphate (SBP), and this reaction is part of the sedoheptulose 1,7-bisphosphate pathway (SBPP). To validate this, the recombinant CLas PFP was heterologously expressed, purified, and biochemically characterized. Biochemical characterization showed comparable affinity for F6P and S7P, with Km values of 66.62 {+/-} 5.4 uM and 57.5 {+/-} 2.85 uM respectively. In-silico analysis showed distinct motifs that reflect specificity for phosphoryl donors among phosphofructokinase isoforms. Biophysical characterization showed that PFP loses conformational stability above 50 degree C and at pH 11.0. The study demonstrates that CLas PFP catalyzes the conversion of S7P to SBP, validating its dual function.

biochemistry↗

bioRxiv2019

Coconut inflorescence sap mediated synthesis of silver nanoparticles and its diverse antimicrobial properties

Green synthesis of nanoparticles (NPs) involves the use of diverse extracts of biological origin as substrates to synthesize nanoparticles and can overcome the hazards associated with chemical methods. Coconut inflorescence sap, which is unfermented phloem sap obtained by tapping of coconut inflorescence, is a rich source of sugars and secondary metabolites. In this study, coconut inflorescence sap was used to synthesize silver nanoparticles (AgNPs). We have initially undertaken metabolomic profiling of coconut inflorescence sap from West Coast Tall cultivar to delineate its individual components. Secondary metabolites constituted the major portion of the inflorescence sap along with sugars, lipids and, peptides. The concentration of silver nitrate, inflorescence sap and incubation temperature for synthesis of AgNPs were optimized. Incubating the reaction mixture at 40{degrees}C was found to enhance AgNP synthesis. The AgNPs synthesized were characterized using UV-Visible spectrophotometry, X-Ray Diffraction (XRD), Fourier Transform Infrared spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM) and Transmission Electron Microscopy (TEM). Antimicrobial property of AgNP was tested in tissue culture of arecanut (Areca catechu L.) where bacterial contamination (Bacillus pumilus) was a frequent occurrence. Significant reduction in the contamination was observed when plantlets were treated with aqueous solutions of 0.01, 0.02 and 0.03% of AgNPs for one hour. Notably, treatment with AgNPs did not affect growth and development of the arecanut plantlets. Cytotoxicity of AgNPs was quantified in HeLa cells. Viability (%) of HeLa cells declined significantly at 10 ppm concentration of AgNP and complete mortality was observed at 60 ppm. Antimicrobial properties of AgNPs synthesized from inflorescence sap were also evaluated and confirmed in human pathogenic bacteria viz., Salmonella sp., Vibrio parahaemolyticus, and Escherichia coli. The study concludes that unfermented inflorescence sap, with above neutral pH, serves as an excellent reducing agent to synthesize AgNPs from Ag+.\n\nO_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC=\"FIGDIR/small/775940v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (64K):\norg.highwire.dtl.DTLVardef@3c3f1forg.highwire.dtl.DTLVardef@1540f6borg.highwire.dtl.DTLVardef@1c069aborg.highwire.dtl.DTLVardef@159772c_HPS_FORMAT_FIGEXP M_FIG Graphical abstract\n\nC_FIG

plant biology↗

bioRxiv2018

DeepProteomics: Protein family classification using Shallow and Deep Networks

The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes. Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation. The laboratory experiments take a considerable amount of time for annotation of the sequences. This arises the need to use computational techniques to classify proteins based on their functions. In our work, we have collected the data from Swiss-Prot containing 40433 proteins which is grouped into 30 families. We pass it to recurrent neural network(RNN), long short term memory(LSTM) and gated recurrent unit(GRU) model and compare it by applying trigram with deep neural network and shallow neural network on the same dataset. Through this approach, we could achieve maximum of around 78% accuracy for the classification of protein families.

genomics↗

bioRxiv2018

Protein Family Classification using Deep Learning

Protein classification is responsible for the biological sequence, we came up with an idea which deals with the classification of proteomics using deep learning algorithm. This algorithm focuses mainly to classify sequences of protein-vector which is used for the representation of proteomics. Selection of the type protein representation is challenging based on which output in terms of accuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Keras embedding used for biological sequences like protein. In this paper we are working on the Protein classification to show the strength and representation of biological sequence of the proteins.

genomics↗