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- Conformational changes; fluorescence; human guanylate binding protein; regulation of GTP hydrolysis 1
- Conformational transition; Docking; Drug discovery; Inhibitors; Kinase; Molecular dynamics; PfCDPK1. 2
- Core to peripheral temperature difference; Lactate clearance; Mean lactate; Pediatric risk of mortality (PRISM II) 1
- Correlates-of-protection; Flavivirus vaccine; Japanese encephalitis; Neutralizing antibody; Tfh cells 1
- Correlates-of-protection; Flavivirus vaccine; Japanese encephalitis; Neutralizing antibody; Tfh cells. 1
- COVID-19 vaccine; Memory antibody response; Polymeric nanoparticles; Protein-subunit vaccine; Receptor binding domain; SARS-CoV-2 vaccine; Virus neutralization. 1
- Crystal structure 1
- CXCR4; Cancer immunotherapy; Metastasis; Mmp9; Mycobacterium indicus pranii. 2
- Cytokine secretion; 1
- Cytokine secretion; Dendritic cells; Immunity; MyD88; Mycobacteria; Tuberculosis. 1
- cytopathic, interferon, NF- B, RNA virus, RelA, burst size, cell death 1
- Deciphering kinase 1
- Dendritic cells; Immunity 1
- dengue virus, zinc, rotavirus, epithelial cells, NF-kappaB 1
- DNA MTases; DNA-protein interactions; N(6)-adenine MTases; Oligomeric MTases; Protein structure-function; Type III R-M systems 1
- EMSA 1
- EMSA; ITC; Mycobacterium tuberculosis; PadR family; Rv3488; crystal structure. 1
- Epigenetic regulation; Fate-bias; Gene regulatory network; Histone variant; Melanocyte; Pigmentation; Specification. 1
- Even though several in silico tools are available for prediction of the phosphorylation sites for mammalian, yeast or plant proteins, currently no software is available for predicting phosphosites for Plasmodium proteins. However, the availability of significant amount of phospho-proteomics data during the last decade and advances in machine learning (ML) algorithms have opened up the opportunities for deciphering phosphorylation patterns of plasmodial system and developing ML-based phosphosite prediction tools for Plasmodium. We have developed Pf-Phospho, an ML-based method for prediction of phosphosites by training Random Forest classifiers using a large data set of 12 096 phosphosites of Plasmodium falciparum and Plasmodium bergei. Of the 12 096 known phosphosites, 75% of sites have been used for training/validation of the classifier, while remaining 25% have been used as completely unseen test data for blind testing. It is encouraging to note that Pf-Phospho can predict the kinase-independent phosphosites with 84% sensitivity, 75% specificity and 78% precision. In addition, it can also predict kinase-specific phosphosites for five plasmodial kinases-PfPKG, Plasmodium falciparum, PfPKA, PfPK7 and PbCDPK4 with high accuracy. Pf-Phospho (http://www.nii.ac.in/pfphospho.html) outperforms other widely used phosphosite prediction tools, which have been trained using mammalian phosphoproteome data. It also has been integrated with other widely used resources such as PlasmoDB, MPMP, Pfam and recently available ML-based predicted structures by AlphaFold2. Currently, Pf-phospho is the only bioinformatics resource available for ML-based prediction of phospho-signaling networks of Plasmodium and is a user-friendly platform for integrative analysis of phospho-signaling along with metabolic and protein-protein interaction networks. 1
- Experimental vaccine 1