Akanksha Rajput

SigMol: repertoire of quorum sensing signaling molecules in prokaryotes

Quorum sensing is a widespread phenomenon in prokaryotes that helps them to communicate among themselves and with eukaryotes. It is driven through quorum sensing signaling molecules (QSSMs) in a density dependent manner that assists in numerous biological functions like biofilm formation, virulence factors secretion, swarming motility, bioluminescence, etc. Despite immense implications, dedicated resources of QSSMs are lacking. Therefore, we have developed SigMol (http://bioinfo.imtech.res.in/manojk/sigmol), a specialized repository of these molecules in prokaryotes. SigMol harbors information on QSSMs pertaining to different quorum sensing signaling systems namely acylated homoserine lactones (AHLs), diketopiperazines (DKPs), 4-hydroxy-2-alkylquinolines (HAQs), diffusible signal factors (DSFs), autoinducer-2 (AI-2) and others. Database contains 1382 entries of 182 unique signaling molecules from 215 organisms. It encompasses biological as well as chemical aspects of signaling molecules. Biological information includes genes, preliminary bioassays, identification assays and applications, while chemical detail comprises of IUPAC name, SMILES and structure. We have provided user-friendly browsing and searching facilities for easy data retrieval and comparison. We have gleaned information of diverse QSSMs reported in literature at a single platform ‘SigMol’. This comprehensive resource will assist the scientific community in understanding intraspecies, interspecies or interkingdom networking and further help to unfold different facets of quorum sensing and related therapeutics.

ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis.

Current Zika virus (ZIKV) outbreaks that spread in several areas of Africa, Southeast Asia, and in pacific islands is declared as a global health emergency by World Health Organization (WHO). It causes Zika fever and illness ranging from severe autoimmune to neurological complications in humans. To facilitate research on this virus, we have developed an integrative multi-omics platform; ZikaVR (http://bioinfo.imtech.res.in/manojk/zikavr/), dedicated to the ZIKV genomic, proteomic and therapeutic knowledge. It comprises of whole genome sequences, their respective functional information regarding proteins, genes, and structural content. Additionally, it also delivers sophisticated analysis such as whole-genome alignments, conservation and variation, CpG islands, codon context, usage bias and phylogenetic inferences at whole genome and proteome level with user-friendly visual environment. Further, glycosylation sites and molecular diagnostic primers were also analyzed. Most importantly, we also proposed potential therapeutically imperative constituents namely vaccine epitopes, siRNAs, miRNAs, sgRNAs and repurposing drug candidates.

Prediction and Analysis of Quorum Sensing Peptides Based on Sequence Features

Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew’s correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like “QSMotifScan”, “ProtFrag”, “MutGen” and “PhysicoProp”. Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm “QSPpred” (freely available at: http://crdd.osdd.net/servers/qsppred).

ge-CRISPR - An integrated pipeline for the prediction and analysis of sgRNAs genome editing efficiency for CRISPR/Cas system.

Genome editing by sgRNA a component of CRISPR/Cas system emerged as a preferred technology for genome editing in recent years. However, activity and stability of sgRNA in genome targeting is greatly influenced by its sequence features. In this endeavor, a few prediction tools have been developed to design effective sgRNAs but these methods have their own limitations. Therefore, we have developed “ge-CRISPR” using high throughput data for the prediction and analysis of sgRNAs genome editing efficiency. Predictive models were employed using SVM for developing pipeline-1 (classification) and pipeline-2 (regression) using 2090 and 4139 experimentally verified sgRNAs respectively from Homo sapiens, Mus musculus, Danio rerio and Xenopus tropicalis. During 10-fold cross validation we have achieved accuracy and Matthew’s correlation coefficient of 87.70% and 0.75 for pipeline-1 on training dataset (T1840) while it performed equally well on independent dataset (V250). In pipeline-2 we attained Pearson correlation coefficient of 0.68 and 0.69 using best models on training (T3169) and independent dataset (V520) correspondingly. ge-CRISPR (http://bioinfo.imtech.res.in/manojk/gecrispr/) for a given genomic region will identify potent sgRNAs, their qualitative as well as quantitative efficiencies along with potential off-targets. It will be useful to scientific community engaged in CRISPR research and therapeutics development.

Computational Exploration of Putative LuxR Solos in Archaea and Their Functional Implications in Quorum Sensing

LuxR solos are unexplored in Archaea, despite their vital role in the bacterial regulatory network. They assist bacteria in perceiving acyl homoserine lactones (AHLs) and/or non-AHLs signaling molecules for establishing intraspecies, interspecies, and interkingdom communication. In this study, we explored the potential LuxR solos of Archaea from InterPro v62.0 meta-database employing taxonomic, probable function, distribution, and evolutionary aspects to decipher their role in quorum sensing (QS). Our bioinformatics analyses showed that putative LuxR solos of Archaea shared few conserved domains with bacterial LuxR despite having less similarity within proteins. Functional characterization revealed their ability to bind various AHLs and/or non-AHLs signaling molecules that involve in QS cascades alike bacteria. Further, the phylogenetic study indicates that Archaeal LuxR solos (with less substitution per site) evolved divergently from bacteria and share distant homology along with instances of horizontal gene transfer. Moreover, Archaea possessing putative LuxR solos, exhibit the correlation between taxonomy and ecological niche despite being the inhabitant of diverse habitats like halophilic, thermophilic, barophilic, methanogenic, and chemolithotrophic. Therefore, this study would shed light in deciphering the role of the putative LuxR solos of Archaea to adapt varied habitats via multilevel communication with other organisms using QS.

In silico analyses of conservational, functional and phylogenetic distribution of the LuxI and LuxR homologs in Gram-positive bacteria

LuxI and LuxR are key factors that drive quorum sensing (QS) in bacteria through secretion and perception of the signaling molecules e.g. N-Acyl homoserine lactones (AHLs). The role of these proteins is well established in Gram-negative bacteria for intercellular communication but remain under-explored in Gram-positive bacteria where QS peptides are majorly responsible for cell-to-cell communication. Therefore, in the present study, we explored conservation, potential function, topological arrangements and evolutionarily aspects of these proteins in Gram-positive bacteria. Putative LuxI/LuxR containing proteins were retrieved using the domain-based strategy from InterPro v62.0 meta-database. Conservational analyses via multiple sequence alignment and domain showed that these are well conserved in Gram-positive bacteria and possess relatedness with Gram-negative bacteria. Further, Gene ontology and ligand-based functional annotation explain their active involvement in signal transduction mechanism via QS signaling molecules. Moreover, Phylogenetic analyses (LuxI, LuxR, LuxI + LuxR and 16s rRNA) revealed horizontal gene transfer events with significant statistical support among Gram-positive and Gram-negative bacteria. This in-silico study offers a detailed overview of potential LuxI/LuxR distribution in Gram-positive bacteria (mainly Firmicutes and Actinobacteria) and their functional role in QS. It would further help in understanding the extent of interspecies communications between Gram-positive and Gram-negative bacteria through QS signaling molecules.

Anti-biofilm Peptides: A New Class of Quorum Quenchers and Their Prospective Therapeutic Applications

Biofilms are the major concerns to the researchers, due to their universal distribution among prokaryotes and involvement in antibiotic drug resistance towards conventional drugs. It led the bacteria to become up to 1000 times resistant towards antibiotics. Therefore, diverse types of anti-biofilm agents are continuously designed to target them namely (phyto) chemicals, peptides, enzymes, biosurfactants, microbial extracts, nanoparticles, and many more. Antibiofilm peptides have demonstrated high potential in targeting biofilm due to their low toxicity, and off-target effects. These peptides are experimentally validated to disrupt most of the biofilms developed on medical devices like catheters, stents, dentures, etc. implicated in nosocomial infections by ESKAPE pathogens. However, one of the important reasons for the peptides, to emerge as a new hope against biofilms, is their wide mode of action against different stages and microbial species. In the present chapter, we are focusing to explore various aspects of this important class of antibiofilm therapeutics.

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

AbstractA number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC50 and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.