In the past decade, the microRNAs (miRNAs) have emerged to be important regulators of gene expression across various species. Several studies have confirmed different types of post-transcriptional modifications at terminal ends of miRNAs. The reports indicate that miRNA modifications are conserved and functionally significant as it may affect miRNA stability and ability to bind mRNA targets, hence affecting target gene repression. Next Generation Sequencing (NGS) of the small RNA (sRNA) provides an efficient and reliable method to explore miRNA modifications. The need for dedicated software, especially for users with little knowledge of computers, to determine and analyze miRNA modifications in sRNA NGS data, motivated us to develop miRMOD. miRMOD is a user-friendly, Microsoft Windows and Graphical User Interface (GUI) based tool for identification and analysis of 5' and 3' miRNA modifications (non-templated nucleotide additions and trimming) in sRNA NGS data. In addition to identification of miRNA modifications, the tool also predicts and compares the targets of query and modified miRNAs. In order to compare binding affinities for the same target, miRMOD utilizes minimum free energies of the miRNA:target and modified-miRNA:target interactions. Comparisons of the binding energies may guide experimental exploration of miRNA post-transcriptional modifications. The tool is available as a stand-alone package to overcome large data transfer problems commonly faced in web-based high-throughput (HT) sequencing data analysis tools. miRMOD package is freely available at http://bioinfo.icgeb.res.in/miRMOD.

Protein secretion is an essential process in all eukaryotes including organisms belonging to the phylum Apicomplexa, which includes many intracellular parasites. The apicomplexan parasites possess a specialized collection of secretory organelles that release a number of proteins to facilitate the invasion of host cells and some of these proteins also participate in immune evasion. Like in other eukaryotes, these parasites possess a series of membrane-bound compartments, namely the endoplasmic reticulum (ER), the intermediate compartments (IC) or vesicular tubular clusters (VTS) and Golgi complex through which proteins pass in a sequential and vectorial fashion. Two sets of proteins; COPI and COPII are important for directing the sequential transfer of material between the ER and Golgi complex.

Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a freely accessible web server pVsupPred at http://bioinfo.icgeb.res.in/pvsup/.

GH7 cellobiohydrolases (CBH1) are vital for the breakdown of cellulose. We had previously observed the enzyme as the most dominant protein in the active cellulose-hydrolyzing secretome of the hypercellulolytic ascomycete-Penicillium funiculosum (NCIM1228). To understand its contributions to cellulosic biomass saccharification in comparison with GH7 cellobiohydrolase from the industrial workhorse-Trichoderma reesei, we natively purified and functionally characterized the only GH7 cellobiohydrolase identified and present in the genome of the fungus.

The outbreak of COVID-19 caused by SARS-CoV-2 virus continually led to infect a large population worldwide. Currently, there is no specific viral protein-targeted therapeutics. The Nucleocapsid (N) protein of the SARS-CoV-2 virus is necessary for viral RNA replication and transcription. The C-terminal domain of N protein (CTD) involves in the self-assembly of N protein into a filament that is packaged into new virions. In this study, the CTD (PDB ID: 6WJI) was targeted for the identification of possible inhibitors of oligomerization of N protein. Herein, multiple computational approaches were employed to explore the potential mechanisms of binding and inhibitor activity of five antiviral drugs toward CTD. The five anti-N drugs studied in this work are 4E1RCat, Silmitasertib, TMCB, Sapanisertib, and Rapamycin. Among the five drugs, 4E1RCat displayed highest binding affinity (-10.95 kcal/mol), followed by rapamycin (-8.91 kcal/mol), silmitasertib (-7.89 kcal/mol), TMCB (-7.05 kcal/mol), and sapanisertib (-6.14 kcal/mol). Subsequently, stability and dynamics of the protein-drug complex were examined with molecular dynamics (MD) simulations. Overall, drug binding increases the stability of the complex with maximum stability observed in the case of 4E1RCat. The CTD-drug complex systems behave differently in terms of the free energy landscape and showed differences in population distribution. Overall, the MD simulation parameters like RMSD, RMSF, Rg, hydrogen bonds analysis, PCA, FEL, and DCCM analysis indicated that 4E1RCat and TMCB complexes were more stable as compared to silmitasertib and sapanisertib and thus could act as effective drug compounds against CTD.Communicated by Ramaswamy H. Sarma.

Drug repositioning continues to be the most effective, practicable possibility to treat COVID-19 patients. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters target cells by binding to the ACE2 receptor via its spike (S) glycoprotein. We used molecular docking-based virtual screening approaches to categorize potential antagonists, halting ACE2-spike interactions by utilizing 450 FDA-approved chemical compounds. Three drug candidates (i.e., anidulafungin, lopinavir, and indinavir) were selected, which show high binding affinity toward the ACE2 receptor. The conformational stability of selected docked complexes was analyzed through molecular dynamics (MD) simulations. The MD simulation trajectories were assessed and monitored for ACE2 deviation, residue fluctuation, the radius of gyration, solvent accessible surface area, and free energy landscapes. The inhibitory activities of the selected compounds were eventually tested in-vitro using Vero and HEK-ACE2 cells. Interestingly, besides inhibiting SARS-CoV-2 S glycoprotein induced syncytia formation, anidulafungin and lopinavir also blocked S-pseudotyped particle entry into target cells. Altogether, anidulafungin and lopinavir are ranked the most effective among all the tested drugs against ACE2 receptor-S glycoprotein interaction. Based on these findings, we propose that anidulafungin is a novel potential drug targeting ACE2, which warrants further investigation for COVID-19 treatment.

Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive form of pancreatic cancer that typically manifests itself at an advanced stage and does not respond to most treatment modalities. The survival rate of a PDAC patient is less than 5%, with a median survival of just a couple of months. A better understanding of the molecular pathology of PDAC is needed to guide research for the development of better clinical treatment modalities for PDAC patients. Gene expression studies performed to date have identified different subtypes of PDAC with prognostic and clinical relevance. Subtypes identified to date are highly heterogeneous since pancreatic cancer is heterogeneous cancer. Tumor microenvironment and stroma constitute a major chunk of PDAC and contribute to the heterogeneity. Better subtyping methods are need of the hour for better prognosis and classification of PDAC for future personalized treatment. In this work, we have performed an integrated analysis of DNA methylation and gene expression datasets to provide better mechanistic and molecular insights into Pancreatic cancers, especially PDAC. The use of varied and diverse datasets has provided valuable insights into different cancer types and can play an integral role in revealing the complex nature of underlying biological mechanisms. We performed subtyping of TCGA-PAAD gene expression and methylation datasets into different subtypes using state-of-the-art normalization methods and unsupervised clustering methods that reveal latent hidden factors, leading to additional insights for subtyping. Differential expression and differential methylation were performed for each of the subtypes obtained from clustering. Our analysis gave a consensus of five cluster solution with relevant pathways like MAPK, MET. The five subtypes corresponded to the tumor and stromal subtypes. This analysis helps in distinguishing and identifying different subtypes based on enriched putative genes. These results help propose novel experimentally-verifiable PDAC subtyping and demonstrate that using varied data sets and integrated methods can contribute to disease prognostication and precision medicine in PDAC treatment.

Microtubules are tubulin polymers present in the eukaryotic cytoskeleton essential for structural stability and cell division that are also roadways for intracellular transport of vesicles and organelles. In the human malaria parasite Plasmodium falciparum, apart from providing structural stability and cell division, microtubules also facilitate important biological activities crucial for parasite survival in hosts, such as egression and motility. Hence, parasite structures and processes involving microtubules are among the most important drug targets for discovering much-needed novel Plasmodium inhibitors. The current study aims to construct reliable and high-quality 3D models of α-, β-, and γ-tubulins using various modeling techniques. We identified a common binding pocket specific to Plasmodium α-, β-, and γ-tubulins. Molecular dynamics simulations confirmed the stability of the Plasmodium tubulin 3D structures. The models generated in the present study may be used for protein-protein and protein-drug interaction investigations targeted toward designing malaria parasite tubulin-specific inhibitors.

While our battle with the COVID-19 pandemic continues, a multitude of Omics data have been generated from patient samples in various studies. Translation of these data into clinical interventions against COVID-19 remains to be accomplished. Exploring host response to COVID-19 in the upper respiratory tract can unveil prognostic markers and therapeutic targets.

Tau tubulin kinase 2 (TTBK2) associated with multiple diseases is one of the kinases which phosphorylates tau and tubulin. Numerous efforts have been made to understand the role of TTBK2 in protein folding mechanisms and misfolding behavior. The misfolded protein intermediates form polymers with unwanted aggregation properties that initiate several diseases, including Alzheimer's. The availability of TTBK2 inhibitors can enhance the understanding of the molecular mechanism of action of the kinase and assist in developing novel therapeutics. In the quest for TTBK2 inhibitors, this study focuses on screening two chemical libraries (ChEMBL and ZINC-FDA). The molecular docking, RO5/absorption, distribution, metabolism, and excretion/toxicity, density functional theory, molecular dynamics (MD) simulations, and molecular mechanics with generalized Born and surface area solvation techniques enabled shortlisting of the four most active compounds, namely, ChEMBL1236395, ChEMBL2104398, ChEMBL3427435, and ZINC000000509440. Moreover, 500 ns MD simulation was performed for each complex, which provided valuable insights into the structural changes in the complexes. The relative fluctuation, solvent accessible surface area, atomic gyration, compactness covariance, and free energy landscapes revealed that the compounds could stabilize the TTBK2 protein. Overall, this study would be valuable for the researchers targeting the development of novel TTBK2 inhibitors.

Influenza still remains one of the most challenging diseases, posing a significant threat to public health. Host lipid rafts play a critical role in influenza A virus (IAV) assembly and budding, however, their role in polyvalent IAV host binding and endocytosis had remained elusive until now. In the present study, we observed co-localization of IAV with a lipid raft marker ganglioside, GM1, on the host surface. Further, we isolated the lipid raft micro-domains from IAV infected cells and detected IAV protein in the raft fraction. Finally, raft disruption using Methyl-β-Cyclodextrin revealed significant reduction in IAV host binding, suggesting utilization of host rafts for polyvalent binding on the host cell surface. In addition to this, cyclodextrin mediated inhibition of raft-dependent endocytosis showed significantly reduced IAV internalization. Interestingly, exposure of cells to cyclodextrin two hours post-IAV binding showed no such reduction in IAV entry, indicating use of raft-dependent endocytosis for host entry. In summary, this study demonstrates that host lipid rafts are selected by IAV as a host attachment factors for multivalent binding, and IAV utilizes these micro-domains to exploit raft-dependent endocytosis for host internalization, a virus entry route previously unknown for IAV.

The processing of miRNA from its precursors is a precisely regulated process and after biogenesis, the miRNAs are amenable to different kinds of modifications by the addition or deletion of nucleotides at the terminal ends. However, the mechanism and functions of such modifications are not well studied in plants. In this study, we have specifically analysed the terminal end non-templated miRNA modifications, using NGS data of rice, tomato and Arabidopsis small RNA transcriptomes from different tissues and physiological conditions. Our analysis reveals template independent terminal end modifications in the mature as well as passenger strands of the miRNA duplex. Interestingly, it is also observed that miRNA sequences terminating with a cytosine (C) at the 3' end undergo a higher percentage of 5' end modifications. The terminal end modifications did not correlate with the miRNA abundances and are independent of tissue types, physiological conditions and plant species. Our analysis indicates that the addition of nucleotides at miRNA ends is not influenced by the absence of RNA dependent RNA polymerase 6. Moreover the terminal end modified miRNAs are also observed amongst AGO1 bound small RNAs and have potential to alter target, indicating its important functional role in repression of gene expression.

The P-loop NTPases constitute one of the largest groups of globular protein domains that play highly diverse functional roles in most of the organisms. Even with the availability of nearly 300 different Hidden Markov Models representing the P-loop NTPase superfamily, not many P-loop NTPases are known in Plasmodium falciparum. A number of characteristic attributes of the genome have resulted into the lack of knowledge about this functionally diverse, but important class of proteins.

Functional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures.

Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM) based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at http://bioinfo.icgeb.res.in/faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections.

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying IDC progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

Both left ventricular (LV) and left atrial (LA) dysfunction and remodelling contribute to adverse outcomes in heart failure with reduced ejection fraction (HFrEF). Danicamtiv is a novel, cardiac myosin activator that enhances cardiomyocyte contraction.

Diagnosis of latent tuberculosis infection (LTBI) using biomarkers in order to identify the risk of progressing to active TB and therefore predicting a preventive therapy has been the main bottleneck in eradication of tuberculosis. We compared two assays for the diagnosis of LTBI: transcript signatures and interferon gamma release assay (IGRA), among household contacts (HHCs) in a high tuberculosis-burden population. HHCs of active TB cases were recruited for our study; these were confirmed to be clinically negative for active TB disease. Eighty HHCs were screened by IGRA using QuantiFERON-TB Gold Plus (QFT-Plus) to identify LTBI and uninfected cohorts; further, quantitative levels of transcript for selected six genes (TNFRSF10C, ASUN, NEMF, FCGR1B, GBP1, and GBP5) were determined. Machine learning (ML) was used to construct models of different gene combinations, with a view to identify hidden but significant underlying patterns of their transcript levels. Forty-three HHCs were found to be IGRA positive (LTBI) and thirty-seven were IGRA negative (uninfected). FCGR1B, GBP1, and GBP5 transcripts differentiated LTBI from uninfected among HHCs using Livak method. ML and ROC (Receiver Operator Characteristic) analysis validated this transcript signature to have a specificity of 72.7%. In this study, we compared a quantitative transcript signature with IGRA to assess the diagnostic ability of the two, for detection of LTBI cases among HHCs of a high-TB burden population; we concluded that a three gene (FCGR1B, GBP1, and GBP5) transcript signature can be used as a biomarker for rapid screening. IMPORTANCE The study compares potential of transcript signature and IGRA to diagnose LTBI. It is first of its kind study to screen household contacts (HHCs) in high TB burden area of India. A transcript signature (FCGR1B, GBP1, & GBP5) is identified as potential biomarker for LTBI. These results can lead to development of point-of-care (POC) like device for LTBI screening in a high TB burdened area.

Eukaryotic cellular machineries are intricately regulated by several molecular mechanisms involving transcriptional control, post-translational control and post-translational modifications of proteins (PTMs). Reversible protein phosphorylation/dephosphorylation process, which involves kinases as well as phosphatases, represents an important regulatory mechanism for diverse pathways and systems in all organisms including human malaria parasite, Plasmodium falciparum. Earlier analysis on P. falciparum protein-phosphatome revealed presence of 34 phosphatases in Plasmodium genome. Recently, we re-analysed P. falciparum phosphatome aimed at identifying parasite specific phosphatases.

Malaria is one of the major global health concerns still prevailing in this 21st century. Even the effect of artemisinin combination therapies (ACT) have declined and causing more mortality across the globe. Therefore, it is important to understand the basic biology of malaria parasite in order to find novel drug targets. Helicases play important role in nucleic acid metabolism and are components of cellular machinery in various organisms. In this manuscript we have performed the biochemical characterization of homologue of DDX17 from Plasmodium falciparum (PfDDX17). Our results show that PfDDX17 is an active RNA helicase and uses mostly ATP for its function. The qRT-PCR experiment results suggest that PfDDX17 is highly expressed in the trophozoite stage and it is localised mainly in the cytoplasm and in infected RBC (iRBC) membrane mostly in the trophozoite stage. The dsRNA knockdown study suggests that PfDDX17 is important for cell cycle progression. These studies report the biochemical functions of PfDDX17 helicase and further augment the fundamental knowledge about helicase families of P. falciparum.