De novo transcriptome profiling and development of novel secondary metabolites based genic SSRs in medicinal plant Phyllanthus emblica L. (Aonla).

Phyllanthus emblica (Aonla, Indian Gooseberry) is known to have various medicinal properties, but studies to understand its genetic structure are limited. Among the various secondary metabolites, ascorbic acid, flavonoids, terpenoids, phenols and tannins possess great potential for its pharmacological applications. Keeping this consideration, we assembled the transcriptome using the Illumina RNASeq500 platform, generating 39,933,248 high-quality paired-end reads assembled into 1,26,606 transcripts. A total of 87,771 unigenes were recovered after isoforms and unambiguous sequences deletion. Functional annotation of 43,377 coding sequences against the NCBI non-redundant (Nr) database search using BlastX yielded 38,692 sequences containing blast hits and found 4685 coding sequences to be unique. The transcript showed maximum similarity to Hevea brasilensis (16%), followed by to Jatropha curcas (12%). Considering key genes involved in the biosynthesis of flavonoids and various classes of terpenoid compounds, thirty EST-SSR primer sequences were designed based on transcriptomic data. Of which, 12 were found to be highly polymorphic with an average of 86.38%. The average value for marker index (MI), effective multiplicity ratio (EMR), resolution power (Rp) and polymorphic information content (PIC) was 7.20, 8.34, 8.64 and 0.80, respectively. Thus, from this study, we developed newly EST-SSRs linked to important genes involved in the secondary metabolites biosynthesis that will be serving as an invaluable genetic resource for crop improvement including the selection of elite genotypes in P. emblica and its closely related Phyllanthaceae species.

Anti-Biofilm: Machine Learning Assisted Prediction of IC50 Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing.

Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC50) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson's correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly 'anti-Biofilm' web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make 'anti-Biofilm' a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.

Integration of Human and Viral miRNAs in Epstein-Barr Virus-Associated Tumors and Implications for Drug Repurposing.

Epstein-Barr virus (EBV) is associated with several tumors, and has substantial relevance for public health. Therapeutics innovation for EBV-related disorders is much needed. In this context, miRNAs are noncoding RNA molecules that play vital roles in EBV infection. miRNA-Seq and RNA-Seq data for EBV-associated clinical samples and cell lines have been generated, but their detailed integrative analyses, and exploitation for drug repurposing against EBV are lacking. Hence, we identified and analyzed the differentially expressed miRNAs (DEmiRs) in EBV-infected cell lines (28) and infected (28) and uninfected human tissue (20) samples using an in-house pipeline. We found significantly enriched host miRNAs like hsa-mir-3651, hsa-mir-1248, and hsa-mir-29c-3p in EBV-infected samples from EBV-associated nasopharyngeal carcinoma and Hodgkin's lymphoma, among others. Furthermore, we also identified significantly enriched novel miRNAs such as hsa-mir-29c-3p, hsa-mir-3651, and hsa-mir-98-3p, which were not previously reported in EBV-related tumors. Differentially expressed mRNAs (DEMs) were identified in EBV-infected cell lines (21) and uninfected human tissue (14) samples. We predicted and selected 1572 DEMs (upregulated) that are targeted by 547 DEmiRs (downregulated). These were further classified into essential (870) and nonessential (702) genes. Moreover, a miRNA-mRNA network was developed for the hub miRNAs. Importantly, we used the DEMs during EBV latent infection types I, II, and III to identify the candidate drugs for repurposing: Glyburide, Levodopa, Nateglinide, and Stiripentol, among others. To the best of our knowledge, this is the first integrative analyses that identified DEmiRs and DEMs as potential therapeutic targets and predicted drugs as potential candidates for repurposing against EBV-related tumors.

Ion-pumping microbial rhodopsin protein classification by machine learning approach.

BACKGROUND: Rhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype. RESULTS: This study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server ( https://bioinfo.imtech.res.in/servers/rhodopred ) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers. CONCLUSION: The overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers.

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing.

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus.

Comparative Assessment of Enamel Microhardness Using Various Remineralising Agents on Artificially Demineralized Human Enamel.

INTRODUCTION: Various remineralizing agents can be used to remineralize initial carious lesions. AIM: The study aims to compare and evaluate the remineralizing efficiency of Remin Pro (VOCO GmbH, Cuxhaven, Germany), tricalcium phosphate, and HealOzone (CurOzone USA Inc., Ontario, Canada) by measuring the microhardness of enamel. MATERIALS AND METHOD: Forty-five mandibular premolars were collected and divided into three groups (A, B, and C). After sectioning mesiodistally, they were subdivided into the control and test groups. The test group was further subdivided into demineralized (A2a, B2a, and C2a) and remineralized (A2b, B2b, and C2b) groups. All test group samples were demineralized by immersing in demineralizing solutions for 24 hours. Afterwards, A2b, B2b, and C2b samples were remineralized by remineralizing agents (Remin Pro, tricalcium phosphate, and HealOzone) for three minutes (twice a day) for 14 days, and then Vickers microhardness testing (VHN) was performed. RESULT: The microhardness values of the demineralized group were lower compared to the samples of the control groups. In the remineralized group, the mean microhardness values were maximum for HealOzone (293.22 kgmm-2), followed by Remin Pro (287.5660 kgmm-2) and then tricalcium phosphate (282.4660 kgmm-2). CONCLUSION: The application of remineralizing paste proved potent in improving the remineralization in the demineralized enamel surface.

AntiVIRmiR: A repository of host antiviral miRNAs and their expression along with experimentally validated viral miRNAs and their targets.

miRNAs play an essential role in promoting viral infections as well as modulating the antiviral defense. Several miRNA repositories have been developed for different species, e.g., human, mouse, and plant. However, 'VIRmiRNA' is the only existing resource for experimentally validated viral miRNAs and their targets. We have developed a 'AntiVIRmiR' resource encompassing data on host/virus miRNA expression during viral infection. This resource with 22,741 entries is divided into four sub-databases viz., 'DEmiRVIR', 'AntiVmiR', 'VIRmiRNA2' and 'VIRmiRTar2'. 'DEmiRVIR' has 10,033 differentially expressed host-viral miRNAs for 21 viruses. 'AntiVmiR' incorporates 1,642 entries for host miRNAs showing antiviral activity for 34 viruses. Additionally, 'VIRmiRNA2' includes 3,340 entries for experimentally validated viral miRNAs from 50 viruses along with 650 viral isomeric sequences for 14 viruses. Further, 'VIRmiRTar2' has 7,726 experimentally validated targets for viral miRNAs against 21 viruses. Furthermore, we have also performed network analysis for three sub-databases. Interactions between up/down-regulated human miRNAs and viruses are displayed for 'AntiVmiR' as well as 'DEmiRVIR'. Moreover, 'VIRmiRTar2' interactions are shown among different viruses, miRNAs, and their targets. We have provided browse, search, external hyperlinks, data statistics, and useful analysis tools. The database available at https://bioinfo.imtech.res.in/manojk/antivirmir would be beneficial for understanding the host-virus interactions as well as viral pathogenesis.

Biofilm-i: A Platform for Predicting Biofilm Inhibitors Using Quantitative Structure-Relationship (QSAR) Based Regression Models to Curb Antibiotic Resistance.

Antibiotic drug resistance has emerged as a major public health threat globally. One of the leading causes of drug resistance is the colonization of microorganisms in biofilm mode. Hence, there is an urgent need to design novel and highly effective biofilm inhibitors that can work either synergistically with antibiotics or individually. Therefore, we have developed a recursive regression-based platform "Biofilm-i" employing a quantitative structure-activity relationship approach for making generalized predictions, along with group and species-specific predictions of biofilm inhibition efficiency of chemical(s). The platform encompasses eight predictors, three analysis tools, and data visualization modules. The experimentally validated biofilm inhibitors for model development were retrieved from the "aBiofilm" resource and processed using a 10-fold cross-validation approach using the support vector machine and andom forest machine learning techniques. The data was further sub-divided into training/testing and independent validation sets. From training/testing data sets the Pearson's correlation coefficient of overall chemicals, Gram-positive bacteria, Gram-negative bacteria, fungus, Pseudomonas aeruginosa, Staphylococcus aureus, Candida albicans, and Escherichia coli was 0.60, 0.77, 0.62, 0.77, 0.73, 0.83, 0.70, and 0.71 respectively via Support Vector Machine. Further, all the QSAR models performed equally well on independent validation data sets. Additionally, we also checked the performance of the random forest machine learning technique for the above datasets. The integrated analysis tools can convert the chemical structure into different formats, search for a similar chemical in the aBiofilm database and design the analogs. Moreover, the data visualization modules check the distribution of experimentally validated biofilm inhibitors according to their common scaffolds. The Biofilm-i platform would be of immense help to researchers engaged in designing highly efficacious biofilm inhibitors for tackling the menace of antibiotic drug resistance.

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches.

Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the "Anti-HCV" platform using machine learning and quantitative structure-activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson's correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The "Anti-HCV" predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.

Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis.

Viral epidemics and pandemics are considered public health emergencies. However, traditional and novel antiviral discovery approaches are unable to mitigate them in a timely manner. Notably, drug repurposing emerged as an alternative strategy to provide antiviral solutions in a timely and cost-effective manner. In the literature, many FDA-approved drugs have been repurposed to inhibit viruses, while a few among them have also entered clinical trials. Using experimental data, we identified repurposed drugs against 14 viruses responsible for causing epidemics and pandemics such as SARS-CoV-2, SARS, Middle East respiratory syndrome, influenza H1N1, Ebola, Zika, Nipah, chikungunya, and others. We developed a novel computational "drug-target-drug" approach that uses the drug-targets extracted for specific drugs, which are experimentally validated in vitro or in vivo for antiviral activity. Furthermore, these extracted drug-targets were used to fetch the novel FDA-approved drugs for each virus and prioritize them by calculating their confidence scores. Pathway analysis showed that the majority of the extracted targets are involved in cancer and signaling pathways. For SARS-CoV-2, our method identified 21 potential repurposed drugs, of which 7 (e.g., baricitinib, ramipril, chlorpromazine, enalaprilat, etc.) have already entered clinical trials. The prioritized drug candidates were further validated using a molecular docking approach. Therefore, we anticipate success during the experimental validation of our predicted FDA-approved repurposed drugs against 14 viruses. This study will assist the scientific community in hastening research aimed at the development of antiviral therapeutics.

Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning.

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.

DrugRepV: a compendium of repurposed drugs and chemicals targeting epidemic and pandemic viruses.

Viruses are responsible for causing various epidemics and pandemics with a high mortality rate e.g. ongoing SARS-CoronaVirus-2 crisis. The discovery of novel antivirals remains a challenge but drug repurposing is emerging as a potential solution to develop antivirals in a cost-effective manner. In this regard, we collated the information of repurposed drugs tested for antiviral activity from literature and presented it in the form of a user-friendly web server named 'DrugRepV'. The database contains 8485 entries (3448 unique) with biological, chemical, clinical and structural information of 23 viruses responsible to cause epidemics/pandemics. The database harbors browse and search options to explore the repurposed drug entries. The data can be explored by some important fields like drugs, viruses, drug targets, clinical trials, assays, etc. For summarizing the data, we provide overall statistics of the repurposed candidates. To make the database more informative, it is hyperlinked to various external repositories like DrugBank, PubChem, NCBI-Taxonomy, Clinicaltrials.gov, World Health Organization and many more. 'DrugRepV' database (https://bioinfo.imtech.res.in/manojk/drugrepv/) would be highly useful to the research community working to develop antivirals.

CoronaVR: A Computational Resource and Analysis of Epitopes and Therapeutics for Severe Acute Respiratory Syndrome Coronavirus-2.

In December 2019, the Chinese city of Wuhan was the center of origin of a pneumonia-like disease outbreak with an unknown causative pathogen. The CDC, China, managed to track the source of infection to a novel coronavirus (2019-nCoV; SARS-CoV-2) that shares approximately 79.6% of its genome with SARS-CoV. The World Health Organization (WHO) initially declared COVID-19 as a Public Health Emergency of International Concern (PHEIC) and later characterized it as a global pandemic on March 11, 2020. Due to the novel nature of this virus, there is an urgent need for vaccines and therapeutics to control the spread of SARS-CoV-2 and its associated disease, COVID-19. Global efforts are underway to circumvent its further spread and treat COVID-19 patients through experimental vaccine formulations and therapeutic interventions, respectively. In the absence of any effective therapeutics, we have devised h bioinformatics-based approaches to accelerate global efforts in the fight against SARS-CoV-2 and to assist researchers in the initial phase of vaccine and therapeutics development. In this study, we have performed comprehensive meta-analyses and developed an integrative resource, "CoronaVR" (http://bioinfo.imtech.res.in/manojk/coronavr/). Predominantly, we identified potential epitope-based vaccine candidates, siRNA-based therapeutic regimens, and diagnostic primers. The resource is categorized into the main sections "Genomes," "Epitopes," "Therapeutics," and Primers." The genome section harbors different components, viz, genomes, a genome browser, phylogenetic analysis, codon usage, glycosylation sites, and structural analysis. Under the umbrella of epitopes, sub-divisions, namely cross-protective epitopes, B-cell (linear/discontinuous), T-cell (CD4+/CD8+), CTL, and MHC binders, are presented. The therapeutics section has different sub-sections like siRNA, miRNAs, and sgRNAs. Further, experimentally confirmed and designed diagnostic primers are earmarked in the primers section. Our study provided a set of shortlisted B-cell and T-cell (CD4+ and CD8+) epitopes that can be experimentally tested for their incorporation in vaccine formulations. The list of selected primers can be used in testing kits to identify SARS-CoV-2, while the recommended siRNAs, sgRNAs, and miRNAs can be used in therapeutic regimens. We foresee that this resource will help in advancing the research against coronaviruses.

NipahVR: a resource of multi-targeted putative therapeutics and epitopes for the Nipah virus.

Nipah virus (NiV) is an emerging and priority pathogen from the Paramyxoviridae family with a high fatality rate. It causes various diseases such as respiratory ailments and encephalitis and poses a great threat to humans and livestock. Despite various efforts, there is no approved antiviral treatment available. Therefore, to expedite and assist the research, we have developed an integrative resource NipahVR (http://bioinfo.imtech.res.in/manojk/nipahvr/) for the multi-targeted putative therapeutics and epitopes for NiV. It is structured into different sections, i.e. genomes, codon usage, phylogenomics, molecular diagnostic primers, therapeutics (siRNAs, sgRNAs, miRNAs) and vaccine epitopes (B-cell, CTL, MHC-I and -II binders). Most decisively, potentially efficient therapeutic regimens targeting different NiV proteins and genes were anticipated and projected. We hope this computational resource would be helpful in developing combating strategies against this deadly pathogen. Database URL: http://bioinfo.imtech.res.in/manojk/nipahvr/.

A comparative evaluation of fracture resistance of endodontically treated premolar teeth reinforced with different prefabricated and custom-made fiber-reinforced post system with two different post lengths: An in vitro study.

AIMS: The aim of this study is to determine the mean failure load for each postsystem and the relationship between post lengths with the mean failure loads. MATERIALS AND METHODS: Ninety single-rooted decoronated mandibular premolar teeth were endodontically treated and randomly assigned to three groups with respect to their post length (2/3rd and ½ of the root length). The first two groups were randomly divided into four subgroups, restored with the following postsystem: polyethylene-woven fiber posts, glass fiber tape, prefabricated carbon, and glass fiber posts. A composite core with no post served as control. All posts were cemented using dual-cure resin cement, and the same was used for core buildup. The standard cores were formed in each group. All the specimens were tested in a universal testing machine, and the load was calculated. RESULTS: One-way analysis of variance (ANOVA) showed that prefabricated glass fiber post had significantly highest fracture resistance when compared to other prefabricated and custom fiber-reinforced composite posts. Two-way ANOVA demonstrated no significant difference among the post lengths. CONCLUSION: The results of this study showed that glass fiber posts showed higher fracture load, but post length did not significantly increase the fracture resistance of endodontically treated teeth.

Management of subgingivally fractured maxillary anterior tooth: a multidisciplinary approach.

The major challenge in traumatic injuries is the management of subgingival fracture of anterior teeth. Forced orthodontic extrusion is a suitable approach for these teeth as it provides both a sound tissue margin for final restoration and creates a periodontal environment (biological width) which is easy for the patient to maintain. Restoration after orthodontic eruption may present a more conservative treatment choice in young patients compared with the prosthetic restoration after extraction. This paper reports a case of the fractured maxillary anterior tooth at the subgingival level that was managed by forced orthodontic extrusion after endodontic therapy followed by aesthetic rehabilitation, a much-forgotten technique not utilised routinely yet conservative and cost-effective.

aBiofilm: a resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance.

Biofilms play an important role in the antibiotic drug resistance, which is threatening public health globally. Almost, all microbes mimic multicellular lifestyle to form biofilm by undergoing phenotypic changes to adapt adverse environmental conditions. Many anti-biofilm agents have been experimentally validated to disrupt the biofilms during last three decades. To organize this data, we developed the 'aBiofilm' resource (http://bioinfo.imtech.res.in/manojk/abiofilm/) that harbors a database, a predictor, and the data visualization modules. The database contains biological, chemical, and structural details of 5027 anti-biofilm agents (1720 unique) reported from 1988-2017. These agents target over 140 organisms including Gram-negative, Gram-positive bacteria, and fungus. They are mainly chemicals, peptides, phages, secondary metabolites, antibodies, nanoparticles and extracts. They show the diverse mode of actions by attacking mainly signaling molecules, biofilm matrix, genes, extracellular polymeric substances, and many more. The QSAR based predictor identifies the anti-biofilm potential of an unknown chemical with an accuracy of ∼80.00%. The data visualization section summarized the biofilm stages targeted (Circos plot); interaction maps (Cytoscape) and chemicals diversification (CheS-Mapper) of the agents. This comprehensive platform would help the researchers to understand the multilevel communication in the microbial consortium. It may aid in developing anti-biofilm therapeutics to deal with antibiotic drug resistance menace.

vhfRNAi: a web-platform for analysis of host genes involved in viral infections discovered by genome wide RNAi screens.

Knockdown of host genes using high-throughput genome-wide RNA interference screens has identified numerous host factors that affect viral infections, which would be helpful in understanding host-virus interactions. We have developed a vhfRNAi web resource based on genome-wide RNAi experiments for viruses. It contains experimental details of 12 249 entries (host factors + restriction factors) for 18 viruses. Simultaneously, this resource encompasses analysis of overlapping genes, genome wide association studies, gene ontology (GO), pathogen interacting proteins, interaction networks and pathway enrichment. Using overlap analysis, it was found that Influenza A virus shared overlapping host genes with the majority of viruses including Hepatitis C virus and Dengue virus 2. In the genome wide association studies analysis, 429 diseases/traits were mapped, of which obesity-related traits were the most common. GO analysis revealed that the major categories belonged to metabolic processes, molecule transport, signal transduction, proteolysis, etc. In the pathogen interacting protein analysis, protein interaction data from different resources can be explored for further understanding of host-virus biology. By pathway enrichment analysis, a total of 8955 genes were mapped on 303 pathways with most of the hits coming from metabolic pathways. We have found 491 genes that are not essential for the host but essential for the virus and can be targeted to inhibit the virus. These may be explored as potential candidates for drug targets. The resource is freely accessible at and will be useful in understanding host-virus biology as well as identification of targets for the development of antiviral therapeutics.

Discovery of Pyrazolopyrimidine Derivatives as Novel Dual Inhibitors of BTK and PI3Kδ.

The aberrant activation of B-cells has been implicated in several types of cancers and hematological disorders. BTK and PI3Kδ are kinases responsible for B-cell signal transduction, and inhibitors of these enzymes have demonstrated clinical benefit in certain types of lymphoma. Simultaneous inhibition of these pathways could result in more robust responses or overcome resistance as observed in single agent use. We report a series of novel compounds that have low nanomolar potency against both BTK and PI3Kδ as well as acceptable PK properties that could be useful in the development of treatments against B-cell related diseases.

SMEpred workbench: A web server for predicting efficacy of chemicallymodified siRNAs.

Chemical modifications have been extensively exploited to circumvent shortcomings in therapeutic applications of small interfering RNAs (siRNAs). However, experimental designing and testing of these siRNAs or chemically modified siRNAs (cm-siRNAs) involves enormous resources. Therefore, in-silico intervention in designing cm-siRNAs would be of utmost importance. We developed SMEpred workbench to predict the efficacy of normal siRNAs as well as cm-siRNAs using 3031 heterogeneous cm-siRNA sequences from siRNAmod database. These include 30 frequently used chemical modifications on different positions of either siRNA strand. Support Vector Machine (SVM) was employed to develop predictive models utilizing various sequence features namely mono-, di-nucleotide composition, binary pattern and their hybrids. We achieved highest Pearson Correlation Coefficient (PCC) of 0.80 during 10-fold cross validation and similar PCC value in independent validation. We have provided the algorithm in the 'SMEpred' pipeline to predict the normal siRNAs from the gene or mRNA sequence. For multiple modifications, we have assembled 'MultiModGen' module to design multiple modifications and further process them to evaluate their predicted efficacies. SMEpred webserver will be useful to scientific community engaged in use of RNAi-based technology as well as for therapeutic development. Web server is available for public use at following URL address: http://bioinfo.imtech.res.in/manojk/smepred .