Abid Qureshi

AVPpred: collection and prediction of highly effective antiviral peptides

In the battle against viruses, antiviral peptides (AVPs) had demonstrated the immense potential. Presently, more than 15 peptide-based drugs are in various stages of clinical trials. Emerging and re-emerging viruses further emphasize the efforts to accelerate antiviral drug discovery efforts. Despite, huge importance of the field, no dedicated AVP resource is available. In the present study, we have collected 1245 peptides which were experimentally checked for antiviral activity targeting important human viruses like influenza, HIV, HCV and SARS, etc. After removing redundant peptides, 1056 peptides were divided into 951 training and 105 validation data sets. We have exploited various peptides sequence features, i.e. motifs and alignment followed by amino acid composition and physicochemical properties during 5-fold cross validation using Support Vector Machine. Physiochemical properties-based model achieved maximum 85% accuracy and 0.70 Matthew’s Correlation Coefficient (MCC). Performance of this model on the experimental validation data set showed 86% accuracy and 0.71 MCC which is far better than the general antimicrobial peptides prediction methods. Therefore, AVPpred—the first web server for predicting the highly effective AVPs would certainly be helpful to researchers working on peptide-based antiviral development. The web server is freely available at http://crdd.osdd.net/servers/avppred.

AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses

Antiviral peptides (AVPs) have exhibited huge potential in inhibiting viruses by targeting various stages of their life cycle. Therefore, we have developed AVPdb, available online at http://crdd.osdd.net/servers/avpdb, to provide a dedicated resource of experimentally verified AVPs targeting over 60 medically important viruses including Influenza, HCV, HSV, RSV, HBV, DENV, SARS, etc. However, we have separately provided HIV inhibiting peptides in ‘HIPdb’. AVPdb contains detailed information of 2683 peptides, including 624 modified peptides experimentally tested for antiviral activity. In modified peptides a chemical moiety is attached for increasing their efficacy and stability. Detailed information include: peptide sequence, length, source, virus targeted, virus family, cell line used, efficacy (qualitative/quantitative), target step/protein, assay used in determining the efficacy and PubMed reference. The database also furnishes physicochemical properties and predicted structure for each peptide. We have provided user-friendly browsing and search facility along with other analysis tools to help the users. Entering of many synthetic peptide-based drugs in various stages of clinical trials reiterate the importance for the AVP resources. AVPdb is anticipated to cater to the needs of scientific community working for the development of antiviral therapeutics.

VIRsiRNAdb: a curated database of experimentally validated viral siRNA/shRNA

RNAi technology has been emerging as a potential modality to inhibit viruses during past decade. In literature a few siRNA databases have been reported that focus on targeting human and mammalian genes but experimentally validated viral siRNA databases are lacking. We have developed VIRsiRNAdb, a manually curated database having comprehensive details of 1358 siRNA/shRNA targeting viral genome regions. Further, wherever available, information regarding alternative efficacies of above 300 siRNAs derived from different assays has also been incorporated. Important fields included in the database are siRNA sequence, virus subtype, target genome region, cell type, target object, experimental assay, efficacy, off-target and siRNA matching with reference viral sequences. Database also provides the users with facilities of advance search, browsing, data submission, linking to external databases and useful siRNA analysis tools especially siTarAlign which align the siRNA with reference viral genomes or user defined sequences. VIRsiRNAdb contains extensive details of siRNA/shRNA targeting 42 important human viruses including influenza virus, hepatitis B virus, HPV and SARS Corona virus. VIRsiRNAdb would prove useful for researchers in picking up the best viral siRNA for antiviral therapeutics development and also for developing better viral siRNA design tools. The database is freely available at http://crdd.osdd.net/servers/virsirnadb.

HIPdb: A Database of Experimentally Validated HIV Inhibiting Peptides

Background Besides antiretroviral drugs, peptides have also demonstrated potential to inhibit the Human immunodeficiency virus (HIV). For example, T20 has been discovered to effectively block the HIV entry and was approved by the FDA as a novel anti-HIV peptide (AHP). We have collated all experimental information on AHPs at a single platform. Descriptions HIPdb is a manually curated database of experimentally verified HIV inhibiting peptides targeting various steps or proteins involved in the life cycle of HIV e.g. fusion, integration, reverse transcription etc. This database provides experimental information of 981 peptides. These are of varying length obtained from natural as well as synthetic sources and tested on different cell lines. Important fields included are peptide sequence, length, source, target, cell line, inhibition/IC50, assay and reference. The database provides user friendly browse, search, sort and filter options. It also contains useful services like BLAST and ‘Map’ for alignment with user provided sequences. In addition, predicted structure and physicochemical properties of the peptides are also included. Conclusion HIPdb database is freely available at http://crdd.osdd.net/servers/hipdb. Comprehensive information of this database will be helpful in selecting/designing effective anti-HIV peptides. Thus it may prove a useful resource to researchers for peptide based therapeutics development.

VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets

Viral microRNAs (miRNAs) regulate gene expression of viral and/or host genes to benefit the virus. Hence, miRNAs play a key role in host–virus interactions and pathogenesis of viral diseases. Lately, miRNAs have also shown potential as important targets for the development of novel antiviral therapeutics. Although several miRNA and their target repositories are available for human and other organisms in literature, but a dedicated resource on viral miRNAs and their targets are lacking. Therefore, we have developed a comprehensive viral miRNA resource harboring information of 9133 entries in three subdatabases. This includes 1308 experimentally validated miRNA sequences with their isomiRs encoded by 44 viruses in viral miRNA ‘VIRmiRNA’ and 7283 of their target genes in ‘VIRmiRtar’. Additionally, there is information of 542 antiviral miRNAs encoded by the host against 24 viruses in antiviral miRNA ‘AVIRmir’. The web interface was developed using Linux-Apache-MySQL-PHP (LAMP) software bundle. User-friendly browse, search, advanced search and useful analysis tools are also provided on the web interface. VIRmiRNA is the first specialized resource of experimentally proven virus-encoded miRNAs and their associated targets. This database would enhance the understanding of viral/host gene regulation and may also prove beneficial in the development of antiviral therapeutics. Database URL: http://crdd.osdd.net/servers/virmirna

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.

siRNAmod: A database of experimentally validated chemically modified siRNAs

Small interfering RNA (siRNA) technology has vast potential for functional genomics and development of therapeutics. However, it faces many obstacles predominantly instability of siRNAs due to nuclease digestion and subsequently biologically short half-life. Chemical modifications in siRNAs provide means to overcome these shortcomings and improve their stability and potency. Despite enormous utility bioinformatics resource of these chemically modified siRNAs (cm-siRNAs) is lacking. Therefore, we have developed siRNAmod, a specialized databank for chemically modified siRNAs. Currently, our repository contains a total of 4894 chemically modified-siRNA sequences, comprising 128 unique chemical modifications on different positions with various permutations and combinations. It incorporates important information on siRNA sequence, chemical modification, their number and respective position, structure, simplified molecular input line entry system canonical (SMILES), efficacy of modified siRNA, target gene, cell line, experimental methods, reference etc. It is developed and hosted using Linux Apache MySQL PHP (LAMP) software bundle. Standard user-friendly browse, search facility and analysis tools are also integrated. It would assist in understanding the effect of chemical modifications and further development of stable and efficacious siRNAs for research as well as therapeutics. siRNAmod is freely available at: http://crdd.osdd.net/servers/sirnamod.

VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

BackgroundSelection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed “VIRsiRNApred” a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.MethodsIn the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.ResultsDuring 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.ConclusionVIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.

AVCpred: an integrated web server for prediction and design of antiviral compounds.

Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure–activity relationship (QSAR)‐based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthews correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10‐fold cross‐validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.

ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and Algorithm for Prediction of Their Inhibitory Efficacy

Allele-specific siRNAs (ASP-siRNAs) have emerged as promising therapeutic molecules owing to their selectivity to inhibit the mutant allele or associated single-nucleotide polymorphisms (SNPs) sparing the expression of the wild-type counterpart. Thus, a dedicated bioinformatics platform encompassing updated ASP-siRNAs and an algorithm for the prediction of their inhibitory efficacy will be helpful in tackling currently intractable genetic disorders. In the present study, we have developed the ASPsiRNA resource (http://crdd.osdd.net/servers/aspsirna/) covering three components viz (i) ASPsiDb, (ii) ASPsiPred, and (iii) analysis tools like ASP-siOffTar. ASPsiDb is a manually curated database harboring 4543 (including 422 chemically modified) ASP-siRNAs targeting 78 unique genes involved in 51 different diseases. It furnishes comprehensive information from experimental studies on ASP-siRNAs along with multidimensional genetic and clinical information for numerous mutations. ASPsiPred is a two-layered algorithm to predict efficacy of ASP-siRNAs for fully complementary mutant (Effmut) and wild-type allele (Effwild) with one mismatch by ASPsiPredSVM and ASPsiPredmatrix, respectively. In ASPsiPredSVM, 922 unique ASP-siRNAs with experimentally validated quantitative Effmut were used. During 10-fold cross-validation (10nCV) employing various sequence features on the training/testing dataset (T737), the best predictive model achieved a maximum Pearson’s correlation coefficient (PCC) of 0.71. Further, the accuracy of the classifier to predict Effmut against novel genes was assessed by leave one target out cross-validation approach (LOTOCV). ASPsiPredmatrix was constructed from rule-based studies describing the effect of single siRNA:mRNA mismatches on the efficacy at 19 different locations of siRNA. Thus, ASPsiRNA encompasses the first database, prediction algorithm, and off-target analysis tool that is expected to accelerate research in the field of RNAi-based therapeutics for human genetic diseases.