In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences.

Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.

Cell-penetrating peptide and antibiotic combination therapy: a potential alternative to combat drug resistance in methicillin-resistant Staphylococcus aureus.

The diverse pattern of resistance by methicillin-resistant Staphylococcus aureus (MRSA) is the major obstacle in the treatment of its infections. The key reason of resistance is the poor membrane permeability of drug molecules. Over the last decade, cell-penetrating peptides (CPPs) have emerged as efficient drug delivery vehicles and have been exploited to improve the intracellular delivery of numerous therapeutic molecules in preclinical studies. Therefore, to overcome the drug resistance, we have investigated for the first time the effects of two CPPs (P3 and P8) in combination with four antibiotics (viz. oxacillin, erythromycin, norfloxacin, and vancomycin) against MRSA strains. We found that both CPPs internalized into the MRSA efficiently at very low concentration (<10 µM) which was non-toxic to bacteria as well as mammalian cells and showed no significant hemolytic activity. However, the combinations of CPPs (≤10 µM) and antibiotics showed high toxicity against MRSA as compared to antibiotics alone. The significant finding is that P3 and P8 could lower the MICs against oxacillin, norfloxacin, and vancomycin to susceptible levels (generally <1 µg/mL) for almost all five clinical isolates. Further, the bacterial cell death was confirmed by scanning electron microscopy as well as propidium iodide uptake assay. Simultaneously, time-kill kinetics revealed the increased uptake of antibiotics. In summary, CPPs assist to restore the effectiveness of antibiotics at much lower concentration, eliminate the antibiotic toxicity, and represent the CPP-antibiotic combination therapy as a potential novel weapon to combat MRSA infections.

ω-Turn: a novel ß-turn mimic in globular proteins stabilized by main-chain to side-chain C−H···O interaction.

Mimicry of structural motifs is a common feature in proteins. The 10-membered hydrogen-bonded ring involving the main-chain C − O in a ß-turn can be formed using a side-chain carbonyl group leading to Asx-turn. We show that the N − H component of hydrogen bond can be replaced by a C(γ) -H group in the side chain, culminating in a nonconventional C − H···O interaction. Because of its shape this ß-turn mimic is designated as ω-turn, which is found to occur ∼ three times per 100 residues. Three residues (i to i + 2) constitute the turn with the C − H···O interaction occurring between the terminal residues, constraining the torsion angles ϕi + 1, ψi + 1, ϕi + 2 and χ'1(i + 2) (using the interacting C(γ) atom). Based on these angles there are two types of ω-turns, each of which can be further divided into two groups. C(ß) -branched side-chains, and Met and Gln have high propensities to occur at i + 2; for the last two residues the carbonyl oxygen may participate in an additional interaction involving the S and amino group, respectively. With Cys occupying the i + 1 position, such turns are found in the metal-binding sites. N-linked glycosylation occurs at the consensus pattern Asn-Xaa-Ser/Thr; with Thr at i + 2, the sequence can adopt the secondary structure of a ω-turn, which may be the recognition site for protein modification. Location between two ß-strands is the most common occurrence in protein tertiary structure, and being generally exposed ω-turn may constitute the antigenic determinant site. It is a stable scaffold and may be used in protein engineering and peptide design.

Designing of peptides with desired half-life in intestine-like environment.

BACKGROUND: In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life. RESULTS: In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides. CONCLUSION: In summary, this study describes a web server 'HLP' that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).

Draft genome sequence of salt-tolerant yeast Debaryomyces hansenii var. hansenii MTCC 234.

Debaryomyces hansenii is one of the most halotolerant species of yeast, and the genome sequence of D. hansenii strain CBS767 is already available. Here we report the 11.46-Mb draft genome of D. hansenii strain MTCC 234, which is even more halotolerant than strain CBS767. Comparative analysis of these sequences would definitely provide further insight into the halotolerance of this yeast.

Genome sequence of the oleaginous red yeast Rhodosporidium toruloides MTCC 457.

We report the de novo assembled 20.05-Mb draft genome of the red yeast Rhodosporidium toruloides MTCC 457, predicted to encode 5,993 proteins, 4 rRNAs, and 125 tRNAs. Proteins known to be unique to oleaginous fungi are present among the predicted proteins. The genome sequence will be valuable for molecular genetic analysis and manipulation of lipid accumulation in this yeast and for developing it as a potential host for biofuel production.

Hmrbase2: a comprehensive database of hormones and their receptors.

PURPOSE: Hormones play a critical role in regulating various physiological processes and any hormonal imbalances can lead to major endocrine disorders. Thus, studying hormones is essential for both the therapeutics and the diagnostics of hormonal diseases. To facilitate this need, we have developed Hmrbase2, a comprehensive platform that provides extensive information on hormones. METHODS: Hmrbase2 is a web-based database which is an update of a previously published database, Hmrbase ( http://crdd.osdd.net/raghava/hmrbase/ ). We collected a large amount of information on peptide and non-peptide hormones and hormone receptors, this information being sourced from Hmrbase, HMDB, UniProt, HORDB, ENDONET, PubChem, and the medical literature. RESULTS: Hmrbase2 contains a total of 12,056 entries, which is more than twice the number of entries contained in the previous version Hmrbase. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones, and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties such as melting point and water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been supplied. Additionally, a similarity search module has been incorporated enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman. CONCLUSIONS: To make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Hmrbase2 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2 .

Genome sequence of the nitroaromatic compound-degrading Bacterium Burkholderia sp. strain SJ98.

We report the 7.85-Mb genome sequence of Burkholderia sp. strain SJ98, isolated from agricultural fields of Assam, India. The draft genome of this strain will be helpful in studying the genetic pathways involved in the degradation of aromatic compounds.

Draft genome sequence of the nitrophenol-degrading actinomycete Rhodococcus imtechensis RKJ300.

We report the 8.231-Mb genome sequence of Rhodococcus imtechensis RKJ300, isolated from pesticide-contaminated soil in Punjab, India. The genome sequence of the strain RKJ300 will be helpful in exploring the molecular pathways involved in the degradation of nitrophenols.

Genome sequence of the marine bacterium Marinilabilia salmonicolor JCM 21150T.

We report the 4.98-Mb genome sequence of Marinilabilia salmonicolor JCM 21150(T), which was isolated from marine mud in the year 1961. The draft genome of strain Marinilabilia salmonicolor JCM 21150(T) contains 4,982,627 bp with a G+C content of 41.92% and 4,227 protein coding genes, 52 tRNAs, and 3 rRNAs.

Genome sequence of the halotolerant bacterium Imtechella halotolerans K1T.

We report the 3.087-Mb genome sequence of Imtechella halotolerans K1(T), isolated from an estuarine water sample collected from Kochi, Kerala, India. Strain K1 was recently reported as a novel genus of the family Flavobacteriaceae.

Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer.

INTRODUCTION: Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs. OBJECTIVES: The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature. METHODS: This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes. RESULTS: Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86. CONCLUSION: We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.

B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood-Brain Barrier Penetrating Peptides.

The blood-brain barrier is a major obstacle in treating brain-related disorders, as it does not allow the delivery of drugs into the brain. We developed a method for predicting blood-brain barrier penetrating peptides to facilitate drug delivery into the brain. These blood-brain barrier penetrating peptides (B3PPs) can act as therapeutics, as well as drug delivery agents. We trained, tested, and evaluated our models on blood-brain barrier peptides obtained from the B3Pdb database. First, we computed a wide range of peptide features. Then, we selected relevant peptide features. Finally, we developed numerous machine-learning-based models for predicting blood-brain barrier peptides using the selected features. The random-forest-based model performed the best with respect to the top 80 selected features and achieved a maximal 85.08% accuracy with an AUROC of 0.93. We also developed a webserver, B3pred, that implements our best models. It has three major modules that allow users to predict/design B3PPs and scan B3PPs in a protein sequence.

Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data.

PURPOSE: Intra-tumor heterogeneity and high mortality among patients with non-small-cell lung carcinoma (NSCLC) emphasize the need to identify reliable prognostic markers unique to each subtype. METHODS: In this study, univariate cox regression and prognostic index (PI)-based approaches were used to develop models for predicting NSCLC patients' subtype-specific survival. RESULTS: Prognostic analysis of TCGA dataset identified 1334 and 2129 survival-specific genes for LUSC (488 samples) and LUAD (497 samples), respectively. Individually, 32 and 271 prognostic genes were found and validated in GSE study exclusively for LUSC and LUAD. Nearly, 9-10% of the validated genes in each subtype were already reported in multiple studies thus highlighting their importance as prognostic biomarkers. Strong literature evidence against these prognostic genes like "ELANE" (LUSC) and "AHSG" (LUAD) instigates further investigation for their therapeutic and diagnostic roles in the corresponding cohorts. Prognostic models built on five and four genes were validated for LUSC [HR = 2.10, p value = 1.86 × 10-5] and LUAD [HR = 2.70, p value = 3.31 × 10-7], respectively. The model based on the combination of age and tumor stage performed well in both NSCLC subtypes, suggesting that despite having distinctive histological features and treatment paradigms, some clinical features can be good prognostic predictors in both. CONCLUSION: This study advocates that investigating the survival-specific biomarkers restricted to respective cohorts can advance subtype-specific prognosis, diagnosis, and treatment for NSCLC patients. Prognostic models and markers described for each subtype may provide insight into the heterogeneity of disease etiology and help in the development of new therapeutic approaches for the treatment of NSCLC patients.

Computational Prediction of the Immunomodulatory Potential of RNA Sequences.

Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).

Topical Delivery of Protein and Peptide Using Novel Cell Penetrating Peptide IMT-P8.

Skin, being the largest organ of the body, is an important site for drug administration. However, most of the drugs have poor permeability and thus drug delivery through the skin is very challenging. In this study, we examined the transdermal delivery capability of IMT-P8, a novel cell-penetrating peptide. We generated IMT-P8-GFP and IMT-P8-KLA fusion constructs and evaluated their internalization into mouse skin after topical application. Our results demonstrate that IMT-P8 is capable of transporting green fluorescent protein (GFP) and proapoptotic peptide, KLA into the skin and also in different cell lines. Interestingly, uptake of IMT-P8-GFP was considerably higher than TAT-GFP in HeLa cells. After internalization, IMT-P8-KLA got localized to the mitochondria and caused significant cell death in HeLa cells signifying an intact biological activity. Further in vivo skin penetration experiments revealed that after topical application, IMT-P8 penetrated the stratum corneum, entered into the viable epidermis and accumulated inside the hair follicles. In addition, both IMT-P8-KLA and IMT-P8-GFP internalized into the hair follicles and dermal tissue of the skin following topical application. These results suggested that IMT-P8 could be a potential candidate to be used as a topical delivery vehicle for various cosmetic and skin disease applications.

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.

In Silico Designing and Screening of Antagonists against Cancer Drug Target XIAP.

X-linked inhibitor of apoptosis (XIAP) is a member of inhibitor of apoptosis (IAP) family and involved in the suppression of apoptosis in cancer cells. This property makes it a therapeutic target for the cancer therapy. In the present study, we have developed QSAR models using chemical descriptors, fingerprints, principal components, docking energy parameters and similarity-based approach against XIAP. We have achieved correlation (R) of 0.803 with R(2) value of 0.645 at 10-fold cross validation using SMOreg algorithm. We have evaluated these models on independent dataset to ascertain its robustness and achieved correlation (R) of 0.793 with R(2) value of 0.628. Further, we have used these models for the screening of FDA approved drugs and drug-like molecules from ZINC database and prioritized them on the basis of their predicted pIC50 values. Docking studies of top hits with XIAP-BIR3 domain shows that Iodixanol (DB01249) and ZINC68678304 have higher binding affinities than well-known tetrapeptide inhibitor, AVPI. We have integrated these models in a web server named as "XIAPin". We hope that this web server will contribute in the designing of nifty antagonists against XIAP.

Computer-Aided Virtual Screening and Designing of Cell-Penetrating Peptides.

Cell-penetrating peptides (CPPs) have proven their potential as versatile drug delivery vehicles. Last decade has witnessed an unprecedented growth in CPP-based research, demonstrating the potential of CPPs as therapeutic candidates. In the past, many in silico algorithms have been developed for the prediction and screening of CPPs, which expedites the CPP-based research. In silico screening/prediction of CPPs followed by experimental validation seems to be a reliable, less time-consuming, and cost-effective approach. This chapter describes the prediction, screening, and designing of novel efficient CPPs using "CellPPD," an in silico tool.