Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach.

The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.

Drug repurposing: databases and pipelines.

The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.

The Use of In Silico Methods to Identify and Assess Antigenic Regions Suitable for the Development of Peptide-based Pan-viral Vaccines.

The constant evolution of pathogenic viral variants and the emergence of new viruses have reinforced the need for broad-spectrum vaccines to combat such threats. The spread of new viral variants leading to epidemic and pandemic infection can be effectively contained, if broad-spectrum vaccines effective against the newer viral variants are readily available. The development of broad-spectrum, pan-neutralising antibodies against viruses which, in general terms, are very antigenically different - such as HIV, influenza virus and paramyxoviruses - has been reported in the literature. The amino acid sequences used to generate a range of approved recombinant anti-viral vaccines were analysed by using in silico methods, with the aim of identifying highly antigenic peptide regions that may be suitable for the development of broad-spectrum peptide-based anti-viral vaccines. This was achieved through the use of open-source data, an algorithm-driven probability matrix, and published in silico prediction tools (SVMTriP, IEDB-AR, VaxiJen 2.0, AllergenFP v. 1.0, AllerTOP v. 2.0, ToxinPred and ProtParam) to evaluate antigenicity, MHC-I and MHC-II binding potential, immunogenicity, allergenicity, toxicity and physicochemical properties. We report a pan-antigenic peptide region with strong affinity for MHC-I and MHC-II, and good immunogenic potential. According to the output from the relevant in silico tools, the peptide was predicted to be non-toxic, non-allergic and to possess the desired physicochemical properties for potentially successful vaccine production. With further investigation and optimisation, this peptide could be considered for use in the development of a broad-spectrum anti-viral vaccine that may protect against emerging new viruses. Our approach of using in silico methods to identify candidate antigenic peptides with the desired physicochemical properties could potentially circumvent the use of some animal studies for peptide vaccine candidate evaluation.

A Comparison of Machine Learning Models for Survival Prediction of Patients with Glioma Using Radiomic Features from MRI Scans.

Background Glioma is a primary, malignant, highly aggressive brain tumor, with patients having an average life expectancy of 14 to 16 months after diagnosis. Magnetic resonance imaging (MRI) scans of these patients can be used to extract and analyze quantifiable features with potential clinical significance. We hypothesize that there is a correlation between radiomic features extracted from MRI scans and survival. Along with clinical data, the radiomic features could be used in survival prediction of patients, providing beneficial information for clinicians to design personalized treatment plans. Methods In our study, we have utilized 3D Slicer for tumor segmentation and feature extraction and performed survival prediction of patients with glioma using four different machine learning models. Results and Conclusion Among the models compared, we have achieved a maximum prediction accuracy of 64.4% using the k-nearest neighbors model, which was trained and tested on a combination of clinical data and radiomic features extracted from MRI images provided in the BraTS 2020 dataset.

Bioinformatics approach used in undergraduate research to predict siRNA as ZIKV therapeutics.

Undergraduate research is an important component of a B.Tech. Biotechnology program. In the present study, a customizable approach designed with open-source bioinformatics tools and databases was introduced to predict siRNAs for ZIKV therapeutics. With minimal prior exposure to bioinformatics, this workflow can be executed with detailed steps as demonstrated in this paper. All software, databases, and servers used in this research are open-source, allowing this project-based learning methodology to be implemented remotely as well. The workflow designed in the present study is flexible and customizable according to the mentor and student's requirements.

The Use of In Silico Tools for the Toxicity Prediction of Potential Inhibitors of SARS-CoV-2.

The current strategy for treating the Covid-19 coronavirus disease involves the repurposing of existing drugs or the use of convalescent plasma therapy, as no specific therapeutic intervention has yet received regulatory approval. However, severe adverse effects have been reported for some of these repurposed drugs. Recently, several in silico studies have identified compounds that are potential inhibitors of the main protease (3-chymotrypsin-like cysteine protease) and the nucleocapsid protein of SARS-CoV-2. An essential step of drug development is the careful evaluation of toxicity, which has a range of associated financial, temporal and ethical limitations. In this study, a number of in silico tools were used to predict the toxicity of 19 experimental compounds. A range of web-based servers and applications were used to predict hepatotoxicity, mutagenicity, acute oral toxicity, carcinogenicity, cardiotoxicity, and other potential adverse effects. The compounds were assessed based on the consensus of results, and were labelled as positive or negative for a particular toxicity endpoint. The compounds were then categorised into three classes, according to their predicted toxicity. Ten compounds (52.6%) were predicted to be non-mutagenic and non-hERG inhibitors, and exhibited zero or low level hepatotoxicity and carcinogenicity. Furthermore, from the consensus of results, all 19 compounds were predicted to be non-mutagenic and negative for acute oral toxicity. Overall, most of the compounds displayed encouraging toxicity profiles. These results can assist further lead optimisation studies and drug development efforts to combat Covid-19.

Inhibition of NarL of Mycobacterium Tuberculosis: an in silico approach.

Mycobacterium tuberculosis (MTB) consumes nitrate as the alternate mechanism of respiration in the absence of oxygen, thus increasing its survival and virulence during latent stage of tuberculosis infection. NarL is a nitrate/nitrite response transcriptional regulatory protein of two-component signal transduction system which regulates nitrate reductase and formate dehydrogenase for MTB adaptation to anaerobic condition. Phosphorylation by sensor kinase (NarX) is the primary mechanism behind the activation of NarL although many response regulators get activated by small molecule phospho-donors in the absence of sensor kinase. Virtual screening was performed using Autodock suite for the compounds from ZINC database against NarL and potential inhibitors was identified to inhibit the activation of NarL by affecting its phosphorylation. Molecular dynamics simulation studies predicted the stability of 1-{1-[(3-nitrophenyl) methyl] piperidin-2-yl} ethan-1-amine in the active site of NarL over 10 ns simulation. Phosphorylation of NarL by small molecule phospho-donors is also investigated in the present study. Here we suggest that nitro benzene - amine piperidine moiety can be an effective lead candidate for developing novel anti-tuberculosis drugs.

Prediction and analysis of surface hydrophobic residues in tertiary structure of proteins.

The analysis of protein structures provides plenty of information about the factors governing the folding and stability of proteins, the preferred amino acids in the protein environment, the location of the residues in the interior/surface of a protein and so forth. In general, hydrophobic residues such as Val, Leu, Ile, Phe, and Met tend to be buried in the interior and polar side chains exposed to solvent. The present work depends on sequence as well as structural information of the protein and aims to understand nature of hydrophobic residues on the protein surfaces. It is based on the nonredundant data set of 218 monomeric proteins. Solvent accessibility of each protein was determined using NACCESS software and then obtained the homologous sequences to understand how well solvent exposed and buried hydrophobic residues are evolutionarily conserved and assigned the confidence scores to hydrophobic residues to be buried or solvent exposed based on the information obtained from conservation score and knowledge of flanking regions of hydrophobic residues. In the absence of a three-dimensional structure, the ability to predict surface accessibility of hydrophobic residues directly from the sequence is of great help in choosing the sites of chemical modification or specific mutations and in the studies of protein stability and molecular interactions.

Identification of proapoptopic, anti-inflammatory, anti- proliferative, anti-invasive and anti-angiogenic targets of essential oils in cardamom by dual reverse virtual screening and binding pose analysis.

BACKGROUND: Cardamom (Elettaria cardamom), also known as "Queen of Spices", has been traditionally used as a culinary ingredient due to its pleasant aroma and taste. In addition to this role, studies on cardamom have demonstrated cancer chemopreventive potential in in vitro and in vivo systems. Nevertheless, the precise poly-pharmacological nature of naturally occurring chemo-preventive compounds in cardamom has still not been fully demystified. METHODS: In this study, an effort has been made to identify the proapoptopic, anti-inflammatory, anti-proliferative, anti-invasive and anti-angiogenic targets of Cardamom's bioactive principles (eucalyptol, alpha-pinene, beta-pinene, d-limonene and geraniol) by employing a dual reverse virtual screening protocol. Experimentally proven target information of the bioactive principles was annotated from bioassay databases and compared with the virtually screened set of targets to evaluate the reliability of the computational identification. To study the molecular interaction pattern of the anti-tumor action, molecular docking simulation was performed with Auto Dock Pyrx. Interaction studies of binding pose of eucalyptol with Caspase 3 were conducted to obtain an insight into the interacting amino acids and their inter-molecular bondings. RESULTS: A prioritized list of target proteins associated with multiple forms of cancer and ranked by their Fit Score (Pharm Mapper) and descending 3D score (Reverse Screen 3D) were obtained from the two independent inverse screening platforms. Molecular docking studies exploring the bioactive principle targeted action revealed that H- bonds and electrostatic interactions forms the chief contributing factor in inter-molecular interactions associated with anti-tumor activity. Eucalyptol binds to the Caspase 3 with a specific framework that is well-suited for nucleophilic attacks by polar residues inside the Caspase 3 catalytic site. CONCLUSION: This study revealed vital information about the poly-pharmacological anti-tumor mode-of-action of essential oils in cardamom. In addition, a probabilistic set of anti-tumor targets for cardamom was generated, which can be further confirmed by in vivo and in vitro experiments.

Comparative reverse screening approach to identify potential anti-neoplastic targets of saffron functional components and binding mode.

BACKGROUND: In the last two decades, pioneering research on anti-tumour activity of saffron has shed light on the role of crocetin, picrocrocin and safranal, as broad spectrum anti-neoplastic agents. However, the exact mechanisms have yet to be elucidated. Identification and characterization of the targets of bioactive constituents will play an imperative role in demystifying the complex anti-neoplastic machinery. METHODS: In the quest of potential target identification, a dual virtual screening approach utilizing two inverse screening systems, one predicated on idTarget and the other on PharmMapper was here employed. A set of target proteins associated with multiple forms of cancer and ranked by Fit Score and Binding energy were obtained from the two independent inverse screening platforms. The validity of the results was checked by meticulously analyzing the post-docking binding pose of the picrocrocin with Hsp90 alpha in AutoDock. RESULTS: The docking pose reveals that electrostatic and hydrogen bonds play the key role in inter-molecular interactions in ligand binding. Picrocrocin binds to the Hsp90 alpha with a definite orientation appropriate for nucleophilic attacks by several electrical residues inside the Hsp90-alpha ATPase catalytic site. CONCLUSION: This study reveals functional information about the anti-tumor mechanism of saffron bioactive constituents. Also, a tractable set of anti-neoplastic targets for saffron has been generated in this study which can be further authenticated by in vivo and in vitro experiments.

Identification of potential apicoplast associated therapeutic targets in human and animal pathogen Toxoplasma gondii ME49.

UNLABELLED: Toxoplasma gondii ME49 is an obligatory intracellular apicomplexa parasite that causes toxoplasmosis in humans, domesticated and wild animals. Waterborne outbreaks of acute toxoplasmosis worldwide reinforce the transmission of Toxoplasma gondii ME49 to humans through contaminated water and may have a greater epidemiological impact than previously believed. In the quest for drug and vaccine target identification subtractive genomics involving subtraction between the host and pathogen genome has been implemented for enlisting essential pathogen specific proteins. Using this approach, our analysis on both human and Toxoplasma gondii ME49 reveals that out of 7987 protein coding sequences of the pathogen, 950 represent essential non human-homologous proteins. Subcellular localization prediction & comparative-biochemical pathway analysis of these essential proteins gives a list of apicoplast-associated proteins having unique pathogen-specific metabolic pathway. These apicoplast-associated enzymes involved in fatty acid biosynthesis pathway of Toxoplasma gondii ME49, may be used as potential drug targets, as the pathway is vital for the protozoan's survival. Structure prediction of drug target proteins was done using fold based recognition method. Screening of the functional inhibitors against these novel targets may result in discovery of novel therapeutic compounds that can be effective against Toxoplasma gondii ME49. ABBREVIATIONS: DEG - Database of Essential Gene, KEGG - Kyoto Encyclopaedia of Genes and Genomes, KAAS - KEGG Automated Annotation Server, PFP - Protein Function Prediction, COG - Cluster of Orthologous Genes.

Structural basis for the function of anti-idiotypic antibody in immune memory.

We had earlier proposed a hypothesis to explain the mechanism of perpetuation of immunological memory based on the operation of idiotypic network in the complete absence of antigen. Experimental evidences were provided for memory maintenance through anti-idiotypic antibody (Ab(2)) carrying the internal image of the antigen. In the present work, we describe a structural basis for such memory perpetuation by molecular modeling and structural analysis studies. A three-dimensional model of Ab(2) was generated and the structure of the antigenic site on the hemagglutinin protein H of Rinderpest virus was modeled using the structural template of hemagglutinin protein of Measles virus. Our results show that a large portion of heavy chain containing the CDR regions of Ab(2) resembles the domain of the hemagglutinin housing the epitope regions. The similarity demonstrates that an internal image of the H antigen is formed in Ab(2), which provides a structural basis for functional mimicry demonstrated earlier. This work brings out the importance of the structural similarity between a domain of hemagglutinin protein to that of its corresponding Ab(2). It provides evidence that Ab(2) is indeed capable of functioning as surrogate antigen and provides support to earlier proposed relay hypothesis which has provided a mechanism for the maintenance of immunological memory.