Academic literature on the topic 'Drug repurposing webserver'

Abstract Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, has been a global concern. While there have been some vaccines and drugs, the rapid emergence of variants due to mutations has threatened public health. As the de novo drug development process is expensive and time-consuming, repurposing existing antiviral drugs against SARS-CoV-2 is an alternative and promising approach to mitigate the current situation. Several studies have indicated that some natural products exhibit inhibitory activities against SARS-CoV-2. This study is aimed at analyzing the potential of natural alkaloids, using various computational tools, as drug candidates against SARS-CoV-2. The molecular docking analysis predicted that naturally occurring alkaloids can bind with RNA-dependent RNA-polymerase (RdRP). The QSAR analysis was conducted by using the way2drug/PASS online web resource, and the pharmacokinetics and toxicity properties of these alkaloids were predicted using pkCSM, SwissADME, and ProTox-II webserver. Among the different alkaloids studied, neferine and berbamine were repurposed as potential drug candidates based on their binding affinity and interactions with RdRP. Further, molecular dynamics simulation of 90 ns revealed the conformational stability of the neferine-RdRP complex.

AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

To cure SARS-CoV-2 infection, the repurposing of conventional antiviral drugs is currently advocated by researchers, though their action is not very effective. The present study, based on in silico methods, was intended to increase the therapeutic potential of conventional drugs: hydroxychloroquine (HCQ), favipiravir (FAV), and remdesivir (REM) by using curcuminoids like curcumin (CUR), bisdemethoxycurcumin (BDMC), and demethoxycurcumin (DMC) as adjunct drugs against SARS-CoV-2 receptor proteins, namely main protease (Mpro) and the S1 receptor-binding domain (RBD). The curcuminoids exhibited similar pharmacokinetic properties to the conventional drugs. The webserver (ANCHOR) predicted greater protein stability for both receptors with a disordered score (<0.5). The molecular docking study showed that the binding energy was highest (−27.47 kcal/mol) for BDMC toward Mpro receptors, while the binding energy of CUR (−20.47 kcal/mol) and DMC (−20.58 kcal/mol) was lower than that of HCQ (−24.58 kcal/mol), FAV (−22.87 kcal/mol), and REM (−23.48 kcal/mol). In the case of S1-RBD, CUR had the highest binding energy (−38.84 kcal/mol) and the lowest was in FAV (−23.77 kcal/mol), whereas HCQ (−35.87 kcal/mol) and REM (−38.44 kcal/mol) had greater binding energy than BDMC (−28.07 kcal/mol) and DMC (−30.29 kcal/mol). Hence, this study envisages that these curcuminoids could be employed in combination therapy with conventional drugs to disrupt the stability of SARS-CoV-2 receptor proteins.

AbstractUnderstanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).

Infection with SARS-CoV-2 coronavirus causes systemic, multi-faceted COVID-19 disease. However, knowledge connecting its intricate clinical manifestations with molecular mechanisms remains fragmented. Deciphering the molecular basis of COVID-19 at the whole-patient level is paramount to the development of effective therapeutic approaches. With this goal in mind, we followed an iterative, expert-driven process to compile data published prior to and during the early stages of the pandemic into a comprehensive COVID-19 knowledge model. Recent updates to this model have also validated multiple earlier predictions, suggesting the importance of such knowledge frameworks in hypothesis generation and testing. Overall, our findings suggest that SARS-CoV-2 perturbs several specific mechanisms, unleashing a pathogenesis spectrum, ranging from “a perfect storm” triggered by acute hyper-inflammation, to accelerated aging in protracted “long COVID-19” syndromes. In this work, we shortly report on these findings that we share with the community via 1) a synopsis of key evidence associating COVID-19 symptoms and plausible mechanisms, with details presented within 2) the accompanying “COVID-19 Explorer” webserver, developed specifically for this purpose (found at https://covid19.molecularhealth.com). We anticipate that our model will continue to facilitate clinico-molecular insights across organ systems together with hypothesis generation for the testing of potential repurposing drug candidates, new pharmacological targets and clinically relevant biomarkers. Our work suggests that whole patient knowledge models of human disease can potentially expedite the development of new therapeutic strategies and support evidence-driven clinical hypothesis generation and decision making.

Deviation from normal healthy conditions, termed as disease, can often be triggered due to the malfunctioning of proteins. Modulating the functions of proteins by administering therapeutic agents (drugs) may alleviate the disease conditions. The majority of the drugs currently available in the market are small organic molecules due to their pharmacological and commercial advantages. These small molecule drugs interact with the protein targets through specific sites on the surface of the protein structure (binding sites). Thus, the structural data of protein-small molecule complexes forms a crucial starting point for most drug discovery programs. The work reported in this thesis deals with understanding various aspects of protein-small molecule interactions. The thesis begins (Chapter 1) with a general introduction on the implication of proteins structural data in drug discovery programs. Chapter 2 provides a fundamental understanding of the general trend in local quality of protein-small molecule crystal complexes deposited in the Protein Data Bank (PDB). Our results suggest ‘seeing is not always believing’ and aims to sensitize the non-crystallographer user community that high-resolution need not always guarantee confident small molecule binding poses. The study indicates 35% of the inspected ~0.28 million protein-small molecule binding site pairs available from ~66000 PDB entries, need serious attention before using those as input in any important applications. Results reported in Chapter 3 suggest that the stereochemical quality of bound small molecules generally agrees well with their crystallographic quality. The findings from this work could be the stepping-stones for developing structure determination technique-independent ligand pose validation tools. The learning from Chapter 3 is extended to Chapter 4 to investigate the stereochemical quality of the small molecules bound to protein structures determined by cryo-EM. Our data shows that the stereochemical quality of small molecules bound to high-resolution protein structures determined by cryo-EM is comparable to high-quality small molecules bound to protein crystal structures. Chapter 5 presents a computational analysis aimed at providing insights into the molecular basis of the specificity of a novel anti-tubercular compound, NU-6027 (identified in a phenotypic screening by experimental collaborators), towards two out of the eleven known Serine-Threonine Protein Kinases in Mycobacterium tuberculosis (Mtb). Chapter 6 reports the development of a freely available web server that facilitates the identification of new uses of old drugs and aid in drug repurposing. In Chapter 7, the principles of ‘neighborhood behavior’ are exploited to identify potential known drugs that could be repurposed against the main protease of SARS-CoV-2. Chapter 8 discusses a virtual screening strategy to identify potential binders of a novel Mtb target, Rv1636 (or the Universal Stress Protein). Collaborators have experimentally validated some of the compounds shortlisted from the computational studies. Chapter 9 summarizes the findings from work reported in the entire thesis and future applications. Overall, this thesis inspects protein-small molecule complexes from a local perspective, aiding the design of rigorous computational experiments that can contribute to solving global unmet medical needs. Interested readers may contact the author directly for Supplementary data at "sohini@iisc.ac.in"
DST-INSPIRE fellowship