scholarly journals Identifying protein subsets and features responsible for improved drug repurposing accuracies using the CANDO platform

2018 ◽  
Author(s):  
William Mangione ◽  
Ram Samudrala
Keyword(s):  
Drug Discovery ◽  
Computational Analysis ◽  
Biological Systems ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Learning Approaches ◽  
Diverse Range ◽  
Compound Libraries ◽  
Ligand Interactions ◽  
Novel Drug

AbstractDrug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 3,733 drugs/compounds that map to 2,030 indications/diseases by predicting their interactions with 46,784 protein structures and relating them via proteomic interaction signatures. The accuracy of the CANDO platform is evaluated using our benchmarking protocol that assesses indication accuracies based on whether or not pairs of drugs associated with the same indication can be captured within a certain cutoff, which is a measure of the drug repurposing recovery rate. To identify subsets of proteins that exhibit the same therapeutic effectiveness as the full set, groups of 8 proteins were randomly selected and subsequently benchmarked 50 times. The resulting protein sets were ranked according to average indication accuracy, pairwise accuracy, and coverage (count of indications with non-zero accuracy). The best 50 subsets of 8 according to each metric were progressively combined into supersets after each iteration and benchmarked. These supersets yield up to 14% improvement in benchmarking accuracy, and represent a 100-1,000 fold reduction in the number of proteins relative to the full set. Protein supersets optimized using independent compound libraries derived from the full library were cross-tested and were shown to reproduce the performance relative to using all 46,784 proteins, indicating that these reduced size supersets are broadly applicable for characterizing drug behavior. Further analysis revealed that sets comprised of proteins with more equitably diverse ligand interactions are important for describing drug behavior. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in computational drug repurposing, and paves the way for the use of machine learning approaches to further improve the accuracy of the CANDO platform and its repurposing potential.Author summaryDrug repurposing is a valuable approach for ameliorating the current problems plaguing drug discovery. We introduce a novel protein subset analysis pipeline that allows us to elucidate features important for drug repurposing accuracies using the Computational Analysis of Novel Drug Opportunities (CANDO) platform. Our platform relates drugs based on the similarity of their interactions with a diverse library of proteins. We subjected all proteins in the platform to a splitting and ranking protocol that ranked protein subsets based on their benchmarking performance. Further analysis of the best performing protein subsets revealed that the most useful proteins for describing how small molecule compounds behave in biological systems are those that are predicted to interact with a structurally diverse range of ligands. We hypothesize that this is a consequence of the multitarget nature of drugs and, conversely, the implied promiscuity of proteins in biological systems. These results may be used to make drug discovery more accurate and efficient by alleviating some of its bottlenecks, bringing us one step further in better understanding how drugs behave in the context of their environments.

19F-NMR in Target-based Drug Discovery

2019 ◽  
Vol 26 (26) ◽  
pp. 4964-4983 ◽  
Author(s):  
CongBao Kang
Keyword(s):  
Drug Discovery ◽  
Conformational Changes ◽  
Protein Structures ◽  
19F Nmr ◽  
Binding Affinities ◽  
Protein Ligand Interactions ◽  
Compound Libraries ◽  
Ligand Interactions ◽  
Reference Ligand ◽  
Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

scholarly journals Shotgun Drug Repurposing Biotechnology to Tackle Epidemics and Pandemics

2020 ◽  
Author(s):  
William Mangione ◽  
Zackary Falls ◽  
Thomas Melendy ◽  
Gaurav Chopra ◽  
Ram Samudrala
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Computational Analysis ◽  
Drug Repurposing ◽  
The Future ◽  
Respiratory Coronavirus ◽  
Novel Drug

In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future. <br>

scholarly journals Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using The CANDO Platform: Implications for Drug Design

2018 ◽  
Author(s):  
William Mangione ◽  
Ram Samudrala
Keyword(s):  
Drug Design ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Learning Approaches ◽  
Drug Candidates ◽  
Protein Library ◽  
Fold Reduction ◽  
Ligand Interactions ◽  
Novel Drug

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100&ndash;1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

scholarly journals Evaluating Performance of Drug Repurposing Technologies

2020 ◽  
Author(s):  
James Schuler ◽  
Zackary Falls ◽  
William Mangione ◽  
Matthew L. Hudson ◽  
Liana Bruggemann ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Drug Discovery ◽  
Computational Analysis ◽  
Drug Repurposing ◽  
Evaluation Metrics ◽  
Drug Discovery And Development ◽  
Evaluation Measures ◽  
Cross Platform ◽  
Patient Use ◽  
Novel Drug

AbstractDrug repurposing technologies are growing in number and maturing. However, comparison to each other and to reality is hindered due to lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross platform comparability, enabling us to continuously strive towards optimal repurposing by decreasing time and cost of drug discovery and development.

scholarly journals Shotgun Drug Repurposing Biotechnology to Tackle Epidemics and Pandemics

2020 ◽  
Author(s):  
William Mangione ◽  
Zackary Falls ◽  
Thomas Melendy ◽  
Gaurav Chopra ◽  
Ram Samudrala
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Computational Analysis ◽  
Drug Repurposing ◽  
The Future ◽  
Respiratory Coronavirus ◽  
Novel Drug

In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future. <br>

scholarly journals Shotgun Drug Repurposing Biotechnology to Tackle Epidemics and Pandemics

2020 ◽  
Author(s):  
William Mangione ◽  
Zackary Falls ◽  
Thomas Melendy ◽  
Gaurav Chopra ◽  
Ram Samudrala
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Computational Analysis ◽  
Drug Repurposing ◽  
The Future ◽  
Respiratory Coronavirus ◽  
Novel Drug

In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future. <br>

scholarly journals Fingerprinting CANDO: Increased Accuracy with Structure and Ligand Based Shotgun Drug Repurposing

2019 ◽  
Author(s):  
James Schuler ◽  
Ram Samudrala
Keyword(s):  
Drug Discovery ◽  
Decision Tree ◽  
Data Fusion ◽  
Large Scale ◽  
Computational Analysis ◽  
Drug Repurposing ◽  
Molecular Fingerprints ◽  
Novel Drug ◽  
Benchmarking Performance ◽  
Random Control

We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to include ligand-based, data fusion, and decision tree pipelines. The first version of CANDO implemented a structure-based pipeline that modeled interactions between compounds and proteins on a large scale, generating compoundproteome interaction signatures used to infer similarity of drug behavior; the new pipelines accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We obtain improved benchmarking performance with the new pipelines across all three evaluation metrics used: average indication accuracy, pairwise accuracy, and coverage. The best performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, thereby enhancing our ability to generate putative therapeutic repurposing candidates, and increasing drug discovery efficiency.

scholarly journals ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing

2020 ◽  
Vol 36 (8) ◽  
pp. 2602-2604 ◽  
Author(s):  
Evangelos Karatzas ◽  
Juan Eiros Zamora ◽  
Emmanouil Athanasiadis ◽  
Dimitris Dellis ◽  
Zoe Cournia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Perfect Match ◽  
Web Server ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
Structural Similarity ◽  
Substructure Search ◽  
Similarity Network ◽  
Compound Libraries ◽  
Network Analysis Metrics

Abstract Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.

scholarly journals Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

2020 ◽  
Author(s):  
Alex Zhavoronkov ◽  
Vladimir Aladinskiy ◽  
Alexander Zhebrak ◽  
Bogdan Zagribelnyy ◽  
Victor Terentiev ◽  
...  
Keyword(s):  
Homology Modelling ◽  
Drug Repurposing ◽  
Hiv Protease ◽  
Learning Approaches ◽  
Protein Targets ◽  
Chemical Libraries ◽  
Internal Resources ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Novel Drug

<div> <div> <div> <p>The emergence of the 2019 novel coronavirus (2019-nCoV), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important 2019-nCoV protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of 2019-nCoV and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked- based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches are being published at www.insilico.com/ncov-sprint/ and will be continuously updated. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. </p> </div> </div> </div>

scholarly journals Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease

2020 ◽  
Vol 13 (11) ◽  
pp. dmm044040 ◽  
Author(s):  
Katie Lloyd ◽  
Stamatia Papoutsopoulou ◽  
Emily Smith ◽  
Philip Stegmaier ◽  
Francois Bergey ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Nuclear Translocation ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Peritoneal Macrophages ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Aseptic Conditions ◽  
Novel Drug

ABSTRACTInflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required.This article has an associated First Person interview with the joint first authors of the paper.

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