Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Abstract Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

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Knowledge graph analytics platform with LINCS and IDG for Parkinson’s disease target illumination

10.1101/2020.12.30.424881 ◽

2021 ◽

Author(s):

Jeremy J Yang ◽

Christopher R Gessner ◽

Joel L Duerksen ◽

Daniel Biber ◽

Jessica L Binder ◽

...

Keyword(s):

Parkinson’S Disease ◽

Human Health ◽

Molecular Basis ◽

Drug Target ◽

Drug Targets ◽

Complex Diseases ◽

Knowledge Graph ◽

Graph Analytics ◽

Novel Drug ◽

Novel Drug Targets

AbstractBackgroundLINCS, “Library of Integrated Network-based Cellular Signatures”, and IDG, “Illuminating the Druggable Genome”, are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson’s disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches.ResultsIntegrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods, using Neo4j. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG’s resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD.ConclusionsThe synergy of LINCS and IDG, via KG methods, empowers graph analytic methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

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Comparative Proteome Analysis of Mycobacterium Tuberculosis Strains - H37Ra, H37Rv, CCDC5180, and CAS/NITR204: A Step Forward to Identify Novel Drug Targets

Letters in Drug Design & Discovery ◽

10.2174/1570180817999200531165148 ◽

2020 ◽

Vol 17 (11) ◽

pp. 1422-1431

Author(s):

Shradheya R.R. Gupta ◽

Ekta Gupta ◽

Avnam Ohri ◽

Sandeep Kumar Shrivastava ◽

Sumita Kachhwaha ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Drug Target ◽

Drug Targets ◽

Virulent Strain ◽

Proteome Analysis ◽

Target Protein ◽

Central Asian ◽

Virulent Strains ◽

Novel Drug ◽

Novel Drug Targets

Background: Mycobacterium tuberculosis is a causative agent of tuberculosis. It is a non-motile, acid-fast, obligatory aerobic bacterium. Finding novel drug targets in Mycobacterium tuberculosis has become extremely important as the bacterium is evolving into a more dangerous multi-drug resistant pathogen. The predominant strains in India belong to the Central-Asian, East- African Indian, and Beijing clad. For the same reason, the whole proteomes of a non-virulent strain (H37Ra), a virulent (H37Rv) and two clinical strains, a Central-Asian clad (CAS/NITR204) and a Beijing clad (CCDC5180) have been selected for comparative study. Selecting a phylogenetically close and majorly studied non-virulent strain is helpful in removing the common and undesired proteins from the study. Objective: The study compares the whole proteome of non-virulent strain with the other three virulent strains to find a unique protein responsible for virulence in virulent strains. It is expected that the drugs developed against identified targets will be specific to the virulent strains. Additionally, to assure minimal toxicity to the host, we also screened the human proteome. Methods: Comparative proteome analysis was used for target identification and in silico validation of identified target protein Rv2466c, identification of the respective ligand of the identified target protein and binding interaction study using Molecular docking and Molecular Dynamic Simulation study were used in this study. Results and Discussion: Finally, eleven proteins were found to be unique in virulent strain only and out of which, Rv2466c (PDB-ID: 4ZIL) was found to be an essential protein and identified as a putative drug target protein for further study. The compound glutathione was found to be a suitable inhibitor for Rv2466c. In this study, we used a comparative proteomics approach to identify novel target proteins. Conclusion: This study is unique as we are assured that the study will move forward the research in a new direction to cure the deadly disease (tuberculosis) caused by Mycobacterium tuberculosis. Rv2466c was identified as a novel drug target and glutathione as a respective ligand of Rv2466c. Discovery of the novel drug target as well as the drug will provide a solution to drug resistance as well as the infection caused by Mycobacterium tuberculosis.

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Metabolic pathway analysis in trypanosomes and malaria parasites

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2001.1040 ◽

2002 ◽

Vol 357 (1417) ◽

pp. 101-107 ◽

Cited By ~ 22

Author(s):

Alan H. Fairlamb

Keyword(s):

Drug Target ◽

Drug Targets ◽

Target Identification ◽

Potential Drug ◽

Metabolic Pathway Analysis ◽

Entire Genome ◽

Microbial Responses ◽

Novel Drug ◽

Potential Drug Targets ◽

Novel Drug Targets

Identification of novel drug targets is required for the development of new classes of drugs to overcome drug resistance and replace less efficacious treatments. In theory, knowledge of the entire genome of a pathogen identifies every potential drug target in any given microbe. In practice, the sheer complexity and the inadequate or inaccurate annotation of genomic information makes target identification and selection somewhat more difficult. Analysis of metabolic pathways provides a useful conceptual framework for the identification of potential drug targets and also for improving our understanding of microbial responses to nutritional, chemical and other environmental stresses. A number of metabolic databases are available as tools for such analyses. The strengths and weaknesses of this approach are discussed.

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Novel drug targets in 2019

Nature Reviews Drug Discovery ◽

10.1038/d41573-020-00052-w ◽

2020 ◽

Vol 19 (5) ◽

pp. 300-300 ◽

Cited By ~ 1

Author(s):

Sorin Avram ◽

Liliana Halip ◽

Ramona Curpan ◽

Tudor I. Oprea

Keyword(s):

Drug Targets ◽

Novel Drug ◽

Novel Drug Targets

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Cyclic Nucleotide Signaling Pathways in Apicomplexan Parasites Provide a Valuable Source for Novel Drug Targets

10.33696/signaling.1.005 ◽

2020 ◽

Keyword(s):

Signaling Pathways ◽

Cyclic Nucleotide ◽

Drug Targets ◽

Valuable Source ◽

Apicomplexan Parasites ◽

Nucleotide Signaling ◽

Novel Drug ◽

Novel Drug Targets

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Identification and Validation of Novel Drug Targets in Tuberculosis

Current Pharmaceutical Design ◽

10.2174/1381612043383223 ◽

2004 ◽

Vol 10 (26) ◽

pp. 3185-3194 ◽

Cited By ~ 35

Author(s):

K. Duncan

Keyword(s):

Drug Targets ◽

Novel Drug ◽

Novel Drug Targets

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Structural Systems Pharmacology: A New Frontier in Discovering Novel Drug Targets

Current Drug Targets ◽

10.2174/1389450111314090003 ◽

2013 ◽

Vol 14 (9) ◽

pp. 952-958 ◽

Cited By ~ 5

Author(s):

Hepan Tan ◽

Xiaoxia Ge ◽

Lei Xie

Keyword(s):

Drug Targets ◽

Structural Systems ◽

Systems Pharmacology ◽

New Frontier ◽

Novel Drug ◽

Novel Drug Targets

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Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

Scientific Reports ◽

10.1038/s41598-021-92701-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Pusheng Quan ◽

Kai Wang ◽

Shi Yan ◽

Shirong Wen ◽

Chengqun Wei ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Drug Target ◽

Target Genes ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Control Group ◽

Drug Candidates ◽

Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

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4E Interacting Protein as a Potential Novel Drug Target for Nucleoside Analogues in Trypanosoma Brucei

Microorganisms ◽

10.3390/microorganisms9040826 ◽

2021 ◽

Vol 9 (4) ◽

pp. 826

Author(s):

Dorien Mabille ◽

Camila Cardoso Santos ◽

Rik Hendrickx ◽

Mathieu Claes ◽

Peter Takac ◽

...

Keyword(s):

Mode Of Action ◽

Drug Target ◽

Drug Targets ◽

De Novo ◽

Treatment Options ◽

Adenosine Kinase ◽

Nucleoside Analogues ◽

Purine Salvage ◽

Interacting Protein ◽

Novel Drug

Human African trypanosomiasis is a neglected parasitic disease for which the current treatment options are quite limited. Trypanosomes are not able to synthesize purines de novo and thus solely depend on purine salvage from the host environment. This characteristic makes players of the purine salvage pathway putative drug targets. The activity of known nucleoside analogues such as tubercidin and cordycepin led to the development of a series of C7-substituted nucleoside analogues. Here, we use RNA interference (RNAi) libraries to gain insight into the mode-of-action of these novel nucleoside analogues. Whole-genome RNAi screening revealed the involvement of adenosine kinase and 4E interacting protein into the mode-of-action of certain antitrypanosomal nucleoside analogues. Using RNAi lines and gene-deficient parasites, 4E interacting protein was found to be essential for parasite growth and infectivity in the vertebrate host. The essential nature of this gene product and involvement in the activity of certain nucleoside analogues indicates that it represents a potential novel drug target.

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