A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases
Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantag...
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2016
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19352727_v10_n1_p_Berenstein http://hdl.handle.net/20.500.12110/paper_19352727_v10_n1_p_Berenstein |
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paper:paper_19352727_v10_n1_p_Berenstein2023-06-08T16:31:50Z A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases chemical compound peptide deformylase antiparasitic agent Article biological activity drug repositioning genome high throughput screening Kinetoplastida mathematical computing neglected disease nonhuman Plasmodium falciparum protein analysis protein domain sequence analysis Trypanosoma cruzi animal biology drug development drug repositioning human isolation and purification mouse Neglected Diseases procedures Animals Antiparasitic Agents Computational Biology Drug Discovery Drug Repositioning Humans Mice Neglected Diseases Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105compounds and several functional relations among 1.67 105proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. © 2016 Berenstein et al. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19352727_v10_n1_p_Berenstein http://hdl.handle.net/20.500.12110/paper_19352727_v10_n1_p_Berenstein |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
chemical compound peptide deformylase antiparasitic agent Article biological activity drug repositioning genome high throughput screening Kinetoplastida mathematical computing neglected disease nonhuman Plasmodium falciparum protein analysis protein domain sequence analysis Trypanosoma cruzi animal biology drug development drug repositioning human isolation and purification mouse Neglected Diseases procedures Animals Antiparasitic Agents Computational Biology Drug Discovery Drug Repositioning Humans Mice Neglected Diseases |
| spellingShingle |
chemical compound peptide deformylase antiparasitic agent Article biological activity drug repositioning genome high throughput screening Kinetoplastida mathematical computing neglected disease nonhuman Plasmodium falciparum protein analysis protein domain sequence analysis Trypanosoma cruzi animal biology drug development drug repositioning human isolation and purification mouse Neglected Diseases procedures Animals Antiparasitic Agents Computational Biology Drug Discovery Drug Repositioning Humans Mice Neglected Diseases A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases |
| topic_facet |
chemical compound peptide deformylase antiparasitic agent Article biological activity drug repositioning genome high throughput screening Kinetoplastida mathematical computing neglected disease nonhuman Plasmodium falciparum protein analysis protein domain sequence analysis Trypanosoma cruzi animal biology drug development drug repositioning human isolation and purification mouse Neglected Diseases procedures Animals Antiparasitic Agents Computational Biology Drug Discovery Drug Repositioning Humans Mice Neglected Diseases |
| description |
Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105compounds and several functional relations among 1.67 105proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. © 2016 Berenstein et al. |
| title |
A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases |
| title_short |
A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases |
| title_full |
A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases |
| title_fullStr |
A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases |
| title_full_unstemmed |
A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases |
| title_sort |
multilayer network approach for guiding drug repositioning in neglected diseases |
| publishDate |
2016 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19352727_v10_n1_p_Berenstein http://hdl.handle.net/20.500.12110/paper_19352727_v10_n1_p_Berenstein |
| _version_ |
1768544932986880000 |