Literature DB >> 29237557

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs.

Michal Brylinski1, Misagh Naderi2, Rajiv Gandhi Govindaraj2, Jeffrey Lemoine3.   

Abstract

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.
Copyright © 2017. Published by Elsevier Ltd.

Entities:  

Keywords:  DrugBank; drug repositioning; drug repurposing; orphan diseases; rare diseases

Mesh:

Substances:

Year:  2017        PMID: 29237557      PMCID: PMC5994353          DOI: 10.1016/j.jmb.2017.12.001

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  43 in total

1.  Scoring function for automated assessment of protein structure template quality.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Proteins       Date:  2004-12-01

2.  HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment.

Authors:  Michael Remmert; Andreas Biegert; Andreas Hauser; Johannes Söding
Journal:  Nat Methods       Date:  2011-12-25       Impact factor: 28.547

3.  Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization.

Authors:  Dong Xu; Yang Zhang
Journal:  Biophys J       Date:  2011-11-15       Impact factor: 4.033

4.  A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  2007-12-28       Impact factor: 11.205

5.  eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands.

Authors:  Michal Brylinski; Wei P Feinstein
Journal:  J Comput Aided Mol Des       Date:  2013-07-10       Impact factor: 3.686

6.  Structure and chemical inhibition of the RET tyrosine kinase domain.

Authors:  Phillip P Knowles; Judith Murray-Rust; Svend Kjaer; Rizaldy P Scott; Sarah Hanrahan; Massimo Santoro; Carlos F Ibáñez; Neil Q McDonald
Journal:  J Biol Chem       Date:  2006-08-23       Impact factor: 5.157

7.  DrugBank: a comprehensive resource for in silico drug discovery and exploration.

Authors:  David S Wishart; Craig Knox; An Chi Guo; Savita Shrivastava; Murtaza Hassanali; Paul Stothard; Zhan Chang; Jennifer Woolsey
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

8.  eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models.

Authors:  Michal Brylinski
Journal:  PLoS Comput Biol       Date:  2014-09-18       Impact factor: 4.475

9.  UniProt: the universal protein knowledgebase.

Authors: 
Journal:  Nucleic Acids Res       Date:  2016-11-29       Impact factor: 16.971

10.  Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score.

Authors:  Shashi Bhushan Pandit; Jeffrey Skolnick
Journal:  BMC Bioinformatics       Date:  2008-12-12       Impact factor: 3.169

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  8 in total

1.  Binding site matching in rational drug design: algorithms and applications.

Authors:  Misagh Naderi; Jeffrey Mitchell Lemoine; Rajiv Gandhi Govindaraj; Omar Zade Kana; Wei Pan Feinstein; Michal Brylinski
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

2.  Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing.

Authors:  Richard D Smith; Jordan J Clark; Aqeel Ahmed; Zachary J Orban; James B Dunbar; Heather A Carlson
Journal:  J Mol Biol       Date:  2019-05-22       Impact factor: 5.469

3.  Data Sharing Advances Rare and Neglected Disease Clinical Research and Treatments.

Authors:  Rachelle J Bienstock
Journal:  ACS Pharmacol Transl Sci       Date:  2019-08-22

4.  eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database.

Authors:  Misagh Naderi; Rajiv Gandhi Govindaraj; Michal Brylinski
Journal:  Gigascience       Date:  2018-08-01       Impact factor: 6.524

5.  Large-scale computational drug repositioning to find treatments for rare diseases.

Authors:  Rajiv Gandhi Govindaraj; Misagh Naderi; Manali Singha; Jeffrey Lemoine; Michal Brylinski
Journal:  NPJ Syst Biol Appl       Date:  2018-03-13

6.  Comparative assessment of strategies to identify similar ligand-binding pockets in proteins.

Authors:  Rajiv Gandhi Govindaraj; Michal Brylinski
Journal:  BMC Bioinformatics       Date:  2018-03-09       Impact factor: 3.169

7.  DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network.

Authors:  Limeng Pu; Rajiv Gandhi Govindaraj; Jeffrey Mitchell Lemoine; Hsiao-Chun Wu; Michal Brylinski
Journal:  PLoS Comput Biol       Date:  2019-02-04       Impact factor: 4.475

8.  NOD: a web server to predict New use of Old Drugs to facilitate drug repurposing.

Authors:  Tarun Jairaj Narwani; Narayanaswamy Srinivasan; Sohini Chakraborti
Journal:  Sci Rep       Date:  2021-06-29       Impact factor: 4.379

  8 in total

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