Literature DB >> 23240691

FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach.

Hongyi Zhou1, Jeffrey Skolnick.   

Abstract

Virtual ligand screening is an integral part of the modern drug discovery process. Traditional ligand-based, virtual screening approaches are fast but require a set of structurally diverse ligands known to bind to the target. Traditional structure-based approaches require high-resolution target protein structures and are computationally demanding. In contrast, the recently developed threading/structure-based FINDSITE-based approaches have the advantage that they are as fast as traditional ligand-based approaches and yet overcome the limitations of traditional ligand- or structure-based approaches. These new methods can use predicted low-resolution structures and infer the likelihood of a ligand binding to a target by utilizing ligand information excised from the target's remote or close homologous proteins and/or libraries of ligand binding databases. Here, we develop an improved version of FINDSITE, FINDSITE(filt), that filters out false positive ligands in threading identified templates by a better binding site detection procedure that includes information about the binding site amino acid similarity. We then combine FINDSITE(filt) with FINDSITE(X) that uses publicly available binding databases ChEMBL and DrugBank for virtual ligand screening. The combined approach, FINDSITE(comb), is compared to two traditional docking methods, AUTODOCK Vina and DOCK 6, on the DUD benchmark set. It is shown to be significantly better in terms of enrichment factor, dependence on target structure quality, and speed. FINDSITE(comb) is then tested for virtual ligand screening on a large set of 3576 generic targets from the DrugBank database as well as a set of 168 Human GPCRs. Excluding close homologues, FINDSITE(comb) gives an average enrichment factor of 52.1 for generic targets and 22.3 for GPCRs within the top 1% of the screened compound library. Around 65% of the targets have better than random enrichment factors. The performance is insensitive to target structure quality, as long as it has a TM-score ≥ 0.4 to native. Thus, FINDSITE(comb) makes the screening of millions of compounds across entire proteomes feasible. The FINDSITE(comb) web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html.

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Year:  2012        PMID: 23240691      PMCID: PMC3557555          DOI: 10.1021/ci300510n

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  44 in total

1.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening.

Authors:  Thomas A Halgren; Robert B Murphy; Richard A Friesner; Hege S Beard; Leah L Frye; W Thomas Pollard; Jay L Banks
Journal:  J Med Chem       Date:  2004-03-25       Impact factor: 7.446

2.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

Authors:  Richard A Friesner; Jay L Banks; Robert B Murphy; Thomas A Halgren; Jasna J Klicic; Daniel T Mainz; Matthew P Repasky; Eric H Knoll; Mee Shelley; Jason K Perry; David E Shaw; Perry Francis; Peter S Shenkin
Journal:  J Med Chem       Date:  2004-03-25       Impact factor: 7.446

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

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

4.  Further evidence for the likely completeness of the library of solved single domain protein structures.

Authors:  Jeffrey Skolnick; Hongyi Zhou; Michal Brylinski
Journal:  J Phys Chem B       Date:  2012-02-13       Impact factor: 2.991

5.  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

6.  3DLigandSite: predicting ligand-binding sites using similar structures.

Authors:  Mark N Wass; Lawrence A Kelley; Michael J E Sternberg
Journal:  Nucleic Acids Res       Date:  2010-05-31       Impact factor: 16.971

7.  Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  J Comput Chem       Date:  2008-07-30       Impact factor: 3.376

8.  FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs.

Authors:  Hongyi Zhou; Jeffrey Skolnick
Journal:  Mol Pharm       Date:  2012-05-21       Impact factor: 4.939

9.  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

10.  TM-align: a protein structure alignment algorithm based on the TM-score.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Nucleic Acids Res       Date:  2005-04-22       Impact factor: 16.971

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  34 in total

1.  Metabolomics identifies the intersection of phosphoethanolamine with menaquinone-triggered apoptosis in an in vitro model of leukemia.

Authors:  Suganthagunthalam Dhakshinamoorthy; Nha-Truc Dinh; Jeffrey Skolnick; Mark P Styczynski
Journal:  Mol Biosyst       Date:  2015-09

2.  Pocket detection and interaction-weighted ligand-similarity search yields novel high-affinity binders for Myocilin-OLF, a protein implicated in glaucoma.

Authors:  Bharath Srinivasan; Sam Tonddast-Navaei; Jeffrey Skolnick
Journal:  Bioorg Med Chem Lett       Date:  2017-07-12       Impact factor: 2.823

Review 3.  Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs.

Authors:  Bharath Srinivasan; Sam Tonddast-Navaei; Ambrish Roy; Hongyi Zhou; Jeffrey Skolnick
Journal:  Med Res Rev       Date:  2018-09-07       Impact factor: 12.944

Review 4.  In silico methods for drug repurposing and pharmacology.

Authors:  Rachel A Hodos; Brian A Kidd; Khader Shameer; Ben P Readhead; Joel T Dudley
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2016-04-15

5.  Why Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods?

Authors:  Jeffrey Skolnick; Hongyi Zhou
Journal:  J Phys Chem B       Date:  2016-10-26       Impact factor: 2.991

6.  Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

Authors:  Gabriele Macari; Daniele Toti; Fabio Polticelli
Journal:  J Comput Aided Mol Des       Date:  2019-10-18       Impact factor: 3.686

Review 7.  Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

Authors:  Jeffrey Skolnick; Hongyi Zhou; Mu Gao
Journal:  Curr Opin Struct Biol       Date:  2013-02-14       Impact factor: 6.809

8.  FINDSITEcomb2.0: A New Approach for Virtual Ligand Screening of Proteins and Virtual Target Screening of Biomolecules.

Authors:  Hongyi Zhou; Hongnan Cao; Jeffrey Skolnick
Journal:  J Chem Inf Model       Date:  2018-10-16       Impact factor: 4.956

9.  Novel small molecule binders of human N-glycanase 1, a key player in the endoplasmic reticulum associated degradation pathway.

Authors:  Bharath Srinivasan; Hongyi Zhou; Sreyoshi Mitra; Jeffrey Skolnick
Journal:  Bioorg Med Chem       Date:  2016-08-13       Impact factor: 3.641

10.  PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity.

Authors:  Ambrish Roy; Bharath Srinivasan; Jeffrey Skolnick
Journal:  J Chem Inf Model       Date:  2015-08-12       Impact factor: 4.956

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