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FINDSITE: a threading-based approach to ligand homology modeling.

ABSTRACT: Ligand virtual screening is a widely used tool to assist in new pharmaceutical discovery. In practice, virtual screening approaches have a number of limitations, and the development of new methodologies is required. Previously, we showed that remotely related proteins identified by threading often share a common binding site occupied by chemically similar ligands. Here, we demonstrate that across an evolutionarily related, but distant family of proteins, the ligands that bind to the common binding site contain a set of strongly conserved anchor functional groups as well as a variable region that accounts for their binding specificity. Furthermore, the sequence and structure conservation of residues contacting the anchor functional groups is significantly higher than those contacting ligand variable regions. Exploiting these insights, we developed FINDSITE(LHM) that employs structural information extracted from weakly related proteins to perform rapid ligand docking by homology modeling. In large scale benchmarking, using the predicted anchor-binding mode and the crystal structure of the receptor, FINDSITE(LHM) outperforms classical docking approaches with an average ligand RMSD from native of approximately 2.5 A. For weakly homologous receptor protein models, using FINDSITE(LHM), the fraction of recovered binding residues and specific contacts is 0.66 (0.55) and 0.49 (0.38) for highly confident (all) targets, respectively. Finally, in virtual screening for HIV-1 protease inhibitors, using similarity to the ligand anchor region yields significantly improved enrichment factors. Thus, the rather accurate, computationally inexpensive FINDSITE(LHM) algorithm should be a useful approach to assist in the discovery of novel biopharmaceuticals.

SUBMITTER: Brylinski M

PROVIDER: S-EPMC2685473 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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FINDSITE: a threading-based approach to ligand homology modeling.

Brylinski Michal M Skolnick Jeffrey J

PLoS computational biology 20090605 6

Ligand virtual screening is a widely used tool to assist in new pharmaceutical discovery. In practice, virtual screening approaches have a number of limitations, and the development of new methodologies is required. Previously, we showed that remotely related proteins identified by threading often share a common binding site occupied by chemically similar ligands. Here, we demonstrate that across an evolutionarily related, but distant family of proteins, the ligands that bind to the common bindi ...[more]

PMID: 19503616

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Project description: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.

Project description:Computational approaches for predicting protein-ligand interactions can facilitate drug lead discovery and drug target determination. We have previously developed a threading/structural-based approach, FINDSITEcomb, for the virtual ligand screening of proteins that has been extensively experimentally validated. Even when low resolution predicted protein structures are employed, FINDSITEcomb has the advantage of being faster and more accurate than traditional high-resolution structure-based docking methods. It also overcomes the limitations of traditional QSAR methods that require a known set of seed ligands that bind to the given protein target. Here, we further improve FINDSITEcomb by enhancing its template ligand selection from the PDB/DrugBank/ChEMBL libraries of known protein-ligand interactions by (1) parsing the template proteins and their corresponding binding ligands in the DrugBank and ChEMBL libraries into domains so that the ligands with falsely matched domains to the targets will not be selected as template ligands; (2) applying various thresholds to filter out falsely matched template structures in the structure comparison process and thus their corresponding ligands for template ligand selection. With a sequence identity cutoff of 30% of target to templates and modeled target structures, FINDSITEcomb2.0 is shown to significantly improve upon FINDSITEcomb on the DUD-E benchmark set by increasing the 1% enrichment factor from 16.7 to 22.1, with a p-value of 4.3 × 10-3 by the Student t-test. With an 80% sequence identity cutoff of target to templates for the DUD-E set and modeled target structures, FINDSITEcomb2.0, having a 1% ROC enrichment factor of 52.39, also outperforms state-of-the-art methods that employ machine learning such as a deep convolutional neural network, CNN, with an enrichment of 29.65. Thus, FINDSITEcomb2.0 represents a significant improvement in the state-of-the-art. The FINDSITEcomb2.0 web service is freely available for academic users at http://pwp.gatech.edu/cssb/FINDSITE-COMB-2 .

Dataset Information

FINDSITE: a threading-based approach to ligand homology modeling.

Publications

FINDSITE: a threading-based approach to ligand homology modeling.

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