16 research outputs found
Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment
The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe. © 2011 Elsevier Ltd. All rights reserved
AI is a viable alternative to high throughput screening: a 318-target study
: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery
Recommended from our members
Ligand Desolvation in Molecular Docking (How, Why, and With What?)
Ultimately, our bodies are biochemical factories of diabolical complexity. As scaffolds, reactors, engines, and signals, proteins are our essential building-blocks. Drugs, with their potential to alleviate symptoms or cure disease, are often small molecules that amplify or extinguish protein function in just the right way. Molecular docking attempts to understand and predict how those small molecule drugs interact with their protein targets inside us. In isolation, both ligand and protein are bathed in water - to bind one another, some water must necessarily depart. At its core, this dissertation is about how to account for desolvation of the ligand upon protein binding. To highlight why ligand desolvation is important, we discover new chemical probes for CXCR4, a protein target implicated in cancer and HIV. En route, we create the LogAUC metric and the DUD-E benchmarking dataset to better assess retrospective docking performance.Our rapid context-dependent ligand desolvation scoring term relates the Generalized-Born effective Born radii for every ligand atom to a fractional desolvation, and then uses this fraction to scale an atom-by-atom decomposition of the full transfer free energy. In a test that fails with no desolvation, our method properly discriminates ligands from highly charges molecules. The method is also flexible, performing well whether the protein binding site is charged or neutral, open or closed. We first retrospectively test ligand desolvation on the 40 original DUD targets, but discover many ways to improve that benchmark. So we construct DUD-E, an improved set with more diverse and biomedically relevant targets, totaling 102 proteins with 22,886 clustered ligands, each with 50 property-matched decoys. To ensure chemotype diversity we cluster the ligands by Bemis-Murcko frameworks. To improve decoys, we add net charge as an additional matched physico-chemical property, and only include the most dissimilar decoys by topology. To test our method prospectively, we screen both a homology model and then a crystal structure of CXCR4. Several of our novel scaffolds are potent and relatively small, with IC50 values as low as 306 nM, ligand efficiencies as high as 0.36, and substantial efficacy in blocking cellular chemotaxis
Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
A key metric to assess molecular docking remains ligand
enrichment
against challenging decoys. Whereas the directory of useful decoys
(DUD) has been widely used, clear areas for optimization have emerged.
Here we describe an improved benchmarking set that includes more diverse
targets such as GPCRs and ion channels, totaling 102 proteins with
22886 clustered ligands drawn from ChEMBL, each with 50 property-matched
decoys drawn from ZINC. To ensure chemotype diversity, we cluster
each target’s ligands by their Bemis–Murcko atomic frameworks.
We add net charge to the matched physicochemical properties and include
only the most dissimilar decoys, by topology, from the ligands. An
online automated tool (http://decoys.docking.org) generates
these improved matched decoys for user-supplied ligands. We test this
data set by docking all 102 targets, using the results to improve
the balance between ligand desolvation and electrostatics in DOCK
3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org
Predicted Biological Activity of Purchasable Chemical Space
Whereas
400 million distinct compounds are now purchasable within
the span of a few weeks, the biological activities of most are unknown.
To facilitate access to new chemistry for biology, we have combined
the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity
to the nearest bioactive to predict activity for every commercially
available molecule in ZINC. This method, which we label SEA+TC, outperforms
both SEA and a naïve-Bayesian classifier via predictive performance
on a 5-fold cross-validation of ChEMBL’s bioactivity data set
(version 21). Using this method, predictions for over 40% of compounds
(>160 million) have either high significance (pSEA ≥ 40),
high
similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of
1382 targets well described by ligands in the literature. Using a
further 1347 less-well-described targets, we predict activities for
an additional 11 million compounds. To gauge whether these predictions
are sensible, we investigate 75 predictions for 50 drugs lacking a
binding affinity annotation in ChEMBL. The 535 million predictions
for over 171 million compounds at 2629 targets are linked to purchasing
information and evidence to support each prediction and are freely
available via https://zinc15.docking.org and https://files.docking.org
Recommended from our members
Automated docking screens: a feasibility study.
Molecular docking is the most practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCK Blaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCK Blaster recapitulates the crystal ligand pose within 2 A rmsd 50-60% of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5% of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5% of 100 property-matched decoys while also posing within 2 A rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available at http://blaster.docking.org
ZINC: A Free Tool to Discover Chemistry for Biology
ZINC is a free public resource for ligand discovery.
The database contains over twenty million commercially available molecules
in biologically relevant representations that may be downloaded in
popular ready-to-dock formats and subsets. The Web site also enables
searches by structure, biological activity, physical property, vendor,
catalog number, name, and CAS number. Small custom subsets may be
created, edited, shared, docked, downloaded, and conveyed to a vendor
for purchase. The database is maintained and curated for a high purchasing
success rate and is freely available at zinc.docking.org
Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set tha