Using Phase for Pharmacophore Modelling. 5th European Life Science Bootcamp March, 2017
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1 Using Phase for Pharmacophore Modelling 5th European Life Science Bootcamp March, 2017
2 Phase: Our Pharmacohore generation tool Significant improvements to Phase methods in 2016 New highly interactive interface in Maestro 11 New common pharmacophore perception algorithm based on pharmacophore-based shape New PhaseHypoScore metric for rank-ordering hypotheses Updated feature definition defaults Simplified command line usage Previously introduced in our last bootcamp series Recording available from
3 Overview Brief introduction of what is a pharmacophore model Basic concept Key considerations How and why pharmacophores can be a very efficient tool in your research How can we use pharmacophores using 2 example cases Create models Validate the models Apply the models
4 What is a pharmacophore model? Pharmacophore model "an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response [1] Abstract concept Collection of features in a specific spatial arrangement Describes a specific binding mode [1] Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998). "Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998)". Pure and Applied Chemistry. 70 (5):
5 Hit identification Core hopping Lead optimization How can they be used? Can be applicable in both ligand-based as well as structurebased projects Versatile Quick
6 Typical Workflow Analyse and Prepare Dataset Create Model Apply Validate Ligand Preparation (Protein preparation if applicable) Conformation Sampling Feature Preception Select method Feature presets Hypothesis settings Required features, number of features etc Visual inspection of the alignment Feature Mapping Binding mode Actives/Inactive Enrichment Validate wit Test Set Database screening Lead optimization Core hopping
7 Pharmacophore Feature Definitions Complete control over feature definitions via SMARTS patterns Updated default feature definitions More reliable assignment of features for conjugated heteroatoms (amidines) All possible hydrophobic features available Feature definition presets allow common modifications without having to change definitions manually or to regenerate sites in database
8 Several Ways to Create Pharmacophore Hypotheses From multiple active ligands From one ligand conformation From apo protein 2 From protein-ligand complex 1 1 Salam, N., Nuti, R., Sherman, W., J Chem Inf Model, 2009, 49(10), Loving, K., Salam, N., Sherman, W., J Comput-Aid Design, 2009, 23(8), 541.
9 Several Ways to Create Pharmacophore Hypotheses From multiple active ligands From one ligand conformation From apo protein 2 From protein-ligand complex 1 1 Salam, N., Nuti, R., Sherman, W., J Chem Inf Model, 2009, 49(10), Loving, K., Salam, N., Sherman, W., J Comput-Aid Design, 2009, 23(8), 541.
10 Phase Common Pharmacophore Generation Algorithm Reference Conformer 1. Generate conformers for all active ligands 2. Pairwise align by pharmacophore-based shape all pairs of active conformers 3. Generate consensus alignments per active ligand 4. Score and rank-order sites in consensus alignments 5. Refine surviving hypothesis by traditional pharmacophore screening 6. Return top-n hypothesis Technical advantages of new common pharmacophore generation algorithm Generate hypotheses with between 3 and 8 sites in a single run Use large numbers of active ligands (>100) Well suited to find hypotheses that match a small fraction of active ligands Highly distributable calculation; short time to hypotheses (<15 min) Refined Alignments Pharmacophore-based Shape Consensus Alignments Consensus Pharmacophore
11 Generating Consensus Alignments Each conformer a i from active A yields a consensus alignment that contains one conformer from each of the other actives B, C, etc. The conformer chosen from B, C, etc. is the one that yields the highest pharmacophore-based shape similarity to a i Shape similarity is computed as a sum of hard-sphere overlaps between pharmacophore sites of the same type: Type Radius Projected Spheres* Acceptor 2.0 Donor 2.0 Hydrophobic 3.0 Negative Ionic 2.0 Positive Ionic 2.0 Aromatic Ring 3.0 * 2.0 Å projected spheres are included to reward alignments with similar orientations of these vector features
12 Why Use Pharmacophore Shape Alignment? Avoids superpositions that would lead to inconsistent ligandreceptor interactions Acceptors point in different directions Preserves the overall shape of a hypothetical binding pocket across all ligands
13 Generating Consensus Pharmacophore A consensus pharmacophore site arises when the required number of actives each contribute a site that lies within 2Å of the corresponding reference site The consensus site is always one of the reference sites (rather than, e.g., the centroid of the sites) If hypotheses containing up to n sites are desired, but the consensus pharmacophore contains more than n sites, all possible subsets of n consensus sites are considered
14 Test Set BEDROC(20) Test Set BEDROC(20) Rank Order Hypothesis by Screening Performance Rank order hypotheses by interpretability and selectivity in virtual screening New PhaseHypoScore metric combines Phase Survival Score with enrichment against the Glide decoy set measured by BEDROC(α=20) 1,2 1 R 2 = 0.10 R 2 = ,2 1 0,8 0,8 0,6 0,6 0,4 0,4 0,2 0, Survival Score 0 0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 PhaseHypoScore Truchon, J., Bayly, C. J. Chem. Inf. Model. 2007, 47,
15 Pharmacophore Hypothesis Generation with E-Pharmacophores from Protein-Ligand Complex -0.6 NA Refine native ligand with Glide XP NA NA NA NA Assess atom-based energy of each feature Hypothesis with desired number of sites
16 Pharmacophore Hypothesis Generation with E-Pharmacophores from Apo Protein Dock 548 fragments into apo site keeping up to 100 poses per fragment Assess atom-based energy of fragments for each fragment Cluster top-scoring 50 fragments by volume and keep most diverse, topscoring fragment from each cluster -0.7 Hypothesis
17 Example Case 1: Generating pharmacophore from proteinligand complex Target: Leukotriene A-4 hydrolase PDB code: 3chp
18 Example Case 2: Identifying Common Pharmacophore Hypotheses Target: Leukotriene A-4 hydrolase PDB code: 3chp Dataset for exercise taken from DUD-E database (dude.docking.org) Training set: 58 ligands
19 Phase Databases Generate PhaseDB without license limitations Improved speed and robustness of Phase DB generation Epik ~ 4x faster than 1 year ago New ConfGen ~25x faster than 1 year ago Phase DB internals are ~5x faster than 1 year ago emolecules Phase DB available Updated monthly with easy upgrade process Lead-, drug-, and fragment-like subsets Screen compounds in DB with Glide, Shape Screening, and Phase
20 Percent of Compounds ConfGen - A New Fast 3D Conformation Generator Employs a fragment-lookup-and-join approach Process ~10 compounds/sec 25x faster than ConfGen Intermediate Good recovery of bioactive conformations Time Per Compound (sec.) Reproduction of Bioactive Conformations New ConfGen ConfGen Intermediate ConfGen Comprehensive New ConfGen ConfGen Intermediate ConfGen Comprehensive RMSD Cutoff (Angstroms)
21 Summary Comprehensive set of tools for generating pharmacohores both from ligand-based as well as structure-based Easy database generation for virtual screening Workflow for generating common pharmacophore hypotheses using large number of active ligands Screening multiple hypotheses/databases in single job
22 Product page Help and support materials Videos Documention &Tutorials Directly accessible from Maestro Help > Tutorials Help > Help Online Support Contact us:
23 Thank you & Questions This concludes our 5th European Life Science Bootcamp series Recordings will be made available on our website shortly Thank you very much for your attention!
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