MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors

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1 MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors Thomas Steinbrecher Senior Application Scientist

2 Typical Docking Workflow Databases ABCDE pocket of XYZ 1. CACDB2010 lead/drug-like set. 2. Phase mining, with multiple hypotheses, of CACDB phase database using ABCDE as queries (shape similarity > 0.6 or top 0.5%). * 3. Fingerprint-based similarity search of whole CACDB using HTS hits as queries (Tanimoto >= 0.6 or top 0.5%). * Glide HTVS Glide XP* 1. Receptor of ABCDE site with HB to Res123 (-C=O and NH) and/or Res 122 (-NH) on chain A. 2. Receptor of ABCDE site with HB to Res123 (-C=O and NH) and/or Res 122 (-NH) on chain B. 3. Receptor of ABCDE site with HB to Res123 (-C=O and NH) on both chain A and B In molecular docking it is challenging to develop a scoring function which is accurate to conduct HTS, eliminate false positives, get good pose prediction and get good ranking. Approximations are built in for computational efficiency which sacrifice the accuracy of prediction. Two conformations of ligands for SP docking Glide shows dependency on input conformations (25 % top scoring) More rigorous methods such as MMGBSA are the Three conformations natural of follow ligands for XP on docking after Glide SP* ConfGen/MM/multiple FFs docking in predicting good binding poses and estimating binding free energies (15 % top scoring) (post-processing of ensemble) Ranking (in a reasonable timeframe)?

3 Typical Discovery Workflow Once a promising lead compound has been identified in a drug discovery program, chemical variations of the lead compound are usually synthesized and tested to identify a molecule that has optimized chemical properties. Molecules in this congeneric series generally have different substituents that are attached to a common molecular core. A key property in this congeneric series of ligands that needs to be optimized is the binding free energy, G(binding). Experimentally, binding free energies are obtained by evaluating the concentration of the compound that is required to inhibit the activity of the protein, e.g., Ki or IC50 data. Computationally, binding free energies for a compound to a particular protein can be calculated from the difference between the free energy of each ligand bound to the protein and the free energies of the components of the complex, i.e.,: G(binding) = G(complex) - ( G(free receptor) - G(free ligand))

4 Intro to MM-GBSA The Molecular Mechanics Generalized Born Surface Area (MM GBSA) method calculates binding free energies for molecules by combining molecular mechanics calculations and continuum (implicit) solvation models. Implicit solvent models are often used to estimate free energies of solute solvent interactions and significantly improve the computational speed and reduce errors in statistical averaging that arise from incomplete sampling of solvent conformations. The molecular mechanics part estimates the enthalpic contributions for the protein ligand interactions. In cases where the ligands in the congeneric series are very similar to one another then, as a first approximation, the entropic contribution to the protein ligand interactions are assumed to be similar across the series and can be neglected in evaluating the relative binding free energies of the ligands.

5 Solvation Free Energy of Macromolecules Macromolecules are charged, polar, irregular objects DG Solv DG DG Solv cavity DG k cavity nonpolar DG A SAS Elec DG Elec DG elec, int DG elec DG cavity

6 Explicit solvent pro accurate standard approach contra large systems boundary artifacts

7 Poisson Boltzmann Equation Contains ionic contributions ( r) ( r) i ( ) The Gold standard of continuum models but hard to solve 2I kt 0 r i Widely applied but slow grid-based solutions are practical

8 Generalized Born models Fast and analytical (good for MD) E Elec i j 1 i j q q rij 2 W i j f q q i GB j ( r ij ) vacuum energy solvation contribution 2 r 2 2 ij f ij( rij) rij ij exp 2 4 ij There are many different "flavors" of GB ij i j

9 Binding Free Energies DG Bind "Corpora non agunt nisi fixata" (No compound is active unless it is bound by a receptor) Paul Ehrlich, 1913

10 The MM-GBSA approach - simulations or snapshots in implicit solvent DG - estimate solution contribution via GB equation plus surface term DG (Bind) (Solv1) DG (Vac) - internal energies via MM-forcefield DG (Solv2) The MM-GBSA thermodynamic cycle DG Bind DG Vacuum DG DG Solv1 Solv2

11 Resources Tasks, Prime MM-GSBA

12 Example Case: FXa

13 Software Demo

14 Quality of Results R 2 = 0.08 R 2 = 0.68 R 2 = 0.04 R 2 = 0.75

15 Example Case: Cox-2

16 MM-GBSA: Considering Flexibility Panel: Tasks, Prime MMGBSA Our COX-2 example: 16 ligands, 8A flex

17 Show relevant properties with Property Tree pic50 Glide GScore MM GBSA DG Binding MM-GBSA: View results

18 MMGSA: What can you expect to see? There is a clear reorientation of key residues to improve binding interactions in the MM-GBSA complex. Comparison of a Glide docked pose (orange) with MMGBSA (cyan) Largest changes are seen in TYR355 with pyrazole nitrogen of ligand (middle right) and SER353 with amine of ligand (bottom right)

19 MMGBSA: A COX-2 Study In this example we explore a set of celecoxib like COX-2 inhibitors sourced from the publication Biava et al, J. Med. Chem. 2010, 53, Orientation of 16 ligands shown similar to binding modes from paper Three major points of variation from core Pyrazoline core belongs to celecoxib. Others own a pyrole core Example of potent inhibitors in COX2 binding site with key residues shown (from paper) cocrystallised inhibitors

20 Protocol for COX-2 study Usual steps of preparing ligands and protein Explicit conformational sampling of the ligands with ConfGen followed by Glide for ligand docking Use of MacroModel to refine poses post-glide and pre-mmgbsa The best scoring poses are explored through MMGBSA and the correlations to experimental activities examined Examine the improved correlation from the MMod refined poses. Run with zero flexibility and 8A flexibility around the ligand See pre-generated graphs

21 Software Demo

22 Predicted V pic50 Glide> MMGBSA 0A Glide> MMGGSA 8A Glide> MM > MMGBSA 8A R R R

23 Thank You For Yor Attention!