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

Databases ABCDE pocket of XYZ

Glide SP*

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%).*

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

Glide XP*

Ranking (in a reasonable timeframe)?

(25 % top scoring)

•Three conformations of ligands for XP docking

•ConfGen/MM/multiple FFs

Glide HTVS

(15 % top scoring)

(post-processing of ensemble)

•Two conformations of ligands for SP docking

•Glide shows dependency on input conformations

Typical Docking Workflow

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. More rigorous methods such as MMGBSA are the natural follow on after docking in predicting good binding poses and estimating binding free energies

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))

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.

Solvation Free Energy of Macromolecules

Macromolecules are charged, polar, irregular objects

+ + + -

-

+

+ + + -

-

+ DGSolv

DGelec, int DGelec

DGcavity

ElecSASnonpolarcavity

EleccavitySolv

GAkG

GGG

DD

DDD

Explicit solvent

pro contra

accurate large systems

standard approach boundary artifacts

Poisson Boltzmann Equation

Contains ionic contributions

The Gold standard of continuum models

but hard to solve

Widely applied but slow

)(2

)()(0 rkT

Irr

i

i

grid-based solutions

are practical

Generalized Born models

Fast and analytical (good for MD)

ji ij

GB

ji

Wji ij

ji

Elecrf

qq

r

qqE

)(

11

2

1

4 0

vacuum energy solvation contribution

2

2

22

4exp)(

ij

ij

ijijijij

rrrf

There are many different "flavors" of GB

jiij

Binding Free Energies

"Corpora non agunt nisi fixata"

(No compound is active unless it is bound by a receptor)

Paul Ehrlich, 1913

DGBind

The MM-GBSA approach

- simulations or snapshots in implicit

solvent

- estimate solution contribution via GB

equation plus surface term

- internal energies via MM-forcefield

The MM-GBSA thermodynamic cycle

DG° (Bind) DG° (Vac)

DG°

(Solv1)

DG°

(Solv2)

21 SolvSolvVacuumBind GGGG DDDD

Resources

• www.schrodinger/kb

• Tasks, Prime MM-GSBA

Example Case: FXa

Software Demo

Quality of Results

R2 = 0.08 R2 = 0.68 R2 = 0.04 R2 = 0.75

Example Case: Cox-2

MM-GBSA: Considering Flexibility • Panel: Tasks, Prime MMGBSA

– Our COX-2 example: 16 ligands, 8A flex

MM-GBSA: View results

• Show relevant properties with Property Tree – pIC50 – Glide GScore – MM GBSA DG Binding

MMGSA: What can you expect to see?

There is a clear re-orientation of key residues to improve binding interactions in the MM-GBSA complex. Largest changes are seen in TYR355 with pyrazole nitrogen of ligand (middle right) and SER353 with amine of ligand (bottom right)

Comparison of a Glide docked pose (orange) with MMGBSA (cyan)

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, 723-733.

• 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

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

Software Demo

Predicted V pIC50

Glide> MMGBSA 0A Glide> MMGGSA 8A Glide> MM > MMGBSA 8A – R2 0.22 R2 0.46 R2 0.65

Thank You For Yor Attention!