Introduction to docking with Lead Finder

Susana Tomasio

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Protein Tool

New upcoming

structure-based

design tool

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Software for computer-assisted drug design

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Molecular docking

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Molecular docking

> Computational technique which finds the predominant binding

mode of a ligand at a protein binding site

> Predicts the interaction energy between two molecules

> Virtual Screening on large libraries of compounds

> 'Experiment' using the computer first

> Fast, saves time and money

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Types of molecular docking

> Protein – ligand

> Rigid protein – rigid/flexible ligand

> Protein - protein

> Both molecules are rigid

> Interaction produces no change in

conformation

> DNA/RNA – ligand

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Protein-ligand docking

> Many docking programs available

> Results depend on the algorithm

> Success of a docking depends on two essential steps:

> Sampling – generation of ligand conformations at the binding site

> Scoring – prediction of the binding affinity of ligand

orientations/conformations

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Sampling: Search algorithms

> Molecular dynamics

> Monte Carlo

> Genetic algorithms

> Fragment-based methods

> Point complementarity methods

> Distance geometry methods

> Systematic searches

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Sampling: Search algorithms

> Molecular dynamics

> Monte Carlo

> Genetic algorithms

> Fragment-based methods

> Point complementarity methods

> Distance geometry methods

> Systematic searches

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Scoring Functions: Tasks and types

> Identification of the correct binding mode for a given ligand

> Pose prediction in docking

> Affinity ranking of compound series

> Ligand design, lead optimization

> Identification of new active ligands

> Virtual screening

> Force field based methods

> Knowledge-based

> Empirical

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Lead Finder – software for modeling protein-ligand interaction

Build model – software preparing full-atomic models of protein structure

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Docking algorithm: Genetic Algorithm (GA)

Pool

Niche

Initial pool of individuals

-dG

Selection of the best individuals

Preliminary optimization

Genetic algorithm

Precise optimization

Picture from BioMolTech

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Scoring functions

> Lead Finder’s scoring functions > semi-empiric molecular mechanical functional that explicitly accounts for

various types of molecular interactions

> Three scoring functions tailored for:> Accurate binding energy predictions – dG-score

> Correct energy-ranking of docked ligand poses – ranking scoring function

> Correct rank-ordering of active and inactive compounds in virtual screening experiments – VS-scoring function

> Same set of energy contributions but weighted with different coefficients

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Lead Finder scoring functions J. Chem. Inf. Model. 2008, 48, 2371-2385

> Van der Waals interactions – 6-12 Lennard-Jones Potential

ΔGVdW = kvdW ∑i ∑j LJij (rij)

> Interactions with metals – 10-12 Lennard-Jones Potential

> Electrostatic interactions

> Protein-ligand – screened Coulomb potential (SCP) with distance

ΔGelec = ∑i ∑j kelec,n (hi,bi) Eelec,n (hi,bi,rij,qi,qj)

where, Eelec,n = 𝑞𝑖𝑞𝑗𝐷𝑛 𝑟𝑖𝑗

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Lead Finder scoring functions J. Chem. Inf. Model. 2008, 48, 2371-2385

> Electrostatic interactions

> Polar contribution of ligand desolvation upon binding – adapted version of

the Born Model

ΔGligand-born = ∑ i

1

2

1

𝐷𝐸𝑆 (𝑅𝐵

,𝑖)−

1

𝐷𝑊(𝑅𝐵,𝑖)

𝑞𝑖2

𝑅𝐵,𝑖

> Hydrogen-bond energy

ΔGHB = khbEhb + khb,lig-pen ΔEhb,lig-pen + khb,prot-pen ΔEhb,prot-pen

Energy of the individual H-bonds:

Ehb = ∑ij k(hi) Ehb,ij

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Lead Finder scoring functions J. Chem. Inf. Model. 2008, 48, 2371-2385

> Protein loss of H-bonds induced by ligand binding

ΔEhb,prot-penalty = ∑ i ∑ j e-𝑟𝑖𝑗

2

1.5

> Non-polar solvation (hydrophobic contacts)

ΔGsol,V = ksol ∑ i/j Si Vj e-𝑟𝑖𝑗

2

3.6

> Internal energy

ΔGinternal = knb (Enb,ES – Enb,W) + k1-4 (E1-4,ES – E1-4,W)

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Lead Finder scoring functions J. Chem. Inf. Model. 2008, 48, 2371-2385

> Entropic losses

> Accounting for freezing ligand’s degrees of freedom upon binding to

protein

ΔGentrop = ktors ntors

ntors - number of freely rotatable bonds in the ligand

ktors – scaling factor

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Major steps in molecular docking

> Protein preparation

> Add Hs; optimization of polar H positions wrt to the ligand

> Optimization of side chain orientations including His, Asn and Gln residues

> Reconstruction of the side chains that are missing in the PDB structure

> Identification of the active site

> Waters and heteroatoms removed

> Ligand preparation

> Explicit assignment of all hydrogen atoms and ionisation state

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Build Model Proteins, 2011, 79, 2693-2710

> Intelligent preparation of protein structure models for docking

> Graph-theoretical approach - TSAR algorithm

> assign optimal ionisation states of protein residues at arbitrary pH

conditions

> based on the screened coulomb potential (SCP) model

> Treats microenvironment-dependent energy of electrostatic interactions as

a function of local hydrophilicity and degree of solvent exposure

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Lead Finder workflow

Raw 3D protein

structure

Optimized 3D protein model

Build Model

Docking a ligand

Stage 1:

Protein model buildingStage 2:

Computing energy grid

map from protein model

Stage 3:

Docking of ligand structure on

the protein’s energy grid map

Predicted values of

- dG

- VS score

- Rank Score

- RMSD in Å (when applicable)

Optimized 3D ligand structure

Creating and saving energy

grid maps

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Energy grid box

> Two ways of defining the position and dimensions

of the energy grid box

> Reference ligand

> Setting coordinates of the centre and dimensions of

the energy grid box

> Size and orientation of the energy grid box

can be customized

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Results

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Docking success rate

> Astex Diverse Set

> Gold reproduces the observed binding mode within 2.0 Å for 81% of the

structures

> In our study one docking was considered successful if the RMSD between

the docked ligand and the bioactive ligand was less than 2.0 Å in 5 or

more runs (out of 10)

% of docking success

Top 10 Top 3 Top 1

Lead Finder 82 76 67

Build Model +

Lead Finder88 86 82

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Docking success rate

FlexX2 Glide SP3 Glide XP4 Gold5 Gold6 Gold7 LigandFit8 MolDock9 Surflex10 All

Original data 46.5 70.2 69.4 72.4 76.5 — — 87.0 70.4 n/a

Lead Finder docking

regime85.0 82.3 81.3 87.3 90.6 92.4 87.3 96.1 96.3 85.0

Lead Finder

screening regime76.5 77.3 77.2 81.3 78.8 83.7 82.3 79.2 76.5 79.0

Number of structures 200 282 268 134 85 92 84 77 81 407

Data provided by BioMolTech

Docking success rate (%) of different software programs obtained on their native test sets and the current Lead Finder benchmarks in docking and screening regimes

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Virtual screening performance

Protein target PDB id ROC EF40 EF70 Number of ligands dG, kcal/mol

Beta-secretase 1m4h 0.98 16.9 16.3 40 -10.8

HIV-1 protease 1pro 0.98 13.2 13.4 50 -11.5

Factor Xa 1fjs 0.98 12.8 11.4 50 -9.7

Estrogen receptor antagonists 3ert 0.97 23.8 15.2 30 -11.6

Ribonuclease A 1qhc 0.95 12.9 8.9 30 -9.0

Epidermal growth factor receptor kinase 1m17 0.95 7.3 8.1 50 -9.4

cAMP-dependent protein kinase 1fmo 0.94 6.2 6.6 50 -10.3

Urokinase-type plasminogen activator 1gj7 0.94 6.9 7.3 20 -9.2

p38 MAP kinase 1kv2 0.92 4.2 5.4 50 -10.8

Acetylcholinesterase 1e66/1eve 0.91 4.3 5.1 30 -8.2

HSP90 1uy6 0.89 3.9 4.5 30 -8.6

Lck kinase 1qpe 0.87 4.6 3.8 40 -8.3

Estrogen receptor agonists 1l2i 0.86 2.3 2.7 30 -9.3

Vascular endothelial growth factor receptor kinase 2 2oh4 0.86 4.0 3.7 50 -8.9

Thermolysin 4tmn 0.86 16.6 3.7 20 -9.5

Neuraminidase 2qwg 0.84 3.7 2.6 30 -7.3

Thymidylate synthase 1f4g 0.77 3.2 2.3 15 -8.7

Progesteron receptor 1sr7 0.76 2.1 2.0 20 -10.4

Data provided by BioMolTech

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Virtual screening performance

Oligopeptide-binding protein 1b5j 1.00 89.4 78.3 16 -15.0

Orotidine-5’-P decarboxylase 1eix 0.99 64.0 26.1 18 -11.0

Protein tyrosine phosphatase 1B 1c84 0.99 55.4 11.7 20 -9.5

Peroxisome proliferator activated receptor gamma 1fm9 0.98 11.8 11.7 50 -11.9

Ribonuclease T1 1rnt 0.97 74.1 35.4 10 -8.5

Thrombin 1c4v 0.96 8.6 10.8 40 -10.2

Trypsin 1qbo 0.95 9.4 9.5 20 -10.6

Thymidine kinase 1kim 0.94 27.8 20.5 10 -8.9

Mineralocorticoid receptor 2aa2 0.94 5.8 10.4 10 -11.2

Poly(ADP-ribose) polymerase 1efy 0.92 5.7 7.6 10 -7.5

Penicillopepsin 1bxo 0.91 8.2 5.5 6 -10.3

Cyclooxygenase-2 1cx2 0.91 4.0 5.1 50 -11.3

Fibroblast growth factor receptor kinase 1fgi 0.86 3.0 3.6 50 -9.6

Angiotensin-converting enzyme 1o86 0.83 2.7 3.8 20 -9.4

Glucocorticoid receptor 3bqd 0.82 2.5 2.7 50 -10.3

Data provided by BioMolTech

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Computing speed

> The time-consuming step is the grid map generation

> Speed of ligand docking calculations depend on the setting of the

genetic algorithm

> More accurate and exhaustive search – default regime

> Faster search but slight decrease of accuracy – screening regime

> Provisional 20-30s per mol

> Number of freely rotatable blonds

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Conclusions

> Good results

> Advanced protonation engine with Build Model

> Covalent docking capable

> Lead Finder is available now!

> Command line

> Built-in in our upcoming protein product

cressetgroup

Thanks to:

susana@cresset-group.com

Thank you!

Oleg Stroganov