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Introduction to docking with Lead Finder
Susana Tomasio
© Cresset
Protein Tool
New upcoming
structure-based
design tool
© Cresset
Software for computer-assisted drug design
© Cresset
Molecular docking
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
Sampling: Search algorithms
> Molecular dynamics
> Monte Carlo
> Genetic algorithms
> Fragment-based methods
> Point complementarity methods
> Distance geometry methods
> Systematic searches
© Cresset
Sampling: Search algorithms
> Molecular dynamics
> Monte Carlo
> Genetic algorithms
> Fragment-based methods
> Point complementarity methods
> Distance geometry methods
> Systematic searches
© Cresset
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
© Cresset
Lead Finder – software for modeling protein-ligand interaction
Build model – software preparing full-atomic models of protein structure
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
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)
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
Results
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
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
© Cresset
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