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Structural biology and drug design
as simple as this …
de Ruyck Jérôme
30/11/2015
Lille - France
Introduction
https://en.wikibooks.org/wiki/Structural_Biochemistry
Introduction
Pre-clinical studies
2-4 years 2 months - 1 year
Chemistry Pharma-cology
Scaling-up
Clinical trials
3-5 years 2-3 years
Phase 1 Phase 2 Phase 3 NDA Phase 4
Introduction
Pre-clinical studies
2-4 years 2 months - 1 year
Chemistry Pharma-cology
Scaling-up
Clinical trials
3-5 years 2-3 years
Phase 1 Phase 2 Phase 3 NDA Phase 4
Scie
ntific
cha
lleng
eM
oney
• Challenging
• Time consuming
• Expensive
Multidisciplinary
approaches
• Efficiency increased
• Time saved
• Cost effectiveness
150 M $
Introduction
In-silico drug-design
BioinformaticsVirtual screening
Molecular modelling
In-vitro drug-design
Medicinal chemistryHigh-throughput screening
Structural biology
Introduction
In-vitro drug design
Medicinal chemistryHigh-throughput screening
Structural biology
In-silico drug design
BioinformaticsVirtual screening
Molecular modelling
Drugs
Structural biology / Molecular modelling
Introduction
In-vitro drug design
Medicinal chemistryHigh-throughput screening
Structural biology
In-silico drug design
BioinformaticsVirtual screening
Molecular modelling
Target
Molecular biology / Bioinformatics
Hits
HTS
Leads
Medicinal chemistry
Pharmacology / PK-PD predictions
Introduction
In-silico drug design
Ligand-based drug design
Structure-based drug design
Drugs
Target Hits
Leads
Outline
Virtual screening
Pharmacophore
Lead design
Pharmacophore
Evaluation
Ligand
Fragment
Ligand-based drug design
Docking
In situ design
Protein
Structure-based drug design
Optimization
Nomenclature
Different kind of inter- and intra-molecular interactions
Polar interactions
Dipoles-DipolesVdW – H-Bond
ElectrostaticSalt bridges
Hydrophobic interactions
Aliphatic
Aromatic
Nomenclature
Aromatic interactionsQuadropular interactions
π – π interactions
π – cation interactions
Nomenclature
Deriving information on molecular systems
without really synthesizing them !
Quantum Mechanics (QM)Computational Chemistry
Theoretical Chemistry
Molecular Mechanics (MM)Molecular ModellingMolecular Dynamics
Quantum mechanics
• Nuclei and electrons separated
• Time consuming
• Applied to small molecules
• Not suitable for proteins
Method Accuracy Max atoms
Semiempirical Low 2000
HF & DFT Medium 500
Perturbation methods High 50
Coupled-cluster Very High 20
Molecular mechanics
• Spheres and springs model
• Very quick
• Can be applied to small molecules
• Suitable for proteins
• Accuracy depends on a force field
=
Streching Bending Torsion Non-bonded
Force field
• Different force fields for different systems
• Proteins• Sugars• Metals • …
• Different parameterization
• Empirical• Semi-empirical (including QM)
=
Streching Bending Torsion Non-bonded
Direct vs Indirect approaches
Virtual screening
Pharmacophore
Lead design
Pharmacophore
LigandIC50 / Ki
2D Structures
Fragment2D Structures
Ligand-based drug design(Indirect approach)
Docking
In situ design
Protein3D structures
Structure-based drug design(Direct approach)
Direct vs Indirect approachesDirect approach
Know receptorDon’t know ligand
Indirect approachDon’t know receptor
Know Ligands
Structural BiologyProtein - ligand interactions
Docking
Direct vs Indirect approachesDirect approach
Know receptorDon’t know ligand
Indirect approachDon’t know receptor
Know Ligands
Statistical methodsPharmacophore
3D-QSAR
?
Ligand-based drug design
Virtual screening
Pharmacophore
Lead design
Pharmacophore
LigandIC50 / Ki
2D Structures
Fragment2D Structures
Optimization Selection Evaluation
Ligand-based drug design
Pharmacophore
A pharmacophore is a geometrical description of molecular features which are necessary for molecular recognition of a ligand by a
biological macromolecule.
Typical pharmacophore features include hydrophobic centroids, aromatic rings, hydrogen bond acceptors or donors, cations, and anions.
Inhibition data
Structural superimposition
Pharmacophore
generation
Example (1)
“A four-point pharmacophore of COX-2 selective inhibitors was derived from a training set of 16 compounds, using the Catalyst program. It consists of a H bond acceptor, two hydrophobic groups and an aromatic ring, in accordance with SAR data of the compounds and with topology of the COX-2 active site. This hypothesis, combined with exclusion volume spheres representing important residues of the COX-2 binding site, was used to virtually screen the Maybridge database. Eight compounds were selected for an in vitro enzymatic assay. Five of them show COX-2 inhibition close to that of nimesulide and rofecoxib, two reference COX-2 selective inhibitors. As a result, structure-based pharmacophore generation was able to identify original lead compounds, inhibiting the COX-2 isoform.”
Example (2)
“Apolar trisubstituted derivatives of harmine show high antiproliferative activity on diverse cancer cell lines. However, these molecules present a poor solubility making these compounds poorly bioavailable. Here, new compounds were synthesized in order to improve solubility while retaining antiproliferative activity. First, polar substituents have shown a higher solubility but a loss of antiproliferative activity.Second, a Comparative Molecular Field Analysis (CoMFA) model was developed, guiding the design and synthesis of eight new compounds. Characterization has underlined the in vitro antiproliferative character of these compounds on five cancerous cell lines, combining with a high solubility at physiological pH, making these molecules druggable. Moreover, targeting glioma treatment, human intestinal absorption and blood brain penetration have been calculated, showing high absorption and penetration properties.”
Structural biology improvement
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
1138
9
1377
6
1630
6
1989
8 2429
5 2873
3 3425
6 4041
0 4661
5 5328
6 6047
3 6784
3
7602
0
8476
3
9364
5
1016
67
2139
2553
2994
3525
4254
5130
5991
6956
7609
8176
8697
9220
9757
1026
3
1081
7
1121
1
13 22 46 75 87 110
136
155
196
236
290
343
408
526
717
908
PDB Stati sti csX-Ray NMR CryoEM
Structure-based drug design
Virtual screening
Lead design
LigandIC50 / Ki
2D Structures
Fragment2D Structures
Docking
In situ design
Protein3D structures
Optimization Selection Evaluation
Structure-based drug design
Structure-based drug design
Virtual screening
Lead design
LigandIC50 / Ki
2D Structures
Fragment2D Structures
Docking
In situ design
Protein3D structures
Optimization Selection Evaluation
Structure-based drug design
Molecular docking
Protein-ligand docking is a computational method that mimics the binding of a ligand to a protein to form a complex.
It predicts the pose of the molecule in the binding site and calculates a score representing the strength of the binding.
Protein
Binding site
Docking
Ligand
Complex
How does it work ?
Protein-ligand docking software works in two different steps
Scoring function
Calculates a score or binding affinity for a
particular pose
Forcefield-basedEmpirical
Knowledge-based potentials
Search algorithm
Generates a large number of poses of a molecule in
the active site
GeneticLamarckian
Simulated annealing
Performs automated docking with full acyclic ligand flexibility, partial cyclic ligand flexibility and partial protein flexibility in and around
active site.
Scoring: includes H-bonding term, pairwise dispersion potential (hydrophobic interactions),
molecular and mechanics term for internal energy.
Example (1)
“Crystal structures of Thermus thermophilus and Bacillus subtilis type 2 IPP isomerases were combined to generate an almost completemodel of the FMN-bound structure of the enzyme. In contrast to previous studies, positions of flexible loops were obtained and carefullyanalyzed by molecular dynamics. Docking simulations find a unique putative binding site for the IPP substrate.”
Example (2)
“A total of 1,990 compounds from the National Cancer Institute (NCI) diversity set with nonredundant structures have been tested to inhibit cancer cell lines with unknown mechanism. Cancer inhibition through EGFR-TK is one of the mechanisms of these compounds. In this work, we performed receptor-based virtual screening against the NCI diversity database. Using two different docking algorithms, AutoDock and Gold, combined with subsequent post-docking analyses, we found eight candidate compounds with high scoring functions that all bind to the ATP-competitive site of the kinase. None of these compounds belongs to the main group of the currently known EGFR-TK inhibitors. Binding mode analyses revealed that the way these compounds complexed with EGFR-TK differs from quinazoline inhibitor binding and the interaction mainly involves hydrophobic interactions. Our results suggest that these molecules could be developed as novel lead compounds in anti-cancer drug design.”
Structure-based drug design
Virtual screening
Lead design
LigandIC50 / Ki
2D Structures
Fragment2D Structures
Docking
In situ design
Protein3D structures
Optimization Selection Evaluation
Structure-based drug design
Fragment-based drug design
Fragment-based drug design is the screening of libraries of fragments with low chemical complexity.
The fragments usually bind the protein target with low affinity (high mM). The fragments selected for follow-up are then optimized by addition of chemical moieties or linked together with the aim of obtaining a highly
potent drug or inhibitor.
Examples (1)
“The search for new drugs is plagued by high attrition rates at all stages in research and development. Chemists have an opportunity to tackle this problem because attrition can be traced back, in part, to the quality of the chemical leads. Fragment-based drug discovery (FBDD) is a new approach, increasingly used in the pharmaceutical industry, for reducing attrition and providing leads for previously intractable biological targets. FBDD identifies low-molecular-weight ligands (~150 Da) that bind to biologically important macromolecules. The three-dimensional experimental binding mode of these fragments is determined using X-ray crystallography or NMR spectroscopy, and is used to facilitate their optimization into potent molecules with drug like properties. Compared with high-throughput-screening, the fragment approach requires fewer compounds to be screened, and, despite the lower initial potency of the screening hits, offers more efficient and fruitful optimization campaigns.”
“X-ray crystallography is an established technique for ligand screening in fragment-based drug-design projects, but the required manual handling steps – soaking crystals with ligand and the subsequent harvesting – are tedious and limit the throughput of the process. Here, an alternative approach is reported: crystallization plates are pre-coated with potential binders prior to protein crystallization and X-ray diffraction is performed directly ‘in situ’ (or in-plate). Its performance is demonstrated on distinct and relevant therapeutic targets currently being studied for ligand screening by X-ray crystallography using either a bending-magnet beamline or a rotating-anode generator. The possibility of using DMSO stock solutions of the ligands to be coated opens up a route to screening most chemical libraries.”
Examples (2)
The future is now …
Deriving information on molecular systems
without really synthesizing them !
Quantum Mechanics (QM)Computational Chemistry
Theoretical Chemistry
Molecular Mechanics (MM)Molecular ModelingMolecular Dynamics
Hybrid QM/MMSimulations
Computational Biology
The ONIOM method
𝐸 (𝑂𝑁𝐼𝑂𝑀 ,𝑅𝑒𝑎𝑙 )=𝐸 (𝑙𝑜𝑤 ,𝑟𝑒𝑎𝑙 )−𝐸 (𝑙𝑜𝑤 ,𝑚𝑜𝑑𝑒𝑙)+𝐸(h h𝑖𝑔 ,𝑚𝑜𝑑𝑒𝑙)
S. Dapprich, et al. 1999 THEOCHEM. 461-462: 1
Inside the mechanism
“Here, we report an integrated quantum mechanics/molecular mechanics (QM/MM) study of the bioorganometallic reaction pathway of the reduction of (E)-4-hydroxy-3-methylbut-2-enyl pyrophosphate (HMBPP) into the so called universal terpenoid precursors isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), promoted by the IspH enzyme. Dehydroxylation of HMBPP is triggered by a proton transfer from Glu126 to the OH group of HMBPP. The reaction pathway is completed by competitive proton transfer from the terminal phosphate group to the C2 or C4 atom of HMBPP.”
Mechanism-based drug design
“Development of novel influenza neuraminidase inhibitors is critical for preparedness against influenza outbreaks. Knowledge of the neuraminidase enzymatic mechanism and transition state analogue, 2-deoxy-2,3-didehydro-N-acetylneuraminic acid, contributed to the development of the first generation anti-neuraminidase drugs, zanamivir and oseltamivir. However, lack of evidence regarding influenza neuraminidase key catalytic residues has limited strategies for novel neuraminidase inhibitor design. Here, we confirm that influenza neuraminidase conserved Tyr406 is the key catalytic residue that may function as a nucleophile; thus, mechanism-based covalent inhibition of influenza neuraminidase was conceived. Crystallographic studies reveal that 2a,3ax-difluoro-N-acetylneuraminic acid forms a covalent bond with influenza neuraminidase Tyr406 and the compound was found to possess potent anti-influenza activity against both influenza A and B viruses. Our results address many unanswered questions about the influenza neuraminidase catalytic mechanism and demonstrate that covalent inhibition of influenza neuraminidase is a promising and novel strategy for the development of next-generation influenza drugs.”
Acknowledgment
• Computational Molecular Systems Biology team
Dr. M. LensinkDr. R. BlosseyDr. J. BouckaertDr. T. DumychDr. E.-M. KrammerIr. G. Brysbaert
• Fundings