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Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Molecular Docking Virtual Screening.

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1 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Molecular Docking Virtual Screening

2 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Overview 1. Introduction 2. Basic concepts 3. Preparation steps of molecular docking 3.1. Basic knowledge 3.2. Target structure 3.2.1. Source 3.2.2. Resolution 3.2.3. Treatment 3.3. Interacting site 3.4. Ligand structure 3.5. Flexibility 3.5.1. Ligand 3.5.2. Macromolecule

3 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Overview 4. Manual docking 5. Automatic docking 5.1. Rules 5.2. Algorithms and methods 5.2.1. Grid method 5.2.2. Sphere method 5.2.3. Incremental method 5.2.4. Genetic algorithm 5.3. Scoring 5.3.1. Force-field 5.3.2. Empirical potential 5.3.3. Knowledge based 6. Applications 6.1. Direct conception

4 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Overview 6.2. Virtual screening 6.2.1. Rules 6.2.2. Databases 6.2.2.1. 1D storage 6.2.2.2. 3D storage 6.2.3. Filtering 6.2.3.1. Redundancy 6.2.3.2. Reactivity & toxicity 6.2.3.3. Drug-like 6.2.3.4. ADMET 6.2.4. Scoring 6.2.5. Assessing quality 6.3. De novo design 7. General conclusions 8. References

5 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 1. Introduction

6 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 1. Introduction molecular docking: prediction of the association between two molecules Experimentally, the interaction process between two compounds is never easy and provides, no to few informations about the structure. We use computational approaches to:  Observe how a compound is structurally placed with (or inside) its partner  Understand the recognition process and establish structure activity/property relationships  Predict on a database of chemical compounds which ones are the most able to interact with the target Molecular docking is mainly applied in the field of medicinal chemistry. However, we can apply this technique to study the biological interactions between two macromolecules (protein/protein or DNA/protein) or any other interactions.

7 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 2. Basic concepts

8 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 2. Basic concepts A drug always acts on a bio-macromolecule (protein, DNA or RNA) as a key (ligand) in a lock (target). Most of the time we wish to directly compete with the substrate. enzyme + Substrate + drug Competitive inhibition: concentration and affinity are key elements for inhibiting the enzyme. It's the most widespread case.

9 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 2. Basic concepts A + B AB  G =  H -T  S Even the most complex biomacromolecules obey to thermodynamic. If  G is negative the reaction will be driven toward the AB formation. If  G is decreased by 2.7 kcal/mol then the dissociation constant (KD) change from 100 to 1 and the association population evolve from 50% to 99% (Boltzmann statistic's). This logarithmic dependency shows the problem of accuracy in molecular modeling

10 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking

11 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.1. Basic knowledge We know the 3D structure of the target and we wish to simulate the interaction of a database of compounds (around 1 million!) One naive approach is to perform molecular dynamics in explicit solvent  Protein is embedded in a box  Ligand is randomly placed in this box  MD predicts the interaction This should work but this requires trajectory in the scale of ms to s whereas we generally perform ns to µs. See David Shaw We need other methods, more direct, since the interaction prediction of two molecules is highly complex and requires tremendous explorations

12 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.2. Target structure 3.2.1. Sources A target 3D structure is required! The PDB (protein databank) ➔ Xray diffraction ● No size limit ● More accurate ● Unique structure (of the crystal) ● Crystallization problems ● Hydrogen are missed ➔ NMR ● Lowest accuracy ● Solution structure ● Size limit around 150 residues (for a protein) ● Average structure ➔ Homology modelling ● Free and quick ● No experimental ● Low precision of sidechains ● Sequence similarity or identity?

13 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking Accuracy is an important parameter: RX 3.2. Target structure 3.2.2. Resolution Here precision, accuracy is very good.

14 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) A protein alpha-helix with different resolution 3.2. Target structure 3.2.2. Resolution 3. Preparation steps of molecular docking

15 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking In NMR the resolution is hard to determine numerically: Generally we look at the RMSD or the number of restraints by residue. 3.2. Target structure 3.2.2. Resolution

16 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.2. Target structure 3.2.2. Resolution For homology modelling (comparative modelling) the resolution has no real meaning. In all cases, it is essential to have a feeling of the target structure resolution at the itneracting site location. For enzyme, generally, this area is the best defined. Beware: for Xray structures some protein parts or atoms may be missed. In this case, we choose to add or not these parts depending of their location or influence for the chemical association. To sum-up, it is always required to gather as much as you can information about the target.

17 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.2. Target structure 3.2.3. Treatment Experimental structures are far from being perfect! You can find in them: o Ions o Water o Soap o Glycosyl o Antibody o Chaperon proteins o Missing atoms… You must clean the pdb file

18 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking Where is the interacting site on the protein? Three major methods:  Experimental complex  Safer method  We need an identical mechanism for ligands  Analysis of structural properties  Cavity detection is complex  More an art than a definite method  Molecular docking of the whole protein  Time consuming and boring  Needs a lot of docking poses (~ 1000) to do statistics  Generally we have “surprising” results 3.3. Interacting site:

19 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.3. Interacting site The cavity detection method "knob & hole". Principe: We consider a sphere of a given volume V. The center of this sphere is placed on the molecular surface (Connoly). We roll this sphere around the molecular surface and we compute the common volume, Vcom, which belongs also to the protein. if Knob Plane Hole

20 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.3. Interacting site "Knob & Hole" cavity detection technique * Knob Hole

21 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.4. Ligand structure Ligands are generally molecular organic compounds. We use GUI software (Graphical User Interface), working with the molecular mechanic theory, such as Maestro, Sybyl, Accelrys, Moe, ICM...

22 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.4. Ligand structure Not an easy step:  No or scarce experimental 3D structures (CSD)  No absolute force-field parameters  Sometimes stereochemistry is not an issue for organic chemist’s ; but not for you.  Ionization states?  Physiological pH?  Atomic type  hybridization  Tautomeric forms  Partial atomic charges  Resonance structures

23 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.5. Flexibility During the interaction, the ligand flexibility is highly engaged whereas the protein (larger molecule) hardly moves. Rigid docking Flexible docking Induced fit docking

24 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking 3.5. Flexibility 3.5.1. Ligand flexibility It is impossible to manage all ligand cartesian coordinates. Thus, only rotatable dihedral angles (torsion) move. Rings are maintained fixed so that they must be correctly minimized. Some questions remain: resonance angle, peptide bond, guanidinium... how to manage them, fixed or rotatable?

25 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 3. Preparation steps of molecular docking Other anecdotic method: make a rigid docking with several ligand conformations. Captopril 3.5. Flexibility 3.5.1. Ligand flexibility

26 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Direct methods are still in development. In Autodock 4.2, the user can define, for few protein residues inside the active site, sidechain torsion angles. 3.5. Flexibility 3.5.2. Target flexibility Advantage:  You choose the amino-acids you want to involve Drawbacks:  Difficult to choose which amino- acids  Only sidechain movements are considered  Possible explusion of the ligand by collapse of the rotatable residues 3. Preparation steps of molecular docking

27 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Indirect methods: A molecular docking is performed. Then a molecular dynamic simulation of the obtained complexe is realized... 3.5. Flexibility 3.5.2. Target flexibility Advantages:  With methods such as MMPBSA you can determine (evaluate) binding free energy  You can explore the physical chemistry of the recognition process  You have access to statistical view of interaction (hydrogen bond lifetime) Drawback:  If the starting structure is not correct... 3. Preparation steps of molecular docking

28 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Indirect methods: A MD simulation is made with the apo protein. Representative structures are then extracted and molecular docking is performed with these targets. 3.5. Flexibility 3.5.2. Target flexibility Advantage:  Real consideration of the apo protein Drawback:  How to extract "representative" target conformations?  What about the molecular docking precision? 3. Preparation steps of molecular docking

29 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 4. Manual docking

30 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 4. Manual docking

31 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 4. Manual docking Looks like a joke... The ligand is placed in the interacting site and the association energy is calculated at each steps. The user manually moves, rotates or translates the compound inside the protein cavity. A new association energy is recorded... etc Advantages:  Quick (and dirty?)  Can be very efficient if the user knows well the interacting site Drawbacks:  Users dependant  You can really obtain stupid results This rudimentary method surprisingly provided interesting results in the past. It is still applicable if only small ligand modifications are explored.

32 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5. Automatic docking

33 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5. Automatic docking 5.1. Rules Principles: Ligand is automatically placed onto the macromolecule. More exhaustive and safer this technique requires long CPU time.

34 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.1. Rules Dreaming about a perfect molecular docking technique:  Reasonable computation time  The global minimum of the ligand/target interaction energy is reached  The calculated free energies reproduce the experimental ones  Experimental interaction patterns observed in XRay complexes are identical Generally the molecular docking simulation can be shared in two steps. DOCKING Searching algorithm: -Conf ormational exploration -Several possible docking poses Scoring function: -Energy quantification -Ranking of docking poses -Clustering 5. Automatic docking

35 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) A box is drawn on the protein macromolecule. Therefore, the interaction will be explored only on this box. This drastically limits the computational time. Beware: o If the box is too small, docking will be false o If the box is too large, exploration must be more intensive and could provides strange "false positive" ligand conformations 5.2. Algorithms and methods 5.2.1. Grid method 5. Automatic docking o Take care of the amino-acids you want to embedded in the box (especially the charged residues)

36 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) For all points (nodes) of the grid a probe atom is positioned. There are as many probes as ligand atom types. A supplemental probe of a +e charge is also considered for the electrostatic computation. The software places iteratively the probe atom in each node points and then compute the energy. These values (tables) are recorded in map files. 5.2. Algorithms and methods 5.2.1. Grid method 5. Automatic docking

37 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) C H H O 1 2 34 5.2. Algorithms and methods 5.2.1. Grid method 5. Automatic docking Formol example:

38 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Now, we have to explore box in order to find the global optimum. It's a "classical" molecular modelling problem... without absolute solution. In docking several exploration methods are used:  Molecular dynamics (global search)  Simulated annealing (global search)  Genetic algorithm (global search)  Conjugated gradient (local serach) Actually, the best method seems to be a genetic algorithm (Lamarckian) followed by some steps of conjugated gradient. 5.2. Algorithms and methods 5.2.1. Grid method 5. Automatic docking

39 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Dihedral angles are translated in genes (binary) 101001010101001011001100111010101010 A random initial population is easily generated 001011010111000101001010011101010101 101010010111101110001010010100111010 110101101011100010100101001110101010 010111110110101110001010010100111010......

40 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Starting population genotypes phenotypes Parents selection fitness fonction Parents selection fitness fonction Children This process is stopped after several defined steps translation Crossing mutation

41 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Phenotype Genotype

42 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Starting population genotypes phenotypes Parents selection fitness fonction Parents selection fitness fonction Children This process is stopped after several defined steps translation Crossing mutation Parents optimisation

43 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Phenotype Genotype

44 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.2. Algorithms and methods 5.2.2. Sphere method 5. Automatic docking

45 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.2. Algorithms and methods 5.2.2. Sphere method 5. Automatic docking

46 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) * * * * * * 5.2. Algorithms and methods 5.2.2. Sphere method 5. Automatic docking

47 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) * assumption: distances between center of spheres correspond to inter-atoms distances (heavy atoms) * * * * * * * * * * * * * 5.2. Algorithms and methods 5.2.2. Sphere method 5. Automatic docking

48 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.2. Algorithms and methods 5.2.2. Sphere method 5. Automatic docking

49 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) The DOCK software used this method. This technique acts more on the shape of molecules than on interactions complementarity. Some issues:  Sphere dimensions?  Matching of sphere centers?  Ligand flexibility? 5.2. Algorithms and methods 5.2.2. Sphere method 5. Automatic docking This old method has proven its efficiency and is still employed.

50 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.2. Algorithms and methods 5.2.3. Incremental method 5. Automatic docking Fragments

51 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Definition of interactions as "umbrellas" 5.2. Algorithms and methods 5.2.3. Incremental method 5. Automatic docking

52 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.2. Algorithms and methods 5.2.3. Incremental method 5. Automatic docking The base fragment is placed by triangulation

53 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.2. Algorithms and methods 5.2.3. Incremental method 5. Automatic docking The second fragment is linked to the first. Torsion exploration is made to find the best pose for this new fragment

54 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 5.2. Algorithms and methods 5.2.3. Incremental method 5. Automatic docking The ligand is then incrementally build In the protein

55 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) The target can only be a protein Umbrella interactions: Hbond electrostatic hydrophobic contact This method tends to overestimate the importance of Hbonds regarding others interactions. 5.2. Algorithms and methods 5.2.3. Incremental method 5. Automatic docking

56 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Aims to describe and quantify the association. Purpose:  Quick computation  Able to compare results with experimental data  Able to distinguish true inhibitors to false positive ligands  Able to rank the ligands 5.3. Scoring 5. Automatic docking

57 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) A force-field (FF) is used to describe the interaction. Based on classical FF such as AMBER or CHARMM. Advantages: Quick Good parameterization based on empirical parameters Drawbacks: Electrostatic is generally overestimated Entropy?? Example : Dock 5.3. Scoring 5.3.1. Force-field 5. Automatic docking

58 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) A function is designed to evaluate free energy of binding instead of interaction energy. 5.3. Scoring 5.3.2. Empirical potential 5. Automatic docking

59 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) These functions are calibrated with experimental data. Advantages:  Safer evaluation of energy  More physical effects are incorporated in the equation  More accurate results Drawbacks:  The function is calibrated with a training set of data. Beware if your system is not "classical".  Sometimes the electrostatic effect is overestimated  Estimation of entropy is far from being correct. Example : FlexX, Autodock, Gold... 5.3. Scoring 5.3.2. Empirical potential 5. Automatic docking

60 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Used only for scoring (after the docking pose) How it works: A statistical analysis is made on a dataset of complex structures form the PDB. ligand/protein atomic distances are recorded. According to the clouds found, a score is given for the atomic distances found in the docking calculation. 5.3. Scoring 5.3.2. Knowledge based 5. Automatic docking

61 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) This technique works well but has no chemical meaning. This type of score ranks more on drug-likeness than on interactions. This technique is sensitive to the studied protein family type. For example, different scoring values are found depending the protein location in cell and its function. This can be an advantage or a drawback. This type of docking scoring (drugscore, ligscore,...) is usually used in consensus scoring Compounds which have a good rank with several scoring functions may be the best ones. No physical interpretation. 5.3. Scoring 5.3.2. Knowledge based 5. Automatic docking

62 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Applications:  Able to localize a ligand inside a biological macromolecule.  Analysis of the interacting binding mode.  Able to draw structure activity relationships. Limitations:  Target flexibility is never taken into account, or scarcely.  Scoring functions are far from being perfect. Energetical interpretations are thus questionable.  Beware of searching parameters.  Generally, several binding modes are proposed... which one should be picked? Software:  Grid method: Autodock, Gold, ICM, Glide  Sphere method: Dock  Incremental construction: FlexX, Ludi 5.4 Conclusions 5. Automatic docking

63 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications

64 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications An iterative work with experimental chemists is made. The purpose is to propose original ideas for getting more active compounds. Requirements: A collaboration with people from experimental fields (chemist/biologist). All people must understand each other! Not so obvious because each field of research has its own logic. Structural analyses must be performed for "all ligands“ The pros and cons: Provides more original compounds than screening. Safer interpretation of results when we compare to virtual screening (see later). Real scientific interactions but needs human and computational time. 6.1. Direct conception

65 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Example 1: exploring a protein cavity with several moities. 6. Applications 6.1. Direct conception

66 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Example 2: Extend a ligand to pick up a new favourable interaction. 6. Applications 6.1. Direct conception

67 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Example 2: Extend a ligand to pick up a new favourable interaction. 6. Applications 6.1. Direct conception

68 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications Ligand n°  G 123-12.3 kcal/mol 22-11.7 kcal/mol 13-10.1 kcal/mol 49 -9.3 kcal/mol 76 -6.5 kcal/mol 6.2. Virtual screening 6.2.1 Rules

69 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Instead of making molecular docking for a small set of defined ligands this computation is extended to a large database. The compounds which will have the best ranks will be purchased and biologically tested. Virtual screening is named by its analogy to all experimental screening methods. Three major steps: 1.Ligand database. If you remove the good ones... You will have nothing at the end. 2.Molecular docking. Even if your database is full of good compounds if you are not able to correctly dock each one... You will have nothing at the end. 3.Ranking. Even if the two previous steps were correctly made, if you are not able to meaningfully rank the ligands... You will have nothing at the end. 6. Applications 6.2. Virtual screening 6.2.1 Rules

70 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Chemical universe: 10 100 à 10 400 compounds. Organic molecules: 10 24 à 10 40 compounds. Synthesized molecules: 10 6 compounds. Acitve molecules: 10 ? molécules. 6. Applications 6.2. Virtual screening 6.2.1 Databases We are looking of a needle in a haystack... if this needle exists.

71 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Numerous chemical databases exist. Some of them are commercial. 6. Applications 6.2. Virtual screening 6.2.1 Databases NameTypeNumber PubchemPublic30 million ChEMBLPublic1 million NCI setPublic140 000 ChemSpiderPublic26 million CoCoCoPublic7 million TCMPublic32 000 ZINCPublic13 million ChemBridgeCommercial700 000 SpecsCommercial240 000 NameTypeNumber IUPHARPublic3 180 AsinexCommercial550 000 EnamineCommercial1.7 million MaybridgeCommercial56 000 WOMBATCommercial263 000 ChemDivCommercial1.5 million ChemnavigatorCommercial55.3 million ACDCommercial3 870 000 MDDRCommercial150 000

72 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6.Applications There are problems of storing chemical data as 3D files:  difficulty to compare chemical composition  it needs high hard-drive access  modification of databases is hard to make can we simplify? Benzene example 6.2. Virtual screening 6.2.2. Databases 6.2.2.1. 1D storage

73 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) The SMILES code gives a benzene with only one line. SMILES: Simplified Molecular Input Line Entry System Others coding system exist (SLN, WLN, STRAPS...), however, they share a similar philosophy and the knowledge of their differences are not for the uninitiated people. 6.Applications 6.2. Virtual screening 6.2.2. Databases 6.2.2.1. 1D storage

74 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Example of SMILES code for a molecule: This system has numerous advantages:  Simple storage (1 line!)  Easy to manage  Generation of virtual library is very easy 6.Applications 6.2. Virtual screening 6.2.2. Databases 6.2.2.1. 1D storage

75 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) A chemical database is SMILES 6.Applications 6.2. Virtual screening 6.2.2. Databases 6.2.2.1. 1D storage

76 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Unfortunately, there are drawbacks of using SMILES coding:  Hydrogens are added at the end for filling the chemical valences  Software are required to transform 1D in 3D. These are generally commercial and have their own drawbacks (CORINA, Omega, ROTATE, CAESAR...)  Smile code is not (yet) unique!!! A molecule might be present twice (or more) 6.Applications 6.2. Virtual screening 6.2.2. Databases 6.2.2.1. 1D storage

77 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) The 3D storage partially solves the 1D problems... but  Storage problems: if a 1D database of 1.5 Go is transformed in 3D, the size is around 132 Go.  Really more difficult to create virtual chemical databases comparing to SMILES code.  Still problem for tautomeric forms and charge 6.Applications 6.2. Virtual screening 6.2.2. Databases 6.2.2.1. 1D storage

78 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) The main problem of chemical databases is that they contain mainly uninteresting compounds. We must filter them to: Eliminate as much as possible uninteresting compounds Spend more computational time for molecular docking calculations. First obvious filter is the redundancy: Sometimes, chemical databases contain the same compounds (even the commercial databases). Why? 1D databases → SMILES code is not unique 3D database → Comparison of compounds is hard to perform 6.2.3.1. Redundancy 6.Applications 6.2. Virtual screening 6.2.3. Filtering

79 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Comparison of 3D information is hard to perform 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.1. Redundancy

80 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Other types of redundancy These three compounds may appear as different in a database!!!!! 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.1. Redundancy

81 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.2. Reactivity and toxicity Some chemical moieties are known to be highly reactive and/or toxic. The compounds which carry these moieties can thus be moved apart.

82 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) The artemisinine counterexample (anti-paludic drug). 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.2. Reactivity and toxicity

83 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) The global assumption of this filtering step is that a biologically molecule looks like... any other biologically active compounds. From this idea (maybe false) several filters can be set:  The 32 types of cycles  The 34 types of moieties  The Lipinski rule From these filters, a score is determined. According to your defined thresholds you will get a database with more or less compounds. 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.3. Drug-like

84 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.3. Drug-like When a ligand interacts with its target it loses some degrees of freedom. This process decreases the association entropy variation and thus increase the free energy of binding. To avoid this fact, there is no other way than to eliminate, as much as possible, ligand degrees of freedom by... making rings. But, keep in mind that:  You must maintain a similar interaction scaffold (the bioactive conformation)  Generally, a ligand without flexibility has difficulties to pass through membrane (distribution) Making rings is thus a smart idea when you are designing biologically active compounds. Some researchers made an inventory of the 32 classical rings classically encountered in drugs.

85 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.3. Drug-like Compounds with one and two rings (5 or 6 membered).

86 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.3. Drug-like Compounds three rings (5 or 6 membered).

87 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.3. Drug-like Other scaffolds

88 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.3. Drug-like

89 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) From a statistical study of 2548 commercially avaible orally active substances, Lipinski defined a rule: the "Lipinski rule's of five". If you want to design an orally available active substance it must follow at least 4 of these 5 points:  A molecular weight lower than 500 g/mol  A logP lower than 5  A number of hydrogen bond donors atoms lower than 5  A number of hydrogen bond acceptors atoms lower than 10  A polar surface lower than 150 Ų 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.3. Drug-like

90 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) ADMET: Adsorption Desorption Metabolism Excretion Toxicity Usually, drugs failed to be marketed during the clinical tests. It is thus essential to remove compounds that have bad AMDET properties. QSAR 2D equations are used to defined the several ADMET properties. With all of these properties a chemical space can be defined. Some software are dedicated to predict pharmacokinetic properties (Volsurf) or toxicity (CORAL) This space is useful to visualize the chemical space and get diverse or similar compounds. 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.4. ADMET

91 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) Chemical descriptors label the axis and colors of the chemical space Statistical tools are useful to analyze this chemical space 6.Applications 6.2. Virtual screening 6.2.3. Filtering 6.2.3.4. ADMET

92 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications The molecular docking calculation is a long step. You can decrease the computational time by two ways:  A low generation of docking poses... In this case, you have to be lucky to get a right-first-time molecular docking calculation.  A highly filtered databases... In this case, you have "few" compounds but, you have to be lucky that the good molecules are not discarded. To sum-up, you have to be lucky (or gifted). The scoring part is the Achille's heel of the structure-based virtual screening. There are 3 main methods of scoring (see previous slides). A consensus scoring is certainly the best way to avoid the major drawbacks of each techniques. 6.2. Virtual screening 6.2.4. Scoring

93 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.2. Virtual screening 6.2.5. Assessing quality Like "classical" molecular docking calculations, if experimental structures of a complex are known, it's interesting to add these compounds in your database.  These compounds, normally, mustn't be discarded during the filtering processes.  We can compare the predicted docked position and the experimental structure. A root mean square deviation (RMSD) can thus be determined: The user should define its threshold value, generally between 0.3 to 2 Å. Despite its simplicity, this metric is far from being perfect (size, interaction, symetry...).

94 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.2. Virtual screening 6.2.5. Assessing quality Number of compounds Interaction energy or score Threshold

95 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.2. Virtual screening 6.2.5. Assessing quality False positive compounds

96 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.2. Virtual screening 6.2.5. Assessing quality False negative compounds

97 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.2. Virtual screening 6.2.5. Assessing quality How to evaluate a good virtual screening procedure? Several groups have developed the used of decoys in the VS strategy. The decoys have been designed to display similar physico-chemical properties of known ligands. For example, the DUD-E (Directory of Useful Decoy Enhanced) contains:  around 102 protein systems (classical drug-target)  for each system, several known ligands are put in a database (average 13)  for each ligand, around 50 decoys (with similar properties) are added  The databases contain, on average, 650 compounds

98 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.2. Virtual screening 6.2.5. Assessing quality

99 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.2. Virtual screening 6.2.5. Assessing quality ROC (Receiver Operating Characteristic) curves: % of active compounds Random Ideal ROC Classical good ROC

100 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications Moon and Howe: ‘‘Given detailed structural knowledge of the target receptor, it should be possible to construct a model of a potential ligand, by algorithmic connection of small molecular fragments, that will exhibit the desired structural and electrostatic complementarity with the receptor.’’ De novo design purpose and challenge: Build an ideal compound inside the protein. If synthesized, this one should be a perfect inhibitor. There are several methods to do this task. All of them have advantages and drawbacks. To date, nothing is perfect! Examples of software: LUDI, CAVEAT, SPROUT, MCSS... Here, we will see only the MCSS strategy. MCSS: Multiple Copy Simultaneaous Search 6.3. De novo design

101 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design We start from an empty protein binding site Thr Lys Phe Trp LeuIle Val Ser

102 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design The binding site is filled with a lot of identical fragments Thr Lys Phe Trp LeuIle Val Ser

103 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design The binding site is filled with a lot of identical fragments Thr Lys Phe Trp LeuIle Val Ser

104 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design Only the best position of the fragment is kept Thr Lys Phe Trp LeuIle Val Ser

105 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design Step by step, the protein binding site is completely filled with fragments at optimal positions. Thr Lys Phe Trp LeuIle Val Ser

106 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design Last step: linkage of all elements Thr Lys Phe Trp LeuIle Val Ser

107 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design First example: the design of novel inhibitor of hepatitis C virus helicase (HCV). Ligbuilder 1 st proposition

108 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design First example: the design of novel inhibitor of hepatitis C virus helicase (HCV). Ligbuilder 2 nd propositionHuman enhancement IC50 = 260 nM

109 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications 6.3. De novo design Second example: the design of inverse agonist of cannabinoid receptor 1 (CB1). TOPAS propositionHuman enhancement IC50 = 4 nMIC50 = 1500 nM

110 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 6. Applications Advantages:  Quick  Provides new ideas of chemical scaffolds  Compounds are here original. It is not the case of virtual screening Drawbacks:  How to synthetize them? New softwares attempt to follow some chemical rules of synthesis...  Sometimes the molecules are generated only for filling the protein cavity and not for inhibit the enzyme.  A final human design is always required 6.3. De novo design

111 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 7. General conclusions

112 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 7. General conclusions Molecular docking is an efficient method to predict the structural interaction of an organic molecule inside a biomacromolecule binding site. However, molecular docking has a weakness for the determation of the interaction energy (scoring function). Generally, molecular docking calculations and their applications don't give an unique solution but rather several solutions. Human has the last word. Molecular docking is mainly applied for the drug-design and get many success.

113 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 7. General conclusions Some successful drugs through molecular docking between 1995 and 2009.

114 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 7. General conclusions Some successful drugs through molecular docking between 1995 and 2009.

115 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 7. General conclusions Some successful drugs through molecular docking between 1995 and 2009. 1)LMC : Leucémie Myéloïde Chronique 2)EGFR : récepteur au facteur de croissance endothélial 3)Cancer pulmonaire non à petites cellules 4)VEGFR : récepteur au facteur de croissance endothélial vasculaire 5)Cancer gastro-intestinal résistant à l’imatinib 6)Lymphome cutané à cellules T 7)INNTI : inhibiteur non nucléosidique de la transcriptase inverse.

116 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 8. References

117 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 8. References Articles:  Shoichet, B.K., D.L. Bodian, and I.D. Kuntz, J. Comp. Chem., 1992. 13(3): p. 380-397.  Meng, E.C., B.K. Shoichet, and I.D. Kuntz, J. Comp. Chem., 1992. 13: p. 505-524.  Kuntz, I.D., J.M. Blaney, S.J. Oatley, R. Langridge, and T.E. Ferrin, J. Mol. Biol., 1982. 161: p. 269-288.  Meng, E.C., D.A. Gschwend, J.M. Blaney, and I.D. Kuntz, Proteins, 1993. 17(3): p. 266-278.  F. Barbault, C. Landon, M. Guenneugues, M. Legrain, et al, Biochemistry 2003. 42 14434-42  D. Eisenberg, E. Schwarz, M. Komaromy and R. Wall, J. Mol. Biol. 1984. 179 125-142.  F. Barbault, B. Ren, J. Rebehmed, C. Teixeira, Y. Luo, et al, Eur. J. Med. Chem. 2008.43 1648- 56.  W. Humphrey, A. Dalke, K. Schulten, J. Mol. Graph. 1996 (14) 33-8  C. Teixeira, N. Serradji, F. Maurel, F. Barbault, Eur. J. Med. Chem. 2009. 44 3524-32  Hu R., Barbault F., Delamar M., Zhang R. Bioorg. Med. Chem. 2009. 17 2400–9  Morris G.M., Huey R., Lindstrom W., Sanner M.F,et al, J Comput Chem 2009. 30 2785–91.  Morris G.M., Goodsell D.S., Halliday R.S., Huey R., Hart W.E., Belew R.K., Olson A.J., J. Comput. Chem. 1998. 19 1639–62  T. Cheng, Q. Li, Z. Zhou, Y. Wang, SH. Bryan., AAPS Journal 2012. 14 133-41  Crivori P, Cruciani G, Carrupt P-A, Testa B., J Med Chem. 2000;43(11):2204–2216.  Toropova AP, Toropov AA, Lombardo A, Roncaglioni A, Benfenati E, Gini G., J Comput. Chem. 2012. doi:10.1002/jcc.22953.  Sharman JL, Benson HE, Pawson AJ, et al., Nucleic Acids Res. 2013;41(D1):D1083–D1088

118 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 8. References Articles :  Nesrine Ben Nasr, "Optimisation de méthodes de criblage virtuel et synthèse de molécules à visée thérapeutique pour le traitement des maladies auto-immunes", 2013, Thesis, CNAM  OMEGA135,213, ROTATE195, CAESAR214  Boström J, Greenwood JR, Gottfries J., J. Mol. Graph. Model. 2003;21(5):449–462  CORINA - http://www.molecular-networks.com/products/corinahttp://www.molecular-networks.com/products/corina  Renner S, Schwab CH, Gasteiger J, Schneider G., J Chem Inf Model. 2006;46(6):2324–2332  Hawkins PCD, Skillman AG, Warren GL, et al, J Chem Inf Model. 2010;50(4):572–584  Li J, Ehlers T, Sutter J, Varma-O’brien S, Kirchmair J., J Chem Inf Model. 2007;47(5):1923–1932  Brooijmans N, Kuntz ID., Annu Rev Biophys Biomol Struct. 2003;32(1):335–373  Boström J., J Comput Aided Mol Des. 2001;15(12):1137–1152  Bissantz C, Folkers G, Rognan D., J Med Chem. 2000;43(25):4759–4767  Pham TA, Jain AN., J Med Chem. 2006;49(20):5856–5868  Irwin JJ, Raushel FM, Shoichet BK., Biochemistry (Mosc). 2005;44(37):12316–12328  Huang N, Shoichet BK, Irwin JJ., J Med Chem. 2006;49(23):6789–6801  DUD - A Directory of Useful Decoys. http://dud.docking.org/http://dud.docking.org/  Spitzer R, Jain AN., J Comput Aided Mol Des. 2012;26(6):687–699  Neves MAC, Totrov M, Abagyan R., J Comput Aided Mol Des. 2012;26(6):675–686  Brozell SR, Mukherjee S, Balius TE, et al, J Comput Aided Mol Des. 2012;26(6):749–773

119 Dr. Florent Barbault, ITODYS (CNRS UMR 7086) 8. References Articles:  Fan H, Irwin JJ, Webb BM, Klebe G, Shoichet BK, Sali A., J Chem Inf Model. 2009;49(11):2512– 2527.  DUD-E: A Database of Useful (Docking) Decoys — Enhanced. http://dude.docking.org/http://dude.docking.org/  Mysinger MM, Carchia M, Irwin JJ, Shoichet BK., J Med Chem. 2012;55(14):6582–6594  Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O. J Med Chem. 2005;48(7):2534–2547  Kirchmair J, Distinto S, Markt P, et al. J Chem Inf Model. 2009;49(3):678–692  Giganti D, Guillemain H, Spadoni J-L, et al., J Chem Inf Model. 2010;50(6):992–1004  Böhm HJ. J Comput Aided Mol Des. 1992;6(1):61–78  Böhm HJ. J Comput Aided Mol Des. 1992;6(6):593–606  Miranker, A.; Karplus, M., PROTEINS: Struct, Funct, and Gene 1991 11:29–34  Bohacek, R. S. & McMartin, C., J Am Chem Soc 1994 116:5560–71  Gisbert Schneider and Karl-Heinz Baringhaus, "De Novo Design: From Models to Molecules"(book)  Kandil, S., Biondaro, S., Vlachakis, D., et al, Bioorg. Med. Chem. Lett 2009 19:2935–7  Wang, R., Gao, Y., and Lai, L., J Mol Model 2000 6:498–516  Rogers-Evans, M., Alanine, A., Bleicher, et al, QSAR Comb Sci 2004 26:426–30  Schneider, G., Neidhart, W., Giller, T., et al, Angew. Chem Int Ed 1999 38:2894–6  Alig, L., Alsenz, J., Andjelkovic, M., Bendels, S., Benardeau, A., et al, J Med Chem 2008 51:2115–27  Alex AA, Millan DS. In: Drug Design Strategies.; 2011 (chapter book)

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