molecular docking and_virtual_screening

Dr. Florent Barbault, ITODYS (CNRS UMR 7086)

Molecular Docking

Virtual Screening


Overview

1. Introduction2. Basic concepts3. Preparation steps of molecular docking

3.1. Basic knowledge3.2. Target structure

3.2.1. Source3.2.2. Resolution3.2.3. Treatment

3.3. Interacting site3.4. Ligand structure3.5. Flexibility

3.5.1. Ligand3.5.2. Macromolecule


Overview

4. Manual docking5. Automatic docking

5.1. Rules5.2. Algorithms and methods

5.2.1. Grid method5.2.2. Sphere method5.2.3. Incremental method5.2.4. Genetic algorithm

5.3. Scoring5.3.1. Force-field5.3.2. Empirical potential5.3.3. Knowledge based

6. Applications6.1. Direct conception


Overview

6.2. Virtual screening6.2.1. Rules6.2.2. Databases

6.2.2.1. 1D storage6.2.2.2. 3D storage

6.2.3. Filtering6.2.3.1. Redundancy6.2.3.2. Reactivity & toxicity6.2.3.3. Drug-like6.2.3.4. ADMET

6.2.4. Scoring6.2.5. Assessing quality

6.3. De novo design7. General conclusions8. References


1. Introduction


1. Introduction

molecular docking: prediction of the association between two molecules

Experimentally, the interaction process between two compounds is never easyand 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 twomacromolecules (protein/protein or DNA/protein) or any other interactions.


2. Basic concepts


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.


2. Basic concepts

A + B AB DG = DH -TDS

KD =A B

AB=1

KA

Even the most complex biomacromolecules obey to thermodynamic.

If DG is negative the reaction will be driven toward the AB formation.

If DG is decreased by 2.7 kcal/mol then the dissociation constant (KD) changefrom 100 to 1 and the association population evolve from 50% to 99% (Boltzmannstatistic's).

This logarithmic dependency shows the problem of accuracy in molecularmodeling

∆Gbind = RTlogKD


3. Preparation steps of molecular docking



3.1. Basic knowledge

We know the 3D structure of the target and we wish to simulate the interactionof 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 thescale of ms to s whereas we generally perform nsto µs.See David Shaw

We need other methods, more direct, since theinteraction prediction of two molecules is highlycomplex and requires tremendous explorations



3.2. Target structure3.2.1. 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 aprotein)●Average structure

➔ Homology modelling● Free and quick●No experimental●Low precision of sidechains●Sequence similarity or identity?



Accuracy is an important parameter: RX

3.2. Target structure

3.2.2. Resolution

Here precision, accuracy is very good.


A protein alpha-helix with different resolution


3.2.2. Resolution




In NMR the resolution is hard to determine numerically:Generally we look at the RMSD or the number of restraints by residue.


3.2.2. Resolution




3.2.2. Resolution

For homology modelling (comparative modelling) the resolution has no realmeaning.

In all cases, it is essential to have a feeling of the target structure resolution at theitneracting site location. For enzyme, generally, this area is the best defined.

Beware: for Xray structures some protein parts or atoms may be missed. In thiscase, we choose to add or not these parts depending of their location or influencefor the chemical association.

To sum-up, it is always required to gather as much as you can information aboutthe target.




3.2.3. Treatment

Experimental structures are far frombeing perfect!

You can find in them:

o Ionso Watero Soapo Glycosylo Antibodyo Chaperon proteinso Missing atoms…

You must clean the pdb file



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:



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 onthe molecular surface (Connoly). We roll this sphere around the molecular surfaceand we compute the common volume, Vcom, which belongs also to the protein.

0 < Vcom ≤V

3

V

3< Vcom <

2V

3

2V

3≤ Vcom < V

if

Knob

Plane

Hole



3.3. Interacting site

"Knob & Hole" cavity detection technique

*

Knob

Hole



3.4. Ligand structure

Ligands are generally molecular organic compounds. We use GUI software(Graphical User Interface), working with the molecular mechanic theory, such asMaestro, Sybyl, Accelrys, Moe, ICM...



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



3.5. Flexibility

During the interaction, the ligand flexibility is highly engaged whereas theprotein (larger molecule) hardly moves.

Rigid docking

Flexible docking

Induced fit docking



3.5. Flexibility3.5.1. Ligand flexibility

It is impossible to manage all ligand cartesian coordinates. Thus, only rotatabledihedral angles (torsion) move. Rings are maintained fixed so that they must becorrectly minimized.

Some questions remain:resonance angle, peptide bond, guanidinium... how to manage them, fixed orrotatable?



Other anecdotic method: make a rigid docking with several ligand conformations.

Captopril

3.5. Flexibility3.5.1. Ligand flexibility


Direct methods are still in development. In Autodock 4.2, the user can define, forfew protein residues inside the active site, sidechain torsion angles.

3.5. Flexibility3.5.2. 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 rotatableresidues



Indirect methods: A molecular docking is performed. Then a molecular dynamicsimulation of the obtained complexe is realized...


Advantages: With methods such as MMPBSA you

can determine (evaluate) bindingfree energy

You can explore the physicalchemistry of the recognition process

You have access to statistical view ofinteraction (hydrogen bond lifetime)

Drawback: If the starting structure is not

correct...



Indirect methods: A MD simulation is made with the apo protein. Representativestructures are then extracted and molecular docking is performed with thesetargets.


Advantage: Real consideration of the apo

protein

Drawback: How to extract "representative"

target conformations? What about the molecular docking

precision?



4. Manual docking


4. Manual docking

Looks like a joke... The ligand is placed in the interacting site and the associationenergy is calculated at each steps.

The user manually moves, rotates or translates the compound inside the proteincavity. 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. Itis still applicable if only small ligand modifications are explored.


5. Automatic docking



5.1. Rules

Principles:Ligand is automatically placed onto the macromolecule. More exhaustive andsafer this technique requires long CPU time.


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



A box is drawn on the proteinmacromolecule. Therefore, theinteraction will be explored only on thisbox. This drastically limits thecomputational time.

Beware:o If the box is too small, docking will be

falseo If the box is too large, exploration

must be more intensive and couldprovides strange "false positive"ligand conformations

5.2. Algorithms and methods5.2.1. Grid method


o Take care of the amino-acids you want to embedded in the box (especiallythe charged residues)


For all points (nodes) of the grida probe atom is positioned.

There are as many probes asligand atom types. Asupplemental probe of a +echarge is also considered for theelectrostatic computation.

The software places iterativelythe probe atom in each nodepoints and then compute theenergy. These values (tables) arerecorded in map files.




C

H

H

O

1

2

34

The evaluation of the interaction energy is instantaneous:

𝐸𝑖𝑛𝑡𝑒𝑟𝑔𝑟𝑖𝑑= 𝐸𝑂4 + 𝐸𝐶

3 + 𝐸𝐻1 + 𝐸𝐻

2

Computationally, the energy calculation is made by tables summations. However, molecule is considered as a list of points without bonds.



Formol example:


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.




Dihedral angles are translated in genes (binary)

101001010101001011001100111010101010

A random initial population is easily generated

001011010111000101001010011101010101101010010111101110001010010100111010110101101011100010100101001110101010010111110110101110001010010100111010......


Starting population

genotypes phenotypes

Parents selectionfitness fonction

Children

This process is stopped after several defined steps

translation

Crossingmutation


Phenotype

Genotype


Starting population

genotypes phenotypes

Parents selectionfitness fonction

Children

This process is stopped after several defined steps

translation

Crossingmutation Parents optimisation


Phenotype

Genotype


5.2. Algorithms and methods5.2.2. Sphere method



**

*

** *




*

assumption: distances between center of spheres correspond to inter-atomsdistances (heavy atoms)

**

*

***

* **

*

***




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?



This old method has proven itsefficiency and is still employed.


5.2. Algorithms and methods5.2.3. Incremental method


NH

N

O

OH

NH2 OH

O

O

OH

NH

NH2

N

OH

O

Fragments


Definition of interactionsas "umbrellas"



OH N




NH

NH2

NH

NH2

The base fragment isplaced by triangulation




NH

NH2

The second fragment islinked to the first.

Torsion exploration is madeto find the best pose for thisnew fragment

O

NH

NH2

OH

O

OH

NH

NH2




O

NH

NH2

N

OO

-

OHThe ligand is then incrementallybuild In the protein


The target can only be a protein

Umbrella interactions: Hbond electrostatic hydrophobic contact

This method tends tooverestimate the importance ofHbonds regarding othersinteractions.




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



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. Scoring5.3.1. Force-field

𝐸 =

𝑖

𝑁𝐵𝑂𝑁𝐷

𝑗=𝑖+1

𝑁𝐵𝑂𝑁𝐷𝑞𝑖𝑞𝑗

𝜀𝑖𝑗𝑟𝑖𝑗+𝐴𝑖𝑗

𝑟𝑖𝑗12 −𝐵𝑖𝑗

𝑟𝑖𝑗6



A function is designed to evaluate free energy of binding instead of interactionenergy.

5.3. Scoring5.3.2. Empirical potential



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. Scoring5.3.2. Empirical potential



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, ascore is given for the atomic distances found in the docking calculation.

5.3. Scoring5.3.2. Knowledge based



This technique works well but has no chemical meaning. This type of score ranksmore 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 itsfunction. This can be an advantage or a drawback.

This type of docking scoring (drugscore, ligscore,...) is usually used in consensusscoring

Compounds which have a goodrank with several scoring functionsmay be the best ones.

No physical interpretation.

5.3. Scoring5.3.2. Knowledge based



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



6. Applications


6. Applications

An iterative work with experimental chemists is made. The purpose is to proposeoriginal 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


Example 1: exploring a protein cavity with several moities.

6. Applications



Example 2: Extend a ligand to pick up a new favourable interaction.

6. Applications



6. Applications

Ligand n° DG

123 -12.3 kcal/mol22 -11.7 kcal/mol13 -10.1 kcal/mol49 -9.3 kcal/mol76 -6.5 kcal/mol

6.2. Virtual screening6.2.1 Rules


Instead of making molecular docking for a small set of defined ligands thiscomputation is extended to a large database.

The compounds which will have the best ranks will be purchased and biologicallytested.

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 atthe end.

3. Ranking. Even if the two previous steps were correctly made, if you arenot able to meaningfully rank the ligands... You will have nothing at theend.

6. Applications6.2. Virtual screening

6.2.1 Rules


Chemical universe:10100 à 10400 compounds.

Organic molecules:1024 à 1040 compounds.

Synthesized molecules:106 compounds.

Acitve molecules:10? molécules.


6.2.1 Databases

We are looking of a needle in a haystack... if this needle exists.


Numerous chemical databases exist. Some of them are commercial.


6.2.1 Databases

Name Type Number

Pubchem Public 30 million

ChEMBL Public 1 million

NCI set Public 140 000

ChemSpider Public 26 million

CoCoCo Public 7 million

TCM Public 32 000

ZINC Public 13 million

ChemBridge Commercial 700 000

Specs Commercial 240 000

Name Type Number

IUPHAR Public 3 180

Asinex Commercial 550 000

Enamine Commercial 1.7 million

Maybridge Commercial 56 000

WOMBAT Commercial 263 000

ChemDiv Commercial 1.5 million

Chemnavigator Commercial 55.3 million

ACD Commercial 3 870 000

MDDR Commercial 150 000


6.Applications

There are problems of storingchemical 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 screening6.2.2. Databases

6.2.2.1. 1D storage


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 similarphilosophy and the knowledge of their differences are not for the uninitiatedpeople.

6.Applications6.2. Virtual screening

6.2.2. Databases6.2.2.1. 1D storage

c1ccccc1 Cc1ccccc1

OH

Oc1ccccc1


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




A chemical database is SMILES




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)




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 isaround 132 Go.

Really more difficult to create virtual chemical databases comparing toSMILES code.

Still problem for tautomeric forms and charge




The main problem of chemical databases is that they contain mainlyuninteresting 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 containthe same compounds (even the commercial databases). Why?

1D databases → SMILES code is not unique3D database → Comparison of compounds is hard to perform

6.2.3.1. Redundancy


6.2.3. Filtering


Comparison of 3D information is hard to perform


6.2.3. Filtering

6.2.3.1. Redundancy


Other types of redundancy

These three compounds may appear as different in a database!!!!!


6.2.3. Filtering

6.2.3.1. Redundancy



6.2.3. Filtering

6.2.3.2. Reactivity and toxicity

Some chemical moieties areknown to be highly reactiveand/or toxic.

The compounds which carrythese moieties can thus bemoved apart.


The artemisinine counterexample (anti-paludic drug).


6.2.3. Filtering

6.2.3.2. Reactivity and toxicity

OO

O O

OH

H


The global assumption of this filtering step is that a biologically molecule lookslike... 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 thresholdsyou will get a database with more or less compounds.


6.2.3. Filtering

6.2.3.3. Drug-like



6.2.3. Filtering

6.2.3.3. Drug-like

When a ligand interacts with its target it loses some degrees of freedom. Thisprocess decreases the association entropy variation and thus increase the freeenergy 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 activecompounds. Some researchers made an inventory of the 32 classical ringsclassically encountered in drugs.



6.2.3. Filtering

6.2.3.3. Drug-like

Compounds with one and two rings (5 or 6 membered).

( )n

n=1,2,3,4,5,6

( )n

n=1,2



6.2.3. Filtering

6.2.3.3. Drug-like

Compounds three rings (5 or 6 membered).



6.2.3. Filtering

6.2.3.3. Drug-like

Other scaffolds



6.2.3. Filtering

6.2.3.3. Drug-like


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 least4 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 Å²


6.2.3. Filtering

6.2.3.3. Drug-like


ADMET:

Adsorption Desorption Metabolism Excretion Toxicity

Usually, drugs failed to be marketed during the clinical tests. It is thus essential toremove 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 arededicated to predict pharmacokinetic properties (Volsurf) or toxicity (CORAL)

This space is useful to visualize the chemical space and get diverse or similarcompounds.


6.2.3. Filtering

6.2.3.4. ADMET


Chemical descriptors labelthe axis and colors of thechemical space

Statistical tools are useful toanalyze this chemical space


6.2.3. Filtering

6.2.3.4. ADMET


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 scoringis certainly the best way to avoid the major drawbacks of each techniques.

6.2. Virtual screening6.2.4. Scoring



6.2.5. Assessing quality

𝑅𝑀𝑆𝐷 =

𝑖=1

𝑁𝑟𝑖 − 𝑟𝑖𝑜

2

𝑁

Like "classical" molecular docking calculations, if experimental structures of acomplex are known, it's interesting to add these compounds in your database.

These compounds, normally, mustn't be discarded during the filteringprocesses.

We can compare the predicted docked position and the experimentalstructure. A root mean square deviation (RMSD) can thus be determined:

The user should define its threshold value, generally between 0.3 to 2 Å. Despiteits simplicity, this metric is far from being perfect (size, interaction, symetry...).




Number of compounds

Interaction energy or scoreThreshold




False positive compounds




False negative compounds




How to evaluate a good virtual screening procedure? Several groups havedeveloped the used of decoys in the VS strategy.

The decoys have been designed to display similar physico-chemical properties ofknown 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




We can compute the Enrichment Factor for x% of selected compounds:

𝐸𝐹𝑥% =𝑅𝑎𝑡𝑖𝑜 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑒 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑥% 𝑜𝑓 𝑠𝑒𝑙𝑒𝑐𝑡𝑒𝑑

𝑅𝑎𝑡𝑖𝑜 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑒 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑤ℎ𝑜𝑙𝑒 𝑑𝑎𝑡𝑎𝑏𝑎𝑠𝑒

EF must be up to 1, because if EF=1 then your molecular docking is equivalent toa random choice!

EF is easy to calculate but: Requires active compounds Not easy to compare virtual screening methods with distinct databases All active compounds are considered similarly. However, some active

molecules are not so much active...




ROC (Receiver Operating Characteristic) curves:

% of false positive: 𝐹𝑃 = 1 −𝑁𝑖𝑛𝑎𝑐𝑡𝑖𝑣𝑒 𝑢𝑛𝑠𝑒𝑙𝑒𝑐𝑡𝑒𝑐𝑡

𝑁𝑡𝑜𝑡𝑎𝑙 𝑜𝑓 𝑖𝑛𝑎𝑐𝑡𝑖𝑣𝑒

% of active compounds

Random

Ideal ROC

Classical good ROC


6. Applications

Moon and Howe: ‘‘Given detailed structural knowledge of the target receptor, itshould be possible to construct a model of a potential ligand, by algorithmicconnection of small molecular fragments, that will exhibit the desired structuraland electrostatic complementarity with the receptor.’’

De novo design purpose and challenge: Build an ideal compound inside theprotein. If synthesized, this one should be a perfect inhibitor.

There are several methods to do this task. All of them have advantages anddrawbacks. To date, nothing is perfect!

Examples of software: LUDI, CAVEAT, SPROUT, MCSS... Here, we will see only theMCSS strategy.

MCSS: Multiple Copy Simultaneaous Search

6.3. De novo design


6. Applications6.3. De novo design

We start from an empty protein binding site

NH3

+

Thr

Lys

Phe

Trp

Leu Ile

Val

Ser

OH

OH



The binding site is filled with a lot of identical fragments

NH3

+

Thr

Lys

Phe

Trp

Leu Ile

Val

Ser

OH

OH

O O-



The binding site is filled with a lot of identical fragments

NH3

+

Thr

Lys

Phe

Trp

Leu Ile

Val

Ser

OH

OH

O O-

O O-

O O-

O O-

O O-

O O-

O

O-O

O-

O

O-

O

O-

O

O-

O

O-

O

O-

O

O-

O

O-



Only the best position of the fragment is kept

NH3

+

Thr

Lys

Phe

Trp

Leu Ile

Val

Ser

OH

OH

O O-

O O-

O O-

O O-

O O-

O O-

O

O-O

O-

O

O-

O

O-

O

O-

O

O-

O

O-

O

O-

O

O-



Step by step, the protein binding site is completely filled with fragments at optimalpositions.

NH3

+

Thr

Lys

Phe

Trp

Leu Ile

Val

Ser

OH

OH

O O-

N

H

H



Last step: linkage of all elements

O O-

N

H

H

NH3

+

Thr

Lys

Phe

Trp

Leu Ile

Val

Ser

OH

OH



First example: the design of novel inhibitor of hepatitis C virus helicase (HCV).

Ligbuilder 1st proposition

N

OH

NH

O

NH

OH

NH2

OH

SHH

H

OH



First example: the design of novel inhibitor of hepatitis C virus helicase (HCV).

Ligbuilder 2nd proposition

O

OH

O

OH

O

O

NH

O

NH

O

Human enhancement

IC50 = 260 nM



Second example: the design of inverse agonist of cannabinoid receptor 1 (CB1).

TOPAS proposition Human enhancement

IC50 = 4 nMIC50 = 1500 nM

N

OO

O

F N

O

O

F

Cl

Cl


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 chemicalrules of synthesis...

Sometimes the molecules are generated only for filling the protein cavity andnot for inhibit the enzyme.

A final human design is always required

6.3. De novo design


7. General conclusions



Molecular docking is an efficient method topredict the structural interaction of anorganic molecule inside a biomacromoleculebinding site.

However, molecular docking has a weaknessfor the determation of the interaction energy(scoring function).

Generally, molecular docking calculations andtheir applications don't give an uniquesolution but rather several solutions. Humanhas the last word.

Molecular docking is mainly applied for thedrug-design and get many success.



Some successful drugs through molecular docking between 1995 and 2009.



Some successful drugs through molecular docking between 1995 and 2009.

1) LMC : Leucémie Myéloïde Chronique2) EGFR : récepteur au facteur de croissance endothélial3) Cancer pulmonaire non à petites cellules4) VEGFR : récepteur au facteur de croissance endothélial vasculaire5) Cancer gastro-intestinal résistant à l’imatinib6) Lymphome cutané à cellules T7) INNTI : inhibiteur non nucléosidique de la transcriptase inverse.


8. References


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