Molecular modelling for In silico drug discovery:modelling small molecules and proteins

Dr Lee Larcombeleelarcombe@gmail.com

Lecture Aim

This lecture aims to provide a basic understanding of the concept of protein and molecular in silico engineering/design as part of the drug development process:-

Introducing theory and approaches, drivers, databases and software – and with a focus on safety and efficacy.

This Lecture Covers

• Drivers for use of computational approaches

• Getting protein structures• Simulation of molecular interactions• Considering safety during design

• We will also highlight key software or data sources along the way

Key Drivers for in silico

Business

Target identification

Lead selection

Lead refinement

Pre-Clinical phases

GenomicsProteomics/MetabolomicsInteraction Networks

Molecular modellingProtein modellingChemoinformatics

Molecular modellingData modellingInteraction Networks

Systems BiologyIn vitroIn vivo

££

£

£

££

£

Ethics Drivers• Use of animals in research

• 3Rs – Refine, Reduce, Replace

• Relevance of animal data for human use• Extrapolation across species

• Improvement of safety for subsequent trials

• Regulatory requirements and change

Extrapolation of data across speciesHow relevant is animal physiology to human physiology ?

Models not available for all diseases

Choice of species can be important• 30% attrition due to no efficacy in man• 10% attrition due to toxicity

For biologics, even more difficult to predict

Part 1: Small Molecule Drugs

8

Safety and Efficacy of Small Molecule Drugs

• Safety: safety issues primarily focus on the potential of the small molecule to have off-target effects, metabolite/breakdown product toxicity, or buildup/non clearance

• Efficacy: efficacy issues focus on bioavailability and good binding kinetics to the right target protein – including variations of that protein (SNPs/mutants)

1st we need a source of molecules: Chemical Repositories

• Databases with safety information (GRS, CAS)

• Databases with structure and vendor/price – individual chemical supply companies - Zinc

• Databases with multiple information types – ChEMBLdb, PubChem, Kegg

ChEMBLdb“The ChEMBL database (ChEMBLdb) contains medicinal chemistry bioassay data, integrated from a wide variety of sources (the literature, deposited data sets, other bioassay databases). Subsets of ChEMBLdb, relating to particular target classes, or disease areas, are exported to smaller databases, These separate data sets, and the entire ChEMBLdb, are available either via ftp downloads, or via bespoke query interfaces, tailored to the requirements of the scientific communities with a specific interest in these research areas”

• Targets: 10,579• Compound records: 1,638,394• Distinct compounds: 1,411,786• Activities: 12,843,338• Publications: 57,156

(release 19)

ChEMBLwww.ebi.ac.uk/chembl/

1. Representation of atomic coordinates

2. Scoring

3. Searching

Basic Requirements for modelling

• How much information do you want to include?

• atoms present• connections between atoms

• bond types• stereochemical configuration• charges• isotopes • 3D-coordinates for atoms

Structure Representation

C8H9NO3

• 3D-coordinates for atoms

Structure Representation

OH

CH2

CHNH2

OH

O

• connections between atoms• bond types

Structure Representation - InChi

http://en.wikipedia.org/wiki/International_Chemical_Identifier

Morphine

InChI=1S/C17H19NO3/c1-18-7-6-17-10-3-5-13(20)16(17)21-15-12(19)4-2-9(14(15)17)8-11(10)18/h2-5,10-11,13,16,19-20H,6-8H2,1H3/t10-,11+,13-,16-,17-/m0/s1

The condensed, 27 character standard InChIKey is a hashed version of the full standard InChI (using the SHA-256 algorithm), designed to allow for easy web searches of chemical compounds.

BQJCRHHNABKAKU-KBQPJGBKSA-N

Energy can be broken into a sum of potential energy terms

Estr stretch

Ebend bend

Etors torsion

EvdW van der Waals

Eel electrostatic

Epol polarization

-+-+

Repulsion

Attraction

+ -++-

+-+

+-+

+- +

E = Ebonds + Eangles + Etorsions + Evdw + Eelectrostatic

θ

φ

Scoring (Energy Functions & Force Fields)

Mol Mechanics (static) – minimisation

Mol Dynamics (dynamic) – laws of motion

MD a bit more complicated … need to know about:

• Classical mechanics

• classical equations of motion (EOM)

• e.g. Newton’s equations of motion

If we know these equations we *could* try to search for ALL possible

structures of Proteins and how they fold e.g. Protein Folding

Searching

• Treat molecule as a set of balls (with mass) connected by rigid rods and springs

• Rods and springs have empirically determined force constants

• Allows one to treat atomic-scale motions in proteins as classical physics problems (OK approximation)

Energy Minimisation Theory

• Efficient way of “polishing and shining” your model• Removes atomic overlaps and unnatural strains in the structure• Stabilizes or reinforces strong hydrogen bonds, breaks weak

ones• Brings molecule to lowest energy

Local minimum vs global minimumMany local minima; only ONE global minimumMethods: Steepest descent, Conjugate gradient, others…

Energy Minimisation

Structures with Low Energyenergy

coordinates

Local minimum

Global minimum

High EnergyLow Energy

Makes small locally steep moves down gradient

Sufficient if starting point already close to optimal solution (e.g. refinement of experimental structure)

Steepest Descent Minimisation

Low Energy

MM calculates just minimum energy state.

MM ignores kinetic energy, does only potential energy

Molecular Mechanics vs Dynamics

Molecules, especially proteins, are not static.• Dynamics can be important to function• Trajectories, not just minimum energy state.

MD takes same force model, but calculates F=ma and calculates velocities of all atoms (as well as positions)

I have no idea where this image came from, but it is a very nice illustation of the comparison. If anyone knows where it is from please let me know!

Why simulate the dynamics of (molecular) systems?

• Molecular systems are not static

• Molecules are in dynamic equilibrium

• Properties are averages over dynamic behaviour of molecules

• Molecular processes are not instantaneous

• Time course (kinetics) of events is important

• Time dependence essential to understand development and regulation

Why?

What can we do with chemical models?

We can investigate structure and similarities of structure between molecules

We can map structural characteristics to properties (SARs)

We can study molecular interactions – particularly with proteins

• Computation to assess binding affinity

• Looks for conformational and electrostatic "fit" between proteins and other molecules

• Optimization: Does position and orientation of the two molecules minimise the total energy? (Computationally intensive)

• Docking small ligands to proteins is a way to find potential drugs. Industrially important!

Interactions – Docking & Screening

• Docking small ligands to proteins is a way to find potential drugs. Industrially important

• A small region of interest (pharmacophore) can be identified, reducing computation

• Empirical scoring functions are not universal

• Various search methods:• Rigid- provides score for whole ligand (accurate)• Flexible- breaks ligands into pieces and docks them

individually

Virtual Screening

So – we need protein (target) structures

http://www.rcsb.org/

The PDB

The PDB was established in 1971 at Brookhaven National Laboratory and originally contained 7 structures. In 1998, the Research Collaboratory for Structural Bioinformatics (RCSB) became responsible for the management of the PDB.

Last year (2013), 9597 structures were deposited from scientists all over the world – this year (2014) so far, 8391

Now totals 105,839 (yesterday) structures

Entries in database - cumulative and by year

Red = total

Blue = yearly

What if there is no structure available?Can we predict structures?

Tertiary structure is dependent on ‘folding’ of the protein.

Recognition, characterisation, and assignment of domains and folds is a major area of structural bioinformatics.

Predicting structure from sequence is one of the biggest challenges...

Historical perspective? Basic secondary structure prediction

Basic methods of secondary structure prediction rely on statistical applications of ‘propensity’

The propensity/inclination/tendency of an amino acid to be in a particular structure based on observation of known datasets

Propensity

n[I][s] / n[I]

n[s] / nP =

P = propensity

I = residue of interest

n[I] = number of residues [I] in the database

n = total number of residues in the database

n[I][s] = number of residues [I] in state of interest i.e. helices

n[s] = number of all residues in the database in the state of interest.

Example

124 / 1640

1246 / 10136P[A] = = 0.61

So, the helical propensity for Alanine where:

• the number of alanines in the database is 1640,

• and the total number of residues in the database is 10136,

• and where the number of alanines found in helices is 124,

• and the total number of residue found in helices is 1246,

would be 0.61

Sliding windows

Propensity values are often assigned using sliding window methods

Sequence: A G T W Y K M C Q N P V

window 1: A G T W Y K M average applied to W

window 2: G T W Y K M C average applied to Y

window 3: T W Y K M C Q average applied to K

Theory that neighboring residues affect local structure

GOR

Method by Garnier, Osguthorpe & Robson (1978).

Uses propensity values for Helix, Sheet, Coil, Turn for each residue from experimentally-determined structures

Analysis done for each state, most probable state is assigned

Sequence EVSAEEIKKHEEKWNKYYGVNAFNLPKELFSKVDEKDRQKYPYNTIGNVFVKGQTSATGV

GOR Sheet ---------------------------------------------SSSSSSSS---SSSS

GOR Helix --------HHHHHHHHH----HHHHHHHHHHHHHHHH-----------------------

GOR Coil --CCCC---------------------------------CCCC-----------------

GOR Turn ------------------TT----------------------------------------

Hydrophobicity

Method by Kyte & Doolittle (1982)

Uses values representing hydrophobicity of residues rather than structural propensity

Applied with a sliding window method

Hydrophobicity

Often helices tend to be more hydrophobic

Internalised regions of a protein are more hydrophobic

Transmembrane domains are hydrophobic

Example Kyte-Doolittle plot

One more - Hydrophilicity

A method by Hopp & Woods (1980)

Experimentally derived values representing residue hydrophilicity

Attempts to determine surface/solvent accessibility - Antigenicity?

Problems

Many of these tools are old - and rely on statistical values from small datasets

They generally cannot achieve better than 60% accuracy (depends on how you measure it!)

60% right is still 40% wrong!!!

!! However – they are still in common use !!

(eg. Emboss tools: garnier & antigenic)

Many scales existhttp://web.expasy.org/protscale/

Application example: Stability

There can be some benefit to using these scales in combination (similar to antigenic) – here using scales for order/disorder, aggregation potential and hydrophobicity to look at protein stability in the absence of structural information

Levinthal’s paradox (1969)

100 residues = 99 peptide bonds

therefore 198 different phi and psi bond angles

3 stable conformations of bond angle = 3198 possible conformations

At a nano/pico second sample rate proteins would not find correct structure for a long time (longer than the age of the Universe!)

Folding is Complex: Is a truly random approach possible?

Proteins fold on a milli/micro second timescale – this is the paradox...

phi

psi

1. proteins do NOT fold from random conformations, which was an assumption of Levinthal's calculation

2. instead, they fold from denatured states that retain substantial 2o, and possibly 3o, structure

• Simulations are computational expensive• Gross approximations in simulations• Nature uses tricks such as

• Posttranslational processing • Chaperones• Environment change

Why are folding simulations so difficult?

How does it work at all?

Complexity & Diversity – potential vs reality

If the average protein contains about 300 amino acids, then there could be a possible 20300 different proteins

(Apparently) this is more than the atoms in the universe!

Yet a human (complex) has only 30,000 proteins

All proteins so far appear to be represented by between 1000 - 5000 fold types

Two reasons for limited fold space

Convergent evolution

Certain folds are biophysically favourable and may have arisen in multiple cases

Divergent evolution

The number of folds seen is limited because they have evolved from a limited number of common ancestor proteins

Despite the evolutionary limitation of the number of existing folds (fold space) it is still complex enough to make classification and

comprehension difficult

Why is Folding Difficult to do?

It's amazing that not only do proteins self-assemble -- fold -- but they do so amazingly quickly: some as fast as a millionth of a second. While this time is very fast on a person's timescale, it's remarkably long for computers to simulate.

In fact, it takes about a day to simulate a nanosecond (1/1,000,000,000 of a second) of dynamics for a reasonable sized protein. (eg Intel core i7 2.66Ghz)

Unfortunately, proteins fold on the tens of microsecond timescale (10,000 nanoseconds). Thus, it would take 10,000 CPU days to simulate folding -- i.e. it would take 30 CPU years! That's a long time to wait for one result!

Folding @ Home folding.stanford.edu

Similar Project:

ab initio protein tertiary structure prediction based on the approach that sequence-dependent local interactions limit or bias segments of the chain to form only distinct sets of local structures

and that non-local interactions select the lowest free-energy tertiary conformations compatible with the local biases.

different models are used to treat the local and non-local interactions.

Rather than attempting a physical model for local sequence-structure relationships, the approach turns to the protein database to look at the distribution of local structures adopted by short sequence segments (fewer than 10 residues in length) in known three-dimensional structures

http://boinc.bakerlab.org/rosetta/

Berkley Open Infrastructure for Network Computing

Infrastructure: BOINChttp://boinc.berkeley.edu/

A: Left, crystal structure of the MarA transcription factor bound to DNA; right, our best submitted model in CASP3.Despite many incorrect details, the overall fold is predicted with sufficient accuracy to allow insights into the mode of DNA binding.

B: Left, the crystal structure of bacteriocin AS-48; middle, our best submitted model in CASP4; right, a structurally and functionally related protein (NK-lysin) identified using this model in a structure-based search of the Protein Data Bank (PDB). The structural and functional similarity is not recognizable using sequence comparison methods (the identity between the two sequences is only 5 percent).

C: Left, crystal structure of the second domain of MutS; middle, our best submitted model for this domain in CASP4; right, a structurally related protein (RuvC) with a related function recognized using the model in a structure-based search of the PDB. The similarity was not recognized using sequence comparison or fold recognition methods.

Some Rosetta@home results

Robetta server http://robetta.bakerlab.org/

Robetta results

This took about 3 weeks to complete

Fold It http://fold-it/portal

A compromise: Homology modelling

If there is no structure for your protein - perhaps there is one for a similar protein.

Sequence alignment tools can be used to compare this to your sequence with unknown structure

Homology searching and sequence alignment is now the first step to protein structure prediction

If homologous proteins are found with structures, unknown can be ‘overlayed’ and structure inferred

Homology Modeling

Based on two assumptions:

1.The structure of a protein is determined by its amino acid sequence alone

2.With evolution, the structure changes more slowly than the sequence - similar sequences may adopt the same structure

Sequence alignment

TEX19 – human protein without a structure.

PDB 2AAM: Crystal structure of a putative glycosidase (tm1410) from thermotoga maritima

Structure inference/alignment

ExPASy - SwissModelSwissModel (swissmodel.expasy.org/)

Phyre2http://www.sbg.bio.ic.ac.uk/phyre2

More annotation http://genome3d.eu/

Using the Models – Docking/Screening

• Choose and prepare target protein• Identify binding pocket• Fit ligand to pocket• Score

• (for screening – repeat!)

Identify the Binding Pocket

• Could identify this by the location of an existing co-crystallised ligand

• Or use surface sphere clusters• Or identify it by clustering of solvent molecules (normally

water)• Perhaps identify it by clustering of fragments (SurFlex

dock protomol)

Binding site based on existing ligand

• Most methods allow you to specify where the site is – perhaps by identifying key residues or based on an existing ligand

• Could use the ‘hole’ left by the ligand as a pocket, or use the ‘surface’ of the ligand as a protomol

Surface Sphere generation• Generate the surface of the target

– Connolly surface

• ‘Rolls’ a sphere the radius of water across the van der Waal’s surface of the target

• Each atom’s centre of van der Waal’s radius acts as a sitepoint for the generation of a sphere on the surface whose centre is perpendicular to the surface at the sitepoint.

• Spheres are then clustered – each cluster is a potential pocket

Identified pocket

Prepare the ligand

• The ligand needs to be prepared too• Drawn & minimised• From a database - & minimised• Extracted from another/the same binding site

• Hydrogens added etc• Minimised/optimised – ready to dock

Docking

• Rigid docking -> ligand is fixed conformationally

• Flexible docking –> ligand is conformationally flexible

• Posable -> ligand is rigid, but moved spacially

Rigid Ligand docking• Centres of spheres

representing the binding pocket act as ‘Site Points’

• The atoms of the ligand are matched to the site points

• Once orientation made, possibly interaction minimised: receptor kept rigid and ligand flexible

Alternatives

Flexible Docking Posable Docking

Rings treated as flexible

Other bonds treated as flexible/rotamers

Rings treated as rigid – ligand fragmented

Rigid docking, but ligands posed conformationally

•Rotated•Twisted•Flipped etc

And repetitively docked to find best fit

Example Interaction – Avidin / Biotin

Virtual Screening• Docking – but repeated with many potential ligands

• Libraries can come from resources such as PubChem/ChEMBLdb – vendors – or other in-house sources

• From specialised databases holding structures suitable for docking

• It is important to have a diversified library especially for rigid docking !

Considering safety & efficacy – “Drug-like”

Lipinski rule of 5 (or Pfizer rule)

‘Compounds which violate at least two of the following conditions have a very low chance of being orally bioavailable’

• MW <500 Da• log P (lipophilicity) <5• number of H bond donors <5• number of H bond acceptors <10

Works well once you have descriptions of small molecules – can be search criteria in databases...

ADME / ADME-Tox• Lipinski rule is really the 1st step in ADME (adsorption,

distribution, metabolism, excretion) modelling

• Structure Activity Relationships (SARs) – similar molecules will behave in similar ways, ie have similar effects.

• Allows for knowledge-based compariative analysis – Tox databases

ChEMBL SARfari(s)

Knowledge-based tox in silico

www.dixa-fp7.eu

Toxicogenomics – Open TG-Gates

HeCaToS http://www.hecatos.eu/

Final Comments

Remember the Key Drivers for in silico approaches

Explore the following Software ToolsAs well as resources mentioned in the slides!

Homology ModellingModeller, Phyre, SwissModel

Model ViewersPymol, Jmol, Rasmol

Molecular Simulation etcGromacs, Tinker, Amber, NAMD, Charmm,

Docking/ScreeningSurflex Dock, Dock, AutoDock, Vina

Graphical Tools/builders/interfacesChimera, Maestro, Ghemical, VMD, DeepView

Suites (companies)Tripos, Accellrys, OpenEye, ChemAxon, Schrodinger, MoE, Yasara

Some are free for academic use, but cost for commercial use

Take note and beware!

Workflow example – free vs paid

ChEMBL

PDB

Discovery Studio

Marvin Sketch

Chimera

Gromacs

Dock

Chimera

ligand

target

get structures

minimisation

dynamics

docking

evaluation

preparation

Commercial suite vs free tools

£££ $$$