Interaction fingerprint: 1D representation of 3D protein-ligand complexes

Interaction fingerprints

Vladimir Chupakhin, UNISTRA, 2011

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Chupakhin VladimirLaboratory of ChemoinformaticsStructural Chemogenomics GroupUniversity of Strasbourg

December 2011

Virtual screening approaches

Ligand –based (QSAR, similarity search,

pharmacophores)


Structure–based(docking, pharmacophores)

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Lock-and-key paradigm

InteractionsLo

ckK

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Molecular docking: main steps

1. Protein and ligand preparation2. Binding site identification

3. Conformational search with scoring of the generated

poses


Geometry of interaction

H-bond length (3.0 Å)

H-bond angle (~175°)

Interactions are geometry!

- Hydrophobic- H-bonds- Ionic- Aromatic- Cation-π

Different type of interactions

Self-docking


Extract ligand

Modify geometryDock to thesame protein

Extract ligand

Calculate RMSD

BlueRedOrange

1.1Å4.3Å

Docking quality: RMSD


δ is the distance between N pairs of equivalent atoms

δ1

δN

Cross-docking


Procedures are the same. But why?Robustness!!!

These fluctuation have huge influence in the docking results

Scoring functions


1. Force-field scoring functions (Dock, AutoDock, GOLD)

2. Empirical scoring functions (ChemScore, PLP, Glide SP/XP)

3. Knowledge-based scoring functions (PMF, DrugScore, ASP, SMoG)

Ligand atoms

Protein atoms

Force-field scoring function

Vladimir Chupakhin, UNISTRA, 2011DOI:10.1038/nrd1549

Protein-ligand interactions energy terms Ligand energy terms

Algorithm (force field based)For a given PL complex1. Calculate the interaction energies

between atoms of the ligand and protein (EvdW + EH-bond) using force field.

2. Calculate internal energy of the ligand (Ewdw + Etorsion) + internal H-bond of the ligand (optionally).

3. Total energy = sum of the energy terms 2 and 3

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Empirical scoring function


Algorithm (additive scheme)1. Define interactions types and

geometries2. Look up at the database of

interaction energies3. Total energy = Sum of the

contribution of the every component (+ geometry term influence)

LUDI

DOI:10.1038/nrd1549

ESF made to reproduce the binding energies or conformations (scoring function depends on the training set used to developed it)

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Knowledge-based scoring function

Vladimir Chupakhin, UNISTRA, 2011DOI:10.1038/nrd1549

Algorithm1. Define interactions types and

geometries2. Look up into the database of LP

atom interactions3. Total score (energy) = Sum of the

interactions scores (energies)(ϒ – adjustable parameter, SAS0 – solvated state

of the solvent accessible ares)

KBSF developed to reproduce the binding pose then energy

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Scoring functions: the purposes


Docking = finding the correct binding

pose

Scoring = predict activity of the compound (Ki,

IC50, etc)

Scoring functions: docking


Docking

Average success to dock compound within RMSD < 2Å is around 70%

15Comparative Assessment of Scoring Functions on a Diverse Test Set, Wang, 2009

Scoring functions: scoring


Scoring

Average success rate to rank compound with correlation coefficient from 55-64%

Comparative Assessment of Scoring Functions on a Diverse Test Set, Wang, 2009 16

GOLD Score failure


pose1

pose2

pose1 pose2GOLD Score 59,19 59,30

RMSD, Å 1,10 4,27

Top scored pose

Molecular scoring functions: problems


1.Problems when binding site is highly charged or highly hydrophobic/ hydrophilic

2.Problems when binging site contains waters, ions, cofactors

3. Fragment-like docking – is very tricky4. Even input conformation can influence

the docking results

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Interaction fingerprints

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Chemical fingerprintFingerprints encode the presence or absence of certain features in a

compound, e.g., fragments.

0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0

KISS: Keep It Short and Simple! Keep It Simple Stupid

Structural Interaction Fingerprints

Zhan Deng, Claudio Chuaqui, and Juswinder Singh Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein−Ligand Binding Interactions (DOI: 10.1021/jm030331x), Biogen Inc.

Detect interactions of the ligandwith every amino acidof the binding site

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Aromatic face to face

Hydrophobic

H-bond(protein donor)

Ionic (protein anion)

Aromatic face to edge

Ionic (protein cation)

1 0 0 0 1 0 0

H-bond(protein acceptor)

Bitstring for 1 residue

Bitstring for the whole binding site – Interaction Fingerprint

100100010000101000000100000010000001 …..Residue 1 Residue 2 Residue 3 Residue 4 Residue 5 Residue X

Interaction Fingerprints : preparation

2007, Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints

Molecular Interaction Fingerprints ~ (IFP)


ILE10 1000000VAL18 1000000ALA31 1000000LYS33 1000000VAL64 1000000PHE80 1010000GLU81 0000100PHE82 1100000LEU83 1001000HIS84 1000000GLN85 1000000ASP86 1000101LEU134 1000000ALA144 1000000ASP145 1000000

Zhan Deng, Claudio Chuaqui, and Juswinder Singh Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein−Ligand Binding Interactions (DOI: 10.1021/jm030331x), Biogen Inc.

1000000100000010000001000000100000010000001000101100000010000001000000

3D 1D (bit string)

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interacting patterns (amino acid can be

represented as residue or an

pharmacophoric point, interacting

fragment of ligand can be encoded as

atom, fragment or pharmacophoric point);

type of interaction (hydrogen bonds,

hydrophobic interactions, etc);

direction of interaction (this parameter

distinguish the direction of interaction: for

example is donor of hydrogen bond protein

or ligand);

strength of interaction and distance

between interacting patterns (these

parameters are research specific);

number of bits per interaction point (one

or many).

Ligand ↔ Receptor

Parameters of IFP

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Gold scoring function failure: IFP wins!


Ligand A07 from LR-complex (PDB ID: 3LFS), docked into CDK2 binding site (PDB ID: 2A0C).

Pose 1 – orange(TCreal_vs_docked – 0.75RMSD – 1.10 Å,Goldscore = 59.20)

Pose 2 – blue(TCreal_vs_docked – 0.52RMSD – 4.27 Å,Goldscore = 59.30)

X-ray pose – brickred

Jaccard (Tanimoto)coefficient


IFP usage• store interactions in useful format• analyze experimental LR-complexes

• quality of docking studies• results clustering (even peptides and PPI)

• analyze docked LR-complexes (drug-like and fragment-like compounds)

• retrieve correct binding pose• retrieve specific binding pose

Use cases for IFP: storage

Useful way to store interaction information from experimentally derived LR-complexes:

• scPDB database – Laboratory of Didier Rognan, UNISTRA, Illkirch (DOI: 10.1021/ci050372x)

• CREDO database (DOI:10.1111/j.1747-0285.2008.00762.x).

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Use cases for IFP: x-ray LR analysis


Compounds

Binding site

Specific interactions

DOI: 10.1021/jm030331x

Use cases for IFP: pose retrieval (1)


RMSD is not 100% correct evaluation function!

DOI: 10.1021/ci600342e

Use cases for IFP: VS


Compare the reference x-ray IFP with IFP of docked poses using Tanimoto coefficient.Compounds

database

Virtual screeningresults

Using standard SF: X% of the real hitsUsing standard SF + TC: X% + up to 20%

Use cases for IFP: PPI


IFP suitable even for analysis of Protein-Protein Interactions!

Use cases for IFP: agonists/antagonists


(A) Procaterol – agonist, (B) Carvediol - antagonist

Selective Structure-Based Virtual Screening for Full and Partial Agonists of the b2 Adrenergic Receptor, DOI: 10.1021/jm800710x

IFP modifications

IFP modifications

IFP modifications: r-SIFt – R-group IFP

LEU831001000

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C R1R2 Benefits: Combinatorial library analysis(~100.000 compounds)

Independent of interaction type!Just the fact of interaction!

DOI: 10.1021/jm050381x

IFP modifications: w-SIFt – weighed IFP

+ Biological Activity

+Machine learning approach: find correlation between bit frequency and activity

DOI: 10.1021/ci800466n

moderateactivity

mostactive

lessactive

Benefits:• help to find what interactions are critical for compound potency• interpretable position dependent scoring function for ligand protein interactions

Binding site independent IFP

Binding site independent IFP

BS-independent IFP: APIF

APIF: A New Interaction Fingerprint Based on Atom Pairs and Its Application to Virtual Screening

Atom Pair

Distance = range

Algorithm1. Detect interaction patterns (Hydrophobic,

HBA, HBD)2. Define distance1 and distance2 for

quadruplet interaction3. Convert distances to distance range4. Map distance range and types ….

QuadrupletIFP

BS-independent IFP: APIF - Quadruplet

Ligand-atom

Protein-atom

Ligand-atom

Protein-atom

Distance 2

Distance 1

Interaction Interaction

1 bit in the APIF

BS-independent IFP: APIF

Benefits:• independent on the binding site• comparable to current scoring functions

BS-independent IFP: Pharm-IF

Algorithm1. Detect interaction patterns (Hydrophobic,

HBA, HBD)2. Define ligand pairs based on ligand atoms

interacting with protein ONLY3. Measure their distance4. Map distance to range (quantization) =

Pharm-IF

Benefits:• independent on the binding site• comparable to current scoring functions

DOI: 10.1021/ci900382e

IFP-based scoring functions

IFP-based scoring functions

IFP-based SF: AuPosSOM


• Dock decoys and compounds with known activity

• Generate vector of interactions (H-bons, hydroph.interactions)

• Train model of the active and incative (vector is input)*

Automatic clustering of docking poses in virtual screening process using self-organizing map - AuPosSOM

f (Input (IFP) = 1 or 0 where 1 – is binder0 – non binder

*Simplified representation

IFP-based SF: RF-Score


A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking – RF-Score DOI:10.1093/bioinformatics/btq112

• Vector of 36 features, each feature is occurrence count for j-iatom pair

• Mechanism of generations: take all atoms around 12A around selected ligand atom, filter out interaction out of cutoff range, sum the result (for each interaction pair).

• PDBBind was used to train Random Forest model• Train model using activity as output and interactions as input

Literature overview: SVM-SP


Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries DOI: 10.1021/ci200078f

• Two types of vectors: SVR-KB (146 features) are knowledge-based pairwise potentials (same as above mentioned but trained with SVR), while SVR-EP is based on physico-chemical properties. SVR-EP vector consist of features extracted from X-score (polar/unpolarSASA, MW, vdW energy, etc)

• SVR-KB is better then SVR-EP

Vector is unique!Vector is atom pair based

Merci bien!Thanks a lot!

Interaction fingerprint: 1D representation of 3D protein-ligand complexes

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