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

The document discusses various molecular docking and scoring methods used in drug discovery, highlighting the importance of interaction fingerprints, which encode the interactions between ligands and protein binding sites. It details different scoring functions, their algorithms, and the challenges they face in predicting binding affinities and poses. Additionally, the use of interaction fingerprints for analyzing experimental ligand-receptor complexes and improving virtual screening is emphasized.

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Interaction fingerprints 1NTERACT10N F1NGERPR1NTS Chupakhin Vladimir Laboratory of Chemoinformatics Structural Chemogenomics Group University of Strasbourg December 2011 1 Vladimir Chupakhin, UNISTRA, 2011
Virtual screening approaches ? Ligand –based (QSAR, similarity search, pharmacophores) Structure–based (docking, pharmacophores) Vladimir Chupakhin, UNISTRA, 2011
Lock-and-key paradigm Interactions Lock Key 3 Vladimir Chupakhin, UNISTRA, 2011
Molecular docking: main steps 1. Protein and ligand preparation 2. Binding site identification 3. Conformational search with scoring of the generated poses 4 Vladimir Chupakhin, UNISTRA, 2011
Geometry of interaction H-bond angle (~175°) H-bond length (3.0 Å) Interactions are geometry!
Different type of interactions - Hydrophobic - H-bonds - Ionic - Aromatic - Cation-π
Self-docking Dock to the Modify geometry same protein Extract ligand Extract ligand Blue Red 4.3Å 1.1Å Orange Calculate RMSD 7 Vladimir Chupakhin, UNISTRA, 2011
Docking quality: RMSD δ1 δ is the distance between N pairs of equivalent atoms δN 8 Vladimir Chupakhin, UNISTRA, 2011
Cross-docking Procedures are the same. But why? Robustness!!! These fluctuation have huge influence in the docking results 9 Vladimir Chupakhin, UNISTRA, 2011
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 Protein atoms atoms 10 Vladimir Chupakhin, UNISTRA, 2011
Force-field scoring function Algorithm (force field based) For a given PL complex 1. 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 Protein-ligand interactions energy terms Ligand energy terms 11 DOI:10.1038/nrd1549 Vladimir Chupakhin, UNISTRA, 2011
Empirical scoring function Algorithm (additive scheme) 1. Define interactions types and geometries 2. Look up at the database of interaction energies 3. Total energy = Sum of the contribution of the every component (+ geometry term influence) ESF made to reproduce the binding energies or conformations (scoring function depends on the training set used to developed it) LUDI 12 DOI:10.1038/nrd1549 Vladimir Chupakhin, UNISTRA, 2011
Knowledge-based scoring function Algorithm 1. Define interactions types and geometries 2. Look up into the database of LP atom interactions 3. 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 13 DOI:10.1038/nrd1549 Vladimir Chupakhin, UNISTRA, 2011
Scoring functions: the purposes Docking = finding Scoring = predict activity the correct binding of the compound (Ki, pose IC50, etc) 14 Vladimir Chupakhin, UNISTRA, 2011
Scoring functions: docking Docking Average success to dock compound within RMSD < 2Å is around 70% Comparative Assessment of Scoring Functions on a Diverse Test Set, Wang, 2009 15 Vladimir Chupakhin, UNISTRA, 2011
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 Vladimir Chupakhin, UNISTRA, 2011
GOLD Score failure pose1 pose2 GOLD Score 59,19 59,30 RMSD, Å 1,10 4,27 pose1 Top scored pose pose2 17 Vladimir Chupakhin, UNISTRA, 2011
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 tricky 4. Even input conformation can influence the docking results 18 Vladimir Chupakhin, UNISTRA, 2011
Interaction fingerprints 19 Vladimir Chupakhin, UNISTRA, 2011
Chemical fingerprint Fingerprints 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 Detect interactions of the ligand with every amino acid of the binding site 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.
Interaction Fingerprints : preparation Aromatic H-bond Ionic (protein Hydrophobic face to edge (protein acceptor) anion) Aromatic H-bond Ionic (protein face to face (protein donor) cation) 1 0 0 0 1 0 0 Bitstring for 1 residue 100100010000101000000100000010000001 ….. Residue 1 Residue 2 Residue 3 Residue 4 Residue 5 Residue X Bitstring for the whole binding site – Interaction Fingerprint 2007, Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints 22
Molecular Interaction Fingerprints ~ (IFP) ILE10 1000000 VAL18 1000000 ALA31 1000000 LYS33 1000000 VAL64 1000000 PHE80 1010000 GLU81 0000100 PHE82 1100000 LEU83 1001000 HIS84 1000000 GLN85 1000000 ASP86 1000101 LEU134 1000000 ALA144 1000000 ASP145 1000000 3D 1D (bit string) 1000000100000010000001000000100000010000001000101100000010000001000000 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. 23 Vladimir Chupakhin, UNISTRA, 2011
Parameters of IFP  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 24
Gold scoring function failure: IFP wins! Pose 1 – orange (TCreal_vs_docked – 0.75 RMSD – 1.10 Å, Goldscore = 59.20) Pose 2 – blue (TCreal_vs_docked – 0.52 RMSD – 4.27 Å, Goldscore = 59.30) X-ray pose – brickred Ligand A07 from LR-complex (PDB ID: 3LFS), docked into CDK2 binding site (PDB ID: 2A0C). Jaccard (Tanimoto) coefficient 25 Vladimir Chupakhin, UNISTRA, 2011
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 Vladimir Chupakhin, UNISTRA, 2011
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). 27
Use cases for IFP: x-ray LR analysis Binding site Compounds Specific interactions DOI: 10.1021/jm030331x Vladimir Chupakhin, UNISTRA, 2011
Use cases for IFP: pose retrieval (1) RMSD is not 100% correct evaluation function! DOI: 10.1021/ci600342e Vladimir Chupakhin, UNISTRA, 2011
Use cases for IFP: VS Compare the reference x-ray IFP with IFP of docked poses using Compounds Tanimoto coefficient. database Virtual screening results Using standard SF: X% of the real hits Using standard SF + TC: X% + up to 20% Vladimir Chupakhin, UNISTRA, 2011
Use cases for IFP: PPI IFP suitable even for analysis of Protein-Protein Interactions! Vladimir Chupakhin, UNISTRA, 2011
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 Vladimir Chupakhin, UNISTRA, 2011
IFP modifications IFP modifications
IFP modifications: r-SIFt – R-group IFP LEU83 110 1001000 C R1R2 Benefits: Combinatorial Independent of interaction type! library analysis Just the fact of interaction! (~100.000 compounds) DOI: 10.1021/jm050381x
IFP modifications: w-SIFt – weighed IFP Biological + Activity + Machine learning less moderate most approach: find active activity active correlation between bit frequency and activity Benefits: • help to find what interactions are critical for compound potency • interpretable position dependent scoring function for ligand protein interactions DOI: 10.1021/ci800466n
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 Distance = range Quadruplet IFP Atom Pair Algorithm 1. Detect interaction patterns (Hydrophobic, HBA, HBD) 2. Define distance1 and distance2 for quadruplet interaction 3. Convert distances to distance range 4. Map distance range and types ….
BS-independent IFP: APIF - Quadruplet Distance 2 Ligand-atom Ligand-atom Interaction Interaction Protein-atom Protein-atom Distance 1 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 Algorithm 1. Detect interaction patterns (Hydrophobic, Benefits: HBA, HBD) • independent on the 2. Define ligand pairs based on ligand atoms interacting with protein ONLY binding site 3. Measure their distance • comparable to current 4. Map distance to range (quantization) = scoring functions Pharm-IF DOI: 10.1021/ci900382e
IFP-based scoring functions IFP-based scoring functions
IFP-based SF: AuPosSOM Automatic clustering of docking poses in virtual screening process using self-organizing map - 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)* f (Input (IFP) = 1 or 0 where 1 – is binder 0 – non binder *Simplified representation 42 Vladimir Chupakhin, UNISTRA, 2011
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-i atom 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 43 Vladimir Chupakhin, UNISTRA, 2011
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/unpolar SASA, MW, vdW energy, etc) • SVR-KB is better then SVR-EP Vector is unique! Vector is atom pair based 44 Vladimir Chupakhin, UNISTRA, 2011
Merci bien! Thanks a lot!