Introduction Drugs • Molecules that can be introduced to change biological activity Structure-Based Drug Design Thomas Funkhouser Princeton University CS597A, Fall 2005 Slide courtesy of Bill Welsh Introduction Structure-Based Drug Design Drug targets Goal: • Enzyme - inhibitors • Given a protein structure, • Receptor - agonists or antagonists and/or its binding site, • Ion channels - blockers and/or its active ligand (possibly bound to protein), • Transporter - update inhibitors find a new molecule that changes the protein’s activity • DNA - blockers Slide courtesy of Bill Welsh Structure-Based Drug Design Structure-Based Drug Design Receptor-based drug design: Ligand-based drug design: • Given a protein structure, • Given an protein structure, and/or its binding site, and/or its binding site, and/or its active ligand (possibly bound to protein), and/or its active ligand (possibly bound to protein), find a new molecule that changes the protein’s activity find a new molecule that changes the protein’s activity Structure-Based Design HIV Protease Inhibitor Example courtesy of Bill Welsh Example courtesy of Joe Corkery 1 Challenges Outline Scoring of chemical models Virtual drug screening • Activity • Ligand-based • Toxicity § 2D structure matching • “Druggability” § 3D structure matching § QSAR • etc. De novo drug design Search of chemical space • Models • Add/remove/replace chemical groups § Simulation • Conformations § Knowledge-based • etc. • Construction algorithms § Incremental construction § Fragment-based § Stochastic optimization Ligand-Based Drug Screening Ligand-Based Drug Screening 17b-Estradiol Androstenediol Progesterone Moxestrol Coumestrol Strategies: HO O HO O HO HO O • 2D matching O OH OH H 3CO OH OH CH 3 O • 3D matching Bisphenol A Diethylstilbestrol Genistein Methoxychlor • Pharmacore matching HO HO O H CO 3 OCH 3 HO OH • Quantitative Structure Activity Relationships (QSAR) OH O OH Cl Cl Cl OH Nafoxidine a- Zearalanol(Zeranol) 4-Hydroxytamoxifen O HO O N N HO OH O H 3CO OH O Tamoxifen Clomifene ICI 164,384 O N O HO (CH ) CO N(CH )C H N 2 10 3 4 9 Cl OH Ligands for Estrogen Receptor Slide courtesy of Bill Welsh 2D Structure Matching 2D Substructure Matching O O Query N N H O N O O H H H N N N OH H 2 N N N NH2 N O N Query N N O O OH N N H H2N HO N N O H N N N N N N N O N N N N N N O N O O O Slide courtesy of Bill Welsh Slide courtesy of Bill Welsh 2 2D Substructure Matching 2D Substructure Matching O Dictionary of Keys H N N-N O N H O-C(-N)-C CH3-Ar-CH3 O C-N-N N-Ar-Ar-O N-C-O N-Ar-O OH > 1 CH3 > 1 N > 1 NH ... Which keys are present? Slide courtesy of Bill Welsh Slide courtesy of Bill Welsh 2D Substructure Matching 2D Substructure Matching * Tanimoto coefficient: Activity data from Uehling et al, J. Med.Chem. 1995 2.5 ) s ∩ t (A B) i = n T 2 u A ∪B ( ) g o l ( e 1.5 c n struct A: 00010100010101000101010011110100 13 bits e r e struct B: 00000000100101001001000011100000 8 bits f f i 1 d y t A & B: 00000000000101000001000011100000 6 bits A ∩B i v i ∪ t A or B: 00010100110101001101010011110100 15 bits A B c 0.5 A T = 6 / 15 = 0.4 0 1.00 0.95 0.90 0.85 Tanimoto similarity Slide courtesy of Bill Welsh Slide courtesy of Bill Welsh Outline 3D Structure Matching Virtual drug screening Search database of molecules for ones with • Ligand-based similar 3D shape and chemistry § 2D structure matching 3D structure matching § QSAR A B De novo drug design • Models § Simulation § Knowledge-based • Construction algorithms § Incremental construction § Fragment-based § Stochastic optimization 3 3D Structure Matching 3D Substructure Matching O a = 8.62+ - 0.58 Angstroms Search database of molecules for ones with N O b = 7.08+- 0.56 Angstroms similar 3D shape and chemistry c a O c = 3.35 +- 0.65 Angstroms O O O O O b N S N N O O O Ai O O N N N Bi O O O N N N O O N O O P O O N O N O P O O N O O O N N N O P O O O O O = − 2 + − 2 N O O d(A, B) Ai B A Bi O O ∈ ∈ Ai A Bi B Slide courtesy of Bill Welsh 3D Substructure Matching 3D Substructure Matching A O(s1) C(u) O(s1) 3.2Å 6.7 3.3 - 4.3 Å Cl O H Cl 4.2-4.7 4.3Å 4.8 O 5.2 6.8 - 7.8 Å O H 5.1-7.1 3.6 - 4.6 Å Flexible [O,S] A Rigid Slide courtesy of Bill Welsh Slide courtesy of Bill Welsh 3D Substructure Matching Pharmacore Matching 17b-Estradiol Androstenediol Progesterone Moxestrol Coumestrol DISTANCE CONSTRAINTS HO HO O HO O O DISTANCE CONSTRAINTS HO ((QQuuaaliltitaatitvivee A Affifninitityy p prreeddicictitoionn m moossttlyly)) O OH OH H 3CO OH OH CH 3 O Bisphenol A BIOPHASE Diethylstilbestrol Genistein Methoxychlor O HO HO H CO OCH HO OH A A' 3 3 OH O OH Cl Cl Cl OH Nafoxidine a- Zearalanol(Zeranol) 4-Hydroxytamoxifen B' RECEPTOR O B RECEPTOR HO O N N HO OH O H 3CO OH O Tamoxifen C C ' Clomifene ICI 164,384 O N O HO (CH ) CO N(CH )C H N 2 10 3 4 9 LIGAND Cl OH Slide courtesy of Bill Welsh Ligands for Estrogen Receptor Slide courtesy of Bill Welsh 4 Pharmacore Matching Outline 17b-Estradiol Androstenediol Progesterone Moxestrol Coumestrol Virtual drug screening HO O HO O HO HO O • Ligand-based O OH OH H 3CO OH OH CH 3 O § 2D structure matching Bisphenol A Diethylstilbestrol Genistein Methoxychlor § 3D structure matching HO HO O H CO OH 12 A 3 OCH 3 HO OH QSAR OH O OH Cl Cl Cl De novo drug design OH Nafoxidine a- Zearalanol(Zeranol) 4-Hydroxytamoxifen • Models O HO O N N HO OH § Simulation O HO H 3CO OH § Knowledge-based O Tamoxifen • Construction algorithms Clomifene ICI 164,384 O N O HO (CH ) CO N(CH )C H § N 2 10 3 4 9 Incremental construction § Fragment-based Cl OH § Stochastic optimization Ligands for Estrogen Receptor Slide courtesy of Bill Welsh QSAR QSAR Learn model for activity as function of descriptors Use model to predict activities for new leads (properties) computed from molecules from their descriptors Compound Activity (pKi) Descriptors (Xi) New Lead: 3 OH "Y" Mol. Vol. (Å ) LogP Dipole Mom (µ) Extract 1 2.34 420 2.8 0.97 2 1.89 332 4.6 2.23 Descriptors 3 0.23 198 -0.3 3.36 4 3.67 467 3.7 0.45 HO 5 2.55 359 -1.5 1.77 V logP µ etc. etc. etc. etc. etc. µ QSARmodel: pKi = 0.52 (Vi) + 0.27 (logPi) - 0.38 (i) Predicted activity of new lead Slide courtesy of Bill Welsh Slide courtesy of Bill Welsh Outline De Novo Drug Design Virtual drug screening General Strategy: • Ligand-based Given a protein structure § 2D structure matching • Build model of binding site § 3D structure matching • Construct molecule that fits model § QSAR De novo drug design • Models § Simulation § Knowledge-based • Construction algorithms § Incremental construction § Fragment-based § Stochastic optimization 5 De Novo Drug Design De Novo Drug Design General Strategy: General Strategy: • Given a protein structure • Given a protein structure Build model of binding site • Build model of binding site • Construct molecule that fits model Construct molecule that fits model Outline Binding Site Models Virtual drug screening • Ligand-based § 2D structure matching § 3D structure matching § QSAR De novo drug design Models § Simulation § Knowledge-based • Construction algorithms § Incremental construction § Fragment-based § Stochastic optimization Example courtesy of Joe Corkery Binding Site Models Outline Simulation Virtual drug screening • e.g., GRID • Ligand-based Dry C3 § 2D structure matching Knowledge-based § 3D structure matching • e.g., X-SITE § QSAR De novo drug design • Models § Simulation N1 O § Knowledge-based Predicted Construction algorithms Binding Site § Incremental construction Model for 1kp8-1-H-ATP-1-_ § Fragment-based using GRID § Stochastic optimization [Goodford85] 6 Incremental Construction Fragment-Based Methods NH2 O Protein N NH2 O O N NH 2 Building O 1. Docking scaffolds 2. Add reactants (substituents) Linking Fragments O N O NH 2 N O NH2 O 3. Find conformation 4. Minimize & Score Slide courtesy of Bill Welsh Slide courtesy of Bill Welsh Stochastic Optimization Summary Monte Carlo search of chemical space: Virtual drug screening • Start from initial drug • Ligand-based • Make random state changes § 2D structure matching (add/delete/move chemical group) § 3D structure matching § • Accept up-hill moves with probability QSAR dictated by “temperature” De novo drug design • Reduce temperature after each move • Models • Stop after temperature gets very small § Simulation § Knowledge-based • Construction algorithms § Incremental construction § Fragment-based § Stochastic optimization Discussion ? 7.