Molecular Modeling and Drug Discovery

1. Molecular Modeling and Drug Discovery Judith Klein-Seetharaman Assistant Professor Department of Pharmacology University of Pittsburgh School of Medicine and School of Computer Science Carnegie Mellon University USA

2. Background • View of living organisms as molecular circuitry: • Molecular circuitry = biochemical processes, that form and recycle molecules in a coordinated and balanced fashion • intended modes of operation = healthy state • aberrant modes of operation = disease state • Diagnosis: • identify the molecular basis of disease • Therapy: • guide biochemical circuitry back to healthy state

3. Information Sources • New technology generates massive amounts of data (often stored in publicly accessible databases): Genomics and Proteomics • Protein and DNA sequences / Whole genome sequences • Protein structure data • Protein pathways and networks • Protein interaction data • Expression data

4. Genomics - ProteomicsMapping Sequence to Protein Structure and Dynamics Primary Sequence MNGTEGPNFY VPFSNKTGVV RSPFEAPQYY LAEPWQFSML AAYMFLLIML GFPINFLTLY VTVQHKKLRT PLNYILLNLA VADLFMVFGG FTTTLYTSLH GYFVFGPTGC NLEGFFATLG GEIALWSLVV LAIERYVVVC KPMSNFRFGE NHAIMGVAFT WVMALACAAP PLVGWSRYIP EGMQCSCGID YYTPHEETNN ESFVIYMFVV HFIIPLIVIF FCYGQLVFTV KEAAAQQQES ATTQKAEKEV TRMVIIMVIA FLICWLPYAG VAFYIFTHQG SDFGPIFMTI PAFFAKTSAV YNPVIYIMMN KQFRNCMVTT LCCGKNPLGD DEASTTVSKT ETSQVAPA Folding 3D Structure

5. Genomics - ProteomicsMapping Sequence to Protein Structure, Dynamics and Function Primary Sequence MNGTEGPNFY VPFSNKTGVV RSPFEAPQYY LAEPWQFSML AAYMFLLIML GFPINFLTLY VTVQHKKLRT PLNYILLNLA VADLFMVFGG FTTTLYTSLH GYFVFGPTGC NLEGFFATLG GEIALWSLVV LAIERYVVVC KPMSNFRFGE NHAIMGVAFT WVMALACAAP PLVGWSRYIP EGMQCSCGID YYTPHEETNN ESFVIYMFVV HFIIPLIVIF FCYGQLVFTV KEAAAQQQES ATTQKAEKEV TRMVIIMVIA FLICWLPYAG VAFYIFTHQG SDFGPIFMTI PAFFAKTSAV YNPVIYIMMN KQFRNCMVTT LCCGKNPLGD DEASTTVSKT ETSQVAPA Folding 3D Structure

6. Disease Challenge 1: Disease Causing Mutations Primary Sequence MNGTEGPNFY VPFSNKTGVV RSPFEAPQYY LAEPWQFSML AAYMFLLIML GFPINFLTLY VTVQHKKLRT PLNYILLNLA VADLFMVFGG FTTTLYTSLH GYFVFGPTGC NLEGFFATLG GEIALWSLVV LAIERYVVVC KPMSNFRFGE NHAIMGVAFT WVMALACAAP PLVGWSRYIP EGMQCSCGID YYTPHEETNN ESFVIYMFVV HFIIPLIVIF FCYGQLVFTV KEAAAQQQES ATTQKAEKEV TRMVIIMVIA FLICWLPYAG VAFYIFTHQG SDFGPIFMTI PAFFAKTSAV YNPVIYIMMN KQFRNCMVTT LCCGKNPLGD DEASTTVSKT ETSQVAPA Folding 3D Structure Complex function within network of proteins

7. Challenge 1: Non-disease causing mutations Primary Sequence MNGTEGPNFY VPFSNKTGVV RSPFEAPQYY LAEPWQFSML AAYMFLLIML GFPINFLTLY VTVQHKKLRT PLNYILLNLA VADLFMVFGG FTTTLYTSLH GYFVFGPTGC NLEGFFATLG GEIALWSLVV LAIERYVVVC KPMSNFRFGE NHAIMGVAFT WVMALACAAP PLVGWSRYIP EGMQCSCGID YYTPHEETNN ESFVIYMFVV HFIIPLIVIF FCYGQLVFTV KEAAAQQQES ATTQKAEKEV TRMVIIMVIA FLICWLPYAG VAFYIFTHQG SDFGPIFMTI PAFFAKTSAV YNPVIYIMMN KQFRNCMVTT LCCGKNPLGD DEASTTVSKT ETSQVAPA Folding 3D Structure Complex function within network of proteins Normal

8. Challenge 1 How can we distinguish functional from non-functional protein sequences? Needed: sequence to structure and function mapping

9. Challenge 2: Which protein is a drug target?

10. ? Challenge 3: How to design a drug in the absence of a structure? Drug Target:

11. Challenge 4: Drug action, efficacy and side effects? Drug Target:

12. Challenges • How can we distinguish functional from non-functional protein sequences? • Which protein is a drug target? • How to design a drug in the absence of a structure? • Understanding drug action, efficacy and side effects • Fundamental Scientific Challenge: • Mapping the relationship between genome sequence and protein structures, dynamics and functions in complex cellular environments

13. Meaning for drug discovery • If one could predict the structure of proteins from sequence, one could discover new drugs at a fast pace • If one could predict the relationship between isozyme and tissue expression, one could design drugs specific to certain tissues • If one could predict the interactions of proteins in different protein networks, one could interpret complex data such as animal models • If one could…

14. Mapping relationships: 7 hierarchical layers • Layer 5. Predicting functional structures (DNA - RNA - proteins - lipids - carbohydrates) • Homology modeling • ab initio • templates • partial information • overall architecture • binding pocket • protein backbone • Layer 6. Molecular interactions • (Protein-ligand, -protein, -DNA, -RNA, -lipid, -carbohydrate) • Layer 7. Gene expression, metabolic and regulatory networks • Layer 1. Sequencing support • (physical mapping, fragment assembly outcome: raw genome sequence) • Layer 2. DNA sequence analysis • Gene finding • non-coding sequences • regulatory sequences finding • orthologous and paralogous sequences • Evolution • Layer 3. Protein sequence analysis • homology detection • alignment • functional annotation • cellular localization • Layer 4. From linear sequence to three-dimensional shapes • conformational space • models for protein (mis)folding • discriminating structures • conformational ambiguity

15. Specific Challenges for Bioinformatics in Drug Discovery • Data needs to be organized, mined and visualized to allow scientific discovery • Linking variety of databases • Linking the different layers • Interpretation of data • Drug discovery

16. Outline Drug Discovery Approach • use the information in the databases and infer information that is not provided directly by genomics and proteomics data: higher level information => piece together all available information - to get detailed picture of a molecular process (or disease) - to identify new protein targets - to develop drugs • based on chemical similarity of known drugs • rational (structure-based) drug design interactively on computer screen • molecular docking (automatic, systematic computer-based prediction of structure and binding affinity of complex) • high-throughput screening and combinatorial chemistry

17. Molecular modeling in drug discovery I. Two case studies for sequence to structure mapping: • Small changes in protein sequence cause dramatic difference in drug binding: COX inhibitors • Large changes in protein sequence still maintain similar structure: G protein coupled receptors • Protein Structure Prediction III. Ligand Docking to Protein Structures

18. Molecular modeling in drug discovery I. Two case studies for sequence to structure mapping: • Small changes in protein sequence cause dramatic difference in drug binding: COX inhibitors • Large changes in protein sequence still maintain similar structure: G protein coupled receptors • Protein Structure Prediction III. Ligand Docking to Protein Structures

19. Case study COX A Wonder Drug: What is the most commonly-taken drug today? It is an effective painkiller. It reduces fever and inflammation when the body gets overzealous in its defenses against infection and damage. It slows blood clotting, reducing the chance of stroke and heart attack in susceptible individuals. It may be an effective addition to the fight against cancer. • Aspirin has been used professionally for a century, and traditionally since ancient times. A similar compound found in willow bark, salicylic acid, has a long history of use in herbal treatment. But only in the last few decades have we understood how aspirin works, and how it might be improved http://www.rcsb.org/pdb/molecules/pdb17_1.html

20. Prostaglandins As you might expect from a drug with such diverse actions, aspirin blocks a central process in the body: Aspirin blocks the production of prostaglandins, key hormones that are used to carry local messages. Unlike most hormones, which are produced in specialized glands and then delivered throughout the body by the blood, prostaglandins are created by cells and then act only in the surrounding area before they are broken down. Prostaglandins control many of these neighborhood processes, including the constriction of muscle cells around blood vessels, aggregation of platelets during blood clotting, and constriction of the uterus during labor. Prostaglandins also deliver and strengthen pain signals and induce inflammation. These many different processes are all controlled by different prostaglandins, but all created from a common precursor molecule. http://www.rcsb.org/pdb/molecules/pdb17_1.html

21. Arachidonic Acid and COX

22. COX = Cyclooxygenase (PDB entry 1prh) performs the first step in the creation of prostaglandins from a common fatty acid. It adds two oxygen molecules to arachidonic acid, beginning a set of reactions. Aspirin blocks the binding of arachidonic acid in the cyclooxygenase active site. The normal messages are not delivered, so we don't feel the pain and don't launch an inflammation response. http://www.rcsb.org/pdb/molecules/pdb17_1.html What does COX do?

23. Structural Organization of COX Two different active sites, collectively prostaglandin synthase: 1, the cyclooxygenase active site discussed; 2, is has an entirely separate peroxidase site, which is needed to activate the heme groups that participate in the cyclooxygenase reaction. Dimer of identical subunits (two cyclooxygenase active sites and two peroxidase active sites in close proximity) Each subunit has a small carbon-rich knob, pointing downward anchoring the complex to the membrane of the endoplasmic reticulum, shown in light blue. The cyclooxygenase active site is buried deep within the protein, and is reachable by a tunnel that opens out in the middle of the knob. This acts like a funnel, guiding arachidonic acid out of the membrane and into the enzyme for processing. PDB entry 4cox http://www.rcsb.org/pdb/molecules/pdb17_1.html

24. Why is there a COX-1 and COX-2? COX-1 and COX-2 are made for different purposes. COX-1 is built in many different cells to create prostaglandins used for basic housekeeping messages throughout the body. COX-2 is built only in special cells and is used for signaling pain and inflammation. Aspirin attacks both. Since COX-1 is targeted, aspirin can lead to unpleasant complications, such as stomach bleeding. Needed: specific compounds that block just COX-2, leaving COX-1 to perform its essential jobs. These drugs are selective pain-killers and fever reducers, without the unpleasant side-effects.

25. Active site Cox 1 (1pth) 1pth

26. COX-2 Active Site (1pxx) Arg120 Val523 (Ile in COX-1) Tyr355

27. Difference between COX-1 and COX-2

28. Summary COX Case Study • Being able to model the effect of small changes in sequence (isoforms) is essential for drug development

29. Molecular modeling in drug discovery I. Two case studies for sequence to structure mapping: • Small changes in protein sequence cause dramatic difference in drug binding: COX inhibitors • Large changes in protein sequence still maintain similar structure: G protein coupled receptors • Protein Structure Prediction III. Ligand Docking to Protein Structures

30. G Protein Coupled Receptors C Cytoplasmic Domain Trans- membrane Domain 1 2 3 4 5 6 7 Extracellular Domain N • Largest family of cell surface receptors • >8000 sequences known • 60% of all known drugs target GPCR

31. Ligand Conformational Changes Signal Transduction Cascade GPCR Function: Signal Transduction

32. Class A: Rhodopsin - like Family Opsins , Odorants, Monoamines, Lipid messengers, Purines , Neuropeptides , Peptide hormones (e.g. platelet activating factor, gonadotropin - releasing hormone, th yrotropin releasing hormone & melatonin ), Glycoprotein hormones, Chemokines , Proteases, Cannabis, Viral Class B: Secretin - like Family Glucagon , Calcitonin , parathyroid hormone, secretin Class C: Metabotropic glutamate and Chemosensor Family mGluR 1 - 7, Calcium sensors, GABA - B Class D: Fungal pheromone Family Class E: c - AMP receptor ( Dictyostelium ) Family Class F: Frizzled/Smoothened family Putative families: Ocular albinism proteins , Drosophila odorant receptors, Plant Mlo receptors , Nematode chemor eceptors , Vomeronasal receptors Putative/ unclassified orphans GPCR Family and Their Ligands

33. Structures of Low Sequence Similarity • Only one structure known, but serves as model for other pharmacologically important GPCR Disulfide Bond Cys110-Cys187

34. Conserved Features The Disulfide Bond is highly conserved across families, but not in putative and orphan receptors

35. Summary GPCR Case Study • Being able to model proteins with low sequence homology is essential to exploit structural information that is hard to get (membrane proteins) but where the impact is very high (>40% of R&D portfolios in companies)

36. Molecular modeling in drug discovery I. Two case studies for sequence to structure mapping: • Small changes in protein sequence cause dramatic difference in drug binding: COX inhibitors • Large changes in protein sequence still maintain similar structure: G protein coupled receptors • Protein Structure Prediction III. Ligand Docking to Protein Structures

37. Modeling Methods and Relation to Sequence Similarity • A. When no information but sequence and physical principles are used • = ab initio structure prediction (Blue Gene IBM ) • B. When other information is used ("ab initio" methods that use pdb information) • Common features: "fold recognition“, requires a method for evaluating the compatibility of a given sequence with a given folding pattern • 3D profiles • Rosetta: conformations from short segments in pdb • Including experimental structural constraints • Threading (=sequence-structure alignment), • Inverse threading and folding experiments • a. using short-range information • b. using short- and long-range information • Predicting structural class only • Predicting active site only • Predicting protein-protein interaction sites • Predicting surface shape?

38. Modeling Methods Continued • C. When a template with known structure must be available: homology modeling • D. Modeling structures based on experimental data • Both NMR and X-ray underdetermine the protein structure. To solve a structure one must minimize a combination of the deviation from the experimental data and the conformational energy: • a. NMR (set of constraints on distances and angles) • b. X-ray crystallography (Fourier transform of the electron density)

40. Evaluating structure prediction • Use rmsd to known structures - defines structural similarity • Critical Assessment of Structure Predictions (CASP) competitions • EVA, EVA submits sequences automatically to different prediction servers shortly before structures are published in pdb

41. Homology Modeling • Database searching for homologous proteins ( Blast the query sequence towards the pdb database ) • Alignment (Pairwise/ Multiple Alignments) • needs minimum 30% sequence identity, but to be useful usually need 40-50% • note that ~30% of genomes have sequence identity of 20% • Model Building • Modeller , Composer etc • Model Refinement and Evaluation • Joy • Procheck etc

42. BLAST (Basic Local Alignment Search Tools) BLAST is a heuristic search method that seeks words of length W (default = 3 in blastp) that score at least T when aligned with the query and scored with a substitution matrix. Words in the database that score T or greater are extended in both directions in an attempt to fina a locally optimal ungapped alignment or HSP (high scoring pair) with a score of at least S or an E value lower than the specified threshold. HSPs that meet these criteria will be reported by BLAST, provided they do not exceed the cutoff value specified for number of descriptions and/or alignments to report.

43. BLOSUM62 Substitution Scoring Matrix. The BLOSUM 62 matrix shown here is a 20 x 20 matrix of which a section is shown here in which every possible identity and substitution is assigned a score based on the observed frequencies of such occurences in alignments of related proteins. Identities are assigned the most positive scores. Frequently observed substitutions also receive positive scores and seldom observed substitutions are given negative scores. The PAM family PAM matrices are based on global alignments of closely related proteins. The PAM1 is the matrix calculated from comparisons of sequences with no more than 1% divergence. Other PAM matrices are extrapolated from PAM1. The BLOSUM family BLOSUM matrices are based on local alignments. BLOSUM 62 is a matrix calculated from comparisons of sequences with no less than 62% divergence. All BLOSUM matrices are based on observed alignments; they are not extrapolated from comparisons of closely related proteins. BLOSUM 62 is the default matrix in BLAST 2.0. Though it is tailored for comparisons of moderately distant proteins, it performs well in detecting closer relationships. A search for distant relatives may be more sensitive with a different matrix. The relationship between BLOSUM and PAM substitution matrices. BLOSUM matrices with higher numbers and PAM matrices with low numbers are both designed for comparisons of closely related sequences. BLOSUM matrices with low numbers and PAM matrices with high numbers are designed for comparisons of distantly related proteins. If distant relatives of the query sequence are specifically being sought, the matrix can be tailored to that type of search. Scoring matrices http://www.ncbi.nlm.nih.gov/Education/

44. Sequence Alignment when homology is low • Hidden Markov Models of Protein Families • Secondary structure prediction methods • Novel alignment methods • Sequence conservation based on property conservation

45. Model Building • Modeller (freeware, http://www.salilab.org/modeller/modeller.html) • Spdbviewer Swissmodel–module (freeware, http://us.expasy.org/spdbv/) • Composer (module of InsightII, commercial version of Modeller)

46. Model Building Principles • Sequentially go from amino acid position to next position • if same amino acid, copy the coordinates • If different amino acid, if the new amino acid has atoms in common with the template, those atoms will be copied, and the rest are computed • At every step, check for steric clashes with previous amino acids • Minimization allowing the position of new amino acid to change • Only at the final stage, bond energy is minimized

47. Model Refinement and Evaluationhttp://cgat.ukm.my/spores/Predictory/evaluation.html • Verify3D (based on surface accessibility) • Procheck (based on phi/psi angle, rmsd deviations) • Joy (based on secondary structure assignments) • WHAT IF (bond length, bond angles, chi values, etc.)

48. WHAT IF Checklist • A WHAT IF check report: what does it mean? • General points • Administrative checks • Nomenclature • Chain name • Weights (occupancy) • Missing atoms and C-terminal oxygens • Symmetry • Consistency • Cell conventions • Matthews' Coefficient • Higher symmetry • Non crystallographic symmetry • Geometry • Chirality • Bond lengths • Bond angles • Torsion Angles: "Evaluation"; "Ramachandran"; "omega"; "Chi1/2" • Rings and planarity: "Planarity"; "Proline Puckering" • Structure • Inside/outside profile • Bumps • Packing quality • Backbone: "number of hits"; "backbone normality"; "peptide flips" • Sidechain rotamers • Water molecules: "floating clusters"; "symmetry relations" • B-factors: "average"; "low B-factors"; "B-factor distribution" • Hydrogen bonds: "Flip check"; "HIS assignments"; "Unsatisfied"

49. Collection of homology models • MODBASE • uses PSI-BLAST plus MODELLER to model and stores coordinates in this database • SWISS-MODEL • automatic structure prediction

50. Play with homology models • www.cs.cmu.edu/~blmt/Seminar/SeminarMaterials/COX • Rasmol is also in this directory, just click on the raswin icon to start program COX 2 Modelling : Template structure : 1PTH.pdb (cox1 in ovis aries) query seq:sequence of 1PXX.pdb (cox2 in mus musculus) model generated using modeller: 2cox.pdb COX 1 Modelling: Template structure : 1PXX.pdb (cox2 in mus musculus) query seq:sequence of 1PTH.pdb (cox1 in ovis aries) model generated using modeller: 1cox.pdb