Molecular Modeling and Drug Discovery Judith Klein-Seetharaman Assistant Professor Department of Pharmacology University of Pittsburgh School of Medicine.

Molecular Modeling and Drug Discovery Judith Klein-Seetharaman Assistant Professor Department of Pharmacology University of Pittsburgh School of Medicine

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

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

Genomics - Proteomics Mapping 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 3D Structure Folding

Genomics - Proteomics Mapping 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 3D Structure Folding

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 3D Structure Folding Complex function within network of proteins 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 3D Structure Folding Complex function within network of proteins Normal Challenge 1: Non-disease causing mutations

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

Challenge 2: Which protein is a drug target?

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

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

Challenges for Bioinformatics in Drug Discovery 1.How can we distinguish functional from non-functional protein sequences? 2.Which protein is a drug target? 3.How to design a drug in the absence of a structure? 4.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

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…

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

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

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 Outline Drug Discovery Approach

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 II.Protein Structure Prediction III. Ligand Docking to Protein Structures

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. http://www.rcsb.org/pdb/molecules/pdb17_1.html 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

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. When aspirin blocks production of prostaglandins, the normal messages are not delivered, so we don't feel the pain and don't launch an inflammation response. 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

Arachidonic Acid and COX

What does COX do? http://www.rcsb.org/pdb/molecules/pdb17_1.html 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.1prh

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. http://www.rcsb.org/pdb/molecules/pdb17_1.html PDB entry 4cox4cox

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. How would you design a drug that only blocks COX-2 and not COX-1?

Structure-based drug design Compare the structures of COX-1 and COX-2 Identify differences that could be exploited Need to know the mechanism of COX inhibition

Mechanism of COX inhibition? Aspirin sterically blocks the binding of arachidonic acid in the cyclooxygenase active site. Ser530 is not a catalytic residue. But it is located in the tunnel that allows entry of arachidonic acid to the active site.

1pth (cox1) Tyr385 Arg120 Hydrophilic side pocket Ile (COX-1) - Val523 (COX-2) BRM SAL DIF Arg120 Tyr385 HIS90 ARG513 1pxx (cox2) Structural Differences COX-1 & COX-2

Hydrophilic Side Pocket HIS90 ARG513 Hydrophilic side pocket

Difference between COX-1 and COX-2

Structural Differences Cox1/2

Rasmol Arg513 His90

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

Structure Analysis How does one determine structures? –Experimentally (X-ray, NMR) –Computationally (ab initio, Rosetta, threading, homology models) How does one access structures? –Pdb –SCOP/CATH How does one analyze structures? –Visualization programs (chime, rasmol, molmol, Insight etc.)

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 1.3D profiles 2.Rosetta: conformations from short segments in pdb 3.Including experimental structural constraints 4.Threading (=sequence-structure alignment), 5.Inverse threading and folding experiments a. using short-range information b. using short- and long-range information 6.Predicting structural class only 7.Predicting active site only 8.Predicting protein-protein interaction sites 9.Predicting surface shape?

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)

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

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

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.

Scoring matrices 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. http://www.ncbi.nlm.nih.gov/Education/

Query: 373 WHPLLPDTFNIEDQEYSFKQFLYNNSILLEHGLTQFVESFTRQIAGRVAGGRNVPIAVQA 432 WHPL+PD+F + Q+YS++QFL+N S+L+++G+ V++F+RQ AGR+ GGRN+ + Sbjct: 363 WHPLMPDSFRVGPQDYSYEQFLFNTSMLVDYGVEALVDAFSRQPAGRIGGGRNIDHHILH 422 Query: 433 VAKASIDQSREMKYQSLNEYRKRFSLKPYTSFEELTGEKEMAAELKALYSDIDVMELYPA 492 VA I +SR ++ Q NEYRKRF +KPYTSF+ELTGEKEMAAEL+ LY DID +E YP Sbjct: 423 VAVDVIKESRVLRLQPFNEYRKRFGMKPYTSFQELTGEKEMAAELEELYGDIDALEFYPG 482 Query: 493 LLVEKPRPDAIFGETMVELGAPFSLKGLMGNPICSPQYWKPSTFGGEVGFKIINTASIQS 552 LL+EK P++IFGE+M+E+GAPFSLKGL+GNPICSP+YWK STFGGEVGF ++ TA+++ Sbjct: 483 LLLEKCHPNSIFGESMIEMGAPFSLKGLLGNPICSPEYWKASTFGGEVGFNLVKTATLKK 542 Query: 553 LICNNVKGCPFTSFNVQDPQ 572 L+C N K CP+ SF+V DP+ Sbjct: 543 LVCLNTKTCPYVSFHVPDPR 562 Blast Result COX-1 vs. COX-2 Score = 745 bits (1924), Expect = 0.0Identities = 343/560 (61%), Positives = 433/560 (77%), Gaps = 1/560 (0%) Query: 13 GLSQAANPCCSNPCQNRGECMSTGFDQYKCDCTRTGFYGENCTTPEFLTRIKLLLKPTPN 72 G NPCC PCQ++G C+ G D+Y+CDCTRTG+ G NCT PE T ++ L+P+P+ Sbjct: 4 GAPAPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPEIWTWLRTTLRPSPS 63 Query: 73 TVHYILTHFKGVWNIVNNIPFLRSLIMKYVLTSRSYLIDSPPTYNVHYGYKSWEAFSNLS 132 +H++LTH + +W+ VN F+R +M+ VLT RS LI SPPTYN+ + Y SWE+FSN+S Sbjct: 64 FIHFLLTHGRWLWDFVN-ATFIRDTLMRLVLTVRSNLIPSPPTYNIAHDYISWESFSNVS 122 Query: 133 YYTRALPPVADDCPTPMGVKGNKELPDSKEVLEKVLLRREFIPDPQGSNXXXXXXXXXXX 192 YYTR LP V DCPTPMG KG K+LPD++ + + LLRR+FIPDPQG+N Sbjct: 123 YYTRILPSVPRDCPTPMGTKGKKQLPDAEFLSRRFLLRRKFIPDPQGTNLMFAFFAQHFT 182 Query: 193 XXXXXXDHKRGPGFTRGLGHGVDLNHIYGETLDRQHKLRLFKDGKLKYQVIGGEVYPPTV 252 K GPGFT+ LGHGVDL HIYG+ L+RQ++LRLFKDGKLKYQ++ GEVYPP+V Sbjct: 183 HQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQMLNGEVYPPSV 242 Query: 253 KDTQVEMIYPPHIPENLQFAVGQEVFGLVPGLMMYATIWLREHNRVCDILKQEHPEWGDE 312 ++ V M YP IP Q AVGQEVFGL+PGLM+YATIWLREHNRVCD+LK EHP WGDE Sbjct: 243 EEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATIWLREHNRVCDLLKAEHPTWGDE 302 Query: 313 QLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNQQFQYQNRIASEFNTLYH 372 QLFQT+RLILIGETIKIVIE+YVQ LSGY +LKFDPELLF QFQY+NRIA EFN LYH Sbjct: 303 QLFQTARLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGAQFQYRNRIAMEFNQLYH 362

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

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

Model Refinement and Evaluation http://cgat.ukm.my/spores/Predictory/evaluation.html http://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.)

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

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

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

1pth (cox1) Tyr385 Arg120 Hydrophilic side pocket Ile (COX-1) - Val523 (COX-2) BRM SAL DIF Arg120 Tyr385 HIS90 ARG513 cox1 model based on 1pxx template 1pxx (cox2) Cox2 model based on 1pth template

1pth (cox1) Tyr385 Arg120 Hydrophilic side pocket Ile (COX-1) - Val523 (COX-2) BRM SAL DIF Arg120 Tyr385 HIS90 ARG513 cox1 model based on 1pxx template 1pxx (cox2) Cox2 model based on 1pth template Steric Block Hydrophilic side pocket

Protein-ligand docking First (if structure is known) or second (after structure prediction) step in a drug design project: find a lead structure (=small molecule which binds to a given target) docking problem - predicting the energetically most favorable complex between a protein and a putative drug molecule For a given protein structure, one can apply docking algorithms to virtually search through the space 2 questions: 1. what does the protein-ligand complex look like 2. what is the affinity with respect to other candidates?

What makes the docking problem hard to solve? 1.Scoring problem –= calculating binding affinity given a protein-ligand complex –no general scoring function is available 2.Large number of degrees of freedom –most important degrees of freedom: 1.relative orientation of the two molecules 2.conformation of the ligand 3.protein conformation 4.water molecules can be between ligand and protein 5.protonation state

Types of Docking Problems 1. Macromolecular docking = two macromolecules are docked, such as protein and DNA, or protein and protein large contact area molecules have fixed overall shape => methods based on geometric properties like shape complementarities alone can be efficiently used to create energetically favorable complexes 2. Small molecule docking = a small molecule is docked to a macromolecule ligand is typically not fixed in shape (as opposed to macromolecular docking) typical ligand size has 5-12 rotatable bonds often fragments of ligand are used for modeling, eg. combinatorial libraries are docked by combining placement for individual building blocks of the library

Steps in Molecular Docking 1.Find a set of compounds to start with - e.g. from inspecting known ligands for a protein (e.g. substrate in an enzyme) 2. compounds from a screening experiment of a combinatorial library (in which there is usually a molecular fragment that is common between all molecules of the library, the core, and the fragments attached to the core are R-groups) 3. compounds from a filtering experiment using other software 4.from varying other lead structures or known ligands 5. virtual screening using a fast docking algorithm (typically from a million molecules) 6. de novo design using fragments of compounds => get several hundred to thousands of ligands to start with

Docking Methods Rigid-body docking algorithms –Historically the first approaches. –Protein and ligand are held fixed in conformational space which reduces the problem to the search for the relative orientation fo the two molecules with lowest energy. –All rigid-body docking methods have in common that superposition of point sets is a fundamental sub- problem that has to be solved efficiently: –Superposition of point sets: minimize the RMSD Flexible ligand docking algorithms –most ligands have large conformational spaces with several low energy states http://www-2.cs.cmu.edu/~blmt/Seminar/SeminarMaterials/interactions.html

Clique-search based approaches = matching characteristic features of the two molecules use a graph to search for compatible matches: the vertices of the graph are all possible matches and edges connect pairs of vertices representing compatible matches compatibility = distance compatibility with in a fixed tolerance epsilon The matches (p1,l1), (p2,l2) are distance-compatible if |d(p1,p2)-d(l1,l2)| < epsilon Example: DOCK program

DOCK = today most widely used molecular docking program starting with the molecular surface of the protein, a set of spheres is created inside the active sties the spheres represent the volume which could be occupied by the ligand: VOLUME is the feature used for matching ligand is represented by spheres inside the ligand For more information: http://www.cmpharm.ucsf.edu/kuntz/dock_demo.html

Largest family of cell surface receptors >8000 sequences known 60% of all known drugs target GPCR C N 1234567 Cytoplasmic Domain Trans- membrane Domain Extracellular Domain G Protein Coupled Receptors

Ligand Conformational Changes Signal Transduction Cascade GPCR Function: Signal Transduction

GPCR Family and Their Ligands

Structure of GPCRs Only one structure known, rhodopsin rhodopsin serves as model for other pharmacologically important GPCR Some GPCR share less than 10% sequence identity Disulfide Bond Cys110-Cys187

Previous Template: Bacteriorhodopsin Halorhodopsin = gray Mammalian Rhodopsin = magenta Sensory Rhodopsin = blue

Improvements Initial homology model can be refined by molecular dynamics simulation including lipid bilayer in the modeling process has been shown to give better models

Examples for Homology Modeling of GPCRs  2 -adrenergic receptor angiotensin II receptor type I (AT 1 ) purinergic GPCRs monocyte chemoattractant-1 (MCP-1) receptor, CCR2 In all cases, models were able to explain site-directed mutagenesis data and could be used to dock ligands. However, ligand-based lead finding (often using the natural ligand as a starting point) is still most widely used for GPCR. Structure-activity relationships (SAR) are derived from them and the resulting pharmacophore models can be used for virtual screening. Need more structures! First global network on structural genomics on GPCRs (MePNet): http://www.mepnet.org http://www.mepnet.org Status: http://www.mepnet.org/index.php?rub=mepnet_1101_0204

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)

Take home messages Structural and functional effects of small changes in sequences Conservation of structure despite large differences in sequences Prediction of structural and functional effects using computational pharmacology to understand disease mechanisms and drug action with the goal of identifying targets and designing drugs against them –Example: Specific Structure of COX and of GPCR –Current hot topics: Complex interactions of proteins within their environment, differences between individuals, systems biology Even with lots of structural information available, prediction of ligand binding affinities is challenging Determination of new structures, especially membrane proteins, is a bottle-neck

Molecular Modeling and Drug Discovery Judith Klein-Seetharaman Assistant Professor Department of Pharmacology University of Pittsburgh School of Medicine.

Similar presentations

Presentation on theme: "Molecular Modeling and Drug Discovery Judith Klein-Seetharaman Assistant Professor Department of Pharmacology University of Pittsburgh School of Medicine."— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Molecular Modeling and Drug Discovery Judith Klein-Seetharaman Assistant Professor Department of Pharmacology University of Pittsburgh School of Medicine.

Similar presentations

Presentation on theme: "Molecular Modeling and Drug Discovery Judith Klein-Seetharaman Assistant Professor Department of Pharmacology University of Pittsburgh School of Medicine."— Presentation transcript:

Similar presentations

About project

Feedback