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drug-poster-resized.qxp:Layout 1
1/25/10
2:57 PM
Page 1
How Do
Work?
Examples from the PDB archive
PROTEINS are tiny molecular machines that perform most of the tasks needed to keep cells alive. These machines are far too small to see, so you might imagine that it is impossible to affect their action. However, drugs can be used to turn proteins on or off. DRUGS are small molecules that bind to proteins and modify their actions. Some very powerful drugs, such as antibiotics or anticancer drugs, are used to completely disable a critical molecular machine. These drugs can kill a bacterial or cancer cell. Other molecules, such as aspirin, gently block less-critical proteins for a few hours. With the use of these drugs, we can make changes inside our own cells, such as the blocking of pain signals. Many structures of drugs that bind to proteins have been determined by scientists. These atomic structures allow us to see how drugs work, and perhaps how to modify them to improve their action. A few examples are shown here. Some of these drugs, like penicillin, were discovered in nature. Other drugs, such as HIV protease inhibitors, were created by using the target protein structure to design new drug molecules. These structures of proteins and drugs, along with many others, can be explored at the RCSB Protein Data Bank (PDB).
Drugs of Signaling Proteins
Antibiotics & Antivirals 1
2
5
Antibiotics and antiviral drugs are specific poisons. They need to kill pathogenic organisms like bacteria and viruses without poisoning the patient at the same time. Often, these drugs attack proteins that are only found in the targeted bacterium or virus and which are crucial for their survival or multiplication. For instance, penicillin attacks the enzyme that builds bacterial cell walls, and HIV protease inhibitors like saquinavir attack an enzyme that is needed for HIV maturation.
2 6
1. D-alanyl-D-alanine carboxypeptidase with penicillin (1pwc) 2. HIV protease with saquinavir (1hxb)
7 3
Anticancer Chemotherapy 3
4
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3. DNA with bleomycin (1mxk) 4. Tubulin with taxol (1jff)
5. Adrenergic receptor with carazolol (2rh1) 6. Prostaglandin H2 synthase with aspirin (1pth). The drug breaks into two pieces when it binds to the enzyme, and the smaller piece (an acetyl group) is attached to the enzyme with a covalent bond. The closeup shows the drug in one piece.
8
4
Cancer cells grow and multiply without control. Since these cells are still similar to normal cells, it is difficult to kill them selectively with drugs that can’t distinguish between the two. Many drugs currently used for cancer chemotherapy attack all growing cells, including cancer cells and normal cells. This causes the severe side effects of cancer chemotherapy, because the drugs attack rapidlygrowing cells in hair follicles and the stomach. Two examples are shown here. Bleomycin attacks DNA in actively growing cells, often cleaving the DNA chain and killing the cell. Paclitaxel (Taxol) binds to tubulin, preventing the action of microtubules during cell division.
Many drugs are designed to keep bodily processes at normal healthy levels. Much of the body’s regulation is done through elaborate communications between cells, so some of the most widely prescribed drugs function by blocking the signaling proteins that allow cells to communicate. G proteincoupled receptors, which transmit signals across cell membranes, are targets for many drugs. For instance, the drug loratadine (Claritin) is used to treat allergies because it blocks the histamine receptor; losartan (Cozaar) is used to treat high blood pressure because it blocks the angiotensin II receptor; and carazolol is one of a large class of beta-blockers that bind to the adrenergic receptor, making it useful for treating heart disease. Signals can also be stopped by blocking the enzymes that create a signaling molecule. Aspirin blocks pain at the source by inhibiting the enzyme cyclooxygenase, which makes pain-signaling prostaglandin molecules.
5
1
Lifestyle Drugs 7
Drug Metabolism 9 You have probably noticed that when you take drugs, the effects gradually wear off in a few hours. Enzymes like cytochrome P450 continually search for drugs and destroy them. This is important because it protects us from poisonous molecules in our diet and in the environment, but it means that we have to take multiple doses of drugs when being treated for a disease.
7. Pancreatic lipase with an alkyl phosphonate inhibitor (1lpb). The drug orlistat shown on the right is similar to the inhibitor found in the crystal structure. 8. HMG-CoA reductase with atorvastatin (1hwk)
9. Cytochrome P450 3A4 with erythromycin (2j0d)
Most drugs mimic the molecules that are normally processed by an enzyme or receptor protein. They bind tightly to the protein and block the site that usually performs the task. For instance, HIV protease normally binds to a protein chain, like the one shown at the left, and clips it into two pieces. Drugs used to treat HIV infection, like saquinavir shown here, are smaller than the protein chain but chemically very similar. The drug binds in a similar position as the peptide, completely blocking the active site so the enzyme is unable to cleave the protein chain. (Image created with the Python Molecular Viewer—mgltools.scripps.edu)
Suicide Inhibitors
Peptide bound (2nxd) and drug bound (1hxb) structures of HIV protease.
About the RCSB PDB: The RCSB Protein Data Bank provides a variety of tools and resources for studying the structures of biological macromolecules and their relationships to sequence, function, and disease. The RCSB PDB is a member of the Worldwide Protein Data Bank, the international collaboration that maintains the PDB archive.
www.pdb.org •
[email protected] ©2009 RCSB PDB • Poster created by David S. Goodsell and Maria Voigt
8
Pharmaceutical scientists have developed a number of drugs that help people modify their own health and bodily function. The drug orlistat (Xenical or alli) blocks the action of pancreatic lipase, and thereby reduces the amount of fat that is absorbed from food. Atorvastatin (Lipitor) and simvastatin (Zocor) lower cholesterol by blocking the action of HMG-CoA reductase, an enzyme involved in the synthesis of cholesterol. These drugs can be used, along with changes in diet and exercise, to help lose weight, regulate cholesterol levels, and control heart disease.
9
Molecular Mimics
6
The RCSB PDB is managed by two members of the RCSB: Rutgers, The State University of New Jersey and the University of California, San Diego. It is supported by funds from the National Science Foundation, the National Institute of General Medical Sciences, the Office of Science, Department of Energy, the National Library of Medicine, the National Cancer Institute, National Institute of Neurological Disorders and Stroke, and the National Institute of Diabetes and Digestive and Kidney Diseases.
Some drugs are particularly effective because they form a chemical bond to the protein target (shown in turquoise), totally disabling it in the process. Penicillin (shown at the bottom with atomic colors) reacts with a serine amino acid in the bacterial enzyme, forming a new covalent bond to the enzyme. This completely blocks the active site, so the enzyme is unable to perform its role in cell wall synthesis. Another suicide inhibitor, aspirin (shown in #6), attaches an acetyl group to its target which blocks an inflammation pathway. Penicillin bound structure of D-alanyl-Dalanine carboxypeptidase (PDB entry 1pwc)
References: 1hwk. E.S. Istvan, J. Deisenhofer (2001) Structural mechanism for statin inhibition of HMG-CoA reductase. Science 292:1160-1164. 1hxb. A. Krohn, S. Redshaw, J.C. Ritchie, B.J. Graves, M.H. Hatada (1991) Novel binding mode of highly potent HIV-proteinase inhibitors incorporating the (R)-hydroxyethylamine isostere. J.Med.Chem. 34:3340-3342. 1jff. J. Lowe, H. Li, K.H. Downing, E. Nogales (2001) Refined structure of alpha beta-tubulin at 3.5 Å resolution. J.Mol.Biol. 313:1045-1057. 1lpb. M.P. Egloff, F. Marguet, G. Buono, R. Verger, C. Cambillau, H. van Tilbeurgh (1995) The 2.46 Å resolution structure of the pancreatic lipase-colipase complex inhibited by a C11 alkyl phosphonate. Biochemistry 34:2751-2762. 1mxk. C. Zhao, C. Xia, Q. Mao, H. Forsterling, E. DeRose, W.E. Antholine, W.K. Subczynski, D.H. Petering (2002) Structures of HO2-Co(III)bleomycin A2 Bound to d(GAGCTC)2 and d(GGAAGCTTCC)2: Structure-reactivity relationships of Co and Fe bleomycins. J.Inorg.Biochem. 91:259-268. 1pth. P.J. Loll, D. Picot, R.M. Garavito (1995) The structural basis of aspirin activity inferred from the crystal structure of inactivated prostaglandin H2 synthase. Nat.Struct.Biol. 2:637-643. 1pwc. N.R. Silvaggi, H.R. Josephine, A.P. Kuzin, R. Nagarajan, R.F. Pratt, J.A. Kelly (2005) Crystal structures of complexes between the R61 DD-peptidase and peptidoglycan-mimetic beta-lactams: a non-covalent complex with a "perfect penicillin". J.Mol.Biol. 345:521-533. 2j0d. M. Ekroos, T. Sjogren (2006) Structural basis for ligand promiscuity in cytochrome P450 3A4. Proc.Natl.Acad.Sci.USA 103:13682-13687. 2nxd. M.D. Altman, E.A. Nalivaika, M. Prabu-Jeyabalan, C.A. Schiffer, B. Tidor (2007) Computational design and experimental study of tighter binding peptides to an inactivated mutant of HIV-1 protease. Proteins 70:678-694. 2rh1. V. Cherezov, D.M. Rosenbaum, M.A. Hanson, S.G. Rasmussen, F.S. Thian, T.S. Kobilka, H.J. Choi, P. Kuhn, W.I. Weis, B.K. Kobilka, R.C. Stevens (2007) High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor. Science 318:1258-1265
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( CHEMISTRY)
Garland Marshall forms the image of a molecule while Douglas Covey studies it
DESIGNING DRUGS WITH COMPUTERS By creating images of molecules on the screen, chemists are learning to tailor drugs to diseases by MARCIA BARTUSIAK
C
hemist Douglas Covey felt very much at home in his labomtory at the Washington University School of Medicine in St. Louis. The maze of glass tubes, whirling centrifuges, and bubbling flasks seemed to be all he needed to carryon his work, the creating and testing of new drugs. Then three years ago he met Garland Marshall, a professor of biophysics at Washington. Marshall told him about a totally new way to confront DISCOVER / AUGUST 1981
his molecules: face to face on a computer screen. Covey was skeptical. "Computers have absolutely nothing to do with my work," he said. Today he admits that he was dead wrong. He has become a true convert to computer chemistry. Part of Covey's research is now done in fl·ont of a cathode-ray tube, where he manipulates ajoy stick and the computer keyboard as though he were playing some SOI·t of electronic space game. At
the flick of a wrist, lines of red, yellow, and green turn and twist before his eyes, each image conveying a bit of information about the electrical charges, structure, and volume of the molecule he may later make in the laboratory. Covey is one of many scientists in universities and drug companies across the country who use computers before turning to their test tubes. On glowing screens, they not only create blueprints for new drugs but also analyze in mi47
CHEMISTRY nute detail the way existing dl'ugs wOl'k in the body. Says Harel Weinstein, a professor of pharmacolog'y at the Mount Sinai School of Medicine in New York City, "Drug companies know they simply cannot be without these computer techniques. They make drug design more rational." How? By helping scientists learn what is necessary, on the molecular level, to cure the body, then enabling them to tailor-make a drug to do thejob. This approach repl'esents a sharp departure from traditional pharmaceutical methods. Ever since primitive man began dabbling (often with fatal results) in things like snake venom and jungle plants in hopes of finding remedies for injuries or disease, drug development has been based chiefly on trial and error-and luck. Peruvians learned to eat the powdered bark of the cinchona tree to cure raging fevers. Old wives' tales recommended the leaves of the foxglove plant for heart trouble. It took modern science to determine that these medicines were not magic potions, but worked because they contained quinine and digitalis, respectively. These drugs and othel's work, scientists think, by finding their way to specific receptors (such as enzymes, DNA molecules, or pmteins in the membrane of a cell) and uniting with them in a kind of molecular embrace that triggers the desired effect on the body-g'etting rid of a headache, for example, or lowering a fever. The computer, with its lightning calculations and vivid graphics, can facilitate the understanding of this union by diagramming the receptor and the drug molecule that will fit it. To do the same thing with the unwieldy wire-and-ball molecular models
Structure of a steroid hormone
48
in most chemistry labs would be impossible. "Besides," recalls Daniel Veber, of Merck Sharp & Dohme Laboratories, "those things were always falling apart in your hands."
T
he computer systems that are doing "molecular mapping" today rapidly digest incredibly large amounts of information and then use it to build a visual model of a drug or chemical. Says Robert Langridge, of the University of California at San Francisco, "I think the Chinese proverb 'One picture is worth ten thousand words' is the best way to describe why the computer is so important to pharmacology. Except that the Chinese probably underestimated the number of words." Langridge and his colleagues at the Computer Graphics Laboratory have developed what is probably the most advanced modeling system now in use (see computer model at the top of the next page), A computer can display the molecular structure of any drug from a listing of thousands contained in its memory. By looking at and analyzing one of these stored models, or one built up on the screen from scratch, chemists can tell if a drug's pal'ticular arrangement of atoms is the molecular "key" that fits into and opens a biological "lock" (the receptor) within the body-perhaps to lower blood pressure, to prevent a pain signal from reaching the brain, or to zap an invading bacterium. "The computer is literally an idea box," says Covey. "This whole approach is helping us avoid the blind alleys before we even step into the lab." Pharmaceutical firms are familiar with those alleys. Out of every 8,000
compounds the companies screen for medicinal use, only one reaches the market. "The computer should help lower those odds," says John Adams, of the Pharmaceutical Manufacturers Association. This means that chemists will not be tied up for weeks, sometimes months, painstakingly assembling test drugs that a computer could show to have little chance of working. The potential saving to the pharmaceutical industry: millions of dollars and thousands of man-hours. Pharmacologists want to use computer chemistry to eliminate the annoying side-effects that drugs often produce when they act like master keys, opening more than one biological lock. For example, one drug used to combat diarrhea not only acts on the intestine but also attaches to a receptor in the brain, where it acts as a mild opiate. To avoid this dual action, chemists would like to manipulate the structure of the drug, first on the computer and then in the laboratory, to make sure it interacts with only one type of receptor. As Horace Brown, of Merck Laboratories, explained to DISCOVER's Wayne Villanueva, "We want to design drugs that are more like rifle bullets than shotgun shells." To find and fit into a receptor, drug molecules must "recognize" receptor molecules. "But how does one molecule recognize another? That's what I'm fascinated with," says Marshall, who along with C. David Barry helped guide the development of Washington University's MMS-X (molecular modeling system) computer. Like other computer chemists, Marshall begins by studying the shapes of the drug molecule and its receptor (when the receptor is
Volume of steroid is shown by a green grid
DISCOVER / AUGUST 1981
known), rotating the models on the computer screen to see them from every possible angle. On the simplest level, a drug and its receptor must fit together like pieces of a jigsaw puzzle. Beyond that, the electrical fields that surround both should attract each other, the way a magnet attracts iron. The computer allows the scientist to calculate and display those effects in graphic form. Using this technique, scientists have figured out how a chemical agent called alloxan works. When fed to laboratory rats, the drug produces many of the symptoms of diabetes. By computing the molecular shape-actually the shape of the cloud of electrons hovering around the molecule-Washington University researchers found that alloxan resembled g'lucose, the sugar molecule that triggers the release of insulin. Both form a sort of four-fingered hand. Alloxan, they concluded, might be fitting into a receptor for glucose and jamming' it. The resulting lack of insulin could have been bringing on the diabetes in the rats. When a chemist wants to know why different-looking drugs act on the same receptor, he can ask the computer to superimpose them all on the screen so he can see how their atoms match up. Marshall, for example, was interested in four chemicals that act like dopamine, a natural substance in the body that helps transmit nerve signals (Parkinson's disease is associated with a
Yellow grid defines the boundaries of the
DISCOVER / AUGUST 1981
lack of dopamine). Once he punched in the proper commands on his computer keyboard and the four molecules merged on the screen, he saw that they all had something in common: a ring' of carbon atoms and one nitrogen atom in the same location. It seemed logical to assume that those atoms were at least part of the electronic key that opens the receptor's door-and a clue to the understanding of dopaminerelated diseases. Covey is using the computer to design a "suicide molecule" that will latch onto and destroy an enzyme (and itself in the process). He is modeling these molecules after steroids, such as the hormones estrogen and testosterone, which determine sexual characteristics. How he uses the computer
for this purpose is shown at the bottom of these pages. At the left, the red lines represent the intricate links between the carbon, oxyg'en, and hydrogen atoms found in a stet'oid called dihydro testosterone. In the next picture, the computer " displays in green the volume that those atoms occupy. The green grid looks like and has been called a hamburget'. To be eft'ective, the green molecular blob must link up with an enzyme. But an enzyme will not accept just any molecule; the steroid must be able to slip between certain barriers, the yellow "hamburger buns" in the third picture. These barriers could be considered the walls of the enzyme; their shape and width are gleaned indirectly, from knowing the size of other drugs that fit into the enzyme. (Says Weinstein, "Determining the structure of a receptor is like trying to describe the beauty of a woman while only knowing what her husband and lovers look like.") In the last screen, the steroid and the enzyme combine to complete the sequence, in what Covey's colleagues call, for obvious reasons, the "Big Mac" model. Having determined all these specifications for normal interaction between a steroid and an enzyme, Covey can design his kamikaze steroid. Like a molecular bomb expert, he varies the formula of the steroid just enough so that it will not only fit between the barriers
The steroid fits into and chemically reacts with the enzyme
49
CHEMISTRY but, once "bitten into" by the enzyme, destroy both the enzyme and itself in the ensuing chemical reaction. "This process may be helpful in treating tumors," says Covey. "Molecules could be designed specifically to seek out and kill an enzyme that keeps a tumor growing. " He has already produced one of his computer-designed steroids, and it has destroyed a bacterial enzyme. His next project is to design a suicide molecule that will attack enzymes in rat tissues. The lise of computer graphics in drug design is so new that it has not yet been used to produce drugs for human use. But at Merck, one computerdesigned compound has been tested on laboratory animals. It is similar to the hormone somatostatin, a long chain of amino acids that helps regulate, among other things, the release of glucagon, which controls blood-sugar levels. Thus an extra dose of this natul'al hormone might help diabetics-if not for one problem: it does not stay in the body long enough to be useful. With their computer, Merck scientists set out to change that. Led by Daniel Veber, they soon found that all the useful work of somatostatin was being done by a group of four amino acids on one side of the molecule; the rest of the long chain was, in a sense, excess baggage. Using computer graphics developed by Peter Gund, they decided to snip off the section that was doing Peering through a large model of a DNA molecule, Miller holds a small molecule that can slip into the hole and stop DNA from working
50
o
cancer agents. Miller has been looking at ways to disrupt the function of DNA, i:i the "double helix" molecule that resembles a spiral staircase and canies the genetic message in its steps. A drug called lucanthone has characteristics that dl'aw it toward DNA molecules. Its approach causes two steps in the molecule to spread apart; it can then slip into the opening, where it becomes a wedg'e that prevents the DNA from transmitting its message. Says Miller, "If you can stop the DNA in a cancer cell from passing along its information, you may be able to stop the disease." But lucanthone is toxic. It attacks the DNA of normal cells as well as cancerous ones. Using its structure as a starting point (it resembles three hexagonal bathroom tiles joined in a row), New Merck compound: from computer Miller has used the computer to demodel to finished product sign close (and, he hopes, nontoxic) cousins of the drug. With its ability to the work and attach it to a shorter carry out millions of calculations, the chemical handle. The resulting com- computer helps Miller decide which of pound-a fine, white powder-stays in his candidates will single out cancer the body anywhere from ten to 40 times cells, get through the cell walls, open as long as the natural hormone and up the DNA, and bind tightly to it. shows nearly all the effects of somato- "It's like writing a recipe," says Milstatin in tests on rats, dogs, and mon- ler. His colleagues, organic chemists keys. If the drug goes into and passes Kevin Potts and Sydney Archer, have clinical tests, it may be given to diabet- made five of the computer-designed ics to improve their response to insu- molecules and turned them over to the lin. Says Veber, "The computer was in- National Institutes of Health, which is strumental in introducing new lines of now testing them fOl' safety and thinking into our work." effectiveness. Miller has also analyzed the way one For Kenneth Miller, a theoretical chemist at Rensselaer Polytechnic In- anti-cancer agent, daunomycin, works; stitute in New York, the computer is a it attacks a specific site on the DNA molmeans of testing and discarding ideas ecule-a certain "word" in the genetic more quickly in his search for anti- code. He thinks this knowledge could lead to some interesting medicine: "I can imagine that in the distant future a sample of someone's tumor will be snipped off and put into a gene machine to have its code read off. Then we'll push some buttons on a computer to design and synthesize a drug that will attack only that code." Does all this mean that the days of the pharmaceutical chemists are numbered, that they, like many others, can be replaced by a computer? Not at all, says Gund. "You can have the most beautiful picture of a molecule on the computer screen, but the big test is whether or not it actually works in the body." Adds Veber, "Sometimes the molecules the computer proposes do absolutely nothing." Electronic logic, it seems, is not infallible. Langridge also hastens to reassure his fellow chemists. "The computer is only a tool," he says. "After all, the most important thing is the person sitting in front of the screen. " DISCOVER / AUGUST 1981
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What is a
Protein?
Proteins play countless roles throughout the biological world, from catalyzing chemical reactions to building the structures of all living things. Despite this wide range of functions all proteins are made out of the same twenty amino acids, but combined in different ways. The way these twenty amino acids are arranged dictates the folding of the protein into its unique final shape. Since protein function is based on the ability to recognize and bind to specific molecules, having the correct shape is critical for proteins to do their jobs correctly.
Primary Structure
Primary structure is the linear sequence of amino acids as encoded by the DNA. This sequence defines how the protein will fold and therefore also defines how it will function. A single change in the amino acid sequence of hemoglobin can cause the proteins to clump together, resulting in the disease sickle cell anemia. one amino acid
Secondary Structure Hydrogen bonds between amino acids form two particularly stable structural elements in proteins: alpha helices and beta sheets. Alpha helices (shown in blue) are the basic structural elements found in hemoglobin, but many other proteins also include beta sheets. The inset highlights the pattern of hydrogen bonds (shown in green) that stabilizes alpha helices.
Tertiary Structure heme
Many functional proteins fold into a compact globular shape, with many carbon-rich amino acids sheltered inside away from the surrounding water. The folded structure of hemoglobin includes a pocket to hold heme, which is the molecule that carries oxygen as it is transported throughout the body.
Quaternary Structure
PDB ID: 1hho
Two or more polypeptide chains can come together to form one functional molecule with several subunits. The four subunits of hemoglobin cooperate so that the complex picks up and delivers more oxygen than is possible with single subunits.
www.rcsb.org •
[email protected]
Protein
Shape & Function
Specific amino acid sequences give proteins their distinct shapes and chemical characteristics. Protein shape is important because many proteins rely on the recognition of specific 3D molecular shapes to function correctly.
Structure
Defense
Collagen forms a strong and flexible triple helix that is widely used throughout the body for structural support. PDB ID: 1bkv
The flexible arms of antibodies have binding sites that can protect the body from disease by recognizing and binding to foreign molecules. PDB ID: 1igt
PDB ID: 1ppi
PDB ID: 2plv
Enzymes Alpha amylase is an enzyme with a specific catalytic site that begins the breakdown of carbohydrates in our saliva.
Communication Insulin is a small, stable protein that can easily maintain its shape while traveling through the blood to regulate blood sugar levels.
PDB ID: 4ins
Transport The calcium pump moves ions across cell membranes allowing the synchronized contraction of muscle cells.
Storage Ferritin forms a hollow shell that stores iron from our food.
PDB ID: 1fha PDB IDs: 1su4 and 1iwo
To learn more about these and other proteins please visit PDB-101 at www.rcsb.org/pdb-101 Text and illustrations by Jessica May and David S. Goodsell
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DOCK THIS:
Drug Design Feeds Drug Development BY KRISTIN COBB, PHD
Once upon a time, not long ago, HIV/AIDS was a scourge, killing anyone who contracted the deadly virus. Now, many people are living with the disease, which they control with drugs initially developed in the 1980s and early 1990s using an approach called computer-aided drug design— the use of computer models to find, build, or optimize drug leads. Armed with information about the 3-D structure of HIV protease, an enzyme essential to the HIV reproductive cycle, computational researchers designed molecules in silico to precisely fit the shape of the enzyme’s active site—as though fitting a key to a lock. The resulting drugs, potent inhibitors of HIV protease and the HIV life cycle, were brought to market in record time and revolutionized the treatment of HIV/AIDS. Around the same time, another anti-viral—Relenza, which treats influenza and was a forerunner to Tamiflu—was also designed using these methods. These HIV and flu drugs are among the best known success stories of computer-aided drug design (see page 23 for both stories). Since those early successes, computer modeling has become an integral part of drug discovery. “Almost everything that has recently moved forward from big pharmaceutical companies to market has involved some sort of collaboration with computational chemistry. It’s like asking, were there chemists involved? Of course there were. It is part of the process,” says Tara Mirzadegan, PhD, head of the computer-aided drug design group at Johnson & Johnson. www.biomedicalcomputationreview.org
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“Almost everything that has recently moved forward from big pharmaceutical companies to market has involved some sort of collaboration with computational chemistry. It’s like asking, were there chemists involved? Of course there were. It is part of the process,” says Tara Mirzadegan. Quite often, computers play a role without making the big splash they did with Relenza and the protease inhibitors. That’s probably because no drug is created solely in silico; the computer is just one of many tools in this process. But as algorithms evolve, computing power explodes, and scientists solve a greater number of 3-D protein structures, computer-aided design has the potential to dramatically cut the cost and time of drug discovery. How? By narrowing down the field of compounds that might help treat a particular disease; by assembling novel drug molecules to disrupt specific disease pathways; and by providing new attack routes against traditionally difficult drug targets. Computers are also increasingly playing a role in optimizing drug leads for bioavailability and safety. Despite the over-hype of computers as the saviors of drug development companies, many still expect this process to bear important fruit. Computer-aided drug design played a critical role in the design of several drugs that are now in late preclinical or early clinical development. Only time will tell which of these, if any, will emerge as drug success stories.
VIRTUAL SCREENING
How it works: In the ideal situation, the 3-D structure of the target molecule (usually an enzyme or receptor) is known, allowing scientists to directly visualize drug-target interactions in silico. Structure-based methods have evolved in two directions since Relenza and the HIV proteases—virtual screening and fragment-based design. In virtual screening, the 3-D struc-
Docked Drug. This 3-dimensional computer graphic shows a candidate drug (a JAK2 inhibitor) docked in the active site of its target protein (JAK2). JAK2 protein is implicated in various myeloproliferative disorders (diseases that produce excess bone marrow cells, such as chronic myelogenous leukemia, or CML) estimated to affect 80,000-100,000 people in the U.S.. Courtesy of SGX Pharmaceuticals, Inc.
ture of a target is screened against libraries of potentially active small molecules. The computer “docks” each compound, or ligand, into the target’s active site and scores its geometric and electrostatic fit. Considerable progress has been made in docking programs in the last two decades, but scientists agree that the problem is complex and that they have yet to find a perfect solution. To
start with, the ligand and protein target are often pictured as a rigid lock and key—but in fact they are dynamic, moving objects that continually change shape and adjust their shapes in response to each other. “Imagine taking a fluffy ball and trying to mold it to optimally fit some kind of a binding site. There are just way too many configurations,” says Dimitris K. Agrafiotis, PhD, vice president of Continues on page 24
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EARLY EXAMPLES: ANTI-VIRAL DRUGS Relenza and the HIV protease inhibitors stand out as the two classic examples of computer-aided drug design. Relenza was developed through a collaboration of Australian scientists, including Jose N. Varghese, PhD, head of structural biology at CSIRO Molecular and Health Technologies. In 1983, Varghese and his colleagues used X-ray crystallography to solve the 3-D structure of the enzyme neuraminidase, one of two potential protein targets on the surface of flu. Neuraminidase plays a critical role in the flu life cycle: after the virus replicates within a host cell, neuraminidase releases the newly formed viral progeny by cleaving a bond between the viral surface protein hemagglutinin and a sugar on the host cell surface, sialic acid. A series of structural experiments revealed important insights. The active site of the enzyme was highly conserved in all strains of flu—both human and animal; the virus routinely escaped antibody recognition by mutating around the periphery of the active site but never changing the active site itself. “Because it was so highly conserved, it seemed clear to us that it must have a very important function,” Varghese says. “So, clearly if one made a molecule that went in there and blocked that site, it would be pretty effective.” A synthetic analog of sialic acid was known to inhibit neuraminidase, but without sufficient potency. Using the crystal structure of neuraminidase bound with this analog, the researchers set out to design a better inhibitor in silico. Computer predictions revealed that a particular guanidinium-for-oxygen substitution would give tight binding. Synthesis of this compound—Relenza—turned out to be tricky, but eventually succeeded. “It bound in nanomolar binding, so it was very tight, and it certainly blocked the virus replication right down to its tracks,” Varghese says. Relenza was licensed to GlaxoSmithKline Inc. in 1990 and approved by the FDA in 1999. Following their lead—and capitilizing on a patent oversight, according to Varghese—Gilead Sciences developed the betterknown neuraminidase inhibitor, Tamiflu (marketed by Roche). Both drugs may be important in the fight against bird flu, Varghese says. Development of the HIV protease inhibitors lagged behind that of the neuraminidase inhibitors by several
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years, but the former won FDA approval sooner (in the mid-1990s) because of the pressing medical need. Dale Kempf, PhD, who is now a distinguished research fellow in Global Pharmaceutical Research and Development at Abbott, was involved in Abbott’s development of ritonavir (brand name Norvir), which started in late 1987. “It’s one of the first examples of the application of genomics for drug design,” he says. When the HIV genome was sequenced and published in the mid1980s, several groups recognized characteristic sequences suggestive of a protease enzyme. Interestingly, the gene encoded only half a protein, which led Kempf and others to realize that the protease must be composed of a dimer—two identical halves that come together to form one active site. This provided a key structural insight even before X-ray crystal structures of the protease were available: the active site had to have a particular type of symmetry, known as C2 or two-fold symmetry (rotation 180 degrees around a central axis yields the identical structure). Kempf’s group used that insight to create a computer model of the protease active site and to design possible inhibitors in silico by starting with a known substrate, chopping off half of the substrate, and rotating the remaining half by 180 degrees. “And when we went into the lab and made those compounds, they turned out to be very potent inhibitors,” Kempf says. Using a combination of the X-ray crystal structures of HIV protease (which had since become available) and computer graphics, they modified these compounds in silico to visualize how certain substitutions would improve characteristics like bioavailability. The first compound with sufficient oral bioavailability, ritonavir, was synthesized in 1991. In 1996, the FDA approved ritonavir in record time (72 days). The total development time—about eight years—was roughly half that of a typical drug, due both to the structure-based approach and to the FDA’s accelerated review. Several other HIV proteases emerged around the same time, including saquinavir (Roche) and nelfinavir (developed by Agouron, now a subsidiary of Pfizer). These drugs helped to revolutionize the treatment of HIV.
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Cancer Interrupted. This three-dimensional computer graphic shows a drug candidate (MET tyrosine kinase inhibitor) bound to its target protein. MET receptor tyrosine kinase controls cell growth, division, and motility and is implicated in a range of cancers, including renal cell carcinoma, gastric cancer, lung cancer, glioblastoma and multiple myeloma. Courtesy of SGX Pharmaceuticals, Inc.
Continued from page 22 informatics at Johnson & Johnson Pharmaceutical Research & Development. “Small molecules—unless they’re very small—tend to be very flexible. They flop around a lot. They can assume a multitude of conformations in 3-D.” If a molecule has five rotatable bonds, then each bond can rotate at many different angles, creating a lot of freedom to take on unique conformations. Most docking programs now account for the flexibility of the ligand by sampling its many conformations and docking each one, but adequately accounting for the flexibility of the target protein is a much more challenging problem. Adding protein flexibility exponentially increases computing demands. “The state of the art today is coming up with sensible simplifications that
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make the problem computationally tractable but still meaningful,” Agrafiotis says. Besides the flexibility of the protein, many docking programs do not adequately account for the influence of water—which surrounds all molecules in living systems. “The mathematical models for defining water and how it shapes itself around the receptor and the drug molecule are still pretty unclear,” says Kent Stewart, PhD, a research fellow in structural biology at Abbott. In addition, the algorithms estimate binding energies using classical Newtonian physics, rather than quantum physics—which also reduces accuracy. “You can calculate the binding energies from some sort of Newtonian point of view, treating atoms as sort of balls attached to springs. Or you can treat it
from a quantum mechanical point of view. Now the quantum mechanical calculations, as you can imagine, are horrendous,” says Jose N. Varghese, PhD, head of structural biology at CSIRO Molecular and Health Technologies. “At this stage, it is a computational challenge.” Methods of scoring how well a small molecule fits a protein’s active site also must trade off between speed and accuracy. “The scoring function that we use has many shortcuts and approximations,” says Mirzadegan. Her group will virtually dock the company’s one million proprietary compounds (which it has purchased or developed over the years) against a given target, and pick the highest ranked 10,000 for biological testing. “We cannot afford docking one compound per day. That would be one
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“The state of the art today is coming up with sensible simplifications that make the problem computationally tractable but still meaningful,” says Dimitris K. Agrafiotis.
million days. So we have to do it in a matter of seconds or sub-seconds.” But increased computing power can help boost the speed of virtual screening without compromising accuracy. In 2000, for instance, Arthur J. Olson, PhD, professor of molecular biology and director of the Molecular Graphics Laboratory at The Scripps Research Institute, started the FightAids@Home project, which uses internet-based grid computing—as was popularized by the SETI@Home project—to do virtual screening for new anti-HIV drugs. “If most people who have computers use only about five percent of the CPU cycles—and the rest of the cycles are just idle—how much wasted or available computing is there?” Olson asks. “It turns out to be an amazing number.” His grid computing project makes use of that idle computer time and helps evaluate drugs for dealing with HIV proteins’ habit of rapidly mutating to escape drug pressures. Fortunately, the 3-D structures have been solved for many of the mutant HIV proteins. With the help of about 500,000 volunteer computers, Olson used AutoDock (a popular docking program that was developed in his lab) to screen 2000 small molecules against several hundred different HIV protease mutants. The program took six months to run; he estimates that on the Scripps super computer, with 300 processors running, it would have taken 50 years. Besides identifying several drug leads, which are now in testing, Olson recognizes an even more important payoff: “When you do such massive dockings, you actually are collecting more than just an answer; you’re collecting a lot of statistics.” Such data could, for example, be used to identify a subset of mutants that represent a spanning set— www.biomedicalcomputationreview.org
Anti-Cancer Key. An anti-cancer drug compound—nutlin—bound to the cancer-causing protein MDM2. Courtesy of RMC Biosciences, Inc.
one that captures all unique interactions with the ligands screened. “Doing docking on only this subset of mutants would free up computer time for screening larger libraries, using more dynamic representations of the protein targets, or using more accurate scoring functions,” he says. The Folding@Home project at Stanford also uses grid computing for drug design. Led by Vijay S. Pande, PhD, associate professor of chemistry and of structural biology, Folding@Home focuses on simulating protein folding and misfolding, but “as
our work matures, we have been looking into the next steps involved in computational drug design,” Pande says. Using distributed computing, his group has devised new, more accurate algorithms for docking and for calculating ligand-protein binding energies. These algorithms are being used in the design of several new drugs, including new inhibitors of the cytokine-cytokine receptor interaction (involved in cancer); novel chaperone inhibitors (also involved in cancer); and novel antibiotics that target the bacterial ribosome. “Distributed computing is a key
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Fragment-based design. Drug companies, such as SGX pharmaceuticals, screen hundreds of fragments in their fragment libraries and identify hits that serve as the building blocks for novel drug candidates. Knowledge of the binding mode of each fragment to its target is combined with advanced computational tools to produce “engineered” drug leads. For example, in this series, a hit is first identified through crystallographic screening (yellow); then chemical groups (red and pink) are added to the bound fragment to increase its binding affinity. Courtesy of SGX Pharmaceuticals, Inc.
Distributed computing is key to developing better, more accurate algorithms for computer-aided drug design, says Vijay Pande. “It allows us to do calculations otherwise impossible.”
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aspect to this, as it allows us to do calculations otherwise impossible,” Pande says.
FRAGMENT-BASED DESIGN
Fragment-based methods take a “Lego” approach to drug design. In a lab, scientists create chemical libraries of small compounds, or fragments—perhaps one-third the size of a typical drug—that are easily linked together. They then screen the libraries for binding activity experimentally, using highthroughput X-ray crystallography (or NMR or mass spectrometry); when a fragment binds to the target, the crystallography provides an exact 3-D picture of the bound fragment in the active site. Next, with the help of computer modeling, fragments are turned into potent drug leads by adding new chemical groups to the initial core fragment or by stitching together several fragments that bind to different points in the active site. “I think this approach is showing quite good promise,” Varghese says. “In fact, with the advent of these modern synchrotrons, scientists can do this fairly quickly—and a lot of pharmaceutical companies are moving in this direction.” The approach offers a combinatorial advantage: “Instead of having a database of say four million compounds
that a really large company would have, you take compounds that are say onethird of the size, and explore them combinatorically. If you explored ten fragments in three different positions, you’d actually explore 1000 combinations. So with a database of something like 400 compounds, you can explore a chemical space that is in the several millions,” says Sir Tom Blundell, FRS, FMedSci, professor and chair of biochemistry at the University of Cambridge. In 1999, Blundell cofounded Astex Therapeutics to do fragment-based methods; the company is now testing a kinase inhibitor—a type of cancer drug—in clinical trials. “The experiment is really one of using crystallography to do your screening. So you’ve pushed the crystallography technology to the point where you can do it so rapidly that it becomes effective to use as a screening tool,” says Siegfried Reich, PhD, vice president of drug discovery at SGX Pharmaceuticals, another company that uses fragment-based methods. (Reich previously helped develop the HIV protease inhibitor nelfinavir at Agouron.) When it was founded in 1999, SGX was named Structural Genomix and its aim was to use high throughput X-ray crystallography to solve a record number of protein structures. But this was not sustainable as a business model. So, in 2000, the comwww.biomedicalcomputationreview.org
“When you’re talking about toxicity, it’s much easier to give a compound to a rat than it is to dock against all possible proteins that are in the rat, even today,” says Art Olson. “But someday, you might be able to do that. We’re certainly creeping up on that.”
pany changed its name to SGX Pharmaceuticals and put its crystallography power to use in drug discovery. One of their lead candidates is a new inhibitor of BCR-ABL, a perpetually active kinase enzyme involved in chronic myelogenous leukemia, or CML. The BCR-ABL inhibitor Gleevec has had enormous success in treating CML patients, but 20 percent are resistant to Gleevec. So scientists at SGX cloned, expressed, purified, and crystallized the Gleevec-resistant protein. Then they screened their fragment library against the wild type and mutant versions of BCR-ABL to find compounds active against both. The fragment hit that eventually led to their lead candidate started with a low binding affinity of just 10 micromolars (i.e., a fairly high concentration of compound was required to bind at least half the protein). This is where the medicinal chemists and structural biologists sit down with the computational chemists, Reich says. Computational chemists virtually build new compounds by adding chemical groups to the starting fragment. For example, they might try linking all the different simple alkyl amines to one of the fragment’s “chemical handles” (sites on the fragment that easily bind to other chemical groups), Reich explains. The computer calculates the binding affinity for each iteration, until it finds one with tight binding. Specialized versions of docking programs are used to calculate the binding affinities. But because you already know exactly how the fragment binds, you start with more information than in virtual screening. By elaborating their initial lead in this www.biomedicalcomputationreview.org
way, SGX got their first hit down to nanomolar potency—i.e. very little of the compound was required in order to bind the protein—in about three months. “That gives you a flavor for how fast this can go,” Reich says.
TRICKY TARGETS
Docking algorithms and fragmentbased methods work well on soluble enzymes that are easily crystallized and contain well-defined pockets where ligands can bind—but many diseases instead involve membrane-bound receptors or protein-protein interactions. Membrane-bound receptors transmit signals from outside to inside the cell. Because the proteins are embedded in the membrane, they cannot easily be crystallized and it is difficult to solve their structures. For example, 25 percent of the top 100 drugs on the market today target G-protein coupled receptors— including the dopamine and serotonin receptors in the brain—but the structure of only one mammalian G-protein coupled receptor is known. When structural information is unavailable, computational chemists use ligand-based methods to hunt for new drug leads. They superimpose a set of ligands with known activity against the target and compare their structural and chemical features. A common pattern, called a pharmacophore, emerges—key functional groups (such as hydrogen bond donors, electrostatic charges, and hydrophobic patches) must be in certain positions. This fingerprint is then used to virtually screen libraries for novel compounds with similar patterns. Ligand-based methods pre-date the struc-
Tricky Target. This computer model of a bacterial cell membrane helped scientists at Polymedix design new antibiotics that mimic the action of the defensin proteins (natural proteins in the body that kill bacteria by puncturing their membranes). Courtesy of Polymedix.
ture-based methods and have helped develop many drugs, including drugs to treat high blood pressure, pain, and depression. Protein-protein interactions occur via surfaces that are often featureless and shallow, and binding affinities can be quite large—so it’s hard for small molecules to disrupt these interactions, says Arthur Olson of Scripps Research Institute. You have to find or design drugs that can bind to multiple footholds, or hot spots, on the protein surface, which is challenging, he says. “I think that this is an area that is really still in its infancy.” But some progress is being made. Kent Stewart of Abbott Labs hopes to control BCL-2, a protein that is overexpressed in certain cancers. It blocks apoptosis (programmed cell death) and thus keeps cancer cells alive. Compared to HIV, Stewart says, which has an actual cave you can dock a molecule into, on
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Cancer Interference. The oncogenic protein BCL-2 helps keep cancer cells alive via a protein-protein interaction. This Bcl-2 inhibitor—developed at Abbott using a fragment-based approach—binds to the BCL-2 protein surface and disrupts the protein-protein interaction. The compound is in late preclinical development. Courtesy of Abbott.
BCL-2, “there’s no such thing as a cave; it’s a very flat and open surface, so it’s hard to get molecules that actually stick,” So, using a fragment-based approach, scientists at Abbott linked together two fragments that bind to the BCL-2 protein surface, resulting in a potent compound that can disrupt the protein-protein interaction. The compound is now in late preclinical development. Some companies have made these difficult targets their niche area. For example, Polymedix’s mission is to develop drugs against membrane-bound targets, protein-protein interactions, and membrane-protein interactions, using a suite of computational tools specifically developed for these aims (by professors William DeGrado, PhD, and Michael Klein, PhD of the University of Pennsylvania). Polymedix is working on a new line of antibiotics that mimic the action of
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defensins—natural proteins found in the body that kill bacteria. “They work similarly to a needle or a corkscrew going into a balloon. They directly attack and perforate the bacterial cell membrane,” says Nicholas Landekic, MBA, President, CEO, and co-founder of Polymedix. Because they do not target bacterial proteins—which can easily evolve to escape drug pressures—defensin-like drugs should not engender bacterial resistance, he says. Scientists at Polymedix built a computational model of a defensin protein inserted into a bacterial cell membrane (a peptide-membrane interaction). Then they virtually transformed the defensin protein into a drug-sized compound. By swapping amino acid groups for chemically analogous small molecule groups, they shrunk the protein while preserving its chemical interactions (electrostatics, lipophilicity, etc.) within the membrane.
The result: drug leads one-tenth the size of the defensins, but about 100-fold more potent and 1000-fold more selective. “So we’ve been able to improve on nature,” Landekic says. The compounds are now being tested in animal studies. “We’ve spent less than 14 million dollars to date since starting Polymedix, so in terms of an efficiency and efficacy rate, I think that’s pretty good,” he adds.
MAKING CHEMICALS INTO DRUGS
Computer-aided methods can identify drug leads with potent activity against a target, but these compounds are far from being drugs. Drugs must also be bioavailable and safe. Safety problems derail many drugs late in development, so identifying potential safety snags early on could save considerable time and money.
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HIV Protease Inhibitor. The second-generation HIV protease inhibitor, Kaletra, was developed at Abbott. Here Kaletra is shown bound to the active site of HIV protease. Courtesy of Abbott.
“How well can we evaluate bioavailability and toxicity in silico? It’s pretty blunt and not a very popular answer: we don’t do very well,” Stewart says. “The biological mechanisms underlying bioavailability and toxicity are complex. So the mathematical models in those areas are still in their infancy,” Olson agrees: We are a long way from being able to simulate a drug’s effect on the entire human body. “When you’re talking about toxicity, it’s
much easier to give a compound to a rat than it is to dock against all possible proteins that are in the rat, even today,” he says. “But someday, you might be able to do that. We’re certainly creeping up on that.” Computers do play a role today, however. Drugs must meet properties that fall under the ADME acronym: be Absorbed by the body, Distributed to the target tissues, and not Metabolized or Excreted too quickly. Software programs check molecules for key features
(known as “Lipinski’s Rule of Five”) that are associated with favorable ADME profiles, such as having five or fewer hydrogen bond donors and a molecular weight below 500. With enough computing power, scientists can also virtually screen a candidate compound against a large panel of proteins from the body, to make sure the compound will not cross react with other enzymes or receptors to cause side effects. To ensure that molecules identified in the computer will have real-world
For the field to progress, says Anthony Nicholls, the current software needs to be more closely scrutinized—using prospective studies that directly compare the impact of computer-aided methods with more traditional drug design approaches. www.biomedicalcomputationreview.org
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“I think in the next seven to ten years, with the computational power that’s coming on line here pretty soon and the steady development in algorithms, computer-aided design is going to make a huge difference,” says Richard Casey.
value, computational scientists benefit from working closely with medicinal chemists during lead identification and optimization. “Medicinal chemists would tell you that there’s lots of intuition involved, so it’s not all computational,” says Hans Wolters, PhD, associate director of informatics at XDx, Inc. For example, he says that as computer scientists became more involved in making drugs, the molecular weight of candidate compounds began to creep up precipitously—to sizes that would not be easily absorbed by the human body. Medicinal chemists help recognize this type of problem early in the process.
DEBATING
THE IMPACT
In the past two decades, although computer-aided drug design has become an integral part of drug discovery, some remain skeptical as to whether these methods are delivering on their promise. The productivity of the pharmaceutical industry has actually declined in the past decade (The FDA approved 58 drugs from 2002 to 2004 compared with 110 from 1994 to 1996, according to the Tufts Center for Drug Development.) Though this is likely due to many factors—in particular, tightening safety standards and the enormous cost and time of clinical trials—the trend has left some wonder30 BIOMEDICAL COMPUTATION REVIEW
ing whether large investments in technology, including computer-aided drug design, are paying significant dividends. Many modeling programs are unreliable, and they are not making a big difference in the real world, cautions Anthony Nicholls, President and CEO of OpenEye Scientific Software, which develops software for computeraided drug design. “It’s all done on faith. It’s all done on the idea that ‘oh, we’re using computers, so it must be better,’” he says. “I think a lot of people are fooling themselves.” He believes that, for the field to progress, the current software needs to be more closely scrutinized—using prospective studies that directly compare the impact of computer-aided methods with more traditional drug design approaches. Other scientists agree that the algorithms are still being refined, but have a more optimistic outlook. They say that progress is steady and that computeraided design is already having an impact. Klaus Klumpp, PhD, an associate director at Roche (who was involved in the development of the HIV protease inhibitor saquinavir), points to a suite of emerging drugs for hepatitis C virus (HCV) as a case in point. HCV was discovered in 1989 and the virus was difficult to grow, so structural information for HCV polymerase and HCV protease became available rel-
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atively late—in the mid-to-late 1990s. By this time, computer-aided drug design was well integrated into big pharmaceutical companies. Several companies quickly identified binding sites and designed inhibitors, many of which are now in early clinical trials. “It is expected to completely change the treatment paradigm for HCV infected patients,” Klumpp says. Richard Casey, PhD, founder and chief scientific officer of RMC Biosciences, Inc., has also witnessed the dramatic effect that computers can have on drug design. His company provides computer-aided drug design services for small and mid-size pharmaceutical companies, which often lack in-house teams. Recently, he made 3-D models and performed in silico docking studies for a mid-size pharmaceutical company that had identified active lead compounds but had no understanding of how they were binding the target, an RNA synthetase. “When they saw this for the first time, it was the ‘aha’ effect: So that’s why this compound has high activity and this compound does not. It was a real eye-opener for them,” Casey says. “I think in the next seven to ten years, with the computational power that’s coming on line here pretty soon and the steady development in algorithms, computeraided design is going to make a huge difference.” ■ www.biomedicalcomputationreview.org
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author of the paper. On top of that, they putational and experimental researchers. modeling complex behaviors of in vivo included a handful of genes, concentrations “We’re just scratching the surface,” Zaman environments and go beyond what we of expressed proteins and the interactions says. “I hope more people start to look at already know.” ■ between those genetic products. They found that geometric organization, a mechanical factor, can influence the direction of division or the polarization of the cell. At the same time selected genes can affect the elasticity or adherence properties of the cell. “This connection works both ways: the genes can influence the mechanics and the mechanics can influence the genes,” he says. This model, which was published in PLoS Computational Biology in Carsten Peterson and his colleagues at Lund University in Sweden have combined genetic and mechaniMay, provides the most inclusive cal signals to accurately simulate the layering of cells in the early embryo. Here, two different views simulation of these factors devel- (external above and cross-sectional below) show various simulations including (a) the pre-set “salt & pepoped to date. per” pattern of the two cell types that determine cell orientation (GATA6 in red; NANOG in blue) next to Even with this progress, the blastocoelic surface (gray); (b) the effect of random movements alone; (c) the effect of setting differresearchers are just at the initial ent adhesion properties for each cell type (a layer forms but positioning isn’t stable or in the right place); stages of building models of how and (d) the addition of stronger adhesion between the NANOG cells and the surrounding trophectoderm, cells respond to their environ- which stabilizes the endoderm in the correct position next to the blastocoel. Reprinted from Krupinski, P, ments. Further work will rely on et al., Simulating the Mammalian Blastocyst—Molecular and Mechanical Interactions Pattern the Embryo, close collaborations between com- PLoS Comput Biol 7(5)1-11(2011).
DE NOVO PROTEIN DESIGN: Designing Novel Proteins that Interact
B
y stringing together amino acids in a prescribed sequence that then folds into a defined structure, nature designs proteins to perform specific functions. Nowadays, computational researchers are doing some protein designing of their own—and it’s bearing some valuable fruit. The goal is to come up with new proteins to perform specific functions and recognize or bind to specific substrates, says Jeffery Saven, PhD, professor of chemistry at the University of Pennsylvania. “What matters is what they can do and what they can recognize,” Saven says. In nature, proteins acquire changes to their sequence of amino acids that lead to new functional forms. In the lab, researchers make chemical changes to an amino acid sequence and test it to see whether it functions in ways they can understand. But by taking the initial
design work in silico, researchers can simplify the experimental workload by honing in on candidates worthy of laboratory work. Essentially, researchers computationally create a multitude of novel amino acid sequences, predict and build models of the new proteins’ likely structures, and model or simulate how they will interact with other molecules. Although this process is no easy matter, progress is being made, as described in a recent review by Pantazes et al. in Current Opinion in Structural Biology. Most notable, perhaps, are the efforts aimed at modeling binding to other proteins and designing new enzymes.
Designs For Binding With Hot Spots
Protein design requires overcoming the difficult challenge of getting a novel pro-
tein to bind to another protein at the correct site, in the correct orientation, and with high affinity. To address that problem, David Baker, PhD, professor of biochemistry at the University of Washington, and his colleagues developed a new and generalizable approach that focuses on a specific patch on a defined target. They computationally place disembodied protein side-chains next to the patch to determine how they interact in the hotspot. Only when they are satisfied with those interactions do they attach it to a protein scaffold with a shape that is complementary for anchoring the hotspot proteins. They then use computational methods to recalculate the energies and make other adjustments aimed at ensuring the appropriate “hotspot” contacts. The researchers also employ a experimental strategy, “yeast display” which expresses designed
Published by Simbios, the NIH National Center for Physics-Based Simulation of Biological Structures
7
DE NOVO PROTEIN DESIGN: DESIGNING NOVEL PROTEINS THAT INTERACT
proteins on the surface of yeast cells, allowing the researchers to test a higher number of designed proteins through flow cytometry in the laboratory than they could by traditional expression and purification methods. In their test case, Baker and his team
would like to be able to explain these failures, because it will help them build refine existing algorithms and build new ones that can better predict protein structures and energies. “People rarely do further analysis on failed designs: they don’t have the resources, the money or the time. So
designed two different novel proteins that bound in the correct orientation—and quite tightly—to the hemaglutinin protein from the especially virulent H1N1 influenza strain from 1918. Those structures could serve as the basis for a new type of flu drug. The work was published in Science in May 2011. Despite finding two successful proteins, it’s worth noting that Baker’s team labtested about 80 other possible designs that didn’t bind. The low success rate raises questions. “We don’t know what happened to the other 80 designs,” Baker says. “Some might have folded into structures that didn’t work.” But he and others
we only tend to learn from success stories,” says Costas Maranas, PhD, professor of chemical engineering at Pennsylvania State University, and a co-author of the Current Opinion in Structural Biology review.
reaction rates by just 6 orders of magnitude (106). For example, in a 2008 Nature paper, Baker and his colleagues achieved this feat with a modest problem, a novel enzyme that catalyzed a Kemp elimination, a reaction not facilitated by existing biological enzymes.
Though a more difficult task than protein binding, building a new catalyst involves a similar strategy. Researchers first must design an appropriate enzyme active site with side chains that position a substrate in an appropriate position to Designing Catalysts facilitate the reaction. In this case, the Computational protein designers are researchers centered their active site also designing new catalysts, a problem around an activated serine. Then they had that requires even more precision than to graft that active site onto a protein designing binding partners. The most effi- backbone structure that would maintain cient natural enzymes speed chemical those configurations. They used their reactions up to 19 orders of magnitude RosettaMatch algorithm to sift through (1019) faster than the reaction would occur thousands of possible structures. on its own. The fastest computationally In further work published in 2010 in designed enzymes, by contrast, enhance Protein Science, Baker and his team sought to explain what makes some designed catalysts more efficient than others. “There are many places where this process can go wrong,” Baker says. A large part of the process is narrowing the experimental possibilities. By using molecular dynamics methods, they were able to rank their computational designs and thereby reduce the number that would need to be tested experimentally. Indeed, had they used this procedure in the 2008 Nature study, only 24 designs would have required experimental testing rather than 120. Such research is a work in progress, Baker says. “What we proved is that you can make enzymes from scratch starting with a computer,“ he says. “Our Flow chart of the key steps in the design of novel proteins. Reprinted with permission from Fleishman et challenge now is to al., Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin, make much more active Science 332:816-821 (2011). catalysts.” ■ 8
BIOMEDICAL COMPUTATION REVIEW
Fall 2011
www.biomedicalcomputationreview.org
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