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  • richardmitnick 11:19 am on September 16, 2016 Permalink | Reply
    Tags: , , , David Baker, Hyperstable peptides, Institute for Protein Design,   

    From U Washington: “Super-stable peptides might be used to create ‘on-demand’ drugs” 

    U Washington

    University of Washington

    Michael McCarthy

    An artist’s conception of a peptide created at the UW Medicine Institute for Protein Design. The backbone structure is shown in pink, and the molecular surface is blue. White indicates the crossbonds that stabilize the peptide’s shape. Vikram Mulligan


    Scientists at the University of Washington’s Institute for Protein Design have shown it is possible to create small, hyperstable peptides that could provide the basis for developing powerful new drugs and diagnostic tests.

    “These super stable peptides provide an ideal molecular scaffold on which it should be possible to design ‘on demand’ a new generation of peptide-based drugs,” said UW Medicine protein engineering pioneer David Baker, who oversaw the research project. He is a UW professor of biochemistry.

    David Baker
    David Baker

    In a study, which appears in the journal Nature, the researchers demonstrate that not only is it possible to design peptides that fold into a wide variety of different conformations, but also that it is possible to incorporate functional groups of chemicals not normally found in peptides.

    Illustrations of designed peptides with different configurations of two structures: tightly wound ribbons and flat, arrow-shaped ribbons.

    Both of these abilities could give designers even greater flexibility to create drugs that act on their molecular targets with high precision. Such drugs should not only be more potent but would also be less likely to have harmful side effects.

    Most drugs work by binding to a key section of a protein in a way that alters how the protein functions, typically by stimulating or inhibiting the protein’s activity. For the binding to occur, the drug must fit into the target site on the protein as a key fits into a lock. How close the lock-and-key fit is can often determine how well the medication works.

    Currently, most prescription drugs are either made of small molecules or much larger proteins. Both classes of drugs have advantages and disadvantages.

    Small molecule drugs, for example, tend to be easy to manufacture, tend to have a long shelf life, and are often easily absorbed. But they often don’t fit the targeted “lock” as selectively as could be hoped. This imperfect fit can result in off-target binding and side-effects that diminish their effectiveness. Protein drugs, on the other hand, often fit their target receptors very well but they are difficult to manufacture, are more unstable, and lose their potency if they are not kept refrigerated. Because of their size and instability, they need to be injected into patients.

    Peptide drugs fall in between these two classes. They are small, so they have many of advantages of small molecule drugs. But they are made of a chain of amino acids, the same components that make up proteins, so they have the potential to achieve the precision of larger protein drugs.

    The power of some tiny peptides can be observed in venomous creatures. A number of poisonous insects and sea creatures produce small peptide toxins. Those are some of the most potent pharmacologically active compounds known. Their potency is among the reasons why medical scientists are interested in tapping into beneficial uses of peptides.

    In the new study, Gaurav Bhardwaj, Vikram Khipple Mulligan, Christopher D. Bahl, senior fellows in the Baker lab, and their colleagues, developed computational methods that are now incorporated in the computer program called Rosetta.

    Rosetta@home, a project running on BOINC software from UC Berkeley
    BOINC WallPaper

    These methods are being used to design peptides ranging from 18 to 47 amino acids in length in 16 different conformations, called topologies.

    Originally developed by Baker and his earlier team, Rosetta uses advanced modelling algorithms to design new proteins by calculating the energies of the biochemical interactions within a protein, and between the protein and its surroundings. Because a protein will assume the shape in which the sum of these interaction energies is at its minimum, the program can calculate which shape a protein will most likely assume in nature.

    The peptides were made hyperstable by designing them to have interior crosslinking structures, called disulfide bonds, which staple together different sections of the peptide. Additional stabilization was secured by tying the two ends of the peptide chain together, a process called cyclization. The resulting constrained peptides were so stable that they were able to survive temperatures to 95 °C, nearly boiling. This survival feat would be impossible for antibody drugs.

    The researchers also showed that the design of these peptides could include amino acids not normally found in proteins. Amino acids have a property called handedness or chirality. Two amino acids can be made of the same atoms but have different arrangements, just as our hands have the same number of fingers but have two mirror-image configurations, right and left. This handedness keeps the right hand from fitting properly into a left-handed glove and vice versa.

    In nature, perhaps due to a chance event billions of years ago, amino acids in living cells are all left-handed. Right-handed amino acids are very rare in naturally occurring proteins. Nevetheless, the researchers were able to insert right-handed amino acids in their designed peptides.

    “Being able to include other types of amino acids allows us to create peptides with a much wider variety of conformations,” said Baker, “and being able to use right-handed amino acids essentially doubles your palette.”

    “By making it possible to create peptides that include ‘unnatural’ amino acids, this approach will allow researchers to explore peptide structures and function that have not been explored by nature through evolution,” Baker said.

    Today’s edition of Nature also has a special supplement, Insight The Protein World. Baker, Po-Ssu, and Scott E. Boyken authored the review article, The coming of age of de novo protein design.

    Also see coverage of the Nature hyperstable peptide design paper in Hutch News by the Fred Hutchinson Cancer Research Center.

    The National Institutes of Health provided partial support for this work through grants P50 AG005136, T32-H600035., GM094597, GM090205, and HHSN272201200025C. Additional funding was provided by The Three Dreamers.

    See the full article here .

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  • richardmitnick 5:18 pm on August 13, 2016 Permalink | Reply
    Tags: , , , David Baker, ,   

    From Rosetta@home: “Designed Protein Containers Push Bioengineering Boundaries” 



    Rosetta@home has posted in their forum a new (July 21, 2016) article, Designed Protein Containers Push Bioengineering Boundaries
    from U Washington’s Institute for Protein Design which I highly recommend for anyone interested in Protein Studies.


    This forum article cites Designed Protein Containers Push Bioengineering Boundariess which goes on to cite Icosahedral protein nanocage – new paper and podcast published in Nature, and “Accurate design of megadalton-scale multi-component icosahedral protein complexes”, published in Science.

    Of this second paper, they write, “In this paper, former Baker lab graduate student Jacob Bale, Ph.D. and collaborators describe the computational design and experimental characterization of ten two-component protein complexes that self-assemble into nanocages with atomic-level accuracy. These nanocages are the largest designed proteins to date with molecular weights of 1.8-2.8 megadaltons and diameters comparable to small viral capsids. The structures have been confirmed by X-ray crystallography (see figure). The advantage of a multi-component protein complex is the ability to control assembly by mixing individually prepared subunits. The authors show that in vitro mixing of the designed subunits occurs rapidly and enables controlled packaging of negatively charged GFP by introducing positive charges on the interior surfaces of the two copmonents.

    The ability to design, with atomic-level precision, these large protein nanostructures that can encapsulate biologically relevant cargo and that can be genetically modified with various functionalities opens up exciting new opportunities for targeted drug delivery and vaccine design.”

    Also referenced in the forum is an article in Science, Jul. 21, 2016 by Robert F. Service This protein designer aims to revolutionize medicines and materials, about Dr David Baker.

    From this Science article, David Baker shows off models of some of the unnatural proteins his team has designed and made.© Rich Frishman

    included also is this video from Science.

    See the full article here.

    Please help promote STEM in your local schools.

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    Stem Education Coalition

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

    About Rosetta

    One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as “chains.” Amino acids can be considered as the “links” in a protein “chain”. Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull its ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a “fold.” The order in which the amino acids are linked determines the protein’s fold. There are many kinds of proteins that vary in the number and order of their amino acids.

    To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta’s strategy for finding low energy shapes looks like this:

    Start with a fully unfolded chain (like a metal chain with its ends pulled).
    Move a part of the chain to create a new shape.
    Calculate the energy of the new shape.
    Accept or reject the move depending on the change in energy.
    Repeat 2 through 4 until every part of the chain has been moved a lot of times.

    We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

    A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as “relaxation.” Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

    Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

    To join this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects to see what else might be of interest to you.

    Rosetta screensaver



  • richardmitnick 2:46 pm on March 30, 2012 Permalink | Reply
    Tags: , , , , David Baker, ,   

    From David Baker at the Rosetta Project – We Need Your Help 

    Dr David baker
    Dr David Baker, Forum moderator, Project administrator, Project developer, Project scientist

    David Baker tells us,

    “In the last two months we believe we have made quite a breakthrough in structure prediction, and are excited to test the new method in CASP10. We need your help though–we are now testing many aspects of the new approach and are seriously limited by available CPU cycles. There are now so many flu inhibitor design and structure prediction jobs queued up on Rosetta@Home that there is an eight day wait before they are getting sent out to you. This would be a great time to temporarily increase Rosetta@Home’s share on your computers and/or recruit new users–we need all the help we can get! thanks! David”

    From The Rosetta web site:

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research [below] for more information). Please join us in our efforts! Rosetta@home is not for profit.

    Disease Related Research

    Comments from David Baker

    “My research group is involved both in fundamental methods development research and in trying to fight disease more directly. Most of the information on this site focuses on basic research, but I thought you might be interested in hearing about some of the disease related work we are doing that you will be contributing to at Rosetta@home.

    Malaria: We are part of a collaborative project headed by Austin Burt at Imperial College in London that is one of the Gates Foundation “Grand Challenge Projects in Global Health”. Malaria is caused by a parasite that spends part of its life cycle inside the mosquito, and is passed along to humans by mosquito bites. The idea behind the project is to make mosquitoes resistant to the parasite by eliminating genes required in the mosquito for the parasite to live. Our part of the project is to use our computer based design methods (ROSETTA) to engineer new enzymes that will specifically target and inactivate these genes.

    Anthrax: We are using ROSETTA to help John Collier’s research group at Harvard build models of anthrax toxin that should contribute to the development of treatments. You can read the abstract of a paper describing some of this work at http://www.pnas.org/cgi/content/abstract/102/45/16409

    HIV: One of the reasons that HIV is such a deadly virus is that it has evolved to trick the immune system. We are collaborating with researchers in Seattle and at the NIH to try to develop a vaccine for HIV. Our role in this project is central–we are using ROSETTA to design small proteins that display the small number of critical regions of the HIV coat protein in a way that the immune system can easily recognize and generate antibodies to. Our goal is to create small stable protein vaccines that can be made very cheaply and shipped all over the world.

    Other viruses: We have been collaborating with Pam Bjorkman’s laboratory at Cal Tech to use the ROSETTA protein-protein docking methodology to build models of herpes simplex virus proteins in complex with human proteins.

    Alzheimer’s disease: Alzheimer’s and many other diseases are likely to be caused by abberant protein folding in which proteins form large aggregated structures called amyloids rather than folding up into their normal biologically active states. A big advance was made recently by David Eisenberg’s research group at UCLA in solving the first structure of an amyloid. We are collaborating with their research group to use the structure to predict which parts of proteins are likely to form amyloids, which will be a first step to blocking amyloid formation and hopefully disease.

    Cancer: Cancer can be caused by mutations in key genes that disrupt normal cellular control processes. We are developing methods for cutting DNA at specific sites in the genome, and we will be targeting sites that are implicated in cancer. After these sites are cut, they should be repaired by the cell using a second, unmutated copy of the gene and the cell should no longer be cancerous. This is a very specific form of gene therapy that, if successful, will circumvent one the main objections to current gene therapy methods; namely, current methods insert the unmutated copy of a gene randomly into the genome, and if the insertion point happens to be near an oncogene, the gene therapy will cure one disease but cause another. Because our methods will target specific sites instead of random sites, they should avoid this pitfall.

    Prostate Cancer: The androgen receptor (AR) binds testosterone and is responsible for normal male development. When the AR becomes hypersensitive to testosterone, prostate cancer is the result. The current treatment for prostate cancer, called “hormone therapy”, involves lowering the amount of testosterone available (sometimes by castration). Many malignant tumors are resistant to this therapy, however, so we are applying our protein design methodology to find different ways to inhibit the AR and to treat prostate cancer. Specifically, we are trying to design proteins that will disable the AR even in the presence of testosterone. We are doing this by designing proteins that will prevent the AR from entering the nucleus of the cell (which is where it does its dirty work), and also preventing it from binding DNA and activating tumor-specific genes even if it does get into the nucleus.

    The above projects are not currently running on BOINC because we don’t yet have an efficient queuing system which lets people submit jobs easily, but look for them soon! Also, rest assured that the structure prediction calculations currently running on your computers will have direct bearing on treating disease. There is a three-fold explanation for this direct relationship between structure prediction and disease treatment:

    Structure prediction and protein design are closely related.

    Improvements in structure prediction lead to improvements in protein design, which in turn can be directly translated into making new enzymes, vaccines, etc. For more information on protein design you might be interested in looking at the review we recently wrote in science which is available at our home page (http://depts.washington.edu/bakerpg).

    Schueler-Furman, O., Wang, C., Bradley, P., Misura, K., Baker, D. (2005). Progress in modeling of protein structures and interactions Science 310, 638-642.

    Structure prediction identifies targets for new drugs.

    When we predict structures for proteins in the human genome on a large scale, we learn about the functions of many proteins, which will help in understanding how cells work and how disease occurs. More directly, we will be able to identify many new potential drug targets for which small molecule inhibitors (drugs) can be designed. To put this in context, one major road-block to developing new treatments for human disease is identifying new “drugable” protein targets. Most new drugs these days interact with the same targets as the old drugs, so these drugs lead to only small improvements in disease treatment. Structure prediction helps us identify new drug targets, and so will help us find innovative, perhaps even breakthrough, treatments for disease.

    Structure prediction allows us to use “rational design” to create new drugs.

    If we know the structure of a protein, we can determine its functional sites, and specifically target those sites to be inactivated by a new drug. Calculation of whether a small molecule (drug) will bind to and inactivate a protein target is similar in many ways to the structure prediction calculations we are doing here–it is basically a problem of finding the lowest energy structure of the protein plus drug system–and we have recently developed a new module in ROSETTA to do this docking problem. Results are very promising, and in the near future your machines will likely be running drug docking calculations along with the vaccine and therapeutic protein design projects described above, in addition to the protein folding calculations you are doing now.

    Please visit the Baker Lab web site to read about

    Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

    Increased Diels-Alderase activity through backbone remodeling guided by Foldit players

    Algorithm discovery by protein folding game players

    Crystal structure of a monomeric retroviral protease solved by protein folding game players

    Computational design of protein inhibitors of Spanish and avian flu hemagglutinin



    Rosetta@home runs on BOINC software from U.C. Berkeley

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