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  1. We've noticed a significant number of high priority projects are trailing behind existing projects. Newer projects are aimed at interpreting and guiding experiments where the full power of Folding@home (F@h) is essential to continue pushing the boundaries of scientific and medical discoveries. The main cause of this issue is the core version needed to run these simulations. Many of our newer SMP projects use the A4 core, which has several scientific advancements, while existing projects use the still important A3 core. The A4 core is not compatible with Clients below version 6.34 and many donors are still running these older Client versions. This presents an opportunity to encourage people to donate their cycles towards these vital A4 projects. To emphasize the scientific importance of these work units, we are boosting the base points of all A4 work units by 10% when uploaded (Note that this bonus will not be reported by V7 or by the 3rd party applications which project PPD but the points will appear when your statistics are credited). The quick return bonuses will be calculated on top of the increased base points. This will start on Monday July 23, 2012, and we will keep this 10% bonus in effect for at least 3 months as a trial period, but plan to keep it longer, as needed. To participate, donors should be running a recent version of the F@h Client. We strongly encourage Windows users to update to the much improved V7 Client. Although F@h Client v6.34 or newer is sufficient to participate for any supported operating system. Please note the Linux and OSX V7 Clients are a work in progress and feedback is welcomed. v7 Clients: Windows/Linux: Visit our home page, http://folding.stanford.edu/English/HomePage Mac OSX: v7 for OSX is still in testing. For a beta copy: https://fah-web.stanford.edu/projects/FAHClient/wiki/BetaRelease Old v6.34+ Clients Windows/Linux: http://folding.stanford.edu/English/DownloadWinOther Voir l'article complet
  2. Guest post from Dr. Xuhui Huang, Hong Kong University of Science and Technology In this post, I want to introduce a new GPU-powered clustering algorithm we recently developed to analyze the large molecular dynamics simulation datasets generated by Folding@home. Folding@home can generate enormous sets of protein structures. A critical step in analyzing these large datasets involves some form of reduction in the dataset, usually in the form of clustering. We recently developed a GPU powered clustering algorithm using the intrinsic properties of a metric space to rapidly accelerate the clustering. Overall, our algorithm is up to two orders of magnitude faster than the CPU implementation, and holds even more promise with the ever increasing performance in GPU hardware. This algorithm should facilitate numerous applications. For example, one of the systems we tested our code on is the human islet amyloid polypeptide (hIAPP) peptide, whose aggregation is implicated in Type 2 diabetes. We hope further analysis of this data will provide insights that will inform the development of treatments for diabetes. Voir l'article complet
  3. Guest post from Dr. Vincent Voelz, Temple University Using protein folding simulations alongside experiments remains challenging because the two techniques often "see" very different things. Simulation trajectories "see" every atom in a single protein in microscopic detail, while experiments often "see" only bulk properties averaged over large ensembles of molecules. For example, in the last few years, we have built kinetic network models of ever larger and slower-folding proteins. These models can have huge numbers of states and many possible folding pathways, yet experimental folding kinetics can be fit to models having only two or three states. In a new paper, we try to bridge these two levels of detail using a combination of simulation and experiment to study the early folding events of ACBP, a 86-residue helix-bundle protein that folds on the ~10 millisecond timescale, one of the largest, slowest-folding proteins we have studied to date. Previous experiments suggested that ACBP folds via a "three-state" mechanism, with an intermediate forming on the ~100 µs timescale. To understand the molecular events underlying the formation of this intermediate, we used Folding@Home to generate tens of thousands of GPU-accelerated trajectories, and stitched these together to build a kinetic network model of the complete folding reaction (see figure below). By comparing our model to the results of state-of-the-art experiments (single-molecule FRET, Trp-Cys quenching, and time-resolved FRET) we found something surprising -- the folding relaxation timescale around ~100 µs corresponds to the heterogeneous formation of unfolded-state structure, rather than some discrete structural state. This work is exciting because it shows that our models can predict atomically detailed mechanistic information about folding (currently very difficult to obtain experimentally) while simultaneously providing accurate predictions of quantities seen in bulk folding experiments. Voir l'article complet
  4. Guest post from Dr. John Chodera, UC Berkeley Kinases [http://en.wikipedia.org/wiki/Kinase] are the molecular logic gates of the cell. These important proteins integrate critical signaling information in every cell of our bodies, becoming active only when specific upstream signals are received. However, in many kinds of cancer, mutations can emerge in one or more kinases that cause them to ignore these regulatory signals and become active all the time. If these kinases are involved in cell division, this can erroneously cause cells to keep dividing even when they shouldn't, potentially resulting in a form of cancer. Our group [http://choderalab.org] is using Folding@Home to understand how some successful anti-cancer therapeutics (like imatinib [http://en.wikipedia.org/wiki/Imatinib]) are able to selectively target the targeted disease-causing kinases while minimally interfering with other normally-functioning kinases. A deeper understanding of this selectivity would help recapitulate the success seen in treating some cancers by aiding the design of novel therapeutics targeting other cancers. Up to now, the origin of this selectivity has been elusive because it appears that highly selective drugs like imatinib can bind in essentially the same way to the highly similar Abl and Src kinases, despite the fact that it binds Abl well and Src poorly (see Figure). It is now believed these differences in binding are due to conformational preferences of the kinase for different geometries, something that had been traditionally hard to study but is well-suited to techniques we originally developed to study protein folding problems on Folding@Home. Stay tuned for future updates on how Folding@Home is helping our study of kinase inhibitors and cancer! Voir l'article complet
  5. Guest post: Dr. Greg Bowman, UC Berkeley We just had a protein folding conference at Stony Brook University in New York that was extremely encouraging. Both the experimental and theoretical communities are very excited about the results we are generating with Folding@home. In particular, they are excited about (i) our increasing ability to make quantitative connections with experiments and (ii) the long timescale dynamics for large proteins we are now able to capture. For example, we recently succeeded in folding an 80-residue protein on 10 millisecond timescales (paper is here). For reference, that’s about twice as many residues and about 1,000 times longer timescales than what most anybody else is able to achieve! There are now multiple experimental groups who are asking us to make predictions for them to test. So, we appreciate all your help and have plenty of new calculations for you to contribute to. Voir l'article complet
  6. To start off FAHcon2012, I gave a talk which included a review of how far Folding@home has come in the last decade. I showed a slide from the very first talk I gave about Folding@home results. That talk was given at Columbia University in August of 2000, and I talked about results from our paper in Science entitled "Screen savers of the world, unite!". That work described the folding of a very small protein (16 amino acids) on a very short timescale (10ns = 10 x 10^-9 seconds!), but still was a major accomplishment for the time. It's exciting to see how far we've come. One way to think about it is in terms of how long of a time scale and length scale we can simulate for protein folding and protein misfolding diseases (such as Aß aggregation in Alzheimer's Disease): Time scales: advancing roughly 1000x every 5 years 2000: 1 to 10ns (Fs peptide) 2005: 1 to 10µs (villin, Aß aggregation of 4 chains) 2010: 1 to 10ms (NTL9, Lambda repressor) 2015: 1 to 10s? Just breaking past a microsecond was a big deal. The fact that we can simulate 10's of milliseconds is very exciting, but I'm really excited about where this appears to be leading, allowing us to tackle really challenging and important problems. It would also mean that through a combination of new methods, algorithms, and hardware advances, we've already increased our capabilities by a million fold in just 10 years (2000 to 2010). We're looking forward to hopefully making it a billion fold in 2015! Length scales: advancing roughly 2x every 5 years 2000: 16 amino acids (Fs) 2005: 35 amino acids (villin) 2010: 80 amino acids (lambda, ACBP) 2015: 160 amino acids? It's also important to note that these are sizes for protein folding. For other problems, such as protein conformational change, we've already tackled much bigger systems. I'm really excited to see what the next 5 years will bring! Voir l'article complet
  7. Here's a guest post from Prof. Dr. Xuhui Huang, from the Hong Kong University of Science and Technology. I had a great time attending the first annual FAH conference and enjoyed the nice summer weather in the Bay area. We had plenty of discussions on the recent progress and future plans of FAH on both scientific and technique sides. I look forward to the future FAH conferences. In my talk, I reported recent results on two projects from our lab. The first one is the development of a new algorithm for the automatic construction of Markov State Model to investigate the conformational dynamics of multi-body systems. This new algorithm holds great potential to help elucidate the aggregation mechanisms of multiple misfolded peptides to form oligomers and eventually fibrils. In the future, we plan to apply this algorithm to study the human islet amyloid polypeptide (hIAPP) peptides, and its aggregation may result in reducing working β-cells in the Type 2 diabetes patients. I have also presented our recent results on applying Markov State Models to elucidate the molecular mechanisms of gene transcription. Transcription is the first step in reading genomic DNA. Transcriptional regulation plays a key role in cell differentiation and other fundamental processes. Misregulation of transcription is a major factor in cancer and other human diseases. Thus, elucidating the mechanism of transcription is crucial for understanding these processes. Our simulation results are able to provide dynamic information for the transcription, and this dynamic information is largely inaccessible to present experimental techniques. Voir l'article complet
  8. Dr. Snow, a new investigator at Colorado State University, gave a presentation focused on upcoming research. A unifying theme of this research is the engineering of new, synthetic proteins with applications in bioenergy & medicine. Specifically, Snow and colleagues are using computational protein design to engineer new cellulase enzymes for more efficient and economical biofuel production. To facilitate these calculations, the Snow group is developing software (SHARPEN) that could be deployed on the Folding@Home network. The technical barriers to developing a SHARPEN F@h core were discussed. Notably, F@h can still contribute to these design problems using existing molecular dynamics simulations. Voir l'article complet
  9. Here's a guest post from Prof. Dr. Michael Shirts (University of Virginia). The open source Gromacs molecular simulation engine has been one of the main simulation tools in Folding@Home for almost a decade. I discussed new tools being introduced in Gromacs 4.6 that will be very useful for biomolecular studies run on Folding@Home. In particular, these new tools will make it much easier to quantitatively estimation the interaction strength of small molecules with proteins. Knowing the strength of these interactions makes it possible to predict how effective proposed new drugs will be. Voir l'article complet
  10. Here's a guest post from Prof. Dr. Peter Kasson (University of Virginia). FahCon2012 was quite an exciting conference. We shared some of our work relating both to new methodology and to our mixed computational & experimental work on influenza. We also enjoyed hearing about many important developments from other FAH researchers. Why do we study influenza? First, influenza kills about 40,000 people each year in the US alone and many more worldwide. These are mostly children under 2 and adults over 60, but all of us who hope to live past 60 and have children we care about find this a matter of some concern. Second, influenza has a proven track record of causing global mass-mortality events, such as 1918. A similar virus today might easily kill in the range of 60 million people, and we’d like to be prepared. Third, influenza is an important model system for understanding other viruses such as HIV and cancer-causing viruses such as HPV, Heptatitis C virus, and Epstein-Barr virus. It may come as a surprise, but many cancers are virus-associated, and these form an important area for prevention. We have done a lot of work on how influenza gets into cells to replicate in the first place. This is an important therapeutic target, and it’s also critical for understanding why viruses like H5N1 “bird flu” have not become efficiently transmissible between people. Some of our new work looks at the protein folding in the membrane required for viral entry. We have some exciting new results that we’ll blog about as soon as they’re published. We also presented new developments on a software package that we’re very excited about: Copernicus. The Kasson, Lindahl, and Pande groups published a paper on Copernicus at SC11 last year, and the Kasson and Lindahl groups have been continuing development extensively. Copernicus essentially makes the back-end control of large-scale simulations much more transparent, so FAH researchers will be more easily able to integrate new methods. It also runs on supercomputers and cloud-computing platforms, so we can use these in addition to FAH, and non-FAH researchers can perform the same style of computation that we do on FAH. Since these changes are all on the server side, FAH donors shouldn’t notice a difference, but we’re excited about the new science that Copernicus can enable. Voir l'article complet
  11. Here's a guest post from Dr. Greg Bowman (UC Berkeley) about FAHcon 2012. I had the opportunity to present two projects at the first Folding@home conference (which was a terrific event!). The first project focused on new protein therapeutics. It has long been known that a protein called IL-2 can help stimulate an immune response, so in theory giving people with diseases like immune deficiencies IL-2 could be tremendously helpful. In practice, however, giving them IL-2 often leads to severe heart problems. To find a better solution, collaborators at Stanford designed a variant of IL-2 that can stimulate an immune response without causing any side effects. However, they couldn't understand how it worked because the two proteins had almost identical structures! Using Folding@home, we showed that IL-2 is a relatively floppy protein while our collaborators' variant is locked into a structure that is poised to stimulate an immune response. The second project highlighted some new methods I've developed that could allow us to predict such behavior so that next time we can go recruit experimentalists instead of waiting for them to bring us interesting problems. Voir l'article complet
  12. Here's a guest post from Dr. Greg Bowman about FAHcon 2012. I had the opportunity to present two projects at the first Folding@home conference (which was a terrific event!). The first project focused on new protein therapeutics. It has long been known that a protein called IL-2 can help stimulate an immune response, so in theory giving people with diseases like immune deficiencies IL-2 could be tremendously helpful. In practice, however, giving them IL-2 often leads to severe heart problems. To find a better solution, collaborators at Stanford designed a variant of IL-2 that can stimulate an immune response without causing any side effects. However, they couldn't understand how it worked because the two proteins had almost identical structures! Using Folding@home, we showed that IL-2 is a relatively floppy protein while our collaborators' variant is locked into a structure that is poised to stimulate an immune response. The second project highlighted some new methods I've developed that could allow us to predict such behavior so that next time we can go recruit experimentalists instead of waiting for them to bring us interesting problems. Voir l'article complet
  13. On Friday May 25 at Stanford University, we had the first "all-hands on deck" scientific conference for the Folding@home Consortium. The goals were to discuss recent scientific advances, share new techniques for how to better use FAH, as well as to plan for new infrastructure advancements in FAH for the next year. I'll blog about some important news from the meeting in future posts. For now, I'll mention that the meeting worked out very well, with lots of new scientific advancements mentioned as well as great discussions on how we can make FAH better from the scientific and donor points of view. Here's a picture of (almost all of) the attendees. Pictured, from top left, going right: TJ Lane (Stanford), Dr. Jason Wagoner (Stanford), Prof. Dr. Vincent Voelz (Temple), Dr. Sidney Elmer (Sandia National Lab), Dr. Fancesco Pontaggia (Brandeis), Dr. Lan Hua (UCSF), Bruce Borden (FoldingForum.org), Joseph Coffland (Cauldron Development), Dr. Diwakar Shukla (Stanford), Dr. Lee-Ping Wang (Stanford), Steven Kearnes (Stanford), Kyle Beauchamp (Stanford), Dr. Greg Bowman (UC Berkeley), Dr. Relly Brandman (UCSF), Robert McGibbon (Stanford), Prof. Dr. Yu-Shan Lin (Tuffs), Prof. Dr. Matt Jacobson (UCSF), Prof. Dr. Jesus Izaguirre (Notre Dame), Prof. Dr. Vijay Pande (Stanford), Prof. Dr. Michael Shirts (University of Virginia), Dr. John Chodera (UC Berkeley/QB3), Prof. Dr. Peter Kasson (University of Virginia), Prof. Dr. Xuhu Huang (Hong Kong). Not shown: Prof. Dr. Chris Snow. Voir l'article complet
  14. We wanted to find a way to express in a single picture the immense collective effort that FAH donors and FAH teams comprise. We had several ideas internally and this is one of my favorite: we made a photo mosaic of the FAHicon out of team logos. We also have a link to a high res version. Voir l'article complet
  15. GUEST POST: Prof. Vincent Voelz, Temple University One of the projects we're excited about in the Voelz Lab is molecular simulation of synthetic polymers called peptoids. These are biomimetic molecules that can fold like proteins, but they have different structural properties. Several peptoids have been identified that can fold into unique three-dimensional structures, but better computational modeling is needed to identify the driving forces for folding and predict stable peptoid structures. If we can develop tools to do this, peptoids have the potential to be an amazing platform to design functionalized nanostructures that can be used for all kinds of applications, from biotherapeutics to nanomaterials. So far, we have shown that modern forcefields can accurately fold peptoids (DOI: http://dx.doi.org/10.1002/bip.21575) and are working with experimental collaborators on blind predictions of peptoid structure (stay tuned for more results here soon). This summer, we hope to be using Folding@home to commence large-scale simulations of peptoid folding for many peptoid sequences, in order to better understand peptoid folding mechanisms and design principles. We look forward to working closely with Folding@Home donors and testers on moving these projects forward -- you will no doubt see us on the forums frequently! One of the projects we're excited about in the <a href="http://voelzlab.org/">Voelz Lab</a> is molecular simulation of synthetic polymers called <a href="http://en.wikipedia.org/wiki/Peptoid">peptoids</a>. These are biomimetic molecules that can fold like proteins, but they have different structural properties. Several peptoids have been identified that can fold into unique three-dimensional structures, but better computational modeling is needed to identify the driving forces for folding and predict stable peptoid structures. If we can develop tools to do this, peptoids have the potential to be an amazing platform to design functionalized nanostructures that can be used for all kinds of applications, from biotherapeutics to nanomaterials. So far, we have shown that modern forcefields can accurately fold peptoids (DOI: http://dx.doi.org/10.1002/bip.21575) and are working with experimental collaborators on blind predictions of peptoid structure (stay tuned for more results here soon). This summer, we hope to be using Folding@Home to commence large-scale simulations of peptoid folding for many peptoid sequences, in order to better understand peptoid folding mechanisms and design principles. We look forward to working closely with Folding@Home donors and testers on moving these projects forward -- you will no doubt see us on the forums frequently! Voir l'article complet
  16. We will have a brief network outage in one of our server rooms today at 7pm PDT. This will not affect Folding@home other than to delay the normal hourly stats update. Once the outage is over, we will restart the update and the stats will be inserted into the db. So, this won't affect donor stats, other than to briefly delay our hourly update this morning. Voir l'article complet
  17. GUEST POST: Prof. Vincent Voelz, Temple University The Voelz Lab just started this past August in the Department of Chemistry at Temple University in Philadelphia, PA. We have just installed two Folding@home servers, and are gearing up to run simulations this summer (which I hope to talk about in future blog posts). In the meantime we have been very lucky to work with the Institute for Computational Molecular Science here at Temple, and a new high-peformance computing cluster to generate some initial data. One of our interests is using molecular simulation to do computational design of folding and binding properties. Design efforts require looking at folding for lots of different possible protein sequences, which is a natural task for a distributed computing platform like Folding@home. We're working on ways to leverage Markov State Models of conformational dynamics to do efficient estimation of the effects of sequence perturbations. A good starting point to test these ideas are to look at proteins for which many sequences have been characterized, to see if we can predict sequence-dependent changes. Many of these sequence mutations are important in human diseases, so we hope to gain insight into these process as well. Voir l'article complet
  18. Here's an update from Prof. Xuhui Huang's lab at Hong Kong University of Science and Technology, another collaborating labortory in the Folding@home consortium. In addition to the molecular recognition processes, another project his lab is working on at the Folding@home platform is to explore the folding free energy landscape of the human islet amyloid polypeptide (hIAPP). hIAPP (also called amylin) is a 37-residue peptide and its aggregation reduces working β-cells in patients with Type 2 diabetes. As an intrinsically disordered protein, hIAPP monomer does not have a folded global minimum in its folding free energy landscape, but contains many stable local minimums. Thus understanding the nature of these locally metastable states can help us to understand the mechanisms of the hIAPP aggregation, and further design small molecules to inhibit the amyloid formation. Just as we have seen in the Pande lab simulations of the Aß peptide in Alzheimer's run on Folding@home previously, this research may offer potential therapeutic agents for Type 2 diabetes. At Folding@home, we are currently running extensive molecular dynamics (MD) simulations and construct Markov State Model to elucidate the free energy landscape of the hIAPP monomer. Projects 2974 and 2975 are related to the above project. We would like to thank all the Folding@home donors for your help to make our research possible. Voir l'article complet
  19. If you'd like to get an email update on the latest news in from Folding@home, please sign up below. We're using Feedblitz and they make it easy to start and end email subscriptions. You can also find the email sign up box on the top right of the blog web page. Your email address: Voir l'article complet
  20. In the past, support for specific GPUs was built into the client. We are working on ways to automatically update this information more easily within the v7 client to support new GPUs, such as the Kepler GPUs which have just came out. While the automatic update isn't ready yet, here is how one can manually do this: 1) Download the GPUs.txt file from https://fah-web.stanford.edu/file-releases/public/GPUs.txt 2) Copy the downloaded GPUs.txt file to the client's run directory. The run directory is also called the data directory. It's the same location as the 'client.db' file. In Windows there is a link to this directory in the start menu. 3) After installing the file you must restart your client. The client has a built-in GPUs.txt which it will use if it does not find one on disk. The client will print a message to the log, very early on, when it reads GPUs.txt from the run directory. In a future version of the v7 client, this will happen automatically, but for now, we are updating this file on our web site and donors can do this update manually for new hardware. Voir l'article complet
  21. The Kasson group has recently published an article in the journal Biochemistry on how influenza binds cell-surface receptors. In this article, we discuss how computational techniques can be used for further analysis of structural and biochemical data on glycan binding by influenza. We review prior work that we have done in collaboration with the Pande group, including research using Folding@home. Those earlier papers can be found here and here. The table-of-contents graphic from our recent paper shows how dynamics the glycan (sugar) residues on the surface of the influenza hemagglutinin protein can be. 20 structures of the glycan molecules are superimposed on the receptor-binding pocket of hemagglutinin. Voir l'article complet
  22. We're installing a new web page for our main site http://folding.stanford.edu. While we're not done quite yet, the main changes are in. Hopefully the new site is cleaner and simpler, both in aesthetics and in ability to navigate. This also coincides with our official rollout of the version 7 (v7) client software for Folding@home. This new client is a complete rewrite with the intention to make it much easier for donors to contribute to Folding@home. In particular, the new client unifies the classic, SMP, and GPU clients into a single download. Also, installation (especially of the more high performance clients such as SMP and GPU) is much easier than before. Finally, the revamped viewer should also be a much better user experience for FAH donors. All in all, our hope is that these combined changes make it much easier for people to understand what we're about and to help contribute to Folding@home. Voir l'article complet
  23. I'm very excited to finally talk about some key new results from our lab. These results have been a long time in coming and in many ways represents a major achievement for Folding@home (FAH) in general, demonstrating that the approach we started 10 years ago can make significant steps forward in our long term goals. Specifically, our long term goals have been to 1) develop new methods to tackle the computational challenges of simulating protein folding; 2) apply these methods to gain new insights into protein folding; 3) use these methods and new insights to simulate Aß protein misfolding, a key process in the toxicity of Alzheimer's Disease (AD); and finally 4) to use those simulations to develop new small molecule drug candidates for AD. In the early years of FAH, we concentrated on the first two goals above. In the last 5-7 years, we have worked to accomplish the third goal. I'm now very excited to report our progress on the last goal –– using FAH for the development of new therapeutic strategies for AD. In a paper just published in the Journal of Medicinal Chemistry, we report on tests of predictions from earlier Folding@home simulations, and how these predictions have led to a new strategy to fight Alzheimer's Disease. While this is not a cure, it is a major step towards our final goal, some light at the end of the tunnel. The next steps, now underway in our lab, are to take this lead compound and help push it towards a viable drug. It's too early to report on our preliminary results there (I like to only talk publicly about work after it's passed through peer review), I'm very excited that the directions set out in this paper do appear to be bearing fruit in terms of a viable drug (not just a drug candidate). I hope I'll have more to say in the coming months! Design of β-Amyloid Aggregation Inhibitors from a Predicted Structural Motif Paul A. Novick†, Dahabada H. Lopes‡, Kim M. Branson†, Alexandra Esteras-Chopo§, Isabella A. Graef§, Gal Bitan‡, and Vijay S. Pande†* †Department of Chemistry, Stanford University, Stanford, California 94305, United States ‡ Department of Neurology, UCLA, Los Angeles, California 90095, United States; Brain Research Institute, UCLA, Los Angeles, California 90095, United States; Molecular Biology Institute, UCLA, Los Angeles, California 90095, United States § Department of Pathology, Stanford University, Stanford, California 94305, United States *Corresponding author. Abstract Drug design studies targeting one of the primary toxic agents in Alzheimer’s disease, soluble oligomers of amyloid β-protein (Aβ), have been complicated by the rapid, heterogeneous aggregation of Aβ and the resulting difficulty to structurally characterize the peptide. To address this, we have developed [Nle35, d- Pro37]Aβ42, a substituted peptide inspired from molecular dynamics simulations which forms structures stable enough to be analyzed by NMR. We report herein that [Nle35, d-Pro37]Aβ42 stabilizes the trimer and prevents mature fibril and β-sheet formation. Further, [Nle35, d-Pro37]Aβ42 interacts with WT Aβ42 and reduces aggregation levels and fibril formation in mixtures. Using ligand-based drug design based on [Nle35, d-Pro37]Aβ42, a lead compound was identified with effects on inhibition similar to the peptide. The ability of [Nle35, d-Pro37]Aβ42 and the compound to inhibit the aggregation of Aβ42 provides a novel tool to study the structure of Aβ oligomers. More broadly, our data demonstrate how molecular dynamics simulation can guide experiment for further research into AD. Voir l'article complet
  24. Today, I'm highlighting the work primarily out of Chris Garcia's lab at Stanford Medical School. The Garcia lab had a very exciting idea on how to re-engineer a very important protein and the Pande lab played a part by providing computer simulations to help understand the mechanism by which the new protein worked. The results are very exciting. Check out the link below for more details. http://medicalxpress.com/news/2012-03-scientists-boost-potency-side-effects.html SUMMARY. The utility of a naturally occurring protein given, sometimes to great effect, as a drug to treat advanced cancers is limited by the severe side effects it sometimes causes. But a Stanford University School of Medicine scientist has generated a mutant version of the protein whose modified shape renders it substantially more potent than the natural protein while reducing its toxicity. Voir l'article complet
  25. A key aspect of Folding@home research has been using computational methods to design new drugs, especially for Alzheimer’s Disease. At the University of Virginia, the Shirts lab is developing methods to leverage the power of Folding@home to develop new drugs to fight disease. Generally, small molecules work as drugs by binding very specifically to certain locations on important proteins. For example, an antibiotic works by binding to a protein on a bacteria, thus interfering with the pathogen's internal workings seriously enough to disable or kill it. By targeting only protein sites that are unique to the pathogen, drugs can act extremely specifically, rather than harming the human body or desired microbes. The exact same principles can toggle very specific parts of our own body's protein machinery on or off, allowing development of drugs that fight diseases of caused by breakdown, mutation, or malfunction our own cellular machinery, like Alzheimer’s Disease, heart disease, diabetes, and many other conditions. However, it is very hard to calculate exactly how tightly a given small molecule will bind to a target protein, or even exactly where and by what mechanism it will bind. A number of computational methods are used in industry today to estimate the binding affinity of small molecules in the process of drug design, but they mostly rely on approximations that are computationally cheap and very approximate, rather than more expensive methods that have the potential to be much more accurate. With Folding@home, we now have the capability to perform rigorous evaluations of these more complete methods, understand their limits, and make them more efficient and reliable. We have been developing our methods working mostly with well-understood model systems, such as FKBP, a protein on the immune system signaling pathway. Once the methods are well-understood, we will be moving on try to design small molecules to treat AIDS (the HIV reverse transcriptase enzyme, required for DNA to replicate) and influenza (various proteins involved in virus cell entry). Such molecules will still require significant effort to make into drugs, since drugs also have to dissolve easily, penetrate cells, and not be broken down to quickly, but being able to predict more easily which molecules interact tightly with the intended targets will be a huge step in the right direction. As part of our efforts to improve Folding@home infrastructure, we are also working to port new versions of the Gromacs molecular simulation platform to Folding@home and improving the interface and integration between Gromacs and Folding@home. Voir l'article complet
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