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Folding@home

Original author(s)	Vijay Pande
Developer(s)	Pande laboratory, Sony, Nvidia, ATI, Cauldron Development
Initial release	October 1, 2000
Stable release	Windows, Linux: 7.1.52 Mac OS X: 6.29.3 PlayStation 3: 1.4
Operating system	Microsoft Windows, Mac OS X, Linux
Platform	Cross-platform
Available in	English
Type	Distributed computing
License	Partially GPL, partially proprietary
Website	folding.stanford.edu

Folding@home (FAH or F@h) is a distributed computing project for simulation of protein folding, computational drug design, and other molecular dynamics for disease research. Folding@home is powered by the idle processing resources of thousands of personal computers and PlayStation 3s from volunteers who have installed the software on these systems. The project primarily attempts to determine the mechanisms of protein folding, (the process by which proteins reach their final three-dimensional structure) and the reasons behind protein misfolding. This is of significant academic interest and has major implications for medical research into Alzheimer's disease, Huntington's disease, and many forms of cancer, among other diseases. To a lesser extent, Folding@home also tries to predict a protein's final structure and determine how other molecules may interact with it, which has applications in drug design. Folding@home is developed and operated by the Pande laboratory at Stanford University, under the leadership of Vijay Pande, and is shared by various scientific institutions and research laboratories across the world in a collaboration known as the Folding@home Consortium.

The project uses statistical simulation methodology that represents a paradigm shift from traditional computational approaches. As part of the project's client-server architecture, the volunteered machines receive simulation Work Units, complete them, and return them to database servers where they are compiled into an overall simulation. Volunteers can track their contributions on the Folding@home website, which can make participation competitive and encourages long-term involvement. The project has pioneered the uses of GPUs, PlayStation 3s, and Message Passing Interface (used for computing on multi-core processors) for distributed computing and scientific research.

Folding@home remains one of the world's fastest computing systems, and currently operates at a computational performance nearly equal to all distributed computing projects under BOINC combined. The project is also the world's most powerful molecular dynamics simulator. This performance from its large-scale computing network has allowed researchers to run computationally expensive atomic-level simulations thousands of times longer than previously achieved. Since its launch on October 1, 2000, the Pande lab has produced 100 scientific research papers as a direct result of the project. These simulations have demonstrated accuracy compared to experimental observations.

Project significance

Proteins are an essential component to many biological functions and participate in virtually all processes within biological cells. They often act as enzymes, performing biochemical reactions including cell signaling, molecular transportation, and cellular regulation. As structural elements, some proteins act as a type of skeleton for cells, and as antibodies, other proteins participate in the immune system. Before a protein can take on these roles, it must fold into a functional three-dimensional structure, a process that often occurs spontaneously and is dependent on interactions within its amino acid sequence. Protein folding is driven by the search to find the most energetically favorable conformation of the protein, i.e. its native state. Thus, understanding protein folding is critical to understanding what a protein does and how it works, and is considered a "holy grail" of computational biology. Despite folding occurring within a crowded cellular environment, it typically proceeds smoothly. However, due to a protein's chemical properties or other factors, proteins may misfold — that is, fold down the wrong pathway and end up misshapen. Unless cellular mechanisms are capable of destroying or refolding such misfolded proteins, they can subsequently aggregate and cause a variety of debilitating diseases. Laboratory experiments studying these processes can be limited in scope and atomic detail, leading scientists to use physics-based computational models that, when complementing experiments, seek to provide a more complete picture of protein folding, misfolding, and aggregation.

Due to the complexity of proteins' conformation space and limitations in computational power, all-atom molecular dynamics simulations have been severely limited in the timescales which they can study. While most proteins typically fold in the order of milliseconds, prior to 2010 simulations could only reach nanosecond to microsecond timescales. General-purpose supercomputers have been used to simulate protein folding, but such systems are intrinsically expensive and typically shared between many different research groups, and because the computations in kinetic models are serial in nature, strong scaling of traditional molecular simulations to these architectures is exceptionally difficult. Additionally, as the protein folding process is stochastic, a limited number of long simulations are not sufficient for comprehensive views of protein folding.

Protein folding does not occur in a single step. Instead, proteins spend the majority of their folding time – nearly 96% in some cases – "waiting" in various intermediate conformational states, each a local thermodynamic free energy minimum in the protein's energy landscape. Through a process known as adaptive sampling, these conformations are used by Folding@home as starting points for a set of simulations trajectories. As the simulations discover more conformations, the trajectories are restarted from them, and a Markov state model (MSM) is gradually created from this cyclic process. MSMs are discrete-time master equation models which map out a biomolecule's conformational and energy landscape by describing its set of distinct structures and the transition rates between them. The adaptive sampling Markov state model approach significantly increases the efficiency of simulation as it avoids computation inside the local energy minimum itself, and is amenable to distributed computing (including on GPUGRID) as it allows for the statistical aggregation of short, independent simulation trajectories. The amount of time it takes to construct a Markov state model is inversely proportional to the number of parallel simulations run, i.e. the number of processors available. In other words, it achieves near-linear parallelization, leading to an approximately four orders of magnitude reduction in overall serial calculation time. A completed MSM illustrates the probability of folding events and pathways from the protein's phase space, may contain tens of thousands of states, and through kinetic clustering of the conformations it can represent these states at an arbitrary resolution. Researchers can use these MSMs to reveal how proteins misfold and to quantitatively compare simulations with experiments. Between 2000 and 2010, the timescales over which Folding@home simulates protein folding have increased by six orders of magnitude.

In 2002, Folding@home used Markov state models to complete approximately a million CPU days of simulations over the span of several months, and in 2011, MSMs parallelized another simulation that required an aggregate 10 million CPU hours of computation. In January 2010, Folding@home used MSMs to simulate the dynamics of the slow-folding 32-residue NTL9 protein out to 1.52 milliseconds, a timescale consistent with experimental folding rate predictions but a thousand times longer than previously achieved. The model consisted of many individual trajectories, each two orders of magnitude shorter. This was the first demonstration that MSMs were capable of statistically capturing folding events that could not be seen by conventional simulation methods. In 2010, Folding@home researcher Greg Bowman was awarded the Thomas Kuhn Paradigm Shift Award from the American Chemical Society for the instrumental development of the open-source MSMBuilder software and for attaining quantitative agreement between theory and experiment. For his work, Pande was awarded the 2012 Michael and Kate Bárány Award for Young Investigators for "developing field-defining and field-changing computational methods to produce leading theoretical models for protein and RNA folding" as well as the 2006 Irving Sigal Young Investigator Award "for his unique approach to employing advances in algorithms that make optimal use of distributed computing, which places his efforts at the cutting edge of simulations. The results have stimulated a re-examination of the meaning of both ensemble and single-molecule measurements, making Dr. Pande’s efforts pioneering contributions to simulation methodology."

Biomedical research

Protein folding is naturally tightly regulated to ensure that it proceeds smoothly. The failure of a protein to fold correctly can result in the development of a variety of diseases including alpha 1-antitrypsin deficiency, Alzheimer's disease, autism, cancer, Creutzfeldt–Jakob disease, cystic fibrosis, Huntington's disease, Mad Cow, Parkinson's disease, sickle-cell anaemia, and type II diabetes. Once it is understood how a protein misfolds, therapeutic intervention can follow, which can use engineered molecules to alter the production of a certain protein, to help destroy a misfolded protein, or to assist in the folding process. Cellular infection by viruses such as HIV and influenza also involve folding events within cellular membranes. Computer-assisted drug design has the potential to expedite and lower the costs of drug discovery. The combination of computational molecular modeling and experimental analysis has the possibility of fundamentally shaping the future of molecular medicine and the rational design of therapeutics. Folding@home is dedicated to producing significant amounts of results about protein folding, the diseases that result from protein misfolding, and the development of novel computational methods for drug design. The goal of the first five years of the project was to make significant advances in understanding folding, while the current goal is to understand misfolding and related disease, especially Alzheimer's disease.

The simulations run on Folding@home used in conjunction with laboratory experiments, but researchers can use it to study how folding in vitro differs from folding in native cellular environments. This is advantageous in studying aspects of folding, misfolding, and its relationship to disease that are exceptionally difficult to observe experimentally. For example, in 2011 Folding@home continued simulations of folding inside a ribosomal exit tunnel, to help scientists better understand how natural confinement and crowding might influence the folding process. Furthermore, scientists typically employ chemical denaturants to unfold proteins from their stable native state. It is not generally known how the denaturant affects the protein's refolding, and it is difficult to experimentally determine if these denatured states contain residual structures which may influence folding behavior. In 2010, Folding@home simulated the unfolded states of Protein L, and predicted the collapse rate in strong agreement with experimental results.

The Pande lab is a non-profit organization and does not sell the results generated by Folding@home. The large data sets from the project are freely available for other researchers to use upon request and some can be accessed from the Folding@home website. The Pande lab has collaborated with other molecular dynamics systems such as the Blue Gene supercomputer, and they share Folding@home's key software with other researchers, so that the algorithms which benefited Folding@home may aid other scientific areas. In 2011 they released the open-source Copernicus software, which is based on Folding@home's MSM and other parallelization techniques and aims to significantly improve the efficiency and scaling of molecular simulations on large computer clusters or supercomputers. Summaries of all of the scientific findings from Folding@home are posted on the Folding@home website after publication. The full publications are available online from an academic library.

Alzheimer's disease

Alzheimer's is an incurable neurodegenerative disease which most most often affects the elderly. It accounts for more than half of all cases of dementia, and as of 2008 it affects more than 24 million people worldwide, with 4.6 million new cases reported each year. Its exact cause remains unknown, but the disease is identified as a protein misfolding disease and is associated with toxic aggregations of the amyloid beta (Aβ) peptide, a fragment of the larger amyloid precursor protein. High concentrations of misfolded Aβ₄₂ causes protein oligomer growth leading to aggregation that in turn contributes to Aβ misfolding. This cyclic process appears to be toxic and leads to neuronal cell death. The oligomer aggregates then collect into dense nontoxic formations known as senile plaques, a pathological marker of Alzheimer's disease. Due to the heterogeneous nature of Aβ oligomer aggregates, experimental techniques such as x-ray crystallography and NMR have had difficulty characterizing their structures. Moreover, atomistic simulations are extremely computationally demanding due to their size and complexity.

Disabling Aβ aggregation using small molecules is regarded as a promising approach to the development of therapeutic drugs for treating Alzheimer's patients. The Pande lab is focusing their research on Alzheimer's with the goal of predicting the aggregate structure and how it develops for drug design approaches as well as developing methods to stop the aggregation process. In 2008 Folding@home simulated the dynamics of Aβ in atomic detail over timescales of the order of tens of seconds. This was significant as previous simulations were about six orders of magnitude shorter. Researchers used the resulting Markov state model to identify a β-Hairpin that was a major source of molecular interactions within the structure. This study helped prepare the Pande lab for future aggregation studies and for further research to find a small peptide which may stabilize the aggregation process. In the same year, Folding@home found several small drug candidates which appear to inhibit the toxicity of Aβ. In 2010, in close cooperation with the Nanomedicine Center for Protein Folding, these drug leads went from the test tube to testing on biological tissue. In 2011, Folding@home completed simulations of several mutations of Aβ that appear to stabilize the aggregate formation, which could aid in the development of therapeutic drug approaches to the disease as well as greatly assisting with experimental NMR spectroscopy studies of the oligomers. Later that year, Folding@home began simulations of various Aβ fragments in order to determine how various natural enzymes affect the structure and folding of Aβ.

Huntington's disease

Huntington's disease is a neurodegenerative genetic disorder that is also associated with protein misfolding and aggregation. Excessive repeats of the glutamine amino acid at the N-terminus of the Huntingtin protein cause aggregation, and although the behavior of the repeats is not completely understood, it does lead to the cognitive decline associated with the disease. As with other aggregates, there is difficulty in experimentally determining its structure. Scientists are using Folding@home to study Huntingtin protein aggregate structure as well as to predict how the aggregate forms, assisting with rational drug design approaches to stop the aggregate formation. The N17 fragment of the Huntingtin protein accelerates this aggregation, and while there have been several proposed mechanisms, its exact role in this process remains largely unknown. Folding@home has simulated this and other fragments in order to elucidate their roles in the disease. Since 2008, its drug design approaches for Alzheimer's disease have been applied to Huntington's, and in 2010, Folding@home researcher Veena Thomas proposed a novel therapeutic strategy for Huntington's which may be funded by the National Institutes of Health. This strategy could be used to bring the results from Folding@home directly to a therapeutic drug.

Cancer

More than half of all known cancers involve mutations of p53, a tumor suppressor protein present in every cell which regulates the cell cycle and signals for cell death in the event of damage to DNA. Specific mutations in p53 can disrupt these functions, allowing an abnormal cell to continue growing unchecked, resulting in the development of tumors. These deleterious mutations may differ between the various types and locations of cancer, and analysis of these mutations is important for understanding the root causes of p53-related cancers. In 2004, Folding@home was used to perform the first molecular dynamics study of the refolding of p53's protein dimer in explicit water which revealed insights that were previously unobtainable, and from it produced the first peer reviewed publication on cancer from a distributed computing project. The following year, it powered a new method to identify the amino acids crucial for the stability of a given protein, which was then used to study mutations of p53. The method demonstrated reasonable success in identifying cancer-promoting mutations and determined the effects of specific mutations which could not otherwise be measured experimentally. Following these studies, the Pande lab expanded their efforts to other p53-related diseases.

Folding@home is also being used to study protein chaperones, heat shock proteins which play essential roles in cell survival by assisting with the folding of other proteins inside the crowded and chemically stressful intracellular environment. Rapidly growing cancer cells rely on specific chaperones, and some chaperones play key roles in chemotherapy resistance. Inhibiting these specific chaperones are seen as potential modes of action for efficient antineoplastic drugs or for reducing the spread of cancer. Using Folding@home and working closely with the Protein Folding Center, the Pande lab hopes to find a drug which inhibits those chaperones involved in cancerous cells. Researchers are also using Folding@home to study other molecules related to cancer, such as the enzyme Src kinase and certain forms of the Engrailed homeodomain. In 2011, Folding@home began simulations of the dynamics of the small knottin protein EETI, which can identify carcinomas in imaging scans by binding to surface receptors of cancer cells. From simulations of this protein, they hope to accelerate research efforts to modify it to identify other diseases or to bind to drugs.

Interleukin 2 (IL-2) is a protein which plays crucial roles in helping T cells of the immune system attack pathogens and tumors. Unfortunately, its use as a cancer treatment is restricted due to serious side effects such as pulmonary edema. IL-2 binds to these pulmonary cells differently than it does to T cells, so IL-2 research involves understanding the differences between these binding mechanisms. In 2012, Folding@home assisted with the discovery of a form of IL-2 which is three hundred times more effective in its immune system role but carries fewer side effects. In experiments, this altered form significantly outperformed natural IL-2 in impeding tumor growth. Pharmaceutical companies have expressed interest in the mutant molecule, and the National Institutes of Health is testing it against a large variety of tumor models in the hopes of accelerating its development as a therapeutic.

Osteogenesis imperfecta

Osteogenesis imperfecta, also known as brittle bone disease, is a incurable genetic bone disorder which can be lethal. Those with the disease are unable to make functional connective bone tissue. This is most commonly due to a mutation in Type-I collagen, which fulfills a variety of structural roles and is the most abundant protein in mammals. The mutation causes a deformation in collagen's triple helix structure, which if not naturally destroyed, leads to abnormal and weakened bone tissue. In 2005, Folding@home tested a new quantum mechanical technique that improved upon previous simulations methods, and which may be useful for future computational studies of collagen. Although researchers have used Folding@home to study collagen folding and misfolding, the interest stands as a pilot project compared to Alzheimer's and Huntington's research.

Viruses

Folding@home is assisting in research towards preventing certain viruses such as influenza and HIV from recognizing and entering biological cells. Influenza in particular has been responsible for periodic high-mortality pandemics, such as the 1918 flu pandemic which may have killed upwards of 100 million people worldwide. Membrane fusion is an essential event for viral infection and involves conformational changes of viral fusion proteins and protein docking. A virus may enter after this process, or a virus may envelop itself in the cell's membrane. Membrane fusion is also crucial to a wide range of biological functions and controlling it has pharmaceutical implications, but the exact molecular mechanisms behind fusion remain largely unknown. Fusion events may involve the interactions of over a half million atoms for hundreds of microseconds. This complexity and timescale makes standard computer simulations exceptionally computationally demanding, so they are typically limited to about ten thousand atoms over tens of nanoseconds: a difference of several orders of magnitude. Moreover, such complex mechanisms are difficult to analyse experimentally. However, in 2006 scientists applied Markov state models and the Folding@home network to gain detailed mechanistic insights into the fusion process.

Using Folding@home for detailed simulations of vesicle fusion, in 2007 the Pande lab introduced a new technique for measuring fusion intermediate topology. In 2009, researchers used Folding@home to study mutations of influenza hemagglutinin, a protein that attaches a virus to its target cell and assists with viral entry. Mutations to hemagglutinin affect the binding affinity to the cell surface receptor glycan of a target species, which determines the infectivity of the virus strain to that species. Knowledge of the effects of hemagglutinin mutations assists in the development of antiviral drugs. In 2011 Folding@home began simulations of the dynamics of the enzyme RNase H, a key component of HIV, in the hopes of designing drugs to deactivate it. As of 2012, Folding@home continues to simulate the folding and interactions of hemagglutinin, complementing experimental studies at the University of Virginia.

Drug design

Drugs function by binding to specific locations on target molecules and causing a certain desired change. Ideally, a drug should act very specifically and bind only to its target without interfering with other biological functions. However, it is difficult to precisely determine where and how tightly two molecules will bind. Due to limitations in computational power, current in silico approaches usually have to trade speed for accuracy; e.g. use rapid protein docking methods instead of computationally expensive free energy calculations. Folding@home's computational performance allows researchers to use both techniques, and evaluate their efficiency and reliability. Computer-assisted drug design has the potential to expedite and lower the costs of drug discovery. In 2010, Folding@home used MSMs and free energy calculations to predict the native structure of the villin protein to within 1.8 Å RMSD from the crystalline structure experimentally determined through X-ray crystallography. This may be important to future protein structure prediction approaches, including for intrinsically unstructured proteins. Scientists have used Folding@home to research drug resistance by studying vancomycin, an antibiotic of "last resort", and beta-lactamase, a protein that can break down antibiotics like penicillin. They hope to be better able to design drugs to deactivate them.

Chemical activity occurs along a protein's active site. Traditional drug design approaches involve tightly binding to this site and blocking its activity, under the assumption that the target protein exists in a single rigid structure. However, this approach only works for approximately 15% of all proteins. Proteins contain allosteric sites which, when bound to by small molecules, can alter a protein's conformation and ultimately affect the protein's activity. These sites are attractive drug targets, but locating them is very computationally expensive. In 2012, Folding@home and MSMs were used to identify allosteric site in three medically relevant proteins: ß lactamase, interleukin-2, and RNase H.

Approximately half of all known antibiotics interfere with the workings of a bacteria's ribosome, a large and complex biochemical machine that performs protein biosynthesis by translating messenger RNA into proteins. Macrolide antibiotics clog the ribosome's exit tunnel, preventing synthesis of essential bacterial proteins. In 2007 the Pande lab received a grant to study and design new antibiotics. In 2008 they used Folding@home to study the interior of this tunnel and how specific molecules may affect it. The full structure of the ribosome has only been recently determined, and Folding@home has also simulated ribosomal proteins, as many of their functions remain largely unknown. Ribosomal research has helped the Pande lab prepare for larger and more complex biomedical problems.

Participation

In addition to reporting active processors, Folding@home also determines its computing performance as measured in FLOPS based on the actual execution time of its calculations. Originally this was reported as native FLOPS, that is, the raw performance from each given type of processing hardware. In March 2009 Folding@home began reporting the performance in both native and x86 FLOPS: the latter being an estimation of how many FLOPS the calculation would take on the standard x86 architecture, which is commonly used as a performance reference. Specialized hardware such as GPUs can efficiently perform certain complex functions in a single FLOP which would otherwise require multiple FLOPS on the x86 architecture. This x86 measurement attempts to even out these hardware differences. Despite using conservative conversions, for the GPU and PS3 clients x86 FLOPS are consistently much greater than their native FLOPS and comprise a large majority of Folding@home's FLOP performance.

In 2007 Guinness recognized Folding@home as the most powerful distributed computing network in the world. As of September 13, 2012, the project has 217,215 active CPUs, 19,623 active GPUs, and 16,252 active PS3s, for a total of 3.654 native petaFLOPS (5.354 x86 petaFLOPS). At the same time, the combined efforts of all distributed computing projects under BOINC totals 6.174 petaFLOPS from 447,720 active hosts. Using the Markov state model approach, Folding@home achieves strong scaling across its user base and gains a near-linear speedup for every additional processor. This large and powerful network allows Folding@home to do work not possible any other way.

Active participation in Folding@home has grown steadily since its launch. In March 2002 Google co-founder Sergey Brin launched Google Compute as add-on for the Google Toolbar. Although limited in functionality and scope, it increased participation in Folding@home from 10,000 up to about 30,000 active CPUs. The program ended in October 2005 in favor of the official Folding@home clients, and is no longer available for the Toolbar. Folding@home also gained participants from Genome@home, another distributed computing project from the Pande lab and a sister project to Folding@home. The goal of Genome@home was protein design and associated applications. Following its official conclusion in March 2004, users were asked to donate computing power to Folding@home instead.

Performance

On September 16, 2007, due in large part to the participation of Playstation 3s, the Folding@home project officially attained a sustained performance level higher than one native petaFLOP, becoming the first computing system of any kind in the world to do so. On May 7, 2008, the project attained a sustained performance level higher than two native petaFLOPS, followed by the three and four native petaFLOPS milestones on August 20 and September 28, 2008 respectively. It was the first computing project to do so. Then on February 18, 2009, Folding@home achieved a performance level of just above five native petaFLOPS. Most recently, on November 10, 2011, Folding@home's performance exceeded six native petaFLOPS with the equivalent of nearly eight x86 petaFLOPS.

Points

Similarly to other distributed computing projects, Folding@home quantitatively assesses user computing contributions to the project through a credit system. All units from a given protein project have uniform base credit, which is determined by benchmarking one or more Work Units from that project on an official reference machine before the project is released. Each user receives these base points for completing every Work Unit, though through the use of a passkey they can receive additional bonus points for reliably and rapidly completing units which are more computationally demanding or have a greater scientific priority. Users may also receive credit for their work by clients on multiple machines. This generates a fair system of equal pay for equal work, and attempts to align credit with the value of the scientific results.

Users can register their contributions under a team, which combine the points of all their members. A user can start their own team, or they can join an existing team. In some cases, a team may have their own community-driven sources of help or recruitment such as an Internet forum. The points can foster friendly competition between individuals and teams to compute the most for the project, which can benefit the folding community and accelerate scientific research. Individual and team statistics are posted on the Folding@home website.

Software

Folding@home software at the user's end involves three primary components: Work Units, cores, and a client.

Work Units

A Work Unit is the protein data that the client is asked to process. Work Units are a fraction of the simulation between the states in a Markov state model. After the Work Unit has been downloaded and completely processed, it is returned and the respective credit points are awarded, and this cycle then repeats automatically. All Work Units have associated deadlines, and if this deadline is exceeded, the user may not get credit and the unit will be automatically reissued to another participant. As protein folding is serial in nature and many Work Units are generated from their predecessors, this allows the overall simulation process to proceed normally if one is not returned after a certain period of time. Due to these deadlines, the minimum system requirement for Folding@home is a Pentium 3 450 MHz CPU with SSE or newer. However, Work Units for high-performance clients have a much shorter deadline than those for the uniprocessor client, as a major part of the scientific benefit is dependent on rapidly completing simulations.

Before public release, Work Units go through several quality assurance steps to keep problematic ones from becoming fully available. These stages include internal testing, closed beta testing, and open beta testing, before a final full release across all of Folding@home. Folding@home's Work Units are normally processed only once, except in the rare event that errors occur during processing. If this occurs for three different users, the unit is automatically pulled from distribution. The Folding@home support forum can be used to differentiate between problematic hardware and a bad Work Unit.

Cores

Specialized molecular dynamics programs, referred to as "FahCores" and often abbreviated "cores", perform the calculations on the Work Unit behind the scenes. A large majority of Folding@home's cores are based on GROMACS, one of the fastest and most popular molecular dynamics software packages available, which largely consists of manually optimized assembly code and hardware optimizations. Although GROMACS is open-source software and there is a cooperative effort between the Pande lab and GROMACS developers, Folding@home uses a closed-source license for data validity reasons. Less active cores include ProtoMol and SHARPEN. Folding@home has used AMBER, CPMD, Desmond, and TINKER, but these have since been retired and are no longer in active service. Some of these cores perform explicit solvation calculations in which the surrounding solvent (usually water) is modeled atom-by-atom; while others perform implicit solvation methods, where the solvent is treated as a mathematical continuum. The core is separate from the client to enable the scientific methods to be updated automatically without requiring a client update. The cores periodically create calculation checkpoints so that if they are interrupted they can resume work from that point upon startup.

Client

Folding@home participants install a client program on their personal computer or on the PlayStation 3 gaming console. The user interacts with the client, which manages the other software components behind the scenes. Through the client, the user may pause the folding process, open an event log, check the work progress, or view personal statistics. The computer clients run continuously in the background at an extremely low priority, utilizing otherwise unused processing power so that normal computer usage is unaffected. The maximum CPU utilization can also be adjusted through client settings. The client connects to a Folding@home server and retrieves a Work Unit and may also download the appropriate core for the client's settings, operating system, and the underlying hardware architecture. After processing, the Work Unit is returned to the Folding@home servers. Computer clients tailor to uniprocessor and multi-core processors systems, as well as graphics processing units. While these latter clients use significantly more resources, the diversity and power of each hardware architecture provides Folding@home with the ability to efficiently complete many different types of simulations in a timely manner, (in a few weeks or months rather than years) which is of significant scientific value. Together, these clients allow researchers to study biomedical questions previously considered impossible to tackle computationally.

Folding@home software developers put significant work goes into minimizing security issues. For example, clients can be downloaded only from the official Folding@home website or its commercial partners. Folding@home's End-User License Agreement forbids public access to the client source code for security and scientific integrity reasons. Each client will upload and download data only from Stanford's Folding@home data servers (over port 8080, with 80 as an alternative) using 2048-bit digital signatures for verification and will only interact with Folding@home computer files. Thus from a security standpoint it behaves in a similar fashion to a web browser, but is even more secure.

Folding@home's first client was a screensaver, which would run Folding@home while the computer was not otherwise in use. In 2004 the Pande lab collaborated with David Anderson to test a supplemental client on the open-source BOINC framework. This client was released to closed beta in April 2005; however, the approach became unworkable and was abandoned in June 2006. BOINC's fixed architecture limits the types of project it can accommodate and thus was not appropriate for Folding@home.

Graphics processing units

The specialized hardware of GPUs is designed to accelerate rendering of 3D graphics applications such as video games and can significantly outperform CPUs for certain types of calculations. Although limited in generality, this makes GPUs one of the most powerful and rapidly growing computational platforms. As such, general purpose GPU computing is the pursuit of many scientists and researchers. However, GPU hardware is difficult to utilize for non-graphics tasks and usually requires significant algorithm restructuring and an advanced understanding of the underlying architecture. Such customization is challenging, especially to researchers with limited software development resources. Folding@home uses the open source OpenMM library, which uses two API levels to interface molecular simulation software to an underlying hardware architecture. With the addition of hardware optimizations, OpenMM-based GPU simulations do not require significant modification but achieve performance nearly equal to hand-tuned GPU code, and greatly outperform CPU implementations.

Prior to 2010 the computational reliability of GPGPU consumer-grade hardware had remained largely unknown, and circumstantial evidence related to the lack of built-in error detection and correction in GPU memory raised reliability concerns. In the first large-scale test of GPU scientific accuracy, a 2010 study of over 20,000 hosts on the Folding@home network detected soft errors in the memory subsystems of two-thirds of the tested GPUs. These errors strongly correlated to board architecture, though the study concluded that reliable GPU computing was very feasible as long as attention is paid to the hardware characteristics, such as through the use of software-side error detection.

The first generation of Folding@home's Windows GPU client (GPU1) was released to the public on October 2, 2006, delivering a 20-30X speedup for certain calculations over its CPU-based GROMACS counterparts. It was the first time GPUs had been used for either distributed computing or major molecular dynamics calculations. GPU1 gave researchers significant knowledge and experience with the development of GPGPU software, but in response to scientific inaccuracies with DirectX, on April 10, 2008 it was succeeded by GPU2, the second generation of the client. Following its introduction, GPU1 was officially retired on June 6. Compared to GPU1, GPU2 was more scientifically reliable and productive, ran on both ATI and CUDA-enabled Nvidia GPUs, and supported more advanced algorithms, larger proteins, and real-time visualization of the protein simulation. Following this, the third generation of Folding@home's GPU client (GPU3) was released on May 25, 2010. While backwards compatible to GPU2, GPU3 was comparatively more stable and efficient, had greater flexibility in its scientific capabilities, and used OpenMM on top of an OpenCL framework. Although the GPU clients do not natively support the Linux operating system, users with Nvidia graphics cards can run them under the WINE software. GPUs remain Folding@home's most powerful platform in terms of FLOPS; as of September 2012 GPU clients account for 76% of the entire project's x86 FLOP throughput.

PlayStation 3

Folding@home can also take advantage of the computing power of PlayStation 3's. At the time of its inception and for certain calculations, its main streaming Cell processor delivered a 20x speed increase over PCs, processing power which could not be found on other systems such as the Xbox 360. The PS3's high speed and efficiency introduced other opportunities for worthwhile optimizations, and significantly changed the tradeoff between computational efficiency and overall accuracy, allowing for the utilization of more complex molecular models at little additional computational cost. This allowed Folding@home to run biomedical calculations that would otherwise be computationally infeasible.

The PS3 client was developed in a collaborative effort between Sony and the Pande lab and was first released as a standalone client on March 23, 2007. Its release made Folding@home the first distributed computing project to utilize PS3s. On September 18 of the following year, the PS3 client became a channel of Life with PlayStation on its launch. In terms of the types of calculations it can perform, at the time of its introduction the client took the middle ground between a CPU's flexibility and a GPU's speed. However, unlike CPUs and GPUs, users cannot perform other activities on their PS3 while running Folding@home. The PS3's uniform console environment makes support easier and makes Folding@home more user friendly. The PS3 also has the ability to stream data quickly to its GPU, and is capable of real-time atomic-level visualizations of the current protein dynamics.

Multi-core processing client

Folding@home can also utilize the parallel processing capabilities of modern multi-core processors. The ability to use several CPU cores simultaneously allows completion of the overall folding simulation much faster. Working together, these CPU cores complete single Work Units proportionately faster than the standard uniprocessor client, which reduces the traditional difficulties of scaling a large simulation to many separate processors. While this approach is not only scientifically valuable, the resulting publications would not have been possible without this computing power.

In November 2006 first-generation symmetric multiprocessing (SMP) clients were publicly released for open beta testing, referred to as SMP1. These clients used Message Passing Interface (MPI) communication protocols for parallel processing, as at that time the GROMACS cores were not designed to be used with multiple threads. This was the first time a distributed computing project had utilized MPI, as it had previously been reserved only for supercomputers, and SMP1 represented a landmark in the simulation of protein folding. Although the clients performed well in Unix-based operating systems such as Linux and Mac's OS-X, they were troublesome under Windows. On January 24, 2010, SMP2, the second generation of the SMP clients and the successor to SMP1, was released as an open beta and replaced the complex MPI with a more reliable thread-based implementation.

SMP2 supported a trial of a special category of "bigadv" Work Units, designed for simulating proteins that are unusually large and computationally intensive and have a great scientific priority. These units originally required a minimum of eight CPU cores, which was later increased on February 7, 2012 to sixteen CPU cores. In addition to these additional hardware requirements over standard SMP2 Work Units, they also require more system resources such as RAM and Internet bandwidth. In return, users who run these are rewarded with a 20% increase over SMP2's bonus point system. The bigadv category allows Folding@home to run particularly demanding simulations on long timescales that had previously required the use of supercomputing clusters and could not be performed anywhere else on Folding@home.

V7

The V7 client is the seventh and latest generation of the Folding@home client software, and is a complete rewrite and unification of the previous clients for Microsoft Windows, Mac OS X and Linux operating systems. Like its predecessors, V7 can also run Folding@home in the background at a very low priority, allowing other applications to use CPU resources as they need. It is designed to make the installation, start-up, and operation more user-friendly for novices, as well as offers greater scientific flexibility to researchers than previous clients. V7 uses Trac for managing its bug tickets so that users can see its development process and provide feedback. It was officially released on March 22, 2012.

V7 consists of four integrated elements. The user typically interacts with V7's open-source GUI, known as FAHControl. This has Novice, Advanced, and Expert user interface modes, and has the ability to monitor, configure, and control many remote folding clients from a single computer. FAHControl directs FAHClient – a back-end application that in turn manages each FAHSlot (or "slot"). Each slot acts as replacement for the previously distinct Folding@home v6 uniprocessor, SMP, or GPU computer clients, as it can download, process, and upload Work Units independently. The FAHViewer function, modeled after the PS3's viewer, displays a real-time 3D rendering, if available, of the protein currently being processed.

Comparison to other molecular systems

Rosetta@home is a distributed computing project aimed at protein structure prediction and is one of the most accurate tertiary structure predictors available. The conformational states from Rosetta's software can be used to initialize a Markov state model as starting points for Folding@home simulations. Conversely, structure prediction algorithms can be improved from thermodynamic and kinetic models and the sampling aspects of protein folding simulations. As Rosetta only tries to predict the final folded state, and not how proteins fold, Rosetta@home and Folding@home are complementary and address very different molecular questions.

Anton is a special-purpose supercomputer constructed for molecular dynamics simulations. As of October 2011 Anton and Folding@home are the two most powerful molecular dynamics systems. Anton is unique in its ability to produce single ultra-long computationally expensive molecular trajectories, such as one in 2010 which reached the millisecond range. However, Anton does not use Markov state models for analysis. In 2011 the Pande lab constructed a MSM from two 100-µs Anton simulations and found alternative folding pathways that were not visible through Anton's traditional analysis. They concluded that there was little difference between MSMs constructed from a limited number of long trajectories or one assembled from many shorter trajectories. In June 2011 Folding@home began additional sampling of an Anton simulation in an effort to better determine how its techniques compare to Anton's methods. However, unlike Folding@home's shorter trajectories, which are more amendable to distributed computing and other parallelization techniques, longer trajectories do not require adaptive sampling to sufficiently sample the protein's phase space. Due to this, it is possible that a combination of Anton's and Folding@home's simulation methods would provide a more thorough sampling of this space.

See also

• Computational biology
• Foldit
• List of distributed computing projects
• Molecular dynamics
• Molecular modeling on GPUs
• Rosetta@home
• Software for molecular modeling
• Storage@home

Notes

Note 1: Supercomputer FLOP performance is assessed by running the legacy LINPACK benchmark. This short-term testing has difficulty in accurately reflecting sustained performance on real-world tasks because LINPACK more efficiently maps to supercomputer hardware. Computing systems also vary in architecture and design, so direct comparison is difficult. Despite this, FLOPS remain the primary speed metric used in supercomputing. In contrast, Folding@home determines its FLOPS using wall clock time by measuring how much time its Work Units take to complete.

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55. Nicholas W. Kelley, Xuhui Huang, Stephen Tam, Christoph Spiess, Judith Frydman and Vijay S. Pande (2009). "The predicted structure of the headpiece of the Huntingtin protein and its implications on Huntingtin aggregation". Journal of Molecular Biology. 388 (5): 919–27. doi:10.1016/j.jmb.2009.01.032. PMC 2677131. PMID 19361448.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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59. L. T. Chong, C. D. Snow, Y. M. Rhee, and V. S. Pande. (2004). "Dimerization of the p53 Oligomerization Domain: Identification of a Folding Nucleus by Molecular Dynamics Simulations". Journal of Molecular Biology. 345 (4): 869–878. doi:10.1016/j.jmb.2004.10.083. PMID 15588832.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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61. Lillian T. Chong, William C. Swope, Jed W. Pitera, and Vijay S. Pande (2005). "Kinetic Computational Alanine Scanning: Application to p53 Oligomerization". Journal of Molecular Biology. 357 (3): 1039–1049. doi:10.1016/j.jmb.2005.12.083. PMID 16457841.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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69. Aron M. Levin, Darren L. Bates, Aaron M. Ring, Carsten Krieg, Jack T. Lin, Leon Su, Ignacio Moraga, Miro E. Raeber, Gregory R. Bowman, Paul Novick, Vijay S. Pande, C. Garrison Fathman, Onur Boyman, and K. Christopher Garcia (2012). "Exploiting a natural conformational switch to engineer an interleukin-2 'superkine'". Nature. 484 (7395): 529–33. Bibcode:2012Natur.484..529L. doi:10.1038/nature10975. PMC 3338870. PMID 22446627.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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77. Peter Kasson. "Membrane Fusion". Kasson lab. Stanford University. Retrieved April 5, 2012.
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79. Peter M. Kasson, Daniel L. Ensign and Vijay S. Pande (2009). "Combining Molecular Dynamics with Bayesian Analysis To Predict and Evaluate Ligand-Binding Mutations in Influenza Hemagglutinin". Journal of the American Chemical Society. 131 (32): 11338–11340. doi:10.1021/ja904557w. PMC 2737089. PMID 19637916.
80. Peter M. Kasson, Vijay S. Pande (2009). "Combining mutual information with structural analysis to screen for functionally important residues in influenza hemagglutinin". Pacific Symposium on Biocomputing: 492–503. PMC 2811693. PMID 19209725.
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82. Vijay Pande (February 24, 2012). "Protein folding and viral infection". Folding@home. typepad.com. Retrieved March 4, 2012.
83. Vijay Pande (February 27, 2012). "New methods for computational drug design". Folding@home. typepad.com. Retrieved April 1, 2012.
84. Guha Jayachandran, M. R. Shirts, S. Park, and V. S. Pande (2006). "Parallelized-Over-Parts Computation of Absolute Binding Free Energy with Docking and Molecular Dynamics". Journal of Chemical Physics. 125 (8): 084901. Bibcode:2006JChPh.125h4901J. doi:10.1063/1.2221680. PMID 16965051.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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86. ^ Gregory Bowman (July 23, 2012). "Searching for new drug targets". Folding@home. typepad.com. Retrieved September 27, 2011.
87. Gregory R. Bowman and Phillip L. Geissler (2012). "Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites". PNAS. 109 (29): 11681. Bibcode:2012PNAS..10911681B. doi:10.1073/pnas.1209309109. {{cite journal}}: Unknown parameter |month= ignored (help)
88. Paula M. Petrone, Christopher D. Snow, Del Lucent, and Vijay S. Pande (2008). "Side-chain recognition and gating in the ribosome exit tunnel". Proceedings of the National Academy of Sciences. 105 (43): 16549. Bibcode:2008PNAS..10516549P. doi:10.1073/pnas.0801795105.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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92. ^ Pande lab (updated daily). "Client Statistics by OS". Folding@home. Stanford University. Retrieved September 13, 2012. {{cite web}}: Check date values in: |date= (help)
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101. Vijay Pande, Stefan Larson (March 4, 2002). "Genome@home Updates". April 15, 2004 Update. Retrieved March 17, 2012.
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126. Dragan Zakic (May 2009). "Community Grid Computing — Studies in Parallel and Distributed Systems" (PDF). Massey University College of Sciences. Massey University. Retrieved April 5, 2012.
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131. Pande lab (July 23, 2012). "Folding@home Passkey FAQ" (FAQ). Folding@home. Stanford University. Retrieved August 20, 2012.
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134. ^ Adam Beberg, Daniel Ensign, Guha Jayachandran, Siraj Khaliq, Vijay Pande (2009). "Folding@home: Lessons From Eight Years of Volunteer Distributed Computing" (PDF). Parallel & Distributed Processing, IEEE International Symposium: 1–8. doi:10.1109/IPDPS.2009.5160922. ISBN 978-1-4244-3751-1. ISSN 1530-2075.{{cite journal}}: CS1 maint: multiple names: authors list (link)
135. Norman Chan (April 6, 2009). "Help Maximum PC's Folding Team Win the Next Chimp Challenge!". Maximumpc.com. Future US, Inc. Retrieved September 6, 2011.
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137. Vijay Pande (April 5, 2011). "More transparency in testing". Folding@home. typepad.com. Retrieved October 14, 2011.
138. Bruce Borden (August 7, 2011). "Re: Gromacs Cannot Continue Further". Folding@home. phpBB Group. Retrieved August 7, 2011.
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140. PantherX (October 31, 2010). "Troubleshooting Bad WUs". Folding@home. phpBB Group. Retrieved August 7, 2011.
141. Carsten Kutzner, David Van Der Spoel, Martin Fechner, Erik Lindahl, Udo W. Schmitt, Bert L. De Groot, and Helmut Grubmüller (2007). "Speeding up parallel GROMACS on high-latency networks". Journal of Computational Chemistry. 28 (12): 2075–2084. doi:10.1002/jcc.20703. PMID 17405124.{{cite journal}}: CS1 maint: multiple names: authors list (link)
142. Berk Hess, Carsten Kutzner, David van der Spoel, and Erik Lindahl (2008). "GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation". Journal of Chemical Theory and Computation. 4 (3): 435–447. doi:10.1021/ct700301q.{{cite journal}}: CS1 maint: multiple names: authors list (link)
143. Pande lab (August 19, 2012). "Folding@home Gromacs FAQ" (FAQ). Folding@home. Stanford University. Retrieved August 20, 2012.
144. Pande lab (August 7, 2012). "Folding@home Frequently Asked Questions (FAQ) Index" (FAQ). Folding@home. Stanford University. Retrieved August 20, 2012.
145. Vijay Pande (September 25, 2009). "Update on new FAH cores and clients". Folding@home. typepad.com. Retrieved February 24, 2012.
146. ^ M. S. Friedrichs, P. Eastman, V. Vaidyanathan, M. Houston, S. LeGrand, A. L. Beberg, D. L. Ensign, C. M. Bruns, V. S. Pande (2009). "Accelerating Molecular Dynamic Simulation on Graphics Processing Units". Journal of Computational Chemistry. 30 (6): 864–72. doi:10.1002/jcc.21209. PMC 2724265. PMID 19191337.{{cite journal}}: CS1 maint: multiple names: authors list (link)
147. Pande lab (August 19, 2012). "Folding@home Petaflop Initiative". Folding@home. Stanford University. Retrieved August 20, 2012.
148. ^ Pande lab (February 10, 2011). "Windows Uniprocessor Client Installation Guide". Folding@home. Stanford University. Retrieved August 20, 2012.
149. PantherX (September 2, 2010). "Re: Can Folding@home damage any part of my PC?". Folding@home. phpBB Group. Retrieved February 25, 2012.
150. ^ Pande lab. "Folding@home Distributed Computing Client". Folding@home. Stanford University. Retrieved August 20, 2012.
151. Vijay Pande (June 28, 2008). "Folding@home's End User License Agreement (EULA)". Folding@home. Retrieved May 15, 2012.
152. ^ Pande lab (May 30, 2012). "Uninstalling Folding@home FAQ". Folding@home. Stanford University. Retrieved August 20, 2012.
153. M. R. Shirts and V. S. Pande. (2000). "Screen Savers of the World, Unite!". Science. 290 (5498): 1903–1904. doi:10.1126/science.290.5498.1903. PMID 17742054.
154. Rattledagger, Vijay Pande (April 1, 2005). "Folding@home client for BOINC in beta "soon"". Boarddigger.com. Anandtech.com. Retrieved April 5, 2012.
155. ^ Pande lab (May 30, 2012). "High Performance FAQ" (FAQ). Folding@home. Stanford University. Retrieved August 20, 2012.
156. John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron Lefohn, Timothy J. Purcell (2007). "A Survey of General-Purpose Computation on Graphics Hardware". Computer Graphics Forum. 26 (1): 80–113. doi:10.1111/j.1467-8659.2007.01012.x.{{cite journal}}: CS1 maint: multiple names: authors list (link)
157. P. Eastman and V. S. Pande (2010). "OpenMM: A Hardware-Independent Framework for Molecular Simulations". Computing in Science and Engineering. 12 (4): 34–39. doi:10.1109/MCSE.2010.27. ISSN 1521-9615.
158. I. Haque and V. S. Pande (2010). "Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU". 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid): 691–696. doi:10.1109/CCGRID.2010.84. ISBN 978-1-4244-6987-1.
159. ^ Pande lab (March 18, 2011). "ATI FAQ" (FAQ). Folding@home. Stanford University. Retrieved March 21, 2012.
160. Vijay Pande (May 23, 2008). "GPU news (about GPU1, GPU2, & NVIDIA support)". Folding@home. typepad.com. Retrieved September 8, 2011.
161. Travis Desell, Anthony Waters, Malik Magdon-Ismail, Boleslaw K. Szymanski, Carlos A. Varela, Matthew Newby, Heidi Newberg, Andreas Przystawik, and David Anderson (2009). "Accelerating the MilkyWay@Home volunteer computing project with GPUs". 8th International Conference on Parallel Processing and Applied Mathematics (PPAM 2009) Part I. pp. 276–288. ISBN 978-3-642-14389-2.{{cite book}}: CS1 maint: multiple names: authors list (link)
162. Vijay Pande (April 10, 2008). "GPU2 open beta". Folding@home. typepad.com. Retrieved September 7, 2011.
163. Vijay Pande (April 15, 2008). "Updates to the Download page/GPU2 goes live". Folding@home. typepad.com. Retrieved September 7, 2011.
164. Vijay Pande (April 11, 2008). "GPU2 open beta going well". Folding@home. typepad.com. Retrieved September 7, 2011.
165. ^ Vijay Pande (April 24, 2010). "Prepping for the GPU3 rolling: new client and NVIDIA FAH GPU clients will (in the future) need CUDA 2.2 or later". Folding@home. typepad.com. Retrieved September 8, 2011.
166. Vijay Pande (May 25, 2010). "Folding@home: Open beta release of the GPU3 client/core". Folding@home. typepad.com. Retrieved September 7, 2011.
167. Joseph Coffland (Pande lab member) (October 13, 2011). "Re: FAHClient V7.1.38 released (4th Open-Beta)". Folding@home. phpBB Group. Retrieved October 15, 2011.
168. "NVIDIA GPU3 Linux/Wine Headless Install Guide". Folding@home. phpBB Group. November 8, 2008. Retrieved September 5, 2011.
169. ^ Edgar Luttmann, Daniel L. Ensign, Vishal Vaidyanathan, Mike Houston, Noam Rimon, Jeppe Øland, Guha Jayachandran, Mark Friedrichs, Vijay S. Pande (2008). "Accelerating Molecular Dynamic Simulation on the Cell processor and PlayStation 3". Journal of Computational Chemistry. 30 (2): 268–274. doi:10.1002/jcc.21054. PMID 18615421.{{cite journal}}: CS1 maint: multiple names: authors list (link)
170. ^ David E. Williams (October 20, 2006). "PlayStation's serious side: Fighting disease". CNN. Retrieved April 5, 2012.
171. Jerry Liao (March 23, 2007). "The Home Cure: PlayStation 3 to Help Study Causes of Cancer". mb.com. Manila Bulletin Publishing Corporation. Retrieved April 5, 2012.
172. Lou Kesten, Associated Press (March 26, 2007). "Week in video-game news: 'God of War II' storms the PS2; a PS3 research project". Post-Gazette.com. Pittsburgh Post-Gazette. Retrieved April 5, 2012.
173. Elaine Chow (September 18, 2008). "PS3 News Service, Life With Playstation, Now Up For Download". Gizmodo.com. Gizmodo. Retrieved April 5, 2012.
174. Vijay Pande (September 18, 2008). "Life with Playstation – a new update to the FAH/PS3 client". Folding@home. typepad.com. Retrieved February 24, 2012.
175. ^ Vijay Pande (June 15, 2008). "What does the SMP core do?". Folding@home. typepad.com. Retrieved September 7, 2011.
176. ^ Vijay Pande (March 8, 2008). "New Windows client/core development (SMP and classic clients)". Folding@home. typepad.com. Retrieved September 30, 2011.
177. Vijay Pande (June 17, 2009). "How does FAH code development and sysadmin get done?". Folding@home. typepad.com. Retrieved October 14, 2011.
178. ^ Peter Kasson (Pande lab member) (July 15, 2009). "new release: extra-large work units". Folding@home. phpBB Group. Retrieved October 9, 2011.
179. Vijay Pande (February 7, 2012). "Update on "bigadv-16", the new bigadv rollout". Folding@home. typepad.com. Retrieved February 9, 2012.
180. Vijay Pande (July 2, 2011). "Change in the points system for bigadv work units". Folding@home. typepad.com. Retrieved February 24, 2012.
181. ^ Pande lab (March 23, 2012). "Windows (FAH V7) Install Guide". Folding@home. Stanford University. Retrieved March 21, 2012.
182. ^ Vijay Pande (March 29, 2011). "Client version 7 now in open beta". Folding@home. typepad.com. Retrieved August 14, 2011.
183. Vijay Pande (March 31, 2011). "Core 16 for ATI released; also note on NVIDIA GPU support for older boards". Folding@home. typepad.com. Retrieved September 7, 2011.
184. Vijay Pande (March 22, 2012). "Web page revamp and v7 rollout". Folding@home. typepad.com. Retrieved March 22, 2012.
185. Lensink MF, Méndez R, Wodak SJ (2007). "Docking and scoring protein complexes: CAPRI 3rd Edition". Proteins. 69 (4): 704–18. doi:10.1002/prot.21804. PMID 17918726. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
186. Gregory R. Bowman and Vijay S. Pande (2009). "Simulated tempering yields insight into the low-resolution Rosetta scoring function". Proteins: Structure, Function, and Bioinformatics. 74 (3): 777–88. doi:10.1002/prot.22210. PMID 18767152.
187. G. R. Bowman and V. S. Pande (2009). Hofmann, Andreas (ed.). "The Roles of Entropy and Kinetics in Structure Prediction". PLoS ONE. 4 (6): e5840. Bibcode:2009PLoSO...4.5840B. doi:10.1371/journal.pone.0005840. PMC 2688754. PMID 19513117.{{cite journal}}: CS1 maint: unflagged free DOI (link)
188. Gen_X_Accord, Vijay Pande (June 11, 2006). "Folding@home vs. Rosetta@home". Rosetta@home forums. University of Washington. Retrieved April 6, 2012.
189. Vijay Pande (October 13, 2011). "Comparison between FAH and Anton's approaches". Folding@home. typepad.com. Retrieved February 25, 2012.
190. ^ Thomas J. Lane, Gregory R. Bowman, Kyle A Beauchamp, Vincent Alvin Voelz, and Vijay S. Pande (2011). "Markov State Model Reveals Folding and Functional Dynamics in Ultra-Long MD Trajectories". Journal of the American Chemical Society. 133 (45): 18413–9. doi:10.1021/ja207470h. PMC 3227799. PMID 21988563.{{cite journal}}: CS1 maint: multiple names: authors list (link)
191. David E. Shaw; et al. (2009). "Millisecond-scale molecular dynamics simulations on Anton". Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (39): 1–11. doi:10.1145/1654059.1654099. ISBN 978-1-60558-744-8. {{cite journal}}: Unknown parameter |author-separator= ignored (help)
192. David E. Shaw; et al. (2010). "Atomic-Level Characterization of the Structural Dynamics of Proteins". Science. 330 (6002): 341–346. Bibcode:2010Sci...330..341S. doi:10.1126/science.1187409. PMID 20947758. {{cite journal}}: Unknown parameter |author-separator= ignored (help)
193. TJ Lane (Pande lab member) (June 6, 2011). "Project 7610 & 7611 in Beta". Folding@home. phpBB Group. Retrieved February 25, 2012.(registration required)
194. Pande lab. "Project 7610 Description". Folding@home. Retrieved February 26, 2012.
195. Imran Haque (Pande lab member) (July 13, 2011). "Re: Are my conversion for GPU flops relativly [sic] correct?". Folding@home. phpBB Group. Retrieved September 12, 2012.
196. Christopher Mims (November 8, 2010). "Why China's New Supercomputer Is Only Technically the World's Fastest". Technology Review. MIT. Retrieved February 25, 2012.
197. Vijay Pande (November 9, 2008). "Re: ATI and NVIDIA stats vs. PPD numbers". Folding@home. phpBB Group. Retrieved September 12, 2012.
198. Vijay Pande (November 9, 2008). "Re: ATI and NVIDIA stats vs. PPD numbers". Folding@home. phpBB Group. Retrieved February 25, 2012.
199. Bruce Borden (July 12, 2011). "Re: Are my conversion for GPU flops relativly [sic] correct?". Folding@home. phpBB Group. Retrieved September 12, 2012.

External links

• Folding@home homepage
• Folding@home news blog
• Folding@home support forum
• Folding@home statistics
• List of Folding@home publications
• "Futures In Biotech" episode about Folding@home
• Video of 1.5ms folding of NTL9
• OpenMM molecular dynamics library

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