Folding@home

Alzheimer's disease is linked to the aggregation of amyloid beta protein fragments in the brain (right). Researchers have used Folding@home to simulate this aggregation process, to better understand the cause of the disease.

Alzheimer's disease is an incurable neurodegenerative disease which most often affects the elderly and accounts for more than half of all cases of dementia. Its exact cause remains unknown, but the disease is identified as a protein misfolding disease. Alzheimer's is associated with toxic aggregations of the amyloid beta (Aβ) peptide, caused by Aβ misfolding and clumping together with other Aβ peptides. These Aβ aggregates then grow into significantly larger senile plaques, a pathological marker of Alzheimer's disease.[49][50][51] Due to the heterogeneous nature of these aggregates, experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) have had difficulty characterizing their structures. Moreover, atomic simulations of Aβ aggregation are highly demanding computationally due to their size and complexity.[52][53]

Preventing Aβ aggregation is a promising method to developing therapeutic drugs for Alzheimer's disease, according to Naeem and Fazili in a literature review article.[54] In 2008, Folding@home simulated the dynamics of Aβ aggregation in atomic detail over timescales of the order of tens of seconds. Prior studies were only able to simulate about 10 microseconds. Folding@home was able to simulate Aβ folding for six orders of magnitude longer than formerly possible. Researchers used the results of this study to identify a beta hairpin that was a major source of molecular interactions within the structure.[55] The 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.[52]

In December 2008, Folding@home found several small drug candidates which appear to inhibit the toxicity of Aβ aggregates.[56] In 2010, in close cooperation with the Center for Protein Folding Machinery, these drug leads began to be tested on biological tissue.[35] 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 therapies for the disease and greatly assist with experimental nuclear magnetic resonance spectroscopy studies of Aβ oligomers.[53][57] Later that year, Folding@home began simulations of various Aβ fragments to determine how various natural enzymes affect the structure and folding of Aβ.[58][59]

Huntington's disease is a neurodegenerative genetic disorder that is 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.[60] As with other aggregates, there is difficulty in experimentally determining its structure.[61] Scientists are using Folding@home to study the structure of the huntingtin protein aggregate and to predict how it forms, assisting with rational drug design methods to stop the aggregate formation.[35] The N17 fragment of the huntingtin protein accelerates this aggregation, and while there have been several mechanisms proposed, its exact role in this process remains largely unknown.[62] Folding@home has simulated this and other fragments to clarify their roles in the disease.[63] Since 2008, its drug design methods for Alzheimer's disease have been applied to Huntington's.[35]

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. Analysis of these mutations helps explain the root causes of p53-related cancers.[64] In 2004, Folding@home was used to perform the first molecular dynamics study of the refolding of p53's protein dimer in an all-atom simulation of water. The simulation's results agreed with experimental observations and gave insights into the refolding of the dimer that were formerly unobtainable.[65] This was the first peer reviewed publication on cancer from a distributed computing project.[66] The following year, Folding@home 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 was reasonably successful in identifying cancer-promoting mutations and determined the effects of specific mutations which could not otherwise be measured experimentally.[67]

Folding@home is also used to study protein chaperones,[35] heat shock proteins which play essential roles in cell survival by assisting with the folding of other proteins in the crowded and chemically stressful environment within a cell. Rapidly growing cancer cells rely on specific chaperones, and some chaperones play key roles in chemotherapy resistance. Inhibitions to these specific chaperones are seen as potential modes of action for efficient chemotherapy drugs or for reducing the spread of cancer.[68] Using Folding@home and working closely with the Center for Protein Folding Machinery, the Pande lab hopes to find a drug which inhibits those chaperones involved in cancerous cells.[69] Researchers are also using Folding@home to study other molecules related to cancer, such as the enzyme Src kinase, and some forms of the engrailed homeodomain: a large protein which may be involved in many diseases, including cancer.[70][71] 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.[72][73]

Interleukin 2 (IL-2) is a protein that helps T cells of the immune system attack pathogens and tumors. However, 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 mutant 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 are testing it against a large variety of tumor models to try to accelerate its development as a therapeutic.[74][75]

Osteogenesis imperfecta, known as brittle bone disease, is an 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,[76] which fulfills a variety of structural roles and is the most abundant protein in mammals.[77] The mutation causes a deformation in collagen's triple helix structure, which if not naturally destroyed, leads to abnormal and weakened bone tissue.[78] In 2005, Folding@home tested a new quantum mechanical method that improved upon prior simulation methods, and which may be useful for future computing studies of collagen.[79] 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.[35]

Folding@home is assisting in research towards preventing some viruses, such as influenza and HIV, from recognizing and entering biological cells.[35] In 2011, Folding@home began simulations of the dynamics of the enzyme RNase H, a key component of HIV, to try to design drugs to deactivate it.[80] Folding@home has also been used to study membrane fusion, an essential event for viral infection and a wide range of biological functions. This fusion involves conformational changes of viral fusion proteins and protein docking,[36] but the exact molecular mechanisms behind fusion remain largely unknown.[81] Fusion events may consist of over a half million atoms interacting for hundreds of microseconds. This complexity limits typical computer simulations to about ten thousand atoms over tens of nanoseconds: a difference of several orders of magnitude.[55] The development of models to predict the mechanisms of membrane fusion will assist in the scientific understanding of how to target the process with antiviral drugs.[82] In 2006, scientists applied Markov state models and the Folding@home network to discover two pathways for fusion and gain other mechanistic insights.[55]

Following detailed simulations from Folding@home of small cells known as vesicles, in 2007, the Pande lab introduced a new computing method to measure the topology of its structural changes during fusion.[83] In 2009, researchers used Folding@home to study mutations of influenza hemagglutinin, a protein that attaches a virus to its host cell and assists with viral entry. Mutations to hemagglutinin affect how well the protein binds to a host's cell surface receptor molecules, which determines how infective the virus strain is to the host organism. Knowledge of the effects of hemagglutinin mutations assists in the development of antiviral drugs.[84][85] As of 2012, Folding@home continues to simulate the folding and interactions of hemagglutinin, complementing experimental studies at the University of Virginia.[35][86]

In March 2020, Folding@home launched a program to assist researchers around the world who are working on finding a cure and learning more about the coronavirus pandemic. The initial wave of projects simulate potentially druggable protein targets from SARS-CoV-2 virus, and the related SARS-CoV virus, about which there is significantly more data available.[87][88][89]

Drugs function by binding to specific locations on target molecules and causing some desired change, such as disabling a target or causing a conformational 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 limits in computing power, current in silico methods usually must trade speed for accuracy; e.g., use rapid protein docking methods instead of computationally costly free energy calculations. Folding@home's computing performance allows researchers to use both methods, and evaluate their efficiency and reliability.[39][90][91] Computer-assisted drug design has the potential to expedite and lower the costs of drug discovery.[38] In 2010, Folding@home used MSMs and free energy calculations to predict the native state of the villin protein to within 1.8 angstrom (Å) root mean square deviation (RMSD) from the crystalline structure experimentally determined through X-ray crystallography. This accuracy has implications to future protein structure prediction methods, including for intrinsically unstructured proteins.[55] Scientists have used Folding@home to research drug resistance by studying vancomycin, an antibiotic drug of last resort, and beta-lactamase, a protein that can break down antibiotics like penicillin.[92][93]

Chemical activity occurs along a protein's active site. Traditional drug design methods involve tightly binding to this site and blocking its activity, under the assumption that the target protein exists in one rigid structure. However, this approach works for approximately only 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 costly. In 2012, Folding@home and MSMs were used to identify allosteric sites in three medically relevant proteins: beta-lactamase, interleukin-2, and RNase H.[93][94]

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.[35] In 2008, they used Folding@home to study the interior of this tunnel and how specific molecules may affect it.[95] The full structure of the ribosome was determined only as of 2011, and Folding@home has also simulated ribosomal proteins, as many of their functions remain largely unknown.[96]

A prion (PrP) is a transmembrane cellular protein found widely in eukaryotic cells. In mammals, it is more abundant in the central nervous system. Although its function is unknown, its high conservation among species indicates an important role in the cellular function. The conformational change from the normal prion protein (PrPc, stands for cellular) to the disease causing isoform PrPSc (stands for prototypical prion disease–scrapie) causes a host of diseases collectly known as transmissible spongiform encephalopathies (TSEs), including Bovine spongiform encephalopathy (BSE) in bovine, Creutzfeldt-Jakob disease (CJD) and fatal insomnia in human, chronic wasting disease (CWD) in the deer family. The conformational change is widely accepted as the result of protein misfolding. What distinguishes TSEs from other protein misfolding diseases is its transmissible nature. The ‘seeding’ of the infectious PrPSc, either arising spontaneously, hereditary or acquired via exposure to contaminated tissues,[97] can cause a chain reaction of transforming normal PrPc into fibrils aggregates or amyloid like plaques consist of PrPSc.[98]

The molecular structure of PrPSc has not been fully characterized due to its aggregated nature. Neither is known much about the mechanism of the protein misfolding nor its kinetics. Using the known structure of PrPc and the results of the in vitro and in vivo studies described below, Folding@home could be valuable in elucidating how PrPSc is formed and how the infectious protein arrange themselves to form fibrils and amyloid like plaques, bypassing the requirement to purify PrPSc or dissolve the aggregates.

The PrPc has been enzymatically dissociated from the membrane and purified, its structure studied using structure characterization techniques such as NMR spectroscopy and X-ray crystallography. Post-translational PrPc has 231 amino acids (aa) in murine. The molecule consists of a long and unstructured amino terminal region spanning up to aa residue 121 and a structured carboxy terminal domain.[98] This globular domain harbors two short sheet-forming anti-parallel β-strands (aa 128 to 130 and aa 160 to 162 in murine PrPc) and three α-helices (helix I: aa 143 to 153; helix II: aa 171 to 192; helix III: aa 199 to 226 in murine PrPc),[99] Helices II and III are anti-parallel orientated and connected by a short loop. Their structural stability is supported by a disulfide bridge, which is parallel to both sheet-forming β-strands. These α-helices and the β-sheet form the rigid core of the globular domain of PrPc.[100]

The disease causing PrPSc is proteinase K resistant and insoluble. Attempts to purify it from the brains of infected animals invariably yield heterogeneous mixtures and aggregated states that are not amenable to characterization by NMR spectroscopy or X-ray crystallography. However, it is a general consensus that PrPSc contains a high percentage of tightly stacked β-sheets than the normal PrPc that renders the protein insoluble and resistant to proteinase. Using techniques of cryoelectron microscopy and structural modeling based on similar common protein structures, it has been discovered that PrPSc contains ß-sheets in the region of aa 81–95 to aa 171, while the carboxy terminal structure is supposedly preserved, retaining the disulfide-linked α-helical conformation in the normal PrPc. These ß-sheets form a parallel left-handed beta-helix.[98] Three PrPSc molecules are believed to form a primary unit and therefore build the basis for the so-called scrapie-associated fibrils.[101] The catalytic activity depends on the size of the particle. PrPSc particles which consist of only 14-28 PrPc molecules exhibit the highest rate of infectivity and conversion.[102]

Despite the difficulty to purify and characterize PrPSc, from the known molecular structure of PrPc and using transgenic mice and N-terminal deletion,[103] the potential ‘hot spots’ of protein misfolding leading to the pathogenic PrPSc could be deduced and Folding@home could be of great value in confirming these. Studies found that both the primary and secondary structure of the prion protein can be of significance of the conversion.

There are more than twenty mutations of the prion protein gene (PRNP) that are known to be associated with or that are directly linked to the hereditary form of human TSEs [56], indicating single amino acids at certain position, likely within the carboxy domain,[99] of the PrPc can affect the susceptibility to TSEs.

The post-translational amino terminal region of PrPc consists of residues 23-120 which make up nearly half of the amino sequence of full-length matured PrPc. There are two sections in the amino terminal region that may influence conversion. First, residues 52-90 contains an octapeptide repeat (5 times) region that likely influences the initial binding (via the octapeptide repeats) and also the actual conversion via the second section of aa 108–124.[104] The highly hydrophobic AGAAAAGA is located between aa residue 113 and 120 and is described as putative aggregation site,[105] although this sequence requires its flanking parts to form fibrillar aggregates.[106]

In the carboxy globular domain,[100] among the three helices, study show that helix II has a significant higher propensity to β-strand conformation.[107] Due to the high conformational flexvoribility seen between residues 114-125 (part of the unstructured N-terminus chain) and the high β-strand propensity of helix II, only moderate changes in the environmental conditions or interactions might be sufficient to induce misfolding of PrPc and subsequent fibril formation.[98]

Other studies of NMR structures of PrPc showed that these residues (~108–189) contain most of the folded domain including both β-strands, the first two α-helices, and the loop/turn regions connecting them, but not the helix III.[103] Small changes within the loop/turn structures of PrPc itself could be important in the conversion as well.[108] In another study, Riek et al. showed that the two small regions of β-strand upstream of the loop regions act as a nucleation site for the conformational conversion of the loop/turn and α-helical structures in PrPc to β-sheet.[99]

The energy threshold for the conversion are not necessarily high. The folding stability, i.e. the free energy of a globular protein in its environment is in the range of one or two hydrogen bonds thus allows the transition to an isoform without the requirement of high transition energy.[98]

From the respective of the interactions among the PrPc molecules, hydrophobic interactions play a crucial role in the formation of β-sheets, a hallmark of PrPSc, as the sheets bring fragments of polypeptide chains into close proximity.[109] Indeed, Kutznetsov and Rackovsky [110] showed that disease-promoting mutations in the human PrPc had a statistically significant tendency towards increasing local hydrophobicity.

In vitro experiments showed the kinetics of misfolding has an initial lag phase followed by a rapid growth phase of fibril formation.[111] It is likely that PrPc goes through some intermediate states, such as at least partially unfolded or degraded, before finally ending up as part of an amyloid fibril.[98]

Computing power of Folding@home and the fastest supercomputer from April 2004 to October 2012. Between June 2007 and June 2011, Folding@home (red) exceeded the performance of Top500's fastest supercomputer (black). However it was eclipsed by K computer in November 2011 and Blue Gene/Q in June 2012.

Supercomputer FLOPS 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 vary in architecture and design, so direct comparison is difficult. Despite this, FLOPS remain the primary speed metric used in supercomputing.[124] In contrast, Folding@home determines its FLOPS using wall-clock time by measuring how much time its work units take to complete.[125]

On September 16, 2007, due in large part to the participation of PlayStation 3 consoles, the Folding@home project officially attained a sustained performance level higher than one native petaFLOPS, becoming the first computing system of any kind to do so.[126][127] Top500's fastest supercomputer at the time was BlueGene/L, at 0.280 petaFLOPS.[128] The following year, on May 7, 2008, the project attained a sustained performance level higher than two native petaFLOPS,[129] followed by the three and four native petaFLOPS milestones in August 2008[130][131] and September 28, 2008 respectively.[132] On February 18, 2009, Folding@home achieved five native petaFLOPS,[133][134] and was the first computing project to meet these five levels.[135][136] In comparison, November 2008's fastest supercomputer was IBM's Roadrunner at 1.105 petaFLOPS.[137] On November 10, 2011, Folding@home's performance exceeded six native petaFLOPS with the equivalent of nearly eight x86 petaFLOPS.[127][138] In mid-May 2013, Folding@home attained over seven native petaFLOPS, with the equivalent of 14.87 x86 petaFLOPS. It then reached eight native petaFLOPS on June 21, followed by nine on September 9 of that year, with 17.9 x86 petaFLOPS.[139] On May 11, 2016 Folding@home announced that it was moving towards reaching the 100 x86 petaFLOPS mark.[140]

Further use grew from increased awareness and participation in the project from the coronavirus pandemic in 2020. On March 20, 2020 Folding@home announced via Twitter that it was running with over 470 native petaFLOPS,[141] the equivalent of 958 x86 petaFLOPS.[142] By March 25 it reached 768 petaFLOPS, or 1.5 x86 exaFLOPS, making it the first exaFLOP computing system.[143]

As of 1 September 2023, the computing power of Folding@home stands at 31 petaFLOPS, or 64 x86 petaFLOPS.[144]

Similarly to other distributed computing projects, Folding@home quantitatively assesses user computing contributions to the project through a credit system.[145] 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.[145] Each user receives these base points for completing every work unit, though through the use of a passkey they can receive added bonus points for reliably and rapidly completing units which are more demanding computationally or have a greater scientific priority.[146][147] Users may also receive credit for their work by clients on multiple machines.[148] This point system attempts to align awarded credit with the value of the scientific results.[145]

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.[149] 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.[145][150][151] Individual and team statistics are posted on the Folding@home website.[145]

If a user does not form a new team, or does not join an existing team, that user automatically becomes part of a "Default" team. This "Default" team has a team number of "0". Statistics are accumulated for this "Default" team as well as for specially named teams.

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 model. After the work unit has been downloaded and completely processed by a volunteer's computer, it is returned to Folding@home servers, which then award the volunteer the credit points. This cycle repeats automatically.[150] 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 occurs serially, and many work units are generated from their predecessors, this allows the overall simulation process to proceed normally if a work unit is not returned after a reasonable period of time. Due to these deadlines, the minimum system requirement for Folding@home is a Pentium 3 450 MHz CPU with Streaming SIMD Extensions (SSE).[148] 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.[152]

Before public release, work units go through several quality assurance steps to keep problematic ones from becoming fully available. These testing stages include internal, beta, and advanced, before a final full release across Folding@home.[153] 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.[154][155] The Folding@home support forum can be used to differentiate between issues arising from problematic hardware and bad work units.[156]

Specialized molecular dynamics programs, referred to as "FahCores" and often abbreviated "cores", perform the calculations on the work unit as a background process. A large majority of Folding@home's cores are based on GROMACS,[150] one of the fastest and most popular molecular dynamics software packages, which largely consists of manually optimized assembly language code and hardware optimizations.[157][158] 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 to help ensure data validity.[159] 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.[4][160][161] 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.[162][163] 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.[150]

Folding@home running on Fedora 25

A Folding@home participant installs a client program on their personal computer. The user interacts with the client, which manages the other software components in the background. Through the client, the user may pause the folding process, open an event log, check the work progress, or view personal statistics.[164] The computer clients run continuously in the background at a very low priority, using idle processing power so that normal computer use is unaffected.[148] The maximum CPU use can be adjusted via client settings.[164][165] 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 are tailored to uniprocessor and multi-core processor systems, and graphics processing units. The diversity and power of each hardware architecture provides Folding@home with the ability to efficiently complete many 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 formerly considered impractical to tackle computationally.[39][150][152]

Professional software developers are responsible for most of Folding@home's code, both for the client and server-side. The development team includes programmers from Nvidia, ATI, Sony, and Cauldron Development.[166] Clients can be downloaded only from the official Folding@home website or its commercial partners, and will only interact with Folding@home computer files. They will upload and download data with Folding@home's data servers (over port 8080, with 80 as an alternate), and the communication is verified using 2048-bit digital signatures.[148][167] While the client's graphical user interface (GUI) is open-source,[168] the client is proprietary software citing security and scientific integrity as the reasons.[169][170][171]

However, this rationale of using proprietary software is disputed since while the license could be enforceable in the legal domain retrospectively, it does not practically prevent the modification (also known as patching) of the executable binary files. Likewise, binary-only distribution does not prevent the malicious modification of executable binary-code, either through a man-in-the-middle attack while being downloaded via the internet,[172] or by the redistribution of binaries by a third-party that have been previously modified either in their binary state (i.e. patched),[173] or by decompiling[174] and recompiling them after modification.[175][176] These modifications are possible unless the binary files – and the transport channel – are signed and the recipient person/system is able to verify the digital signature, in which case unwarranted modifications should be detectable, but not always.[177] Either way, since in the case of Folding@home the input data and output result processed by the client-software are both digitally signed,[148][167] the integrity of work can be verified independently from the integrity of the client software itself.

Folding@home uses the Cosm software libraries for networking.[150][166] Folding@home was launched on October 1, 2000, and was the first distributed computing project aimed at bio-molecular systems.[178] Its first client was a screensaver, which would run while the computer was not otherwise in use.[179][180] In 2004, the Pande lab collaborated with David P. Anderson to test a supplemental client on the open-source BOINC framework. This client was released to closed beta in April 2005;[181] however, the method became unworkable and was shelved in June 2006.[182]

The specialized hardware of graphics processing units (GPU) is designed to accelerate rendering of 3-D graphics applications such as video games and can significantly outperform CPUs for some types of calculations. GPUs are one of the most powerful and rapidly growing computing platforms, and many scientists and researchers are pursuing general-purpose computing on graphics processing units (GPGPU). However, GPU hardware is difficult to use for non-graphics tasks and usually requires significant algorithm restructuring and an advanced understanding of the underlying architecture.[183] Such customization is challenging, more so to researchers with limited software development resources. Folding@home uses the open-source OpenMM library, which uses a bridge design pattern with two application programming interface (API) levels to interface molecular simulation software to an underlying hardware architecture. With the addition of hardware optimizations, OpenMM-based GPU simulations need no significant modification but achieve performance nearly equal to hand-tuned GPU code, and greatly outperform CPU implementations.[162][184]

Before 2010, the computing reliability of GPGPU consumer-grade hardware was 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 traits, such as software-side error detection.[185]

The first generation of Folding@home's GPU client (GPU1) was released to the public on October 2, 2006,[182] delivering a 20–30 times speedup for some calculations over its CPU-based GROMACS counterparts.[186] It was the first time GPUs had been used for either distributed computing or major molecular dynamics calculations.[187][188] 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.[186][189] Following the introduction of GPU2, GPU1 was officially retired on June 6.[186] Compared to GPU1, GPU2 was more scientifically reliable and productive, ran on ATI and CUDA-enabled Nvidia GPUs, and supported more advanced algorithms, larger proteins, and real-time visualization of the protein simulation.[190][191] Following this, the third generation of Folding@home's GPU client (GPU3) was released on May 25, 2010. While backward compatible with GPU2, GPU3 was more stable, efficient, and flexibile in its scientific abilities,[192] and used OpenMM on top of an OpenCL framework.[192][193] Although these GPU3 clients did not natively support the operating systems Linux and macOS, Linux users with Nvidia graphics cards were able to run them through the Wine software application.[194][195] GPUs remain Folding@home's most powerful platform in FLOPS. As of November 2012, GPU clients account for 87% of the entire project's x86 FLOPS throughput.[196]

Native support for Nvidia and AMD graphics cards under Linux was introduced with FahCore 17, which uses OpenCL rather than CUDA.[197]

The PlayStation 3's Life With PlayStation client displayed a 3-D animation of the protein being folded

From March 2007 until November 2012, Folding@home took advantage of the computing power of PlayStation 3s. At the time of its inception, its main streaming Cell processor delivered a 20 times speed increase over PCs for some calculations, processing power which could not be found on other systems such as the Xbox 360.[39][198] The PS3's high speed and efficiency introduced other opportunities for worthwhile optimizations according to Amdahl's law, and significantly changed the tradeoff between computing efficiency and overall accuracy, allowing the use of more complex molecular models at little added computing cost.[199] This allowed Folding@home to run biomedical calculations that would have been otherwise infeasible computationally.[200]

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.[39][201] Its release made Folding@home the first distributed computing project to use PS3s.[202] On September 18 of the following year, the PS3 client became a channel of Life with PlayStation on its launch.[203][204] In the types of calculations it can perform, at the time of its introduction, the client fit in between a CPU's flexibility and a GPU's speed.[150] However, unlike clients running on personal computers, users were unable to perform other activities on their PS3 while running Folding@home.[200] The PS3's uniform console environment made technical support easier and made Folding@home more user friendly.[39] The PS3 also had the ability to stream data quickly to its GPU, which was used for real-time atomic-level visualizing of the current protein dynamics.[199]

On November 6, 2012, Sony ended support for the Folding@home PS3 client and other services available under Life with PlayStation. Over its lifetime of five years and seven months, more than 15 million users contributed over 100 million hours of computing to Folding@home, greatly assisting the project with disease research. Following discussions with the Pande lab, Sony decided to terminate the application. Pande considered the PlayStation 3 client a "game changer" for the project.[205][206][207]

Folding@home can use the parallel computing abilities of modern multi-core processors. The ability to use several CPU cores simultaneously allows completing the full simulation far faster. Working together, these CPU cores complete single work units proportionately faster than the standard uniprocessor client. This method is scientifically valuable because it enables much longer simulation trajectories to be performed in the same amount of time, and reduces the traditional difficulties of scaling a large simulation to many separate processors.[208] A 2007 publication in the Journal of Molecular Biology relied on multi-core processing to simulate the folding of part of the villin protein approximately 10 times longer than was possible with a single-processor client, in agreement with experimental folding rates.[209]

In November 2006, first-generation symmetric multiprocessing (SMP) clients were publicly released for open beta testing, referred to as SMP1.[182] 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.[152] This was the first time a distributed computing project had used MPI.[210] Although the clients performed well in Unix-based operating systems such as Linux and macOS, they were troublesome under Windows.[208][210] 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.[147][166]

SMP2 supports a trial of a special category of bigadv work units, designed to simulate proteins that are unusually large and computationally intensive and have a great scientific priority. These units originally required a minimum of eight CPU cores,[211] which was raised to sixteen later, on February 7, 2012.[212] Along with these added hardware requirements over standard SMP2 work units, they require more system resources such as random-access memory (RAM) and Internet bandwidth. In return, users who run these are rewarded with a 20% increase over SMP2's bonus point system.[213] The bigadv category allows Folding@home to run especially demanding simulations for long times that had formerly required use of supercomputing clusters and could not be performed anywhere else on Folding@home.[211] Many users with hardware able to run bigadv units have later had their hardware setup deemed ineligible for bigadv work units when CPU core minimums were increased, leaving them only able to run the normal SMP work units. This frustrated many users who invested significant amounts of money into the program only to have their hardware be obsolete for bigadv purposes shortly after. As a result, Pande announced in January 2014 that the bigadv program would end on January 31, 2015.[214]

A sample image of the V7 client in Novice mode running under Windows 7. In addition to a variety of controls and user details, V7 presents work unit information, such as its state, calculation progress, ETA, credit points, identification numbers, and description.

The V7 client is the seventh and latest generation of the Folding@home client software, and is a full rewrite and unification of the prior clients for Windows, macOS, and Linux operating systems.[215][216] It was released on March 22, 2012.[217] Like its predecessors, V7 can 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, and offer greater scientific flexibility to researchers than prior clients.[218] V7 uses Trac for managing its bug tickets so that users can see its development process and provide feedback.[216]

V7 consists of four integrated elements. The user typically interacts with V7's open-source GUI, named FAHControl.[168][219] This has Novice, Advanced, and Expert user interface modes, and has the ability to monitor, configure, and control many remote folding clients from one computer. FAHControl directs FAHClient, a back-end application that in turn manages each FAHSlot (or slot). Each slot acts as replacement for the formerly 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 3-D rendering, if available, of the protein currently being processed.[215][216]

In 2014, a client for the Google Chrome and Chromium web browsers was released, allowing users to run Folding@home in their web browser. The client used Google's Native Client (NaCl) feature on Chromium-based web browsers to run the Folding@home code at near-native speed in a sandbox on the user's machine.[220] Due to the phasing out of NaCL and changes at Folding@home, the web client was permanently shut down in June 2019.[221]

In July 2015, a client for Android mobile phones was released on Google Play for devices running Android 4.4 KitKat or newer.[222][223]

On February 16, 2018, the Android client, which was offered in cooperation with Sony, was removed from Google Play. Plans were announced to offer an open source alternative in the future.[224]