Protein folding does not occur in one step. uses Markov state models, like the one diagrammed here, to model the possible shapes and folding pathways a protein can take as it condenses from its initial randomly coiled state (left) into its native 3-D structure (right). Moreover, as protein folding is a stochastic process (i.e., random) and can statistically vary over time, it is challenging computationally to use long simulations for comprehensive views of the folding process. Further, because the computations in kinetic models occur serially, strong scaling of traditional molecular simulations to these architectures is exceptionally difficult. General-purpose supercomputers have been used to simulate protein folding, but such systems are intrinsically costly and typically shared among many research groups. While most proteins typically fold in the order of milliseconds, before 2010, simulations could only reach nanosecond to microsecond timescales. ĭue to the complexity of proteins' conformation or configuration space (the set of possible shapes a protein can take), and limits in computing power, all-atom molecular dynamics simulations have been severely limited in the timescales that they can study. Laboratory experiments studying these processes can be limited in scope and atomic detail, leading scientists to use physics-based computing models that, when complementing experiments, seek to provide a more complete picture of protein folding, misfolding, and aggregation. Unless cellular mechanisms can destroy or refold misfolded proteins, they can subsequently aggregate and cause a variety of debilitating diseases. 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. Despite folding occurring within a crowded cellular environment, it typically proceeds smoothly. 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. Protein folding is driven by the search to find the most energetically favorable conformation of the protein, i.e., its native state. 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 and interactions of the amino acids with their surroundings. As structural elements, some proteins act as a type of skeleton for cells, and as antibodies, while other proteins participate in the immune system. They often act as enzymes, performing biochemical reactions including cell signaling, molecular transportation, and cellular regulation. Proteins are an essential component to many biological functions and participate in virtually all processes within biological cells. It starts in an unstable random coil state and finishes in its native state conformation. Results from the project's simulations agree well with experiments. Since its launch on October 1, 2000, was involved in the production of 226 scientific research papers. This level of performance from its large-scale computing network has allowed researchers to run computationally costly atomic-level simulations of protein folding thousands of times longer than formerly achieved. With heightened interest in the project as a result of the COVID-19 pandemic, the system achieved a speed of approximately 1.22 exaflops by late March 2020 and reached 2.43 exaflops by April 12, 2020, making it the world's first exaflop computing system.
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Volunteers can track their contributions on the website, which makes volunteers' participation competitive and encourages long-term is one of the world's fastest computing systems. As part of the client–server model network architecture, the volunteered machines each receive pieces of a simulation (work units), complete them, and return them to the project's database servers, where the units are compiled into an overall simulation.
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The project uses statistical simulation methodology that is a paradigm shift from traditional computing methods. The project utilizes graphics processing units (GPUs), central processing units (CPUs), and ARM processors like those on the Raspberry Pi for distributed computing and scientific research.
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is currently based at the University of Pennsylvania and led by Greg Bowman, a former student of Vijay Pande. This includes the process of protein folding and the movements of proteins, and is reliant on simulations run on volunteers' personal computers. Microsoft Windows, macOS, Linux, PlayStation 3 (discontinued as of firmware version 4.30)įoldingathome ( FAH or is a distributed computing project aimed to help scientists develop new therapeutics for a variety of diseases by the means of simulating protein dynamics.