Editing Folding@home (section)

== Background ==
{{further|Protein folding}}
[[File:Protein folding.png|thumb|A protein before and after folding. It starts in an unstable [[random coil]] state and finishes in its native state conformation.]]

[[Protein]]s are an essential component to many biological functions and participate in virtually all processes within [[Cell (biology)|biological cell]]s. They often act as [[enzyme]]s, performing biochemical reactions including [[cell signaling]], molecular transportation, and [[Cell cycle#Regulation of eukaryotic cell cycle|cellular regulation]]. As structural elements, some proteins act as a type of [[cytoskeleton|skeleton for cells]], and as [[antibodies]], while other proteins participate in the [[immune system]]. Before a protein can take on these roles, it must fold into a functional [[Protein tertiary structure|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. 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]].<ref name="10.1371/journal.pcbi.1000452"/><ref name="10.1126/science.309.5731.78b"/> Despite folding occurring within a [[macromolecular crowding|crowded cellular environment]], it typically proceeds smoothly. However, due to a protein's chemical properties or other factors, proteins may [[protein misfolding|misfold]], that is, fold down the wrong pathway and end up misshapen. Unless cellular mechanisms can destroy or refold misfolded proteins, they can subsequently [[Protein aggregation|aggregate]] and cause a variety of debilitating diseases.<ref name="10.1002/iub.117"/> 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.<ref name="10.1016/j.abb.2007.05.014"/><ref name="10.1016/j.cbpa.2008.02.011"/>

Due to the complexity of proteins' conformation or [[Configuration space (physics)|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. While most proteins typically fold in the order of milliseconds,<ref name="10.1016/j.abb.2007.05.014"/><ref name="10.1146/annurev.biophys.34.040204.144447"/> before 2010, simulations could only reach nanosecond to microsecond timescales.<ref name="10.1021/ja9090353"/> General-purpose [[supercomputer]]s have been used to simulate protein folding, but such systems are intrinsically costly and typically shared among many research groups. Further, because the computations in kinetic models occur serially, strong [[scalability|scaling]] of traditional molecular simulations to these architectures is exceptionally difficult.<ref name="978-1-58603-796-3"/><ref name="10.1002/bip.10219"/> 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.<ref name="10.1016/j.sbi.2010.10.006"/><ref name="10.1137/06065146X"/>

[[File:ACBP MSM from Folding@home.tiff|thumb|Folding@home uses [[Markov state model]]s, 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).]]
Protein folding does not occur in one step.<ref name="10.1002/iub.117"/> Instead, proteins spend most of their folding time, nearly 96% in some cases,<ref name="10.1016/j.sbi.2011.12.001"/> ''waiting'' in various intermediate [[protein conformation|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 (mathematics)|set]] of simulation trajectories. As the simulations discover more conformations, the trajectories are restarted from them, and a [[Hidden Markov model|Markov state model]] (MSM) is gradually created from this cyclic process. MSMs are [[discrete-time]] [[master equation]] models which describe a biomolecule's conformational and energy landscape as a set of distinct structures and the short transitions between them. The adaptive sampling Markov state model method 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.<ref name="Simulation FAQ"/> 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 linear [[parallelization]], leading to an approximately four [[orders of magnitude]] reduction in overall serial calculation time. A completed MSM may contain tens of thousands of sample states from the protein's [[phase space]] (all the conformations a protein can take on) and the transitions between them. The model illustrates folding events and pathways (i.e., routes) and researchers can later use kinetic clustering to view a coarse-grained representation of the otherwise highly detailed model. They can use these MSMs to reveal how proteins misfold and to quantitatively compare simulations with experiments.<ref name="10.1016/j.ymeth.2010.06.002"/><ref name="10.1016/j.sbi.2010.10.006"/><ref name="10.1021/ct900620b"/>

Between 2000 and 2010, the length of the proteins Folding@home has studied have increased by a factor of four, while its timescales for protein folding simulations have increased by six orders of magnitude.<ref name="typepad: how far FAH has come"/> In 2002, Folding@home used Markov state models to complete approximately a million [[CPU]] days of simulations over the span of several months,<ref name="10.1038/nature01160"/> and in 2011, MSMs parallelized another simulation that required an aggregate 10&nbsp;million CPU hours of computing.<ref name="10.1073/pnas.1010880108"/> In January 2010, Folding@home used MSMs to simulate the dynamics of the slow-folding 32-[[amino acid|residue]] NTL9 protein out to 1.52&nbsp;milliseconds, a timescale consistent with experimental folding rate predictions but a thousand times longer than formerly achieved. The model consisted of many individual trajectories, each two orders of magnitude shorter, and provided an unprecedented level of detail into the protein's energy landscape.<ref name="10.1016/j.ymeth.2010.06.002"/><ref name="10.1021/ja9090353"/><ref name="10.3390/ijms11125292"/> In 2010, Folding@home researcher Gregory Bowman was awarded the [[Thomas Kuhn#Thomas Kuhn Paradigm Shift Award|Thomas Kuhn Paradigm Shift Award]] from the [[American Chemical Society]] for the development of the [[open-source software|open-source]] MSMBuilder software and for attaining quantitative agreement between theory and experiment.<ref name="2010 KPS award"/><ref name="MSMBuilder source"/> 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",<ref name="Biophysical society names recipients"/> and the 2006 Irving Sigal Young Investigator Award for his simulation results which "have stimulated a re-examination of the meaning of both ensemble and single-molecule measurements, making Pande's efforts pioneering contributions to simulation methodology."<ref name="FAH Awards"/>