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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 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.