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Protein structure prediction is a critical problem linked to drug design,mutation detection, and protein synthesis, among other applications. To thisend, evolutionary data has been used to build contact maps which aretraditionally minimized as energy functions via gradient descent based schemeslike the L-BFGS algorithm. In this paper we present what we call theAlternating Metropolis-Hastings (AMH) algorithm, which (a) significantlyimproves the performance of traditional MCMC methods, (b) is inherentlyparallelizable allowing significant hardware acceleration using GPU, and (c)can be integrated with the L-BFGS algorithm to improve its performance. Thealgorithm shows an improvement in energy of found structures of 8.17% to 61.04%(average 38.9%) over traditional MH and 0.53% to 17.75% (average 8.9%) overtraditional MH with intermittent noisy restarts, tested across 9 proteins fromrecent CASP competitions. We go on to map the Alternating MH algorithm to aGPGPU which improves sampling rate by 277x and improves simulation time to alow energy protein prediction by 7.5x to 26.5x over CPU. We show that ourapproach can be incorporated into state-of-the-art protein prediction pipelinesby applying it to both trRosetta2's energy function and the distogram componentof Alphafold1's energy function. Finally, we note that specially designedprobabilistic computers (or p-computers) can provide even better performancethan GPUs for MCMC algorithms like the one discussed here.

An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction