Computational design of new proteins is often performed by optimizing the amino acid sequence. This sequence is characterized by an energy (lower energy means better propensity to form the desired 3D structure) that is sampled and minimized. Here, we use the parallel tempering algorithm to accelerate this task. ESMfold was used to predict the structures of the sampled proteins and calculate energy. Starting from random amino acid sequences, each sequence was sampled using the Monte Carlo method at one of a series of temperatures, and these replicas were being exchanged by the parallel tempering method. A series of 100 or 200 residue proteins was designed to maximize confidence in structure prediction and globularity and minimize surface hydrophobic residues. We show that parallel tempering is a viable alternative to Monte Carlo sampling without replica exchanges and simulated annealing or related energy-based protein design methods, especially in the situation where a continuous flow of designed sequences is desired.

Department of Biochemistry and Microbiology University of Chemistry and Technology Prague Prague 6 Czech Republic

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