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In this study, we propose an analytic statistical mechanics approach to solve a fundamental problem in biological physics called protein design. Protein design is an inverse problem of protein structure prediction, and its solution is the amino acid sequence that best stabilizes a given conformation. Contrary to previous computational physics studi...
Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In general, the computation of the protein design involves a double loop: A loop for amino acid sequence changes and a loop for an exhaustive conformational search for each am...
A novel protein design method using Bayesian learning is proposed in this work. We consider a posterior probability of amino acid sequences by taking into account water and assuming a prior of sequences. For some instances of a target conformation of a two-dimensional (2D) lattice Hydrophobic-Polar (HP) model, our method successfully finds an amino...