Lattice protein design using Bayesian learning
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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 acid sequence for which the target conformation has a unique ground state. However, the performance was not as good for 3D lattice HP models compared with 2D models. Furthermore, we find a strong linearity between the chemical potential of water and the number of surface residues, thereby revealing the relationship between protein structure and the effect of water molecules. The advantage of our method is that it greatly reduces computation time, because it does not require long calculations for the partition function corresponding to an exhaustive conformational search. As our method uses a general form of Bayesian learning and statistical mechanics and is not limited to lattice HP proteins, the results presented here elucidate some heuristics used successfully in previous protein design methods.
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