Macromolecular Potential Energy Decoder Based on Graphical Model

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Knowing tertiary structure is important to understand and predict protein function. However, it is an open question how to predict the tertiary structure of proteins from a sequence of amino acids. In this project, Slice Chain Max-Sum (SCMS) algorithm has been proposed. This method represents the potential function of a protein molecule as a factor graph, which is a kind of a graphical model. The factor graph is converted into a linearly structured one according to a slicing of the molecule in 3D space. Based on the converted graph, max-sum search is performed in combination with node-wise local MCMC sampling that approximates continuous variables by discrete ones. Experimental results show that SCMS is more efficient than conventional MCMC method. It is also shown that improved version of SCMS (i.e. SCMS2.0) outperforms MCMC method that is reinforced by the quasi-Newton method.