Approximate Inference and Side-chain Prediction

Side-chain prediction is an important subtask in the protein-folding problem. We show that finding a minimal energy side-chain configuration is equivalent to performing inference in an undirected graphical model. The graphical model is relatively sparse yet has many cycles. We used this equivalence to assess the performance of approximate inference algorithms in a real-world setting. Specifically, we were interested in two questions: (1) which approximate inference algorithms give superior performance and (2) how does this performance compare to the state-of-the-art in computational biology. We looked at three tasks in side-chain graphical models — finding the minimal energy configuration, finding the M best configurations and approximating the free energy and conformational entropy. In all three subtasks we found that belief propagation gave the best results among the approximate inference algorithms and in many cases it outperformed the state-of-the-art in algorithms developed in the computational biology field.

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