Approximate Inference and Protein-Folding

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Chen Yanover, Yair Weiss


Side-chain prediction is an important subtask in the protein-folding problem. We show that finding a minimal energy side-chain con(cid:173) figuration 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. Specifi(cid:173) cally we compared belief propagation (BP), generalized BP (GBP) and naive mean field (MF). In cases where exact inference was possible, max-product BP al(cid:173) ways found the global minimum of the energy (except in few cases where it failed to converge), while other approximation algorithms of similar complexity did not. In the full protein data set, max(cid:173) product BP always found a lower energy configuration than the other algorithms, including a widely used protein-folding software (SCWRL).