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2010

Guiding belief propagation using domain knowledge for protein-structure determination

9 years 3 months ago
Guiding belief propagation using domain knowledge for protein-structure determination
A major bottleneck in high-throughput protein crystallography is producing protein-structure models from an electrondensity map. In previous work, we developed Acmi, a probabilistic framework for sampling all-atom protein-structure models. Acmi uses a fully connected, pairwise Markov random field to model the 3D location of each non-hydrogen atom in a protein. Since exact inference in this model is intractable, Acmi uses loopy belief propagation (BP) to calculate marginal probability distributions. In cases of approximation, BP's message-passing protocol becomes a crucial design decision. Previously, Acmi took a naive, round-robin protocol to sequentially process messages. Others have proposed informed methods for message scheduling by ranking messages based on the amount of new information they contain. These information-theoretic measures, however, fail in the highly connected, large output space domain of proteinstructure inference. In this work, we develop a framework for usi...
Ameet Soni, Craig A. Bingman, Jude W. Shavlik
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where BCB
Authors Ameet Soni, Craig A. Bingman, Jude W. Shavlik
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