A. Soni & J. Shavlik (2012).
Probabilistic Ensembles for Improved Inference in Protein-Structure Determination. Journal of Bioinformatics and Computational Biology (JBCB). doi: http://dx.doi.org/10.1142/S0219720012400094
An extended version of this paper can be found in The Proceedings of the ACM International Conference on Bioinformatics and Computational Biology 2011.
This publication is available remotely.
Protein X-ray crystallography -- the most popular method for determining protein structure -- remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the three-dimensional (3D) location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.
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