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Probabilistic cross-link analysis and experiment planning for high-throughput elucidation of protein structure

¹ Department of Computer Science and ² Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, USA
³ International Institute of Molecular and Cell Biology, 02-109 Warsaw, Poland

(RECEIVED April 30, 2004; FINAL REVISION August 20, 2004; ACCEPTED August 20, 2004)

Emerging high-throughput techniques for the characterization of protein and protein-complex structures yield noisy data with sparse information content, placing a significant burden on computation to properly interpret the experimental data. One such technique uses cross-linking (chemical or by cysteine oxidation) to confirm or select among proposed structural models (e.g., from fold recognition, ab initio prediction, or docking) by testing the consistency between cross-linking data and model geometry. This paper develops a probabilistic framework for analyzing the information content in cross-linking experiments, accounting for anticipated experimental error. This framework supports a mechanism for planning experiments to optimize the information gained. We evaluate potential experiment plans using explicit trade-offs among key properties of practical importance: discriminability, coverage, balance, ambiguity, and cost. We devise a greedy algorithm that considers those properties and, from a large number of combinatorial possibilities, rapidly selects sets of experiments expected to discriminate pairs of models efficiently. In an application to residue-specific chemical cross-linking, we demonstrate the ability of our approach to plan experiments effectively involving combinations of cross-linkers and introduced mutations. We also describe an experiment plan for the bacteriophage Tfa chaperone protein in which we plan dicysteine mutants for discriminating threading models by disulfide formation. Preliminary results from a subset of the planned experiments are consistent and demonstrate the practicality of planning. Our methods provide the experimenter with a valuable tool (available from the authors) for understanding and optimizing cross-linking experiments.

Keywords: protein structure prediction; protein–protein complexes; experiment design; cross-linking mass spectrometry; disulfide trapping; structural genomics

Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.04846604.

Reprint requests to: Alan M. Friedman, Department of Biological Sciences, Lilly Hall, Purdue University, West Lafayette, IN 47907, USA; e-mail: afried{at}purdue.edu; fax: (765) 496-1189; or Chris Bailey-Kellogg, Department of Computer Science, 6211 Sudikoff Laboratory, Dartmouth College, Hanover, NH 03755, USA; e-mail: cbk{at}cs.dartmouth.edu; fax: (603) 646-1672.

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