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 residuespecific 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 A growing number of groups have demonstrated the utility of cross-linking for identifying geometric features of protein and complex structure. In contrast to other biophysical techniques, such as FRET (Dong et al. 2000) and EPR spin labeling (Voss et al. 1995;Gaponenko et al. 2000), which yield a number of approximate distances, cross-linking generally provides only the information that some pairs of residues are closer than a maximal cross-linking distance at some point during the reaction. That this information can be sufficient for discriminating among predicted structural models has been demonstrated (Cohen and Sternberg 1980;Swaney 1986;Haniu et al. 1993;Scaloni et al. 1998;Kwaw et al. 2000;Young et al. 2000;Chen et al. 2001;Schilling et al. 2003;Trester-Zedlitz et al. 2003). As illustrated in Figure 1, initial computational analyses provide possible models of a protein's structure, for example, by fold recognition (Godzik 2003;Kurowski and Bujnicki 2003) (Simons et al. 1997;Kihara et al. 2001), or a complex's interaction, for example, by docking (Smith and Sternberg 2002). Specific sites ...