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

ABSTRACT: 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 p

SUBMITTER: Ye X 

PROVIDER: S-EPMC2287312 | biostudies-literature | 2004 Dec

REPOSITORIES: biostudies-literature