Reducing Mass Degeneracy in SAR by MS by Stable Isotopic Labeling

Proceedings of the Twentieth International Conference on Machine Learning, 2000

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 2000

Contents

Mass spectrometry (MS) promises to be an invaluable tool for functional genomics, by supporting low- cost, high-throughput experiments. However, large- scale MS faces the potential problem of mass degeneracy | indistinguishable masses for multiple biopolymer fragments (e.g. from a limited proteolytic digest). This paper studies the tasks of planning and interpreting MS experiments that use selective isotopic labeling, thereby substantially reducing potential mass degeneracy. Our algorithms support an experimental- computational protocol called Structure-Activity Relation by Mass Spectrometry (SAR by MS), for elucidating the function of protein-DNA and protein- protein complexes. SAR by MS enzymatically cleaves a crosslinked complex and analyzes the resulting mass spectrum for mass peaks of hypothesized fragments. Depending on binding mode, some cleavage sites will be shielded; the absence of anticipated peaks implicates corresponding fragments as either part of the interaction region or inaccessible due to conformational change upon binding. Thus different mass spectra provide evidence for different structure-activity relations. We address combinatorial and algorithmic questions in the areas of data analysis (constraining binding mode based on mass signature) and experiment planning (determining an isotopic labeling strategy to reduce mass degeneracy and aid data analysis). We explore the computational complexity of these problems, obtaining upper and lower bounds. We report experimental results from implementations of our algorithms.