Ultra-sharp metal and nanotube-based probes for applications in scanning microscopy and neural recording.

This paper discusses several methods for manufacturing ultra-sharp probes, with applications geared toward, but not limited to, scanning microscopy (STM, AFM) and intra-cellular recordings of neural signals. We present recipes for making tungsten, platinum/iridium alloy, and nanotube fibril tips. Electrical isolation methods using Parylene-C or PMMA are described.

Reducing mass degeneracy in SAR by MS by stable isotopic labeling.

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.

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data.

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Deferring assignment eliminates the bottleneck faced by traditional approaches, which begin by correlating peaks among dozens of experiments. JIGSAW utilizes only four experiments, none of which requires 13C-labeled protein, thus dramatically reducing both the amount and expense of wet lab molecular biology and the total spectrometer time. Results for three test proteins demonstrate that JIGSAW correctly identifies 79-100% of alpha-helical and 46-65% of beta-sheet NOE connectivities and correctly aligns 33-100% of secondary structure elements. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations. It could be useful for quick structural assays to speed data to the biologist early in an investigation and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

Reducing mass degeneracy in SAR by MS by stable isotopic labeling.

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.