Direct Observation of Millisecond to Second Motions in Proteins by Dipolar CODEX NMR Spectroscopy

We present a site-resolved study of stow (ms to s) motions in a protein in the solid (microcrystalline) state performed with the use of a modified version of the centerband-only detection of exchange (CODEX) NMR experiment. CODEX was originally based on measuring changes in molecular orientation by means of the chemical shift anisotropy (CSA) tensor, and in our modification, angular reorientations of internuclear vectors are observed. The experiment was applied to the study of stow (15)N-(1)H motions of the SH3 domain of chicken a-spectrin. The protein was perdeuterated with partial back-exchange of protons at labile sites. This allowed indirect (proton) detection of (15)N nuclei and thus a significant enhancement of sensitivity. The diluted proton system also made negligible proton-driven spin diffusion between (15)N nuclei, which interferes with the molecular exchange (motion) and hampers the acquisition of dynamic parameters. The experiment has shown that approximately half of the peaks in the 2D (15)N-(1)H correlation spectrum exhibit exchange in a different extent. The correlation time of the slow motion for most peaks is 1 to 3 s. This is the first NMR study of the internal dynamics of proteins in the solid state on the millisecond to second time scale with site-specific spectral resolution that provides both time-scale and geometry information about molecular motions.

CONTRA: Improving the performance of dynamic investigations in natural abundance organic solids by mirror-symmetric constant-time CODEX

We present a minor but essential modification to the CODEX 1D-MAS exchange experiment. The new CONTRA method, which requires minor changes of the original sequence only, has advantages over the previously introduced S-CODEX, since it is less sensitive to artefacts caused by finite pulse lengths. The performance of this variant, including the finite pulse effect, was confirmed by SIMPSON calculations and demonstrated on a number of dynamic systems. (C) 2007 Elsevier Inc. All rights reserved.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.