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Probabilistic Assignment of Chemical Shift Data for Semi-Automatic Amino Acid Recognition

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Hooge,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Hooge, J. (2010). Probabilistic Assignment of Chemical Shift Data for Semi-Automatic Amino Acid Recognition. Poster presented at 11th Conference of Junior Neuroscientists of Tübingen (NeNa 2010), Heiligkreuztal, Germany.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BE08-4
Abstract
manner. First the backbone resonances are assigned. This is usually achieved from sequential information provided by three chemical shifts: CA, CB and C’. Once the sequence is solved, the second assignment step takes place. For this purpose, the CA-CB and HA chemical shifts are used as a start point for assignment of the side chain resonances, thus connecting the backbone resonances to their respective side chains. This strategy is unfortunately limited by
the size of the protein due to increasing signal overlap and missing signals. Therefore, amino acid recognition is in many cases not possible as the CA-CB chemical shift pattern is not sufficient to discriminate between the 20 amino acids. As a result, the first step of the strategy
described above remains tedious and time consuming. The combination of modern NMR techniques with new spectrometers now provide information that was not always accessible
in the past, due to sensitivity problems. These experiments can be applied efficiently to measure a protein size up to 45 kDa and furthermore provide a unique combination of
sequential carbon spin system information. The assignment process can thus benefit from a maximum knowledge input, containing âallâ backbone and side chain chemical shifts as
well as an immediate amino acid recognition from the side chain spin system. We propose to extend the software PASTA (Protein ASsignment by Threshold Accepting) to achieve
a general sequential assignment of backbone and side-chain resonances in a semi- to fullautomatic per-residue approach. PASTA will offer the possibility to achieve the sequential assignment using any kind of chemical shifts (carbons and/or protons) that can provide sequential information combined with an amino acid recognition feature based on carbon spin system analysis.