This is usually achieved from sequential information provided by three chemical shifts: CA, CB and C’. First the backbone resonances are assigned. 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.
#Auto assign carbons mestrenova software#
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. The assignment process can thus benefit from a maximum knowledge input, containing all backbone and side chain chemical shifts as well as an immediate amino acid recognition from the side chain spin system. 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 combination of modern NMR techniques with new spectrometers now provide information that was not always accessible in the past, due to sensitivity problems. As a result, the first step of the strategy described above remains tedious and time consuming. 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. This strategy is unfortunately limited by the size of the protein due to increasing signal overlap and missing signals. For this purpose, the C ?-C ? and H ? 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. Once the sequence is solved, the second assignment step takes place. This is usually achieved from sequential information provided by three chemical shifts: C ?, C ? and C O. Traditionally, resonance assignment of protein backbone and side chains is done in a two-steps manner.
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