Protein chemical shift detection software tools | NMR-based proteomics data analysis
NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.
A database system for empirical prediction of backbone chemical shifts (N, HN, HA, CA, CB, CO) using a combination of backbone phi, psi torsion angles and sidechain chi1 angles from a given protein with known PDB coordinates.
Detects referencing errors and to recalibrate the 1H and 13C chemical shift scales if needed. The analysis requires only that the signals be identified with distinct residue types (intra-residue spin systems). LACS allows errors in calibration to be detected and corrected in advance of sequence-specific assignments and secondary structure determinations. Signals that do not fit the linear model (outliers) deserve scrutiny since they could represent errors in identifying signals with a particular residue, or interesting features such as a cis-peptide bond. LACS provides the basis for the automated detection of such features and for testing reassignment hypotheses. Early detection and correction of errors in referencing and spin system identifications can improve the speed and accuracy of chemical shift assignments and secondary structure determinations. The input format is NMRSTAR 2.1 (BMRB) format, and. the result will be returned via email in a couple of minutes.
Uses the PDB (Protein Data Bank) and BMRB (Biological Magnetic Resonance Bank) archives and performs a statistical analysis, which takes into account the solvent accessibility of all heavy atoms, the secondary structure information and the reported chemical shift values. VASCO corrects and reports chemical shift referencing inconsistencies, identifies individual chemical shift outliers and provides a Z-score for each deviation from the expected mean value. The method can be extended to use other information (e.g., dihedral angles) by further subdividing the data and to other nuclei and/or molecule types (e.g., RNA and DNA), although this only becomes possible with increasing data archive size.
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