Dataset Information

Automated projection spectroscopy (APSY).

ABSTRACT: This work presents the automated projection spectroscopy (APSY) method for the recording of discrete sets of j projections from N-dimensional (N > or = 3) NMR experiments at operator-selected projection angles and automatic identification of the correlation cross peaks. The result from APSY is the fully automated generation of the complete or nearly complete peak list for the N-dimensional NMR spectrum from a geometric analysis of the j experimentally recorded, low-dimensional projections. In the present implementation of APSY, two-dimensional projections of the N-dimensional spectrum are recorded by using techniques developed for projection-reconstruction spectroscopy [Kupce,E.& Freeman, R. (2004) J. Am. Chem. Soc. 126, 6429-6440]. All projections are peak-picked with the available automated routine atnos. The previously undescribed algorithm GAPRO (geometric analysis of projections) uses vector algebra to identify subgroups of peaks in different projections that arise from the same resonance in the N-dimensional spectrum, and from these subgroups it calculates the peak positions in the N-dimensional frequency space. Unambiguous identification thus can be achieved for all cross peaks that are not overlapped with other peaks in at least one of the N dimensions. Because of the correlation between the positions of corresponding peaks in multiple projections, uncorrelated noise is efficiently suppressed, so that APSY should be quite widely applicable for correlation spectra of biological macromolecules, which have intrinsically low peak density in the N-dimensional spectral space.

SUBMITTER: Hiller S

PROVIDER: S-EPMC1182451 | biostudies-literature | 2005 Aug

REPOSITORIES: biostudies-literature

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Automated projection spectroscopy (APSY).

Hiller Sebastian S Fiorito Francesco F Wüthrich Kurt K Wider Gerhard G

Proceedings of the National Academy of Sciences of the United States of America 20050725 31

This work presents the automated projection spectroscopy (APSY) method for the recording of discrete sets of j projections from N-dimensional (N > or = 3) NMR experiments at operator-selected projection angles and automatic identification of the correlation cross peaks. The result from APSY is the fully automated generation of the complete or nearly complete peak list for the N-dimensional NMR spectrum from a geometric analysis of the j experimentally recorded, low-dimensional projections. In th ...[more]

PMID: 16043707

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Project description:Single-molecule force spectroscopy (SMFS) provides a powerful tool to explore the dynamics and energetics of individual proteins, protein-ligand interactions, and nucleic acid structures. In the canonical assay, a force probe is retracted at constant velocity to induce a mechanical unfolding/unbinding event. Next, two energy landscape parameters, the zero-force dissociation rate constant (ko) and the distance to the transition state (Δx‡), are deduced by analyzing the most probable rupture force as a function of the loading rate, the rate of change in force. Analyzing the shape of the rupture force distribution reveals additional biophysical information, such as the height of the energy barrier (ΔG‡). Accurately quantifying such distributions requires high-precision characterization of the unfolding events and significantly larger data sets. Yet, identifying events in SMFS data is often done in a manual or semiautomated manner and is obscured by the presence of noise. Here, we introduce, to our knowledge, a new algorithm, FEATHER (force extension analysis using a testable hypothesis for event recognition), to automatically identify the locations of unfolding/unbinding events in SMFS records and thereby deduce the corresponding rupture force and loading rate. FEATHER requires no knowledge of the system under study, does not bias data interpretation toward the dominant behavior of the data, and has two easy-to-interpret, user-defined parameters. Moreover, it is a linear algorithm, so it scales well for large data sets. When analyzing a data set from a polyprotein containing both mechanically labile and robust domains, FEATHER featured a 30-fold improvement in event location precision, an eightfold improvement in a measure of the accuracy of the loading rate and rupture force distributions, and a threefold reduction of false positives in comparison to two representative reference algorithms. We anticipate FEATHER being leveraged in more complex analysis schemes, such as the segmentation of complex force-extension curves for fitting to worm-like chain models and extended in future work to data sets containing both unfolding and refolding transitions.

Dataset Information

Automated projection spectroscopy (APSY).

Publications

Automated projection spectroscopy (APSY).

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