Dataset Information

Tunnelling readout of hydrogen-bonding-based recognition.

ABSTRACT: Hydrogen bonding has a ubiquitous role in electron transport and in molecular recognition, with DNA base pairing being the best-known example. Scanning tunnelling microscope images and measurements of the decay of tunnel current as a molecular junction is pulled apart by the scanning tunnelling microscope tip are sensitive to hydrogen-bonded interactions. Here, we show that these tunnel-decay signals can be used to measure the strength of hydrogen bonding in DNA base pairs. Junctions that are held together by three hydrogen bonds per base pair (for example, guanine-cytosine interactions) are stiffer than junctions held together by two hydrogen bonds per base pair (for example, adenine-thymine interactions). Similar, but less pronounced effects are observed on the approach of the tunnelling probe, implying that attractive forces that depend on hydrogen bonds also have a role in determining the rise of current. These effects provide new mechanisms for making sensors that transduce a molecular recognition event into an electronic signal.

SUBMITTER: Chang S

PROVIDER: S-EPMC2698135 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Publications

Tunnelling readout of hydrogen-bonding-based recognition.

Chang Shuai S He Jin J Kibel Ashley A Lee Myeong M Sankey Otto O Zhang Peiming P Lindsay Stuart S

Nature nanotechnology 20090322 5

Hydrogen bonding has a ubiquitous role in electron transport and in molecular recognition, with DNA base pairing being the best-known example. Scanning tunnelling microscope images and measurements of the decay of tunnel current as a molecular junction is pulled apart by the scanning tunnelling microscope tip are sensitive to hydrogen-bonded interactions. Here, we show that these tunnel-decay signals can be used to measure the strength of hydrogen bonding in DNA base pairs. Junctions that are he ...[more]

PMID: 19421214

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Project description:Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

Dataset Information

Tunnelling readout of hydrogen-bonding-based recognition.

Publications

Tunnelling readout of hydrogen-bonding-based recognition.

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