Biomedical subjects

Jens Meiler

Publications and source records attributed to Jens Meiler.

24 records · Page 2Linked to original sources

PubMed2003

PROSHIFT: protein chemical shift prediction using artificial neural networks.

The importance of protein chemical shift values for the determination of three-dimensional protein structure has increased in recent years because of the large databases of protein structures with assigned chemical shift data. These databases have allowed the investigation of the quantitative relationship between chemical shift values obtained by liquid state NMR spectroscopy and the three-dimensional structure of proteins. A neural network was trained to predict the (1)H, (13)C, and (15)N of proteins using their three-dimensional structure as well as experimental conditions as input parameters. It achieves root mean square deviations of 0.3 ppm for hydrogen, 1.3 ppm for carbon, and 2.6 ppm for nitrogen chemical shifts. The model reflects important influences of the covalent structure as well as of the conformation not only for backbone atoms (as, e.g., the chemical shift index) but also for side-chain nuclei. For protein models with a RMSD smaller than 5 A a correlation of the RMSD and the r.m.s. deviation between the predicted and the experimental chemical shift is obtained. Thus the method has the potential to not only support the assignment process of proteins but also help with the validation and the refinement of three-dimensional structural proposals. It is freely available for academic users at the PROSHIFT server: www.jens-meiler.de/proshift.html

Amino Acid Sequence↗

PubMed2002

A structural model of the complex formed by phospholamban and the calcium pump of sarcoplasmic reticulum obtained by molecular mechanics.

Phospholamban (PLN) is an intrinsic membrane protein of 52 amino acids that modulates the activity of the reticular Ca(2+) ion pump. We recently solved the three-dimensional structure of chemically synthesized, unphosphorylated, monomeric PLN (C41F) by high-resolution nuclear magnetic resonance spectroscopy in chloroform/methanol. The structure is composed of two alpha-helical regions connected by a beta turn (Type III). We used this structure and the crystallographic structure of the sarcoplasmic reticulum calcium pump (SERCA) recently determined by Toyoshima and co-workers and modeled into its E(2) form by Stokes (1KJU) or by Toyoshima (1FQU). We applied restrained and unrestrained energy optimizations and used the AMBER molecular mechanics force field to model the complex formed between PLN and the pump. The results indicate that transmembrane helix 6 (M6) of the SERCA pump is energetically favored, with respect to the other transmembrane helices, as the PLN binding partner within the membrane and is the only one of these helices that also permits contact between the N-terminal residues of PLN and the critical cytosolic binding loop region of the pump. This result is in agreement with published biochemical data and with the predictions of previous mutagenesis work on the membrane sector of the pump. The model reveals that PLN does not span the entire width of the membrane, that is, its hydrophobic C-terminal end is located near the center of the transmembrane region of the SERCA pump. The model also shows that interaction with M6 is stabilized by additional contacts made by PLN to M4. The contact between the N-terminal portion of PLN and the pump is stabilized by a number of salt and hydrogen-bond bridges, which may be abolished by phosphorylation of PLN. The contacts between the cytosolic portions of PLN and the pump are only observed in the E(2) conformation of the pump. Our model of the complex also offers a plausible structural explanation for the preference of protein kinase A for phosphorylation of Ser16 of PLN.

Animals↗

PubMed2002

Model-free analysis of protein backbone motion from residual dipolar couplings.

On the basis of the measurement of NH residual dipolar couplings (RDCs) in 11 different alignment media, an RDC-based order parameter is derived for each residue in the protein ubiquitin. Dipolar couplings are motionally averaged in the picosecond to millisecond time range and, therefore, reflect motion slower than the inverse overall tumbling correlation time of the protein. It is found that there is considerable motion that is slower than the correlation time and could not be detected with previous NMR methodology. Amplitudes and anisotropies of the motion can be derived from the model-free analysis. The method can be applied provided that at least five sufficiently different alignment media can be found for the biomolecule under investigation.

Computer Simulation↗

PubMed2002

Genius: a genetic algorithm for automated structure elucidation from 13C NMR spectra.

The automated structure elucidation of organic molecules from experimentally obtained properties is extended by an entirely new approach. A genetic algorithm is implemented that uses molecular constitution structures as individuals. With this approach, the structure of organic molecules can be optimized to meet experimental criteria, if in addition a fast and accurate method for the prediction of the used physical or chemical features is available. This is demonstrated using 13C NMR spectrum as readily obtainable information. By means of artificial neural networks a fast and accurate method for calculating the 13C NMR spectrum of the generated structures exists. The method is implemented and tested successfully for organic molecules with up to 18 non-hydrogen atoms.

Algorithms↗

PubMed2002

Using neural networks for (13)c NMR chemical shift prediction-comparison with traditional methods.

Interpretation of (13)C chemical shifts is essential for structure elucidation of organic molecules by NMR. In this article, we present an improved neural network approach and compare its performance to that of commonly used approaches. Specifically, our recently proposed neural network (J. Chem. Inf. Comput. Sci. 2000, 40, 1169-1176) is improved by introducing an extended hybrid numerical description of the carbon atom environment, resulting in a standard deviation (std. dev.) of 2.4 ppm for an independent test data set of approximately 42,500 carbons. Thus, this neural network allows fast and accurate (13)C NMR chemical shift prediction without the necessity of access to molecule or fragment databases. For an unbiased test dataset containing 100 organic structures the accuracy of the improved neural network was compared to that of a prediction method based on the HOSE code (hierarchically ordered spherical description of environment) using SPECINFO. The results show the neural network predictions to be of quality (std. dev. = 2.7 ppm) comparable to that of the HOSE code prediction (std. dev. = 2.6 ppm). Further we compare the neural network predictions to those of a wide variety of other (13)C chemical shift prediction tools including incremental methods (CHEMDRAW, SPECTOOL), quantum chemical calculation (GAUSSIAN, COSMOS), and HOSE code fragment-based prediction (SPECINFO, ACD/CNMR, PREDICTIT NMR) for the 47 (13)C-NMR shifts of Taxol, a natural product including many structural features of organic substances. The smallest standard deviations were achieved here with the neural network (1.3 ppm) and SPECINFO (1.0 ppm).

Journal Article↗

PubMedDate not supplied

Validation of structural proposals by substructure analysis and 13C NMR chemical shift prediction.

The 2D NMR-guided computer program COCON can be extremely valuable for the constitutional analysis of unknown compounds, if its results are evaluated by neural network-assisted 13C NMR chemical shift and substructure analyses. As instructive examples, data sets of four differently complex marine natural products were thoroughly investigated. As a significant step towards a true automated structure elucidation, it is shown that the primary COCON output can be safely diminished to less than 1% of its original size without losing the correct structural proposal.

Journal Article↗