Hydration shells exchange charge with their protein.

Investigation of the interaction between a protein and its hydration shells is an experimental and theoretical challenge. Here, we used ultrasonic pressure waves in aqueous solutions of a protein to explore the conformational states of the protein and its interaction with its hydration shells. In our experiments, the amplitude of an ultrasonic pressure wave is gradually increased (0-20 atm) while we simultaneously measure the Raman spectra from the hydrated protein (ß-lactoglobulin and lysozyme). We detected two types of spectral changes: first, up to 70% increase in the intensity of the fluorescence background of the Raman spectrum with a typical relaxation time of 30-45 min. Second, we detect changes in the vibrational Raman spectra. To clarify these results we conducted similar experiments with aqueous solutions of amino acids and ethanol. These experiments led us to conclude that, without the presence of an ultrasonic pressure, a protein and its hydration shells are in thermodynamic and charge equilibrium, i.e. a protein and its hydration shells exchange charges. The ultrasonic wave disrupts these equilibria which are regained within 30-45 min after the ultrasonic pressure is shut off.

Fiber ring laser with a feedback mirror.

We describe the spectral and power features of a ytterbium-doped double-clad photonic crystal fiber laser that is operated in a ring configuration with an external mirror that feeds back only one of its two output beams. We compare the operation of the laser with and without an external feedback mirror. We find that the feedback mirror reduces significantly the spectral and power fluctuations. It is also responsible for an interesting spectral phenomenon: The laser frequency is drifting periodically over 9 nm at a rate of 2 nm/s from a short wavelength to a longer wavelength and vice versa.

Enkephalins: Raman spectral analysis and comparison as function of pH 1-13.

Raman spectral studies are carried out on Leu- and Met-enkephalin as a function of the pH value in the range of 1-13. The molecules are dissolved in KCl solvent and the pH is controlled at each value. Spectral analyses reveal the dependence of the structural conformation on the pH, and a comparison of the two molecules is made in three frequency regions: the tyrosine Fermi doublet (850-830 cm(-1)), aromatic side chains (1650-1550 cm(-1)), and carboxylate (1430-1400 cm(-1)). All regions and frequencies are presented, discussed, and compared for the two molecules.

Protein secondary structure: category assignment and predictability.

In the last decade, the prediction of protein secondary structure has been optimized using essentially one and the same assignment scheme known as DSSP. We present here a different scheme, which is more predictable. This scheme predicts directly the hydrogen bonds, which stabilize the secondary structures. Single sequence prediction of the new three category assignment gives an overall prediction improvement of 3.1% and 5.1% compared to the DSSP assignment and schemes where the helix category consists of alpha-helix and 3(10)-helix, respectively. These results were achieved using a standard feed-forward neural network with one hidden layer on a data set identical to the one used in earlier work.

Neural-network analysis of the vibrational spectra of N-acetyl L-alanyl N'-methyl amide conformational states.

Density-functional theory (DFT) calculations utilizing the Becke 3LYP hybrid functional have been carried out for N-acetyl L-alanine N'-methylamide and examined with respect to the effect of water on the structure, the vibrational frequencies, vibrational absorption (VA), vibrational circular dichroism (VCD), Raman spectra, and Raman optical activity (ROA) intensities. The large changes due to hydration in the structures, and the relative stability of the conformer, reflected in the VA, VCD, Raman spectra, and ROA spectra observed experimentally, are reproduced by the DFT calculations. A neural network has been constructed for reproducing the inverse scattering data (we infer the structural coordinates from spectroscopic data) that the DFT method could produce. The purpose of the network has also been to generate the large set of conformational states associated with each set of spectroscopic data for a given conformer of the molecule by interpolation. Finally the neural network performances are used to monitor a sensitivity analysis of the importance of secondary structures and the influence of the solvent. The neural network is shown to be good in distinguishing the different conformers of the small alanine peptide, especially in the gas phase.

Applications of neural network prediction of conformational states for small peptides from spectra and of fold classes.

Electronic structures of small peptides were calculated 'ab initio' with the help of Density Functional Theory (DFT) and molecular dynamics that rendered a set of conformational states of the peptides. For the structures of these states it was possible to derive atomic polar tensors that allowed us to construct vibrational spectra for each of the conformational states with low energy. From the spectra, neural networks could be trained to distinguish between the various states and thus be able to generate a larger set of relevant structures and their relation to secondary structures of the peptides. The calculations were done both with solvent atoms (up to ten water molecules) and without, and hence the neural networks could be used to monitor the influence of the solvent on hydrogen bond formation. The calculations at this stage only involved very short peptide fragments of a few alanine amino acids but already at this stage they could be compared with reasonable agreements to experiments. The neural networks are shown to be good in distinguishing the different conformers of the small alanine peptides, especially when in the gas phase. Also the task of predicting protein fold-classes, defined from line-geometry, seems promising.

Microwave-enhanced folding and denaturation of globular proteins.

It is shown that microwave irradiation can affect the kinetics of the folding process of some globular proteins, especially beta-lactoglobulin. At low temperature the folding from the cold denatured phase of the protein is enhanced, while at a higher temperature the denaturation of the protein from its folded state is enhanced. In the latter case, a negative temperature gradient is needed for the denaturation process, suggesting that the effects of the microwaves are nonthermal. This supports the notion that coherent topological excitations can exist in proteins. The application of microwaves hold promises for a wide range of biotechnological applications, such as protein synthesis, protein aggregation, etc., and may have implications for biological systems as well.

Prediction of protein secondary structure at 80% accuracy.

Secondary structure prediction involving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure. An overall performance of 77.2%-80.2% (77.9%-80.6% mean per-chain) for three-state (helix, strand, coil) prediction was obtained when evaluated on a commonly used set of 126 protein chains. The method uses profiles made by position-specific scoring matrices as input, while at the output level it predicts on three consecutive residues simultaneously. The predictions arise from tenfold, cross validated training and testing of 1032 protein sequences, using a scheme with primary structure neural networks followed by structure filtering neural networks. With respect to blind prediction, this work is preliminary and awaits evaluation by CASP4.

Microwave enhanced kinetics observed in ORD studies of a protein.

Microwaves are shown to affect the kinetics of conformational changes of the protein beta-lactoglobulin. Microwaves can accelerate conformational changes in the direction towards the equilibrium state. This applies both for the folding and the unfolding processes. Cold denaturing thermal unfolding of the proteins is accelerated by negative temperature gradients. Microwave irradiation of the protein solution heated it by about 0.3 degree, and hence the observed acceleration of denaturing is therefore non-thermal.

Prediction of protein hydration sites from sequence by modular neural networks.

The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with two separate neural networks. These predictions are used as input together with protein sequences for networks predicting hydration of residues, backbone atoms and sidechains. These networks are trained with protein crystal structures. The prediction of hydration is improved by adding information on secondary structure and solvent accessibility and, using actual values of these properties, residue hydration can be predicted to 77% accuracy with a Matthews coefficient of 0.43. However, predicted property data with an accuracy of 60-70% result in less than half the improvement in predictive performance observed using the actual values. The inclusion of property information allows a smaller sequence window to be used in the networks to predict hydration. It has a greater impact on the accuracy of hydration site prediction for backbone atoms than for sidechains and for non-polar than polar residues. The networks provide insight into the mutual interdependencies between the location of ordered water sites and the structural and chemical characteristics of the protein residues.

Coherent topological phenomena in protein folding.

A theory is presented for coherent topological phenomena in protein dynamics with implications for protein folding and stability. We discuss the relationship to the writhing number used in knot diagrams of DNA. The winding state defines a long-range order along the backbone of a protein with long-range excitations, 'wring' modes, that play an important role in protein denaturation and stability. Energy can be pumped into these excitations, either thermally or by an external force.

An update of the DEF database of protein fold class predictions.

An update is given on the Database of Expected Fold classes (DEF) that contains a collection of fold-class predictions made from protein sequences and a mail server that provides new predictions for new sequences. To any given sequence one of 49 fold-classes is chosen to classify the structure related to the sequence with high accuracy. The updated prediction system is developed using data from the new version of the 3D-ALI database of aligned protein structures and thus is giving more reliable and more detailed predictions than the previous DEF system.

Protein folding and wring resonances.

The polypeptide chain of a protein is shown to obey topological constraints which enable long range excitations in the form of wring modes of the protein backbone. Wring modes of proteins of specific lengths can therefore resonate with molecular modes present in the cell. It is suggested that protein folding takes place when the amplitude of a wring excitation becomes so large that it is energetically favorable to bend the protein backbone. The condition under which such structural transformations can occur is found, and it is shown that both cold and hot denaturation (the unfolding of proteins) are natural consequences of the suggested wring mode model. Native (folded) proteins are found to possess an intrinsic standing wring mode.

Molecular wring resonances in chain molecules.

It is shown that the eigenfrequency of collective twist excitations in chain molecules can be in the megahertz and gigahertz range. Accordingly, resonance states can be obtained at specific frequencies, and phenomena that involve structural properties can take place. Chain molecules can alter their conformation and their ability to function, and a breaking of the chain can result. It is suggested that this phenomenon forms the basis for effects caused by the interaction of microwaves and biomolecules, e.g., microwave assisted hydrolysis of chain molecules.

Protein distance constraints predicted by neural networks and probability density functions.

We predict interatomic Calpha distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taking the context of the two residues into account. These two methods are used to predict whether distances in independent test sets were above or below given thresholds. We investigate which distance thresholds produce the most information-rich constraints and, in turn, the optimal performance of the two methods. The predictions are based on a data set derived using a new threshold which defines when sequence similarity implies structural similarity. We show that distances in proteins are predicted more accurately by neural networks than by probability density functions. We show that the accuracy of the predictions can be further increased by using sequence profiles. A threading method based on the predicted distances is presented. A homepage with software, predictions and data related to this paper is available at http://www.cbs.dtu.dk/services/CPHmodels/.

Distance distributions in proteins: a six-parameter representation.

We present a statistical analysis of protein structures based on interatomic C alpha distances. The overall distance distributions reflect in detail the contents of sequence-specific substructures maintained by local interactions (such as alpha-helixes) and longer range interactions (such as disulfide bridges and beta-sheets). We also show that a volume scaling of the distances makes distance distributions for protein chains of different length superimposable. Distance distributions were also calculated specifically for amino acids separated by a given number of residues. Specific features in these distributions are visible for sequence separations of up to 20 amino acid residues. A simple representation, which preserves most of the information in the distance distributions, was obtained using six parameters only. The parameters give rise to canonical distance intervals and when predicting coarse-grained distance constraints by methods such as data-driven artificial neural networks, these should preferably be selected from these intervals. We discuss the use of the six parameters for determining or reconstructing 3-D protein structures.

Prediction of O-glycosylation of mammalian proteins: specificity patterns of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase.

The specificity of the enzyme(s) catalysing the covalent link between the hydroxyl side chains of serine or threonine and the sugar moiety N-acetylgalactosamine (GalNAc) is unknown. Pattern recognition by artificial neural networks and weight matrix algorithms was performed to determine the exact position of in vivo O-linked GalNAc-glycosylated serine and threonine residues from the primary sequence exclusively. The acceptor sequence context for O-glycosylation of serine was found to differ from that of threonine and the two types were therefore treated separately. The context of the sites showed a high abundance of proline, serine and threonine extending far beyond the previously reported region covering positions -4 through +4 relative to the glycosylated residue. The O-glycosylation sites were found to cluster and to have a high abundance in the N-terminal part of the protein. The sites were also found to have an increased preference for three different classes of beta-turns. No simple consensus-like rule could be deduced for the complex glycosylation sequence acceptor patterns. The neural networks were trained on the hitherto largest data material consisting of 48 carefully examined mammalian glycoproteins comprising 264 O-glycosylation sites. For detection neural network algorithms were much more reliable than weight matrices. The networks correctly found 60-95% of the O-glycosylated serine/threonine residues and 88-97% of the non-glycosylated residues in two independent test sets of known glycoproteins. A computer server using E-mail for prediction of O-glycosylation sites has been implemented and made publicly available. The Internet address is NetOglyc@cbs.dtu.dk.

Prediction of hypervariable CDR-H3 loop structures in antibodies.

The structure of the most variable antibody hypervariable loop, CDR-H3, has been predicted from amino acid sequence alone. In contrast to other approaches predictions are made for loop lengths up to 17 residues. The predictions have been achieved using artificial neural networks which are trained on a large set of loops from the Brookhaven Protein Databank which have structures similar to CDR-H3. The loop structures are described by the two backbone dihedral angles phi and psi for each residue. For 21 CDR-H3 loops unique to the neural network, the prediction of dihedral angles leads to an average root mean square deviation in the Cartesian coordinates of 2.65 A. The present method, when combined with existing modelling protocols, provides an important addition to the structural prediction of the complementarity determining regions of antibodies.

The DEF data base of sequence based protein fold class predictions.

A new method for predicting protein fold-classes and protein domains from sequence data is constructed and used for generating a data base of protein fold-class assignments. Any given sequence of amino acids is assigned a specific prediction of one out of 45 typical protein fold-classes, a prediction of one out of 4 super fold-classes for the content of secondary structures and a profile of fold-class predictions along the sequence. The prediction accuracy for the super fold-classes is around 91% correct and 82% correct for the specific fold-classes. This accuracy is maintained down to a few percent of sequence identity.