Two-dimensional coordination polymers of Zn(II) and Cd(II) derived from 3,3′,5,5′-azobenzenetetracarboxylic acid exhibiting solvent facilitated structure reversibility

Two Cd(II) and Zn(II) coordination polymers based on 3,3′,5,5′-azobenzenetetracarboxylic acid (H₄abtc): [Cd₂(abtc)(H₂O)₆]·DMF·0.5H₂O (1) and [Zn₂(abtc)(bpy)(H₂O)₂]·DMF·H₂O (2) are synthesized and structurally characterized. Both 1 and 2 are 2D polymers but interconnected by solvent molecules to generate 3D suprastructures. Solvent expulsion leads to rupture of both structures, but upon re-exposure to the solvent mixture they exhibit remarkable ability to regain the original structure reversibly from the almost amorphous solvent-expelled form. Compounds with such structural flexibility and reversibility are expected to have some useful functionality.     

Machine learning in protein structure prediction

Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.     

The influence of zinc on apical uptake of cadmium in the gills and cadmium influx to the circulatory system in zebrafish (Danio rerio)

Zn (0-16 microM) effects on apical Cd uptake from the water into the branchial epithelium and influx of Cd from the water to the circulatory system in zebrafish (Danio rerio) were studied in three experiments. Apical Cd uptake was decreased by Zn in all three experiments. In fish exposed to 1-600 nM Cd (experiment 1), apical Cd uptake showed saturation kinetics at 2 and 4 microM Zn, and a competitive interaction was indicated. At 16 microMZn, Cd uptake increased linearly. Cadmium influx did not show saturation kinetics, but was inhibited by 16 microM Zn at low Cd exposure concentrations. In fish exposed to 0.1-600 nM Cd (experiment 2), Cd uptake was inhibited by 16 microM Zn, whereas at 30 nM Cd uptake was inhibited by 2 microM Zn. Similarly, 2 microM Zn did not influence Cd uptake in fish exposed to 0.1-2 nM Cd (experiment 3), whereas 2 microM Zn inhibited uptake at 8-30 nM Cd. Zinc also inhibited Cd influx at higher Cd concentrations. However, at lower Cd exposures, a Zn-induced increased influx was indicated. Zinc influences the Cd uptake and influx processes at several sites in the branchial epithelial cells, indicating that influx of Zn2+ and Cd2+ occurs through common pathways.     

Predicting protein three-dimensional structure

The current state of the art in modeling protein structure has been assessed, based on the results of the CASP (Critical Assessment of protein Structure Prediction) experiments. In comparative modeling, improvements have been made in sequence alignment, sidechain orientation and loop building. Refinement of the models remains a serious challenge. Improved sequence profile methods have had a large impact in fold recognition. Although there has been some progress in alignment quality, this factor still limits model usefulness. In ab initio structure prediction, there has been notable progress in building approximately correct structures of 40-60 residue-long protein fragments. There is still a long way to go before the general ab initio prediction problem is solved. Overall, the field is maturing into a practical technology, able to deliver useful models for a large number of sequences.     

Crystal structure of the protein kinase domain of yeast AMP-activated protein kinase Snf1

AMP-activated protein kinase (AMPK) is a master metabolic regulator, and is an important target for drug development against diabetes, obesity, and other diseases. AMPK is a hetero-trimeric enzyme, with a catalytic (alpha) subunit, and two regulatory (beta and gamma) subunits. Here we report the crystal structure at 2.2A resolution of the protein kinase domain (KD) of the catalytic subunit of yeast AMPK (commonly known as SNF1). The Snf1-KD structure shares strong similarity to other protein kinases, with a small N-terminal lobe and a large C-terminal lobe. Two negative surface patches in the structure may be important for the recognition of the substrates of this kinase.     

A critique of the utility of the prediction of protein secondary structure

The Chou-Fasman predictive algorithm for determining the secondary structure of proteins from the primary sequence is reviewed. Many examples of its use are presented which illustrate its wide applicability, such as predicting (a) regions with the potential for conformational change, (b) sequences which are capable of assuming several conformations in different environments, (c) effects of single amino acid mutations, (d) amino acid replacements in synthesis of peptides to bring about a change in conformation, (e) guide to the synthesis of polypeptides with definitive secondary structure,e.g. signal sequences, (f) conformational homologues from varying sequences and (g) the amino acid requirements for amphiphilicα-helical peptides.     

Physical principles of protein structure and protein folding

Physical principles determining the protein structure and protein folding are reviewed: (i) the molecular theory of protein secondary structure and the method of its prediction based on this theory; (ii) the existence of a limited set of thermodynamically favourable folding patterns of α- and β-regions in a compact globule which does not depend on the details of the amino acid sequence; (iii) the moderns approaches to the prediction of the folding patterns of α- and β-regions in concrete proteins; (iv) experimental approaches to the mechanism of protein folding. The review reflects theoretical and experimental works of the author and his collaborators as well as those of other groups.     

Distance matrix-based approach to protein structure prediction

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r _ij ² ] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r _ij is greater or less than a cutoff value r _cutoff. We have performed spectral decomposition of the distance matrices $ {\mathbf{D}} = \sum {\lambda_{k} {\mathbf{v}}_{k} {\mathbf{v}}_{k}^{T} } Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r _ij²] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r _ij is greater or less than a cutoff value r _cutoff. We have performed spectral decomposition of the distance matrices , in terms of eigenvalues and the corresponding eigenvectors and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r ²—the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r ² from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 ?, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 ?. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement ().     

A new topological method to measure protein structure similarity

A method for the quantitative evaluation of structural similarity between protein pairs is developed that makes use of a Delaunay-based topological mapping. The result of the mapping is a three-dimensional array which is representative of the global structural topology and whose elements can be used to construe an integral scoring scheme. This scoring scheme was tested for its dependence on the protein length difference in a pairwise comparison, its ability to provide a reasonable means for structural similarity comparison within a family of structural neighbors of similar length, and its sensitivity to the differences in protein conformation. It is shown that such a topological evaluation of similarity is capable of providing insight into these points of interest. Protein structure comparison using the method is computationally efficient and the topological scores, although providing different information about protein similarity, correlate well with the distance root-mean-square deviation values calculated by rigid-body structural alignment.     

Environmental features are important in determining protein secondary structure

We have investigated amino acid features that determine secondary structure: (1) the solvent accessibility of each side chain, and (2) the interaction of each side chain with others one to four residues apart. Solvent accessibility is a simple model that distinguishes residue environment. The pairwise interactions represent a simple model of local side chain to side chain interactions. To test the importance of these features we developed an algorithm to separate alpha-helices, beta-strands, and "other" structure. Single residue and pairwise probabilities were determined for 25,141 samples from proteins with <30% homology. Combining the features of solvent accessibility with pairwise probabilities allows us to distinguish the three structures after cross validation at the 82.0% level. We gain 1.4% to 2.0% accuracy by optimizing the propensities, demonstrating that probabilities do not necessarily reflect propensities. Optimization of residue exposures, weights of all probabilities, and propensities increased accuracy to 84.0%.     

Evolutionary computer programming of protein folding and structure predictions

In order to understand the mechanism of protein folding and to assist the rational de-novo design of fast-folding, non-aggregating and stable artificial enzymes it is very helpful to be able to simulate protein folding reactions and to predict the structures of proteins and other biomacromolecules. Here, we use a method of computer programming called "evolutionary computer programming" in which a program evolves depending on the evolutionary pressure exerted on the program. In the case of the presented application of this method on a computer program for folding simulations, the evolutionary pressure exerted was towards faster finding deep minima in the energy landscape of protein folding. Already after 20 evolution steps, the evolved program was able to find deep minima in the energy landscape more than 10 times faster than the original program prior to the evolution process.     

Thionein/metallothionein control Zn(II) availability and the activity of enzymes

Fundamental issues in zinc biology are how proteins control the concentrations of free Zn(II) ions and how tightly they interact with them. Since, basically, the Zn(II) stability constants of only two cytosolic zinc enzymes, carbonic anhydrase and superoxide dismutase, have been reported, the affinity for Zn(II) of another zinc enzyme, sorbitol dehydrogenase (SDH), was determined. Its log K is 11.2 +/- 0.1, which is similar to the log K values of carbonic anhydrase and superoxide dismutase despite considerable differences in the coordination environments of Zn(II) in these enzymes. Protein tyrosine phosphatase 1B (PTP 1B), on the other hand, is not classified as a zinc enzyme but is strongly inhibited by Zn(II), with log K = 7.8 +/- 0.1. In order to test whether or not metallothionein (MT) can serve as a source for Zn(II) ions, it was used to control free Zn(II) ion concentrations. MT makes Zn(II) available for both PTP 1B and the apoform of SDH. However, whether or not Zn(II) ions are indeed available for interaction with these enzymes depends on the thionein (T) to MT ratio and the redox poise. At ratios [T/(MT + T) = 0.08-0.31] prevailing in tissues and cells, picomolar concentrations of free Zn(II) are available from MT for reconstituting apoenzymes with Zn(II). Under conditions of decreased ratios, nanomolar concentrations of free Zn(II) become available and affect enzymes that are not zinc metalloenzymes. The match between the Zn(II) buffering capacity of MT and the Zn(II) affinity of proteins suggests a function of MT in controlling cellular Zn(II) availability.     

2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts

Chemical shifts of amino acids in proteins are the most sensitive and easily obtainable NMR parameters that reflect the primary, secondary, and tertiary structures of the protein. In recent years, chemical shifts have been used to identify secondary structure in peptides and proteins, and it has been confirmed that ¹H^α, ¹³C^α, ¹³C^β, and ¹³C′ NMR chemical shifts for all 20 amino acids are sensitive to their secondary structure. Currently, most of the methods are purely based on one-dimensional statistical analyses of various chemical shifts for each residue to identify protein secondary structure. However, it is possible to achieve an increased accuracy from the two-dimensional analyses of these chemical shifts. The 2DCSi approach performs two-dimension cluster analyses of ¹H^α, ¹H^N, ¹³C^α, ¹³C^β, ¹³C′, and ¹⁵N^H chemical shifts to identify protein secondary structure and the redox state of cysteine residue. For the analysis of paired chemical shifts of 6 data sets, each of the 20 amino acids has its own 15 two-dimension cluster scattering diagrams. Accordingly, the probabilities for identifying helix and extended structure were calculated by using our scoring matrix. Compared with existing the chemical shift-based methods, it appears to improve the prediction accuracy of secondary structure identification, particularly in the extended structure. In addition, the probability of the given residue to be helix or extended structure is displayed, allows the users to make decisions by themselves. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Grant sponsor: National Science Council of ROC; Grant numbers: NSC-94-2323-B006- 001, NSC-93-2212-E-006.     

基于遗传算法的蛋白质结构预测方法

遗传算法源于自然界的进化规律,是一种自适应启发式概率性迭代式全局搜索算法。本文主要介绍了GA的基本原理,算法及优点;总结GA在蛋白质结构预测中建立模型和执行策略,以及多种算法相互结合预测蛋白质结构的研究进展。     

NMR structure of the human doppel protein

The NMR structure of the recombinant human doppel protein, hDpl(24-152), contains a flexibly disordered "tail" comprising residues 24-51, and a globular domain extending from residues 52 to 149 for which a detailed structure was obtained. The globular domain contains four alpha-helices comprising residues 72-80 (alpha1), 101-115 (alpha2(a)), 117-121 (alpha2(b)), and 127-141 (alpha3), and a short two-stranded anti-parallel beta-sheet comprising residues 58-60 (beta1) and 88-90 (beta2). The fold of the hDpl globular domain thus coincides nearly identically with the structure of the murine Dpl protein. There are close similarities with the human prion protein (hPrP) but, similar to the situation with the corresponding murine proteins, hDpl shows marked local differences when compared to hPrP: the beta-sheet is flipped by 180 degrees with respect to the molecular scaffold formed by the four helices, and the beta1-strand is shifted by two residues toward the C terminus. A large solvent-accessible hydrophobic cleft is formed on the protein surface between beta2 and alpha3, which has no counterpart in hPrP. The helix alpha2 of hPrP is replaced by two shorter helices, alpha2(a) and alpha2(b). The helix alpha3 is shortened by more than two turns when compared with alpha3 of hPrP, which is enforced by the positioning of the second disulfide bond in hDpl. The C-terminal peptide segment 144-149 folds back onto the loop connecting beta2 and alpha2. All but four of the 20 conserved residues in the globular domains of hPrP and hDpl appear to have a structural role in maintaining a PrP-type fold. The conservation of R76, E96, N110 and R134 in hDpl, corresponding to R148, E168, N183 and R208 in hPrP suggests that these amino acid residues might have essential roles in the so far unknown functions of PrP and Dpl in healthy organisms.     

Solution structure of the ribosome-associated cold shock response protein Yfia of Escherichia coli

The solution structure of the ribosome-associated cold shock response protein Yfia of Escherichia coli was determined by nuclear magnetic resonance with a RMSD of 0.6A. Yfia shows a global beta-alpha-beta-beta-beta-alpha folding topology similar to its homologue HI0257 of Haemophilus influenzae and the double-strand-binding domain of Drosophila Staufen protein. Yfia and HI0257 differ in their surface charges and in the composition of their flexible C-termini, indicating their specificity to different target molecules. Both proteins exhibit a hydrophobic and polar region, which probably functions as interaction site for protein complex formation. Despite their similarity to the dsRBD fold, Yfia does not bind to model fragments of 16S ribosomal RNA as determined by NMR titration and gel shift experiments.     

The configuration of the Cu2+ binding region in full-length human prion protein

The cellular prion protein (PrP^C) is a Cu²⁺ binding protein connected to the outer cell membrane. The molecular features of the Cu²⁺ binding sites have been investigated and characterized by spectroscopic experiments on PrP^C-derived peptides and the recombinant human full-length PrP^C (hPrP-[23-231]). The hPrP-[23-231] was loaded with ⁶³Cu under slightly acidic (pH 6.0) or neutral conditions. The PrP^C/Cu²⁺-complexes were investigated by extended X-ray absorption fine structure (EXAFS), electron paramagnetic resonance (EPR), and electron nuclear double resonance (ENDOR). For comparison, peptides from the copper-binding octarepeat domain were investigated in different environments. Molecular mechanics computations were used to select sterically possible peptide/Cu²⁺ structures. The simulated EPR, ENDOR, and EXAFS spectra of these structures were compared with our experimental data. For a stoichiometry of two octarepeats per copper the resulting model has a square planar four nitrogen Cu²⁺ coordination. Two nitrogens belong to imidazole rings of histidine residues. Further ligands are two deprotonated backbone amide nitrogens of the adjacent glycine residues and an axial oxygen of a water molecule. Our complex model differs significantly from those previously obtained for shorter peptides. Sequence context, buffer conditions and stoichiometry of copper show marked influence on the configuration of copper binding to PrP^C. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.