Experimental Structure Determination Research Articles

Metacaspase Activity of Arabidopsis thaliana Is Regulated by S-Nitrosylation of a Critical Cysteine Residue

Nitric oxide (NO) regulates a number of signaling functions in both animals and plants under several physiological and pathophysiological conditions. S-Nitrosylation linking a nitrosothiol on cysteine residues mediates NO signaling functions of a broad spectrum of mammalian proteins, including caspases, the main effectors of apoptosis. Metacaspases are suggested to be the ancestors of metazoan caspases, and plant metacaspases have previously been shown to be genuine cysteine proteases that autoprocess in a manner similar to that of caspases. We show that S-nitrosylation plays a central role in the regulation of the proteolytic activity of Arabidopsis thaliana metacaspase 9 (AtMC9) and hypothesize that this S-nitrosylation affects the cellular processes in which metacaspases are involved. We found that AtMC9 zymogens are S-nitrosylated at their active site cysteines in vivo and that this posttranslational modification suppresses both AtMC9 autoprocessing and proteolytic activity. However, the mature processed form is not prone to NO inhibition due to the presence of a second S-nitrosylation-insensitive cysteine that can replace the S-nitrosylated cysteine residue within the catalytic center of the processed AtMC9. This cysteine is absent in caspases and paracaspases but is conserved in all reported metacaspases.

Polarization ratio and effective mass in InP nanowires: Effect of crystallographic axis

Indium phosphide nanowires grown along different crystallographic axes---namely, the [001], [101], and [111] directions of zinc-blende structure---are investigated using a first-principles derived semi-empirical pseudopotential theory, aimed at understanding the effects of wire orientation on band structure, polarization ratio, and effective masses of semiconductor nanowires. Band energies over entire Brillouin zone are determined, and are found to exhibit different characteristics for three types of wires in terms of band dispersion and the location of orbital energy. A pronounced dispersion hump is revealed to exist in the lowest conduction band for the [001] and [111] wires, but not for the [101] wires. On the other hand, the [001] and [111] wires are shown to have very different orbital energy for the top valence state at the zone boundary $X$ point---being $\ensuremath{-}6.8\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ in the former and $\ensuremath{-}6.2\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ in the latter. These differences provide specific and useful suggestion to encourage experimental determination of the band structure in InP nanowires. As another key result, we study the polarization ratio in wires of different orientations. Our calculations show that, given the same lateral size, the [111] wires yield the highest polarization ratio as compared to wires along the other two directions, while simultaneously possessing larger band-edge photoluminescence transition intensity. The [111] wires are thus suggested to be better suitable for optical device applications. Interestingly, we also found that polarization ratio displays a different size dependence than transition intensity does. More specifically, the polarization ratio is predicted to increase with the decreasing size, which is opposite to the behavior as exhibited by the optical transition intensity. The polarization ratios in the [101] and [111] wires of $11.7\phantom{\rule{0.3em}{0ex}}\mathrm{\AA{}}$ diameter are shown to approach the limit of 100%. In addition to polarization ratio, we further determine the electron and hole masses for wires of different crystallographic axes. For the [101] and [111] wires, the hole masses are revealed to be $\ensuremath{\sim}0.25$, which are markedly smaller than the values $(\ensuremath{\geqslant}1.0)$ along the same direction in bulk. This result demonstrates an interesting possibility of obtaining in nanowires a high hole mobility that is not available in bulk. An explanation for the anomaly in the hole mass is suggested and is associated with the existence of an electronic band transition.

Identifying cysteines and histidines in transition‐metal‐binding sites using support vector machines and neural networks

Accurate predictions of metal-binding sites in proteins by using sequence as the only source of information can significantly help in the prediction of protein structure and function, genome annotation, and in the experimental determination of protein structure. Here, we introduce a method for identifying histidines and cysteines that participate in binding of several transition metals and iron complexes. The method predicts histidines as being in either of two states (free or metal bound) and cysteines in either of three states (free, metal bound, or in disulfide bridges). The method uses only sequence information by utilizing position-specific evolutionary profiles as well as more global descriptors such as protein length and amino acid composition. Our solution is based on a two-stage machine-learning approach. The first stage consists of a support vector machine trained to locally classify the binding state of single histidines and cysteines. The second stage consists of a bidirectional recurrent neural network trained to refine local predictions by taking into account dependencies among residues within the same protein. A simple finite state automaton is employed as a postprocessing in the second stage in order to enforce an even number of disulfide-bonded cysteines. We predict histidines and cysteines in transition-metal-binding sites at 73% precision and 61% recall. We observe significant differences in performance depending on the ligand (histidine or cysteine) and on the metal bound. We also predict cysteines participating in disulfide bridges at 86% precision and 87% recall. Results are compared to those that would be obtained by using expert information as represented by PROSITE motifs and, for disulfide bonds, to state-of-the-art methods.

Air−Water Henry's Law Constants for PCB Congeners: Experimental Determination and Modeling of Structure−Property Relationship

A modified gas-purging technique was used for the determination of Henry's law constants (HLCs) for four non-ortho- and eight mono-ortho-substituted polychlorinated biphenyls (PCBs). The method involves measurement of a compound's concentration in only the water phase while that compound is being stripped isothermally from the solution at a known gas flow rate. HLCs were calculated from the slope of a plot of ln(Cn) versus (1/V)n, where (Sigma 1/V)n = 1/V0 + 1/V1 + ... + 1/V(n-1). The HLCs ranged from 5.6 to 21.8 Pa m3/mol, with an average precision of 13%, and they are comparable to values in the literature. Meta-analysis technique and principal component regression (PCR) were applied to model the relationship between experimentally determined HLC values of 94 PCB congeners and the congeners' structures. Cross-validation yields an optimal model with two principal components. Statistical analysis suggests that HLCs of PCBs are primarily affected by meta-chlorine substitution, a relationship which has never been discussed in the literature. The substitution of chlorines on the biphenyl rings generally leads to smaller HLCs. The predicted HLCs are in good agreement with the experimentally determined values.

Medium-energy ion-scattering study of the structure of cleanTiO2(110)−(1×1)

$100\phantom{\rule{0.3em}{0ex}}\mathrm{keV}$ ${\mathrm{H}}^{+}$ medium-energy ion scattering has been applied to investigate the surface relaxations of the clean rutile ${\mathrm{TiO}}_{2}(110)\text{\ensuremath{-}}(1\ifmmode\times\else\texttimes\fi{}1)$ surface structure. A set of blocking curves in four different incident directions show clear differences between the surface and bulk attributable to surface relaxation. Optimized values of the surface relaxation parameters to give the best fit to the surface structure are generally in quite good agreement with a recent experimental determination of this structure using quantitative low-energy electron diffraction. In particular, both solutions favor an outward relaxation of the bridging O atoms on the surface rather than the strong inward displacement favored by the only previous experimental structure determination based on surface x-ray diffraction.

An Intricate Molecule: Aluminum Triiodide. Molecular Structure of AlI3 and Al2I6 from Electron Diffraction and Computation.

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Evaluation of the Information Content in Infrared Spectra for Protein Secondary Structure Determination

Fourier-transform infrared spectroscopy is a method of choice for the experimental determination of protein secondary structure. Numerous approaches have been developed during the past 15 years. A critical parameter that has not been taken into account systematically is the selection of the wavenumbers used for building the mathematical models used for structure prediction. The high quality of the current Fourier-transform infrared spectrometers makes the absorbance at every single wavenumber a valid and almost noiseless type of information. We address here the question of the amount of independent information present in the infrared spectra of proteins for the prediction of the different secondary structure contents. It appears that, at most, the absorbance at three distinct frequencies of the spectra contain all the nonredundant information that can be related to one secondary structure content. The ascending stepwise method proposed here identifies the relevance of each wavenumber of the infrared spectrum for the prediction of a given secondary structure and yields a particularly simple method for computing the secondary structure content. Using the 50-protein database built beforehand to contain as little fold redundancy as possible, the standard error of prediction in cross-validation is 5.5% for the α-helix, 6.6% for the β-sheet, and 3.4% for the β-turn.

Theoretical Study of the Distortion from Regular Tetrahedral Structure of M(NH2)4 Complexes

Theoretical study on tetrakis-amido complexes (M(NR2)4, M = Ti, V, Cr, and Mo; R = H, Me) is presented. At first a rough investigation of the potential energy surface indicates that all stationary points are of S4 or D2 symmetry depending on the coupled rotations of the NR2 groups. Qualitative correlation diagrams are calculated within S4 or D2 symmetry constraint between two limiting structures of D2d symmetry. DFT (B3LYP) calculations on these two paths are presented for unsubstituted complexes (R = H) and the various minima are optimized and characterized. These results are discussed in the light of the correlation diagrams. Finally, optimization of the different minima has been performed on substituted species (R = Me) and the theoretical results are shown to be in good agreement with the experimental structural determination when available.

Domain boundary prediction based on profile domain linker propensity index

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. In this work, a novel index at the profile level is presented, namely, the profile domain linker propensity index (PDLI), which uses the evolutionary information of profiles for domain linker prediction. The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. PDLI is then obtained by the frequencies of binary profiles in domain linkers as compared to those in domains. A smooth and normalized numeric profile is generated for any amino acid sequences from which the domain linkers can be predicted. Testing on the Structural Classification of Proteins (SCOP) database and CASP6 targets shows that PDLI outperforms other indexes at the amino acid level.

Crystal Structure of Human 3-Hydroxy-3-methylglutaryl-CoA Lyase: INSIGHTS INTO CATALYSIS AND THE MOLECULAR BASIS FOR HYDROXYMETHYLGLUTARIC ACIDURIA

3-Hydroxy-3-methylglutaryl-CoA (HMG-CoA) lyase is a key enzyme in the ketogenic pathway that supplies metabolic fuel to extrahepatic tissues. Enzyme deficiency may be due to a variety of human mutations and can be fatal. Diminished activity has been explained based on analyses of recombinant human mutant proteins or, more recently, in the context of structural models for the enzyme. We report the experimental determination of a crystal structure at 2.1 A resolution of the recombinant human mitochondrial HMG-CoA lyase containing a bound activator cation and the dicarboxylic acid 3-hydroxyglutarate. The enzyme adopts a (betaalpha)(8) barrel fold, and the N-terminal barrel end is occluded. The structure of a physiologically relevant dimer suggests that substrate access to the active site involves binding across the cavity located at the C-terminal end of the barrel. An alternative hypothesis that involves substrate insertion through a pore proposed to extend through the barrel is not compatible with the observed structure. The activator cation ligands included Asn(275), Asp(42),His(233), and His(235); the latter three residues had been implicated previously as contributing to metal binding or enzyme activity. Arg(41), previously shown to have a major effect on catalytic efficiency, is also located at the active site. In the observed structure, this residue interacts with a carboxyl group of 3-hydroxyglutarate, the hydrolysis product of the competitive inhibitor 3-hydroxyglutaryl-CoA required for crystallization of human enzyme. The structure provides a rationale for the decrease in enzyme activity due to clinical mutations, including H233R, R41Q, D42H, and D204N, that compromise active site function or enzyme stability.

An Intricate Molecule: Aluminum Triiodide. Molecular Structure of AlI3 and Al2I6 from Electron Diffraction and Computation

The molecular structure of aluminum triiodide was investigated in the gas phase by high-temperature gas-phase electron diffraction and high-level computations. The geometries of monomeric, AlI3, and dimeric, Al2I6, molecules were determined from two separate experiments carried out under carefully controlled conditions to prevent decomposition. This is the first experimental determination of the dimer structure by modern techniques. The computed geometrical parameters strongly depend on the applied methods and basis sets as well as on core-valence correlation effects. The electron diffraction thermal average bond length, r(g), of AlI3 at 700 K is 2.448(6) A; while those of Al2I6 at 430 K are 2.456(6) A (terminal) and 2.670(8) A (bridging). The equilibrium geometry of the monomer molecule is planar with D(3h) symmetry. The dimer molecule is extremely floppy, and it is difficult to determine the symmetry of its equilibrium geometry by computation, as it is sensitive to the applied methods. MP2 and CCSD calculations find the Al2I6 molecule puckered with C(2v) symmetry (although with a very small barrier at planarity), while density functional methods give a structure with a planar central ring of D(2h) symmetry. Comparison of the computed vibrational frequencies with the gas-phase experimental ones favors the D(2h) symmetry structure.

Recent Advances in Molecular Modeling and Medicinal Chemistry Aspects of Phospho-Glycoprotein&#

Phospho-glycoprotein (P-gp) is an efflux transporter expressed in many organs (ex: kidney, lung, liver and spleen) and in hormone producing or responsive tissues (ex: adrenal cortex, testis and placenta). It is involved in many important physiological functions. Among them the major one is extrusion of xenobiotics in order to detoxify the cells. This property of P-gp is associated with multidrug resistance (MDR) for many pathological conditions. While the experimental determination of three-dimensional structure is not yet successful, the transmembrane (TM) 5, 6, 11 and 12 are sensitive to mutations and contain substrate binding sites. Designing of potential and selective inhibitors of P-gp is still hampered by a lack of information upon the three dimensional structure of P-gp. The design of P-gp inhibitors was traditionally driven by quantitative structure activity relationship studies, which is complicated by factors such as different types of assays, multiple drug binding sites and diverse chemical structures. Clearly a conclusive and predictive SAR does not seem to be practical, despite progress in the last few years towards more specific SAR suggesting well defined structural features responsible for activity. Advances made recently in solving the crystal structure of prokaryotic ATP binding cassette proteins (ABC) transporters, Ec-MsbA, Vc-MsbA and BtuCD yielded suitable templates for construction of homology models of P-gp. Few molecular dynamics (MD) simulations aimed at elucidating the functional dynamics of ABC transporters have provided useful insights to their mechanism and structure. The present review aims at the general overview of importance, expression, structure, organization and drug binding sites of P-gp. This review also highlights recent developments in the homology modeling, molecular dynamics simulations of P-gp and progress in QSAR, pharmacophore modeling of P-gp modulators.

Crystal structure prediction with the supramolecular synthon approach: Experimental structures of 2-amino-4-ethylphenol and 3-amino-2-naphthol and comparison with prediction

Two earlier crystal structure predictions (CSP) of aminophenol compounds are checked against experimental structure determinations. One of the predictions classified originally as “good” is verified and is now seen to be somewhat correct. The other prediction, which was classified earlier as “unclear”, is incorrect. The two experimental crystal structures are characterized by small and large synthons both of which contain O–H⋯N and N–H⋯O hydrogen bonds. The evolving nature of the CSP exercise is noted.

The Structures of Ag55+and Ag55-: Trapped Ion Electron Diffraction and Density Functional Theory

We report the experimental structure determination of cold, mass selected Ag(55)(+/-) cluster ions using the recently developed technique of trapped ion electron diffraction. By comparison of experimental and theoretical molecular scattering functions and consideration of computed total energies, we show that Ag(55)(+) constitutes an ideal Mackay icosahedron, whereas Ag(55)(-) is a weakly Jahn-Teller distorted icosahedron. Isomers of other structural types, for example, decahedral or close-packed, could be ruled out. The candidate structures were obtained by density functional theory calculations.

Prediction of Multimolecular Assemblies by Multiple Docking

The majority of proteins function when associated in multimolecular assemblies. Yet, prediction of the structures of multimolecular complexes has largely not been addressed, probably due to the magnitude of the combinatorial complexity of the problem. Docking applications have traditionally been used to predict pairwise interactions between molecules. We have developed an algorithm that extends the application of docking to multimolecular assemblies. We apply it to predict quaternary structures of both oligomers and multi-protein complexes. The algorithm predicted well a near-native arrangement of the input subunits for all cases in our data set, where the number of the subunits of the different target complexes varied from three to ten. In order to simulate a more realistic scenario, unbound cases were tested. In these cases the input conformations of the subunits are either unbound conformations of the subunits or a model obtained by a homology modeling technique. The successful predictions of the unbound cases, where the input conformations of the subunits are different from their conformations within the target complex, suggest that the algorithm is robust. We expect that this type of algorithm should be particularly useful to predict the structures of large macromolecular assemblies, which are difficult to solve by experimental structure determination.

Comparison between experimental and theoretical determination of the local structure of theGaAs1−yNydilute nitride alloy

We present a combined experimental and theoretical study of the local structure of the $\mathrm{Ga}{\mathrm{As}}_{1\ensuremath{-}y}{\mathrm{N}}_{y}$ dilute nitride alloy. Experimental results obtained by x-ray absorption spectroscopy have been compared with first-principles density-functional supercell calculations and with the predictions of three different valence force field models. Both experiments and calculations find that inclusion of N induces static disorder in the $\mathrm{Ga}\text{\ensuremath{-}}\mathrm{As}$ bond length distribution. An increase of the $\mathrm{Ga}\text{\ensuremath{-}}\mathrm{As}$ bond length upon N incorporation in gallium arsenide has been observed; this is due to the competing effects of the decrease of the free lattice parameter and the tensile strain due to pseudomorphic growth. The different theoretical calculations reproduce more or less accurately this bond length expansion; we discuss the performance of the different valence force field models in predicting the measured bond lengths.

PPRODO: Prediction of protein domain boundaries using neural networks

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multidomain proteins but also for the experimental structure determination. Since protein sequences of multiple domains may contain much information regarding evolutionary processes such as gene-exon shuffling, this information can be detected by analyzing the position-specific scoring matrix (PSSM) generated by PSI-BLAST. We have presented a method, PPRODO (Prediction of PROtein DOmain boundaries) that predicts domain boundaries of proteins from sequence information by a neural network. The network is trained and tested using the values obtained from the PSSM generated by PSI-BLAST. A 10-fold cross-validation technique is performed to obtain the parameters of neural networks using a nonredundant set of 522 proteins containing 2 contiguous domains. PPRODO provides good and consistent results for the prediction of domain boundaries, with accuracy of about 66% using the +/-20 residue criterion. The PPRODO source code, as well as all data sets used in this work, are available from http://gene.kias.re.kr/ approximately jlee/pprodo/.

Automated use of mutagenesis data in structure prediction

In the absence of experimental structural determination, numerous methods are available to indirectly predict or probe the structure of a target molecule. Genetic modification of a protein sequence is a powerful tool for identifying key residues involved in binding reactions or protein stability. Mutagenesis data is usually incorporated into the modeling process either through manual inspection of model compatibility with empirical data, or through the generation of geometric constraints linking sensitive residues to a binding interface. We present an approach derived from statistical studies of lattice models for introducing mutation information directly into the fitness score. The approach takes into account the phenotype of mutation (neutral or disruptive) and calculates the energy for a given structure over an ensemble of sequences. The structure prediction procedure searches for the optimal conformation where neutral sequences either have no impact or improve stability and disruptive sequences reduce stability relative to wild type. We examine three types of sequence ensembles: information from saturation mutagenesis, scanning mutagenesis, and homologous proteins. Incorporating multiple sequences into a statistical ensemble serves to energetically separate the native state and misfolded structures. As a result, the prediction of structure with a poor force field is sufficiently enhanced by mutational information to improve accuracy. Furthermore, by separating misfolded conformations from the target score, the ensemble energy serves to speed up conformational search algorithms such as Monte Carlo-based methods.

Molecular Modelling of Drug Targets: The Past, the Present and the Future

Most currently used therapeutic drugs have an enzyme or a membrane-bound receptor as site of action. The sequencing of the human and other genomes has provided a potential to identify many hitherto unknown proteins that might serve as new drug targets. To achieve this, knowledge about three-dimensional protein structures is crucial for the understanding of their functional mechanisms, and for a rational drug design. Over the last decade atomic resolution crystal structures of soluble proteins have been reported in a rapidly increasing number, but the detailed three-dimensional structures are still unknown for the majority of membrane proteins since their membrane association makes experimental structure determinations complicated. Computerized modelling of protein structures, based on experimentally determined structures of homologue proteins, may be a useful methodological alternative, especially for membrane proteins. In the past, molecular modelling of transporters and G-protein-coupled receptors was based on low-resolution structural data obtained by cryo-electron microscopy. Recent high-resolution crystal structure determinations of a G-protein-coupled receptor, rhodopsin, and several different transporter proteins and ion channels have enabled construction of more accurate receptor and transporter models. For the future, collaborative structural genomics initiatives aim at determining the three-dimensional structure of all known proteins, based on a combination of experimental structure determination and molecular modelling. Development of still more powerful computer hardware and software will enable extensive studies of the protein structure and dynamics of new potential drug targets, but raises a new challenge in the validation and calibration of computerized methods of biosimulations.

Immucillins as Antibiotics for T‐Cell Proliferation and Malaria

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