A new protein structure of P-II class snake venom metalloproteinases: it comprises metalloproteinase and disintegrin domains

A new metalloproteinase-disintegrin, named Jerdonitin, was purified from Trimeresurus jerdonii venom with a molecular weight of 36 kDa on SDS-PAGE. It dose-dependently inhibited ADP-induced human platelet aggregation with IC50 of 120 nM. cDNA cloning and sequencing revealed that Jerdonitin belonged to the class II of snake venom metalloproteinases (SVMPs) (P-II class). Different from other P-II class SVMPs, metalloproteinase and disintegrin domains of its natural protein were not separated, confirmed by internal peptide sequencing. Compared to other P-II class SVMPs, Jerdonitin has two additional cysteines (Cys219 and Cys238) located in the spacer domain and disintegrin domain, respectively. They probably form a disulfide bond and therefore the metalloproteinase and disintegrin domains cannot be separated by posttranslationally processing. In summary, comparison of the amino acid sequences of Jerdonitin with those of other P-II class SVMPs by sequence alignment and phylogenetic analysis, in conjunction with natural protein structure data, suggested that it was a new type of P-II class SVMPs. (C) 2003 Elsevier Inc. All rights reserved.

Multifractal analysis of geomagnetic storm and solar flare indices and their class dependence

The multifractal properties of two indices of geomagnetic activity, D st (representative of low latitudes) and a p (representative of the global geomagnetic activity), with the solar X-ray brightness, X l , during the period from 1 March 1995 to 17 June 2003 are examined using multifractal detrended fluctuation analysis (MF-DFA). The h(q) curves of D st and a p in the MF-DFA are similar to each other, but they are different from that of X l , indicating that the scaling properties of X l are different from those of D st and a p . Hence, one should not predict the magnitude of magnetic storms directly from solar X-ray observations. However, a strong relationship exists between the classes of the solar X-ray irradiance (the classes being chosen to separate solar flares of class X-M, class C, and class B or less, including no flares) in hourly measurements and the geomagnetic disturbances (large to moderate, small, or quiet) seen in D st and a p during the active period. Each time series was converted into a symbolic sequence using three classes. The frequency, yielding the measure representations, of the substrings in the symbolic sequences then characterizes the pattern of space weather events. Using the MF-DFA method and traditional multifractal analysis, we calculate the h(q), D(q), and τ (q) curves of the measure representations. The τ (q) curves indicate that the measure representations of these three indices are multifractal. On the basis of this three-class clustering, we find that the h(q), D(q), and τ (q) curves of the measure representations of these three indices are similar to each other for positive values of q. Hence, a positive flare storm class dependence is reflected in the scaling exponents h(q) in the MF-DFA and the multifractal exponents D(q) and τ (q). This finding indicates that the use of the solar flare classes could improve the prediction of the D st classes.

Evaluating viewpoint entropy for ribbon representation of protein structure

While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon’s Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative su- perfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non-abstract objects. In a second study, we asked 7 molecular biology experts to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy. Our results show that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy is highly dependent on the complexity of the structure: while most participants agree on good viewpoints for small, non-globular structures with few secondary structure elements, viewpoint preference varies considerably for complex structures. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.

Protein Block Expert (PBE): a web-based protein structure analysis server using a structural alphabet

Encoding protein 3D structures into 1D string using short structural prototypes or structural alphabets opens a new front for structure comparison and analysis. Using the well-documented 16 motifs of Protein Blocks (PBs) as structural alphabet, we have developed a methodology to compare protein structures that are encoded as sequences of PBs by aligning them using dynamic programming which uses a substitution matrix for PBs. This methodology is implemented in the applications available in Protein Block Expert (PBE) server. PBE addresses common issues in the field of protein structure analysis such as comparison of proteins structures and identification of protein structures in structural databanks that resemble a given structure. PBE-T provides facility to transform any PDB file into sequences of PBs. PBE-ALIGNc performs comparison of two protein structures based on the alignment of their corresponding PB sequences. PBE-ALIGNm is a facility for mining SCOP database for similar structures based on the alignment of PBs. Besides, PBE provides an interface to a database (PBE-SAdb) of preprocessed PB sequences from SCOP culled at 95% and of all-against-all pairwise PB alignments at family and superfamily levels. PBE server is freely available at http://bioinformatics.univ-reunion.fr/ PBE/.

Protein Hydration and Water Structure: X-ray Analysis of a Closely Packed Protein Crystal with Very Low Solvent Content

Low-humidity monoclinic lysozyme, resulting from a water-mediated transformation, has one of the lowest solvent contents (22% by volume) observed in a protein crystal. Its structure has been solved by the molecular replacement method and refined to an R value of 0.175 for 7684 observed reflections in the 10–1.75 Å resolution shell. 90% of the solvent in the well ordered crystals could be located. Favourable sites of hydration on the protein surface include side chains with multiple hydrogen-bonding centres, and regions between short hydrophilic side chains and the main-chain CO or NH groups of the same or nearby residues. Major secondary structural features are not disrupted by hydration. However, the free CO groups at the C terminii and, to a lesser extent, the NH groups at the N terminii of helices provide favourable sites for water interactions, as do reverse turns and regions which connect β-structure and helices. The hydration shell consists of discontinuous networks of water molecules, the maximum number of molecules in a network being ten. The substrate-binding cleft is heavily hydrated, as is the main loop region which is stabilized by water interactions. The protein molecules are close packed in the crystals with a molecular coordination number of 14. Arginyl residues are extensively involved in intermolecular hydrogen bonds and water bridges. The water molecules in the crystal are organized into discrete clusters. A distinctive feature of the clusters is the frequent occurrence of three-membered rings. The protein molecules undergo substantial rearrangement during the transformation from the native to the low-humidity form. The main-chain conformations in the two forms are nearly the same, but differences exist in the side-chain conformation. The differences are particularly pronounced in relation to Trp 62 and Trp 63. The shift in Trp 62 is especially interesting as it is also known to move during inhibitor binding.

An automated approach to network features of protein structure ensembles

Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of 2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

mulPBA: an efficient multiple protein structure alignment method based on a structural alphabet

The increasing number of available protein structures requires efficient tools for multiple structure comparison. Indeed, multiple structural alignments are essential for the analysis of function, evolution and architecture of protein structures. For this purpose, we proposed a new web server called multiple Protein Block Alignment (mulPBA). This server implements a method based on a structural alphabet to describe the backbone conformation of a protein chain in terms of dihedral angles. This sequence-like' representation enables the use of powerful sequence alignment methods for primary structure comparison, followed by an iterative refinement of the structural superposition. This approach yields alignments superior to most of the rigid-body alignment methods and highly comparable with the flexible structure comparison approaches. We implement this method in a web server designed to do multiple structure superimpositions from a set of structures given by the user. Outputs are given as both sequence alignment and superposed 3D structures visualized directly by static images generated by PyMol or through a Jmol applet allowing dynamic interaction. Multiple global quality measures are given. Relatedness between structures is indicated by a distance dendogram. Superimposed structures in PDB format can be also downloaded, and the results are quickly obtained. mulPBA server can be accessed at www.dsimb.inserm.fr/dsimb_tools/mulpba/.