ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre- processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein–protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus

Fosmidomycin Uptake into Plasmodium and Babesia-Infected Erythrocytes Is Facilitated by Parasite-Induced New Permeability Pathways

., a mouse malaria parasite. and related parasites. Our data provide further evidence that parasite-induced new permeability pathways may be exploited as routes for drug delivery

SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles

Structural flexibility of proteins has an important influence on molecular recognition and enzymatic function. In modeling, structure ensembles are therefore often applied as a valuable source of alternative protein conformations. However, their usage is often complicated by structural artifacts and inconsistent data annotation. Here, we present SIENA, a new computational approach for the automated assembly and preprocessing of protein binding site ensembles. Starting with an arbitrarily defined binding site in a single protein structure, SIENA searches for alternative conformations of the same or sequentially closely related binding sites. The method is based on an indexed database for identifying perfect <i>k</i>-mer matches and a recently published algorithm for the alignment of protein binding site conformations. Furthermore, SIENA provides a new algorithm for the interaction-based selection of binding site conformations which aims at covering all known ligand-binding geometries. Various experiments highlight that SIENA is able to generate comprehensive and well selected binding site ensembles improving the compatibility to both known and unconsidered ligand molecules. Starting with the whole PDB as data source, the computation time of the whole ensemble generation takes only a few seconds. SIENA is available via a Web service at www.zbh.uni-hamburg.de/siena

ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations

The usage of conformational ensembles constitutes a widespread technique for the consideration of protein flexibility in computational biology. When experimental structures are applied for this purpose, alignment techniques are usually required in dealing with structural deviations and annotation inconsistencies. Moreover, many application scenarios focus on protein ligand binding sites. Here, we introduce our new alignment algorithm ASCONA that has been specially geared to the problem of aligning multiple conformations of sequentially similar binding sites. Intense efforts have been directed to an accurate detection of highly flexible backbone deviations, multiple binding site matches within a single structure, and a reliable, but at the same time highly efficient, search algorithm. In contrast, most available alignment methods rather target other issues, e.g., the global alignment of distantly related proteins that share structurally conserved regions. For conformational ensembles, this might not only result in an overhead of computation time but could also affect the achieved accuracy, especially for more complicated cases as highly flexible proteins. ASCONA was evaluated on a test set containing 1107 structures of 65 diverse proteins. In all cases, ASCONA was able to correctly align the binding site at an average alignment computation time of 4 ms per target. Furthermore, no false positive matches were observed when searching the same query sites in the structures of other proteins. ASCONA proved to cope with highly deviating backbone structures and to tolerate structural gaps and moderate mutation rates. ASCONA is available free of charge for academic use at http://www.zbh.uni-hamburg.de/ascona

Chemical activation of a high-affinity glutamate transporter in human erythrocytes and its implications for malaria-parasite-induced glutamate uptake

Human erythrocytes have a low basal permeability to L-glutamate and are not known to have a functional glutamate transporter. Here, treatment of human erythrocytes with arsenite was shown to induce the uptake of L-glutamate and D-aspartate, but not that of D-glutamate or L-alanine. The majority of the arsenite-induced L-glutamate influx was via a high-affinity, Na+-dependent system showing characteristics of members of the "excitatory amino acid transporter" (EAAT) family. Western blots and immunofluorescence assays revealed the presence of a member of this family, EAAT3, on the erythrocyte membrane. Erythrocytes infected with the malaria parasite Plasmodium falciparum take up glutamate from the extracellular environment. Although the majority of uptake is via a low-affinity Na+-independent pathway there is, in addition, a high-affinity uptake component, raising the possibility that the parasite activates the host cell glutamate transporter

Discriminative Chemical Patterns: Automatic and Interactive Design

The classification of molecules with respect to their inhibiting, activating, or toxicological potential constitutes a central aspect in the field of cheminformatics. Often, a discriminative feature is needed to distinguish two different molecule sets. Besides physicochemical properties, substructures and chemical patterns belong to the descriptors most frequently applied for this purpose. As a commonly used example of this descriptor class, SMARTS strings represent a powerful concept for the representation and processing of abstract chemical patterns. While their usage facilitates a convenient way to apply previously derived classification rules on new molecule sets, the manual generation of useful SMARTS patterns remains a complex and time-consuming process. Here, we introduce SMARTSminer, a new algorithm for the automatic derivation of discriminative SMARTS patterns from preclassified molecule sets. Based on a specially adapted subgraph mining algorithm, SMARTSminer identifies structural features that are frequent in only one of the given molecule classes. In comparison to elemental substructures, it also supports the consideration of general and specific SMARTS features. Furthermore, SMARTSminer is integrated into an interactive pattern editor named SMARTSeditor. This allows for an intuitive visualization on the basis of the SMARTSviewer concept as well as interactive adaption and further improvement of the generated patterns. Additionally, a new molecular matching feature provides an immediate feedback on a pattern’s matching behavior across the molecule sets. We demonstrate the utility of the SMARTSminer functionality and its integration into the SMARTSeditor software in several different classification scenarios

Index-Based Searching of Interaction Patterns in Large Collections of Protein–Ligand Interfaces

Comparison of three-dimensional interaction patterns in large collections of protein–ligand interfaces is a key element for understanding protein–ligand interactions and supports various steps in the structure-based drug design process. Different methods exist that provide query systems to search for geometrical patterns in protein–ligand complexes. However, these tools do not meet all of the requirements, which are high query variability, an adjustable search set, and high retrieval speed. Here we present a new tool named PELIKAN that is able to search for a variety of geometrical queries in large protein structure collections in a reasonably short time. The data are stored in an SQLite database that can easily be constructed from any set of protein–ligand complexes. We present different test queries demonstrating the performance of the PELIKAN approach. Furthermore, two application scenarios show the usefulness of PELIKAN in structure-based design endeavors