Protein-ligand interactions are the fundamental basis for molecular design in pharmaceutical research, biocatalysis, and agrochemical development. Especially hydrogen bonds are known to have special geometric requirements and therefore deserve a detailed analysis. In modeling approaches a more general description of hydrogen bond geometries, using distance and directionality, is applied. A first study of their geometries was performed based on 15 protein structures in 1982. Currently there are about 95 000 protein-ligand structures available in the PDB, providing a solid foundation for a new large-scale statistical analysis. Here, we report a comprehensive investigation of geometric and functional properties of hydrogen bonds. Out of 22 defined functional groups, eight are fully in accordance with theoretical predictions while 14 show variations from expected values. On the basis of these results, we derived interaction geometries to improve current computational models. It is expected that these observations will be useful in designing new chemical structures for biological applications.

The generation of sets of low-energy conformations for a given molecule is a central task in drug design. Herein we present a new conformation generator called CONFECT that builds on our previously published library of torsion patterns. Conformations are generated as well as ranked by means of normalized frequency distributions derived from the Cambridge Structural Database (CSD). Following an incremental construction approach, conformations are selected from a systematic enumeration within energetic boundaries. The new tool is benchmarked in several different ways, indicating that it allows the efficient generation of high-quality conformation ensembles. These ensembles are smaller than those produced by state-of-the-art tools, yet they effectively cover conformational space.

The accurate handling of different chemical file formats and the consistent conversion between them play important roles for calculations in complex cheminformatics workflows. Working with different cheminformatic tools often makes the conversion between file formats a mandatory step. Such a conversion might become a difficult task in cases where the information content substantially differs. This paper describes UNICON, an easy-to-use software tool for this task. The functionality of UNICON ranges from file conversion between standard formats SDF, MOL2, SMILES, PDB, and PDBx/mmCIF via the generation of 2D structure coordinates and 3D structures to the enumeration of tautomeric forms, protonation states, and conformer ensembles. For this purpose, UNICON bundles the key elements of the previously described NAOMI library in a single, easy-to-use command line tool.

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.

The formation of molecular complexes between proteins and small organic substances is a fundamental concept of life. Biochemical experiments from X‐ray crystallography to isothermal titration calorimetry (ITC) are applied in large‐scale providing data for the analysis of the structural foundations of binding affinity. In recent years, several, mostly publically available databases emerged containing affinity data and structural information. These databases are central for the construction of complex models describing interaction geometries and correlate structural features to the strength of binding. Binding affinity databases reflect the knowledge of affinity measurements from many sources, mostly scientific and patent literature. A critical aspect is the data quality, which is affected by transcription errors during database construction as well as experimental uncertainties. The Protein Data Bank (PDB) is the central resource for macromolecular biological structures containing nearly 100,000 data entries today. Sophisticated geometric databases have been constructed based on this allowing for complex queries about the spatial arrangement of functional groups and their interactions. For scientists working in molecular design like medicinal chemists, access to this information can substantially support the process of creating new molecular entities specifically interacting with proteins of interest. WIREs Comput Mol Sci 2014, 4:562–575. doi: 10.1002/wcms.1192 This article is categorized under: Structure and Mechanism > Molecular Structures Computer and Information Science > Chemoinformatics Computer and Information Science > Databases and Expert Systems

Acetylcholinesterase (AChE, EC 3.1.1.7) is a serine hydrolase [1], which belongs to the a ⁄ b hydrolase family [2,3]. The enzyme hydrolyses a broad range of ester and amide substrates, showing the highest specificity for acetylselenocholine, acetylthiocholine (ATCh) and acetylcholine (ACh) [4]. Substrate cleavage proceeds via a two-step mechanism: acylation of the enzyme, followed by deacylation involving a water molecule [5][6][7]. This process is mediated by the catalytic triad Ser200-His440-Glu327 (Torpedo californica AChE, TcAChE, numbering [8]) located within the active site at the bottom of a 20 Å deep gorge. Substrate binding is facilitated by another component of the active site, the anionic site, which is characterized by several conserved aromatic residues, such as Trp84 and Phe330. These residues have been shown to interact with the quaternary ammonium groups of ACh or ATCh via cation-p interactions [7][8][9][10][11][12]. Further stabilization of the quaternary moiety arises from an electrostatic interaction with the acidic side-chain of Glu199 [7,12]. A second substrate-binding site, the peripheral anionic site (PAS), lies essentially on the The hydrolysis of acetylthiocholine by acetylcholinesterase from Electrophorus electricus was investigated in the presence of the inhibitors tacrine, gallamine and compound 1. The interaction of the enzyme with the substrate and the inhibitors was characterized by the parameters K I , a¢, b or b, K m and V max , which were determined directly and simultaneously from nonlinear Michaelis-Menten plots. Tacrine was shown to act as a mixedtype inhibitor with a strong noncompetitive component (a¢ % 1) and to completely block deacylation of the acyl-enzyme. In contrast, acetylcholinesterase inhibition by gallamine followed the 'steric blockade hypothesis', i.e. only substrate association to as well as substrate ⁄ product dissociation from the active site were reduced in the presence of the inhibitor. The relative efficiency of the acetylcholinesterase-gallamine complex for the catalysis of substrate conversion was determined to be 1.7-25% of that of the free enzyme. Substrate hydrolysis and the inhibition of acetylcholinesterase were also investigated in the presence of 6% acetonitrile, and a competitive pseudo-inhibition was observed for acetonitrile (K I = 0.25 m). The interaction of acetylcholinesterase with acetonitrile and tacrine or gallamine resulted in a seven-to 10-fold increase in the K I values, whereas the principal mode of inhibition was not affected by the organic solvent. The determination of the inhibitory parameters of compound 1 in the presence of acetonitrile revealed that the substance acts as a hyperbolic mixed-type inhibitor of acetylcholinesterase. The complex formed by the enzyme and the inhibitor still catalysed product formation with 8.7-9.6% relative efficiency.

The inside cover picture shows the conformational ensemble of AMP generated by CONFECT, a novel knowledge‐based conformer generator. Conformations are generated by using lists of “usual” torsion angles derived from CSD‐based frequency histograms (top left). One of the bioactive conformations of AMP is shown in the binding site of the protein from PDB complex 3TV2 (bottom right). For more details, see the Full Paper by Matthias Rarey et al. on

Nowadays, computational approaches are an integral part of life science research. Problems related to interpretation of experimental results, data analysis, or visualization tasks highly benefit from the achievements of the digital era. Simulation methods facilitate predictions of physicochemical properties and can assist in understanding macromolecular phenomena. Here, we will give an overview of the methods developed in our group that aim at supporting researchers from all life science areas. Based on state-of-the-art approaches from structural bioinformatics and cheminformatics, we provide software covering a wide range of research questions. Our all-in-one web service platform ProteinsPlus (http://proteins.plus) offers solutions for pocket and druggability prediction, hydrogen placement, structure quality assessment, ensemble generation, protein-protein interaction classification, and 2D-interaction visualization. Additionally, we provide a software package that contains tools targeting cheminformatics problems like file format conversion, molecule data set processing, SMARTS editing, fragment space enumeration, and ligand-based virtual screening. Furthermore, it also includes structural bioinformatics solutions for inverse screening, binding site alignment, and searching interaction patterns across structure libraries. The software package is available at http://software.zbh.uni-hamburg.de.