Oliver Koch

Structure-Based Design of Inhibitors of Protein-Protein Interactions: Mimicking Peptide Binding Epitopes

Protein–protein interactions (PPIs) are involved at all levels of cellular organization, thus making the development of PPI inhibitors extremely valuable. The identification of selective inhibitors is challenging because of the shallow and extended nature of PPI interfaces. Inhibitors can be obtained by mimicking peptide binding epitopes in their bioactive conformation. For this purpose, several strategies have been evolved to enable a projection of side chain functionalities in analogy to peptide secondary structures, thereby yielding molecules that are generally referred to as peptidomimetics. Herein, we introduce a new classification of peptidomimetics (classes A–D) that enables a clear assignment of available approaches. Based on this classification, the Review summarizes strategies that have been applied for the structure-based design of PPI inhibitors through stabilizing or mimicking turns, β-sheets, and helices.

Development of Novel Potent Orally Bioavailable Oseltamivir Derivatives Active against Resistant Influenza A

With the emergence of oseltamivir-resistant influenza viruses and in view of a highly pathogenic flu pandemic, it is important to develop new anti-influenza agents. Here, the development of neuraminidase (NA) inhibitors that were designed to overcome resistance mechanisms along with unfavorable pharmacokinetic (PK) properties is described. Several 5-guanidino- and 5-amidino-based oseltamivir derivatives were synthesized and profiled for their anti-influenza activity and in vitro and in vivo PK properties. Amidine 6 and guanidine 7 were comparably effective against a panel of different A/H1N1 and A/H3N2 strains and also inhibited mutant A/H1N1 neuraminidase. Among different prodrug strategies pursued, a simple amidoxime ethyl ester (9) exhibited a superior PK profile with an oral bioavailability of 31% (rats), which is comparable to oseltamivir (36%). Thus, bioisosteric replacement of the 5-guanidine with an acetamidine-in the form of its N-hydroxy prodrug-successfully tackled the two key limitations of currently used NA inhibitors, as exemplified with oseltamivir.

Turns revisited: a uniform and comprehensive classification of normal, open, and reverse turn families minimizing unassigned random chain portions.

Turns are irregular secondary structure elements with a hydrogen bond or a specific Cα‐Cα distance between the first and the last residue. They are up to six residues in length. Here, we present a uniform classification for all normal (COi – NHi+n hydrogen bond), open (a Cαi‐Cαi+n distance up to 10 Å), and reverse (NHi – COi+i hydrogen bond) turn families based on current structural data. Considering the large amount of data evaluated, this classification likely covers quite comprehensively most of the possible conformations of turns. All turn structures of a nonredundant dataset of 1903 protein chains were retrieved using Relibase and clustered using emergent self‐organizing maps. This leads to three normal, four open, and five reverse turn families with several new turn‐types. Based on the amino acid propensities, the achieved separation into normal, open, and reverse turn families seems convincing. In combination with β‐sheet and helix classification on average 96% of the given protein chain can now be successfully classified. Proteins 2009.

Cooperative effects in hydrogen-bonding of protein secondary structure elements: a systematic analysis of crystal data using Secbase.

A systematic analysis of the hydrogen‐bonding geometry in helices and β sheets has been performed. The distances and angles between the backbone carbonyl O and amide N atoms were correlated considering more than 1500 protein chains in crystal structures determined to a resolution better than 1.5 Å. They reveal statistically significant trends in the H‐bond geometry across the different secondary structural elements. The analysis has been performed using Secbase, a modular extension of Relibase (Receptor Ligand Database) which integrates information about secondary structural elements assigned to individual protein structures with the various search facilities implemented into Relibase. A comparison of the mean hydrogen‐bond distances in α helices and 310 helices of increasing length shows opposing trends. Whereas in α helices the mean H‐bond distance shrinks with increasing helix length and turn number, the corresponding mean dimension in 310 helices expands in a comparable series. Comparing similarly the hydrogen‐bond lengths in β sheets there is no difference to be found between the mean H‐bond length in antiparallel and parallel β sheets along the strand direction. In contrast, an interesting systematic trend appears to be given for the hydrogen bonds perpendicular to the strands bridging across an extended sheet. With increasing number of accumulated strands, which results in a growing number of back‐to‐back piling hydrogen bonds across the strands, a slight decrease of the mean H‐bond distance is apparent in parallel β sheets whereas such trends are obviously not given in antiparallel β sheets. This observation suggests that cooperative effects mutually polarizing spatially well‐aligned hydrogen bonds are present either in α helices and parallel β sheets whereas such influences seem to be lacking in 310 helices and antiparallel β sheets. Proteins 2005.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

Prediction of turn types in protein structure by machine‐learning classifiers

We present machine learning approaches for turn prediction from the amino acid sequence. Different turn classes and types were considered based on a novel turn classification scheme. We trained an unsupervised (self‐organizing map) and two kernel‐based classifiers, namely the support vector machine and a probabilistic neural network. Turn versus non‐turn classification was carried out for turn families containing intramolecular hydrogen bonds and three to six residues. Support vector machine classifiers yielded a Matthews correlation coefficient (mcc) of ∼0.6 and a prediction accuracy of 80%. Probabilistic neural networks were developed for β‐turn type prediction. The method was able to distinguish between five types of β‐turns yielding mcc > 0.5 and at least 80% overall accuracy. We conclude that the proposed new turn classification is distinct and well‐defined, and machine learning classifiers are suited for sequence‐based turn prediction. Their potential for sequence‐based prediction of turn structures is discussed. Proteins 2009.

Visual Analysis of Biological Activity Data with Scaffold Hunter

The growing interest in chemogenomics approaches over the last years has led to an increasing amount of data regarding chemical and the corresponding biological activity space. The resulting data, collected in either in‐house or public databases, need to be analyzed efficiently to speed‐up the increasingly difficult task of drug discovery. Unfortunately, the discovery of new chemical entities or new targets for known drugs (‘drug repurposing’) is not suitable to a fully automated analysis or a simple drill down process. Visual interactive interfaces that allow to explore chemical space in a systematic manner and facilitate analytical reasoning can help to overcome these problems. Scaffold Hunter is a tool for the visual analysis of chemical compound databases that provides integrated visualization and analysis of biological activity data and fosters the interactive exploration of data imported from a variety of sources. We describe the features and illustrate the use by means of an exemplary analysis workflow.

Molecular Dynamics Reveal Binding Mode of Glutathionylspermidine by Trypanothione Synthetase

The trypanothione synthetase (TryS) catalyses the two-step biosynthesis of trypanothione from spermidine and glutathione and is an attractive new drug target for the development of trypanocidal and antileishmanial drugs, especially since the structural information of TryS from Leishmania major has become available. Unfortunately, the TryS structure was solved without any of the substrates and lacks loop regions that are mechanistically important. This contribution describes docking and molecular dynamics simulations that led to further insights into trypanothione biosynthesis and, in particular, explains the binding modes of substrates for the second catalytic step. The structural model essentially confirm previously proposed binding sites for glutathione, ATP and two Mg2+ ions, which appear identical for both catalytic steps. The analysis of an unsolved loop region near the proposed spermidine binding site revealed a new pocket that was demonstrated to bind glutathionylspermidine in an inverted orientation. For the second step of trypanothione synthesis glutathionylspermidine is bound in a way that preferentially allows N1-glutathionylation of N8-glutathionylspermidine, classifying N8-glutathionylspermidine as the favoured substrate. By inhibitor docking, the binding site for N8-glutathionylspermidine was characterised as druggable.

Identification of M. tuberculosis thioredoxin reductase inhibitors based on high-throughput docking using constraints.

A virtual screening campaign is presented that led to small molecule inhibitors of thioredoxin reductase of Mycobacterium tuberculosis (MtTrxR) that target the protein-protein interaction site for the substrate thioredoxin (Trx). MtTrxR is a promising drug target because it dominates the Trx-dependent hydroperoxide metabolism and the reduction of ribonucleotides, thus facilitating survival and proliferation of M. tuberculosis. Moreover, MtTrxR sufficiently differs from its human homologs to suggest the possibility of selective inhibition if the MtTrxR-Trx interaction site is targeted. To this end, high-throughput docking of 6.5 million virtual compounds to the thioredoxin binding site of MtTrxR combined with constraints as filtering steps was applied. A total of 170 high-scoring compounds yielded 18 compounds that inhibited MtTrxR with IC50 values up to the low micromolar range, thus revealing that the protein-protein interaction site of MtTrxR is indeed druggable. Most importantly, selectivity toward MtTrxR in comparison to human TrxR (HsTrxR) is also demonstrated.

What Can We Learn from Bioactivity Data? Chemoinformatics Tools and Applications in Chemical Biology Research.

The ever increasing bioactivity data that are produced nowadays allow exhaustive data mining and knowledge discovery approaches that change chemical biology research. A wealth of chemoinformatics tools, web services, and applications therefore exists that supports a careful evaluation and analysis of experimental data to draw conclusions that can influence the further development of chemical probes and potential lead structures. This review focuses on open-source approaches that can be handled by scientists who are not familiar with computational methods having no expert knowledge in chemoinformatics and modeling. Our aim is to present an easily manageable toolbox for support of every day laboratory work. This includes, among other things, the available bioactivity and related molecule databases as well as tools to handle and analyze in-house data.