Magnet for the Needle in Haystack: "Crystal Structure First" Fragment Hits Unlock Active Chemical Matter Using Targeted Exploration of Vast Chemical Spaces.

Fragment-based drug discovery (FBDD) has successfully led to approved therapeutics for challenging and "undruggable" targets. In the context of FBDD, we introduce a novel, multidisciplinary method to identify active molecules from purchasable chemical space. Starting from four small-molecule fragment complexes of protein kinase A (PKA), a template-based docking screen using Enamine's multibillion REAL Space was performed. A total of 93 molecules out of 106 selected compounds were successfully synthesized. Forty compounds were active in at least one validation assay with the most active follow-up having a 13,500-fold gain in affinity. Crystal structures for six of the most promising binders were rapidly obtained, verifying the binding mode. The overall success rate for this novel fragment-to-hit approach was 40%, accomplished in only 9 weeks. The results challenge the established fragment prescreening paradigm since the standard industrial filters for fragment hit identification in a thermal shift assay would have missed the initial fragments.

Identification of Functionally Related Enzymes by Learning-to-Rank Methods.

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work, we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.

Cavities tell more than sequences: exploring functional relationships of proteases via binding pockets.

Computational approaches play an increasingly important role for the analysis and prediction of selectivity profiles. As most of the successfully administered small molecule drugs bind in depressions on the surface of proteins, physicochemical properties of the pocket-exposed amino acids play a central role in ligand recognition during the binding event. Cavbase is an approach to describe binding sites in terms of the exposed physicochemical properties and to compare them independent of their sequence and fold homology. Classification of proteins by means of their binding-site properties is a promising approach to obtain information necessary for selectivity modeling. For this purpose, the workflow clusterScore has been developed to explore the important parameters of a clustering procedure, which will allow an accurate classification of proteins. It has been successfully applied on two diverse and challenging data sets. The predicted number of clusters, as suggested by clusterScore and the subsequent clustering of proteins are in agreement with the EC and Merops classifications. Furthermore, putative cross-reactivity mapped between calpain-1 and cysteine cathepsins on structural level has so far only been described based on ligand data. In a benchmark study using ligand topology, binding site, and sequence information of eleven serine proteases, the emerging clusters indicate a pronounced correlation between the cavity and ligand data. These results emphasize the importance of binding-site information which should be considered for ligand design during lead optimization cycles. The program clusterScore is freely available and can be downloaded from our Web site www.agklebe.de.

Investigation of specificity determinants in bacterial tRNA-guanine transglycosylase reveals queuine, the substrate of its eucaryotic counterpart, as inhibitor.

Bacterial tRNA-guanine transglycosylase (Tgt) catalyses the exchange of the genetically encoded guanine at the wobble position of tRNAs(His,Tyr,Asp,Asn) by the premodified base preQ1, which is further converted to queuine at the tRNA level. As eucaryotes are not able to synthesise queuine de novo but acquire it through their diet, eucaryotic Tgt directly inserts the hypermodified base into the wobble position of the tRNAs mentioned above. Bacterial Tgt is required for the efficient pathogenicity of Shigella sp, the causative agent of bacillary dysentery and, hence, it constitutes a putative target for the rational design of anti-Shigellosis compounds. Since mammalian Tgt is known to be indirectly essential to the conversion of phenylalanine to tyrosine, it is necessary to create substances which only inhibit bacterial but not eucaryotic Tgt. Therefore, it seems of utmost importance to study selectivity-determining features within both types of proteins. Homology models of Caenorhabditis elegans Tgt and human Tgt suggest that the replacement of Cys158 and Val233 in bacterial Tgt (Zymomonas mobilis Tgt numbering) by valine and accordingly glycine in eucaryotic Tgt largely accounts for the different substrate specificities. In the present study we have created mutated variants of Z. mobilis Tgt in order to investigate the impact of a Cys158Val and a Val233Gly exchange on catalytic activity and substrate specificity. Using enzyme kinetics and X-ray crystallography, we gained evidence that the Cys158Val mutation reduces the affinity to preQ1 while leaving the affinity to guanine unaffected. The Val233Gly exchange leads to an enlarged substrate binding pocket, that is necessary to accommodate queuine in a conformation compatible with the intermediately covalently bound tRNA molecule. Contrary to our expectations, we found that a priori queuine is recognised by the binding pocket of bacterial Tgt without, however, being used as a substrate.

Novel type II fatty acid biosynthesis (FAS II) inhibitors as multistage antimalarial agents.

Malaria is a potentially fatal disease caused by Plasmodium parasites and poses a major medical risk in large parts of the world. The development of new, affordable antimalarial drugs is of vital importance as there are increasing reports of resistance to the currently available therapeutics. In addition, most of the current drugs used for chemoprophylaxis merely act on parasites already replicating in the blood. At this point, a patient might already be suffering from the symptoms associated with the disease and could additionally be infectious to an Anopheles mosquito. These insects act as a vector, subsequently spreading the disease to other humans. In order to cure not only malaria but prevent transmission as well, a drug must target both the blood- and pre-erythrocytic liver stages of the parasite. P. falciparum (Pf) enoyl acyl carrier protein (ACP) reductase (ENR) is a key enzyme of plasmodial type II fatty acid biosynthesis (FAS II). It has been shown to be essential for liver-stage development of Plasmodium berghei and is therefore qualified as a target for true causal chemoprophylaxis. Using virtual screening based on two crystal structures of PfENR, we identified a structurally novel class of FAS inhibitors. Subsequent chemical optimization yielded two compounds that are effective against multiple stages of the malaria parasite. These two most promising derivatives were found to inhibit blood-stage parasite growth with IC(50) values of 1.7 and 3.0 µM and lead to a more prominent developmental attenuation of liver-stage parasites than the gold-standard drug, primaquine.

The use of isothermal titration calorimetry and molecular dynamics to show variability in DNA transfection performance.

The mechanism causing variability in DNA transfection efficacy for low-molecular-weight pDMAEMA (poly(2-(dimethylamino)ethyl methacrylate) and pDMAEMA-b-pHEMA (poly(2-(dimethyl amino)ethylmethacrylate)-block-poly(2-hydroxyl methacrylate)) has so far remained unclear, apart from the evidence of beneficial effects of the pHEMA grafting. This study has explicitly characterized the electrostatically driven self-assembly process of linear polymethacrylate polymers with DNA-generating nanocarriers for efficient gene transfection. Isothermal titration calorimetry (ITC) showed clear differences in binding-heat profiles of homo-polycationic and pHEMA grafted polymers with DNA. Polyethylene imine, a branched polycationic polymer of 25kDa with high transfection potential that has previously been successfully used in transfection experiments, demonstrated a heat flow profile that was partly identical to pDMAEMA-b-pHEMA. Computational molecular dynamics (MD) simulated the folding process of polymer in water from a linear to a coiled state: homo-pDMAEMA and pHEMA grafts reduced their overall positive charge accessibility upon folding, down to 45% and 63%, respectively. The homo-pDMAEMA formed the globular conformation more preferably than pHEMA grafts, thus impeding electrostatic interaction with DNA. These findings substantiate the known disadvantage of low-molecular-weight linear polymers compared to higher-molecular-weight polymers in transfection performance; here we have disclosed the ability of a non-cationic chain elongation to be beneficial for the self-assembly process. The combination of MD and ITC has proved to be a suitable approach for carrier-payload interaction studies and may be used to predict the efficacy of a polymer as a nanocarrier from the flexibility of its structure.

Superposition and alignment of labeled point clouds.

Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A labeled point cloud is a finite set of points, where each point is not only associated with a position in three-dimensional space, but also with a discrete class label that represents a specific property. This type of model is especially suitable for modeling biomolecules such as proteins and protein binding sites, where a label may represent an atom type or a physico-chemical property. Proceeding from this representation, we address the question of how to compare two labeled points clouds in terms of their similarity. Using fuzzy modeling techniques, we develop a suitable similarity measure as well as an efficient evolutionary algorithm to compute it. Moreover, we consider the problem of establishing an alignment of the structures in the sense of a one-to-one correspondence between their basic constituents. From a biological point of view, alignments of this kind are of great interest, since mutually corresponding molecular constituents offer important information about evolution and heredity, and can also serve as a means to explain a degree of similarity. In this paper, we therefore develop a method for computing pairwise or multiple alignments of labeled point clouds. To this end, we proceed from an optimal superposition of the corresponding point clouds and construct an alignment which is as much as possible in agreement with the neighborhood structure established by this superposition. We apply our methods to the structural analysis of protein binding sites.