Binding to large enzyme pockets: small-molecule inhibitors of trypanothione reductase.

The causative agents of the parasitic disease human African trypanosomiasis belong to the family of trypanosomatids. These parasitic protozoa exhibit a unique thiol redox metabolism that is based on the flavoenzyme trypanothione reductase (TR). TR was identified as a potential drug target and features a large active site that allows a multitude of possible ligand orientations, which renders rational structure-based inhibitor design highly challenging. Herein we describe the synthesis, binding properties, and kinetic analysis of a new series of small-molecule inhibitors of TR. The conjunction of biological activities, mutation studies, and virtual ligand docking simulations led to the prediction of a binding mode that was confirmed by crystal structure analysis. The crystal structures revealed that the ligands bind to the hydrophobic wall of the so-called "mepacrine binding site". The binding conformation and potency of the inhibitors varied for TR from Trypanosoma brucei and T. cruzi.

Targeting dynamic pockets of HIV-1 protease by structure-based computational screening for allosteric inhibitors.

We present the discovery of low molecular weight inhibitors of human immunodeficiency virus 1 (HIV-1) protease subtype B that were identified by structure-based virtual screening as ligands of an allosteric surface cavity. For pocket identification and prioritization, we performed a molecular dynamics simulation and observed several flexible, partially transient surface cavities. For one of these presumable ligand-binding pockets that are located in the so-called "hinge region" of the identical protease chains, we computed a receptor-derived pharmacophore model, with which we retrieved fragment-like inhibitors from a screening compound pool. The most potent hit inhibited protease activity in vitro in a noncompetitive mode of action. Although attempts failed to crystallize this ligand bound to the enzyme, the study provides proof-of-concept for identifying innovative tool compounds for chemical biology by addressing flexible protein models with receptor pocket-derived pharmacophore screening.

Fractal Dimensions of Macromolecular Structures.

Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less-explored geometric features of macromolecules is molecular surface irregularity, or 'roughness', which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large-scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand-binding pockets and protein-protein interfaces, exhibit greater local fluctuations in their fractal dimensions than 'inert' surface areas. We expect approximately 22 % of a protein's surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology.

Scrutinizing MHC-I binding peptides and their limits of variation.

Designed peptides that bind to major histocompatibility protein I (MHC-I) allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2K(b) is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2K(b) in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012).

Significance estimation for sequence-based chemical similarity searching (PhAST) and application to AuroraA kinase inhibitors.

BACKGROUND: Chemical similarity searching allows the retrieval of preferred screening molecules from a compound database. Candidates are ranked according to their similarity to a reference compound (query). Assessing the statistical significance of chemical similarity scores helps prioritizing significant hits, and identifying cases where the database does not contain any promising compounds. METHOD: Our text-based similarity measure, Pharmacophore Alignment Search Tool (PhAST), employs pair-wise sequence alignment. We adapted the concept of E-values as significance estimates and employed a sampling technique that incorporates the principle of importance sampling in a Markov chain Monte Carlo simulation to generate distributions of random alignment scores. These distributions were used to compute significance estimates for similarity scores in a preliminary prospective virtual screen for inhibitors of Aurora A kinase. CONCLUSION: Assessing the significance of compound similarity computed with PhAST allows for a statistically motivated identification of candidate screening compounds. Inhibitors of Aurora A kinase were retrieved from a large compound library.

Homology model adjustment and ligand screening with a pseudoreceptor of the human histamine H4 receptor.

A computer-assisted method for the generation of pseudoreceptor models is presented together with two practical applications. From a three-dimensional alignment of known histamine H(4) receptor ligands, a pseudoreceptor model of the putative ligand binding site was constructed and used for virtual screening of a large collection of commercially available compounds. Two bioactive chemotypes were retrieved, demonstrating the general applicability of the approach. The pseudoreceptor model was also used to find the putative ligand binding pocket within the transmembrane receptor domain. For each frame of a molecular dynamics simulation of a homology-based H(4) receptor model, we automatically extracted potential ligand binding pockets and used their compatibility with the pseudoreceptor as a selection criterion. The best-matching pocket fits perfectly with existing mutation data and previously published hypotheses suggesting Glu182(5.46) as the preferred binding partner of a positively charged moiety of H(4) receptor ligands. This new pseudoreceptor approach has demonstrated its suitability for both structure-based prioritization of protein receptor models, and ligand-based virtual screening with the aim to perform scaffold hopping.