Karl-Heinz Baringhaus

Scaffold diversity of natural products: inspiration for combinatorial library design

Natural products contain scaffold structures that can be systematically exploited for the design of combinatorial compound libraries with druglike properties. We review approaches for scaffold identification, and compare properties and pharmacophoric features of drugs and natural products. In particular, an application of the self-organizing map technique is presented for natural product-derived compound and library design.

Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface.

Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.

Identification of the bile acid-binding site of the ileal lipid-binding protein by photoaffinity labeling, matrix-assisted laser desorption ionization-mass spectrometry, and NMR structure.

The ileal lipid-binding protein (ILBP) is the only physiologically relevant bile acid-binding protein in the cytosol of ileocytes. To identify the bile acid-binding site(s) of ILBP, recombinant rabbit ILBP photolabeled with 3-azi- and 7-azi-derivatives of cholyltaurine was analyzed by a combination of enzymatic fragmentation, gel electrophoresis, and matrix-assisted laser desorption ionization (MALDI)-mass spectrometry. The attachment site of the 3-position of cholyltaurine was localized to the amino acid triplet His100-Thr101-Ser102using the photoreactive 3,3-azo-derivative of cholyltaurine. With the corresponding 7,7-azo-derivative, the attachment point of the 7-position could be localized to the C-terminal part (position 112–128) as well as to the N-terminal part suggesting more than one binding site for bile acids. By chemical modification and NMR structure of ILBP, arginine residue 122 was identified as the probable contact point for the negatively charged side chain of cholyltaurine. Consequently, bile acids bind to ILBP with the steroid nucleus deep inside the protein cavity and the negatively charged side chain near the entry portal. The combination of photoaffinity labeling, enzymatic fragmentation, MALDI-mass spectrometry, and NMR structure was successfully used to determine the topology of bile acid binding to ILBP.

The scaffold hopping potential of pharmacophores

The goal of scaffold hopping is to replace the chemical core structure by a novel chemical motif while keeping the biological activity of the molecule. As pharmacophores define chemical features essential for biological activity, they can be successfully employed to guide scaffold replacements. To this end, various novel approaches have recently been developed and applied.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules.

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

Fast similarity searching and screening hit analysis

Similarity searching allows a fast identification of analogues to biologically active molecules. Depending on the applied similarity metrics, either structurally close analogues or more diverse compounds can be identified. This is of particular interest for the analysis of high-throughput screening (HTS) hits. A combination of similarity searching and data mining applied to HTS data derives early structure-activity relationships to guide a subsequent optimization of hits.:

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.

Phototoxicity – from molecular descriptors to classification models

Potential photoactivation of certain pharmaceuticals, cosmetic ingredients and natural products by sunlight (e.g., phenothiazines, arylsulfonamides, or coumarins) has to be considered early on in development in order to avoid serious adverse effects (for example phototoxic or photoallergic reactions). Current clinical trial registration guidelines (FDA May 2003 [1], EMEA Dec. 2002 [2]) recommend photosafety testing of molecules if they exhibit strong absorption bands between 290-700nm and if they are significantly partitioned in human skin or eyes. The UV absorption coefficients and the tissue partitioning of a compound are considered as important factors for phototoxic effects. However, the rationalization and prediction of phototoxicity by (quantitative) structure-property relationships ((Q)SPR) offers a valuable strategy to reduce experimental testing if an appropriate precision level of the underlying model is guaranteed. A diverse data set of known phototoxicants and non-phototoxicants including various molecular chemotypes (90 % of them are pharmaceuticals) was compiled. After geometry optimization the maximum absorption wavelength of each compound was calculated by semi-empirical methods followed by subsequent computation of molecular descriptors. Our insilico analysis (e.g., PLS and recursive partitioning) of quantum chemical as well as classical molecular descriptors (e.g., LUMO, HOMO/LUMO gap, electron affinity, ionization energy, molecular fragments, physicochemical descriptors such as logD, pKa and logPeff) has led to predictive photosafety classifiers. Model validation was performed with a proprietary external test set of an in vitro photosafety assay (3T3 neutral red assay). Our photosafety models are currently applied in a prospective manner in the prioritization, classification and labeling of newly designed molecules.

Identification and Application of Antitarget Activity Hotspots to Guide Compound Optimization

The optimization of a lead structure to a development candidate often requires removal of undesirable antitarget activities. To this end, we have developed an approach to extract antitarget activity hotspots from larger databases and to transfer this knowledge onto novel chemical series. These antitarget activity hotspots will be captured as pairs of informative molecules, which are chemically closely related, but differ significantly in biological activity. We illustrate the application of antitarget activity hotspots as informative compound pairs for the optimization of side effects in lead structures for relevant antitargets in pharmaceutical research. The use for prospective design requires establishing a structural link between known antitarget hotspot pairs and a new lead structure: we employ 3D‐based similarity comparison for this task. The entire workflow serves as idea generator in early optimization. The feasibility of this approach is demonstrated in several optimization problems related to hERG inhibition, and CYP3A4 inhibition. Several structural examples demonstrate the ability of the 3D‐shape searching to identify related scaffolds and the usefulness of the antitarget hotspot information to guide optimization by modulating the undesirable antitarget activity. Such a concept based on the analysis of local similarities and the transfer to 3D‐related series is especially promising in those cases, where the construction of antitarget QSAR models fails to detect local SAR trends for guiding the next optimization cycle.

Efficient Approximation of Ligand Rotational and Translational Entropy Changes upon Binding for Use in MM-PBSA Calculations

A major uncertainty in binding free energy estimates for protein-ligand complexes by methods such as MM-PB(GB)SA or docking scores results from neglecting or approximating changes in the configurational entropies (ΔSconfig.) of the solutes. In MM/PB(GB)SA-type calculations, ΔSconfig. has usually been estimated in the rigid rotor, harmonic oscillator approximation. Here, we present the development of a computationally efficient method (termed BEERT) to approximate ΔSconfig. in terms of the reduction in translational and rotational freedom of the ligand upon protein-ligand binding (ΔSR/T), starting from the flexible molecule approach. We test the method successfully in binding affinity computations in connection with MM-PBSA effective energies describing changes in gas-phase interactions and solvation free energies. Compared to related work by Ruvinsky and co-workers, clustering bound ligand poses based on interactions with the protein rather than structural similarity of the poses, and an appropriate averaging over single entropies associated with an individual well of the energy landscape of the protein-ligand complex, were found to be crucial. Employing three data sets of protein-ligand complexes of pharmacologically relevant targets for validation, with up to 20, in part related ligands per data set, spanning binding free energies over a range of ≤7 kcal mol-1, reliable and predictive linear models to estimate binding affinities are obtained in all three cases (R2 = 0.54-0.72, p < 0.001, root mean squared error S = 0.78-1.44 kcal mol-1; q2 = 0.34-0.67, p < 0.05, root mean squared error sPRESS = 1.07-1.36 kcal mol-1). These models are markedly improved compared to considering MM-PBSA effective energies alone, scoring functions, and combinations with ΔSconfig. estimates based on the number of rotatable bonds, rigid rotor, harmonic oscillator approximation, or interaction entropy method. As a limitation, our method currently requires a target-specific training data set to identify appropriate scaling coefficients for the MM-PBSA effective energies and BEERT ΔSR/T. Still, our results suggest that the approach is a valuable, computationally more efficient complement to existing rigorous methods for estimating changes in binding free energy across structurally (weakly) related series of ligands binding to one target.