Gudrun Lange

Crystal Structure of 1-Deoxy-D-xylulose 5-Phosphate Synthase, a Crucial Enzyme for Isoprenoids Biosynthesis.

Isopentenyl pyrophosphate (IPP) is a common precursor for the synthesis of all isoprenoids, which have important functions in living organisms. IPP is produced by the mevalonate pathway in archaea, fungi, and animals. In contrast, IPP is synthesized by a mevalonate-independent pathway in most bacteria, algae, and plant plastids. 1-Deoxy-d-xylulose 5-phosphate synthase (DXS) catalyzes the first and the rate-limiting step of the mevalonate-independent pathway and is an attractive target for the development of novel antibiotics, antimalarials, and herbicides. We report here the first structural information on DXS, from Escherichia coli and Deinococcus radiodurans, in complex with the coenzyme thiamine pyrophosphate (TPP). The structure contains three domains (I, II, and III), each of which bears homology to the equivalent domains in transketolase and the E1 subunit of pyruvate dehydrogenase. However, DXS has a novel arrangement of these domains as compared with the other enzymes, such that the active site of DXS is located at the interface of domains I and II in the same monomer, whereas that of transketolase is located at the interface of the dimer. The coenzyme TPP is mostly buried in the complex, but the C-2 atom of its thiazolium ring is exposed to a pocket that is the substrate-binding site. The structures identify residues that may have important roles in catalysis, which have been confirmed by our mutagenesis studies.

Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function

The HYDE scoring function consistently describes hydrogen bonding, the hydrophobic effect and desolvation. It relies on HYdration and DEsolvation terms which are calibrated using octanol/water partition coefficients of small molecules. We do not use affinity data for calibration, therefore HYDE is generally applicable to all protein targets. HYDE reflects the Gibbs free energy of binding while only considering the essential interactions of protein–ligand complexes. The greatest benefit of HYDE is that it yields a very intuitive atom-based score, which can be mapped onto the ligand and protein atoms. This allows the direct visualization of the score and consequently facilitates analysis of protein–ligand complexes during the lead optimization process. In this study, we validated our new scoring function by applying it in large-scale docking experiments. We could successfully predict the correct binding mode in 93% of complexes in redocking calculations on the Astex diverse set, while our performance in virtual screening experiments using the DUD dataset showed significant enrichment values with a mean AUC of 0.77 across all protein targets with little or no structural defects. As part of these studies, we also carried out a very detailed analysis of the data that revealed interesting pitfalls, which we highlight here and which should be addressed in future benchmark datasets.

A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function

The estimation of free energy of binding is a key problem in structure-based design. We developed the scoring function HYDE based on a consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes. HYDE is applicable to all types of protein targets since it is not calibrated on experimental binding affinity data or protein–ligand complexes. The comprehensible atom-based score of HYDE is visualized by applying a very intuitive coloring scheme, thereby facilitating the analysis of protein–ligand complexes in the lead optimization process. In this paper, we have revised several aspects of the former version of HYDE which was described in detail previously. The revised HYDE version was already validated in large-scale redocking and screening experiments which were performed in the course of the Docking and Scoring Symposium at 241st ACS National Meeting. In this study, we additionally evaluate the ability of the revised HYDE version to predict binding affinities. On the PDBbind 2007 coreset, HYDE achieves a correlation coefficient of 0.62 between the experimental binding constants and the predicted binding energy, performing second best on this dataset compared to 17 other well-established scoring functions. Further, we show that the performance of HYDE in large-scale redocking and virtual screening experiments on the Astex diverse set and the DUD dataset respectively, is comparable to the best methods in this field.

Towards an Integrated Description of Hydrogen Bonding and Dehydration: Decreasing False Positives in Virtual Screening with the HYDE Scoring Function

We developed a new empirical scoring function, HYDE, for the evaluation of protein–ligand complexes. HYDE estimates binding free energy based on two terms for dehydration and hydrogen bonding only. The essential feature of this scoring function is the integrated use of log P‐derived atomic increments for the prediction of free dehydration energy and hydrogen bonding energy. Taking the dehydration of atoms within the interface into account shows that some atoms contribute favorably to the overall score, while others contribute unfavorably. For instance, hydrogen bond functions are penalized if they are dehydrated unless they can overcompensate this loss by forming a hydrogen bond with excellent geometry. The main stabilizing contribution represents the removal of apolar groups from the water: the hydrophobic effect. Initial studies using the DUD dataset show that with HYDE, there is a significant decrease in false positives, a reasonable categorization of compounds as either non‐binders, weak, medium or strong binders, and in particular, there is a generally applicable and thermodynamically sensible cutoff score below which there is a high likelihood that the compound is indeed a binder.

Evidence of water molecules--a statistical evaluation of water molecules based on electron density.

Water molecules play important roles in many biological processes, especially when mediating protein-ligand interactions. Dehydration and the hydrophobic effect are of central importance for estimating binding affinities. Due to the specific geometric characteristics of hydrogen bond functions of water molecules, meaning two acceptor and two donor functions in a tetrahedral arrangement, they have to be modeled accurately. Despite many attempts in the past years, accurate prediction of water molecules-structurally as well as energetically-remains a grand challenge. One reason is certainly the lack of experimental data, since energetic contributions of water molecules can only be measured indirectly. However, on the structural side, the electron density clearly shows the positions of stable water molecules. This information has the potential to improve models on water structure and energy in proteins and protein interfaces. On the basis of a high-resolution subset of the Protein Data Bank, we have conducted an extensive statistical analysis of 2.3 million water molecules, discriminating those water molecules that are well resolved and those without much evidence of electron density. In order to perform this classification, we introduce a new measurement of electron density around an individual atom enabling the automatic quantification of experimental support. On the basis of this measurement, we present an analysis of water molecules with a detailed profile of geometric and structural features. This data, which is freely available, can be applied to not only modeling and validation of new water models in structural biology but also in molecular design.

Large-Scale Analysis of Hydrogen Bond Interaction Patterns in Protein–Ligand Interfaces

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.

Nearly no Scoring Function Without a Hansch‐Analysis

Corwin Hansch is well‐known as the father, inventor, and promoter of quantitative structure‐activity relationships. Usually, QSAR is seen as a ligand‐based design method correlating molecular structure or property descriptors to biological activity. QSAR is seldom mentioned in relation to structure‐based approaches, although it is the centerpiece of nearly every empirical scoring function. QSAR techniques are applied on various levels, from the fitting of scoring terms to biological affinity data in empirical scoring functions up to the fine‐tuning of individual aspects of protein‐ligand interactions. In the following, we report on current findings for our scoring approach HYDE, which are based upon both the idea of QSAR and Hansch’s historical logP data. We relate the molecular surface area of 594 diverse compounds to their experimental octanol/water partition coefficients aiming at new insights in hydrogen bonding and dehydration energies of solutes. Donors and acceptors which are far from each other contribute with nearly constant increments to the logP value. The solubility in the aqueous phase is however not increased with the number of hydrogen bonds a polar group is able to form. Although signs are found that these facts have been known for many years, they have implications for modern scoring function design.

Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures

Macromolecular structures resolved by X-ray crystallography are essential for life science research. While some methods exist to automatically quantify the quality of the electron density fit, none of them is without flaws. Especially the question of how well individual parts like atoms, small fragments, or molecules are supported by electron density is difficult to quantify. While taking experimental uncertainties correctly into account, they do not offer an answer on how reliable an individual atom position is. A rapid quantification of this atomic position reliability would be highly valuable in structure-based molecular design. To overcome this limitation, we introduce the electron density score EDIA for individual atoms and molecular fragments. EDIA assesses rapidly, automatically, and intuitively the fit of individual as well as multiple atoms (EDIAm) into electron density accompanied by an integrated error analysis. The computation is based on the standard 2fo - fc electron density map in combination with the model of the molecular structure. For evaluating partial structures, EDIAm shows significant advantages compared to the real-space R correlation coefficient (RSCC) and the real-space difference density Z score (RSZD) from the molecular modelers point of view. Thus, EDIA abolishes the time-consuming step of visually inspecting the electron density during structure selection and curation. It supports daily modeling tasks of medicinal and computational chemists and enables a fully automated assembly of large-scale, high-quality structure data sets. Furthermore, EDIA scores can be applied for model validation and method development in computer-aided molecular design. In contrast to measuring the deviation from the structure model by root-mean-squared deviation, EDIA scores allow comparison to the underlying experimental data taking its uncertainty into account.

HYDEing the false positives – scoring for lead optimization

It is highly desirable to have a scoring function that provides guidance for the design of compounds with optimized bioactivity. HYDE [1] is such a scoring function, considering the essential interactions in protein-ligand complexes. HYDE describes consistently hydrogen bonds, the hydrophobic effect and desolvation. Its basic principle is a well balanced assessment of the energetics of desolvation. Compared to most other scoring functions HYDE is not calibrated on affinity data, we use octanol/water partition data of small molecules for calibration. Only three major factors are taken into consideration: (a) local hydrophobicity, (b) solvent accessible surface, and (c) contact surface area. Based on these, energetically favorable and unfavorable contributions to the binding affinity can be assessed on an atomic level. Atomic contributions can be visualized, which turns out to be particularly helpful in a lead-optimization setup. One may immediately identify energetically unfavorable arrangements, like a polar group without a counter-part in an otherwise hydrophobic pocket or two hydrogen bond acceptors facing each other. Medicinal chemists will immediately have ideas how to alter a given structure in order to gain activity. It is demonstrated that HYDE is able to distinguish between strong binders, weak binders, and non-binders considering several p38 MAP kinase inhibitors [2]. In a congeneric series of thrombin inhibitors [3] it is shown that HYDE is able to score and rank single atom exchanges correctly.

Placement of Water Molecules in Protein Structures: From Large-Scale Evaluations to Single-Case Examples

Water molecules are of great importance for the correct representation of ligand binding interactions. Throughout the last years, water molecules and their integration into drug design strategies have received increasing attention. Nowadays a variety of tools are available to place and score water molecules. However, the most frequently applied software solutions require substantial computational resources. In addition, none of the existing methods has been rigorously evaluated on the basis of a large number of diverse protein complexes. Therefore, we present a novel method for placing water molecules, called WarPP, based on interaction geometries previously derived from protein crystal structures. Using a large, previously compiled, high-quality validation set of almost 1500 protein-ligand complexes containing almost 20 000 crystallographically observed water molecules in their active sites, we validated our placement strategy. We correctly placed 80% of the water molecules within 1.0 Å of a crystallographically observed one.