Interest in lipid interactions with proteins and other biomolecules is emerging not only in fundamental biochemistry but also in the field of nanobiotechnology where lipids are commonly used, for example, in carriers of mRNA vaccines. The outward-facing components of cellular membranes and lipid nanoparticles, the lipid headgroups, regulate membrane interactions with approaching substances, such as proteins, drugs, RNA, or viruses. Because lipid headgroup conformational ensembles have not been experimentally determined in physiologically relevant conditions, an essential question about their interactions with other biomolecules remains unanswered: Do headgroups exchange between a few rigid structures, or fluctuate freely across a practically continuous spectrum of conformations? Here, we combine solid-state NMR experiments and molecular dynamics simulations from the NMRlipids Project to resolve the conformational ensembles of headgroups of four key lipid types in various biologically relevant conditions. We find that lipid headgroups sample a wide range of overlapping conformations in both neutral and charged cellular membranes, and that differences in the headgroup chemistry manifest only in probability distributions of conformations. Furthermore, the analysis of 894 protein-bound lipid structures from the Protein Data Bank suggests that lipids can bind to proteins in a wide range of conformations, which are not limited by the headgroup chemistry. We propose that lipids can select a suitable headgroup conformation from the wide range available to them to fit the various binding sites in proteins. The proposed inverse conformational selection model will extend also to lipid binding to targets other than proteins, such as drugs, RNA, and viruses.
Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC 50 10 µM. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions.Target deconvolution of phenotypic screening hits 1 consists of identifying the macromolecular targets of small molecules exhibiting some kind of phenotypic activity (e.g. whole-cell activity) 2 . It is a prerequisite to gain mechanistic understanding of observable activity and has proven helpful for drug development 3,4 . Indeed, the combination of phenotypic screening with target deconvolution constitutes an attractive alternative strategy for the discovery of molecularly targeted therapies. However, reliable computational methods for target prediction, also known as target fishing or polypharmacology prediction [5][6][7][8][9] , are crucial for this application. Target prediction tools are also used to predict drug side-effects 10 and drug repositioning opportunities 9 . The need for target prediction methods is exacerbated by the resurgence of phenotypic drug discovery [11][12][13] , as this trend has boosted the availability of new hits whose phenotypic activities are still to be explained mechanistically. A landmark study 3 has shown that, despite a more intense focus on target-based drug discovery, most first-in-class drug approvals come from phenotypic screens. This realisation has contributed to many more research projects using this type of screens (e.g. large-scale empirical screening projects on cancer cell lines [14][15][16][17][18] or pathogen cultures) 19,20 . In turn, more phenotypic data has resulted in more accurate in silico models to predict new hits, whether this prediction is done from the chemical structure of molecules [21][22][23][24] or more recently complemented with molecular profiles characterising the system in which the...
DHQ2 (type II dehydroquinase), which is an essential enzyme in Helicobacter pylori and Mycobacterium tuberculosis and does not have any counterpart in humans, is recognized to be an attractive target for the development of new antibacterial agents. Computational and biochemical studies that help understand in atomic detail the catalytic mechanism of these bacterial enzymes are reported in the present paper. A previously unknown key role of certain conserved residues of these enzymes, as well as the structural changes responsible for triggering the release of the product from the active site, were identified. Asp89*/Asp88* from a neighbouring enzyme subunit proved to be the residue responsible for the deprotonation of the essential tyrosine to afford the catalytic tyrosinate, which triggers the enzymatic process. The essentiality of this residue is supported by results from site-directed mutagenesis. For H. pylori DHQ2, this reaction takes place through the assistance of a water molecule, whereas for M. tuberculosis DHQ2, the tyrosine is directly deprotonated by the aspartate residue. The participation of a water molecule in this deprotonation reaction is supported by solvent isotope effects and proton inventory studies. MD simulation studies provide details of the required motions for the catalytic turnover, which provides a complete overview of the catalytic cycle. The product is expelled from the active site by the essential arginine residue and after a large conformational change of a loop containing two conserved arginine residues (Arg109/Arg108 and Arg113/Arg112), which reveals a previously unknown key role for these residues. The present study highlights the key role of the aspartate residue whose blockage could be useful in the rational design of inhibitors and the mechanistic differences between both enzymes.
The binding mode of several substrate analogues, (2R)‐2‐benzyl‐3‐dehydroquinic acids 4, which are potent reversible competitive inhibitors of type II dehydroquinase (DHQ2), the third enzyme of the shikimic acid pathway, has been investigated by structural and computational studies. The crystal structures of Mycobacterium tuberculosis and Helicobacter pylori DHQ2 in complex with one of the most potent inhibitor, p‐methoxybenzyl derivative 4 a, have been solved at 2.40 Å and 2.75 Å, respectively. This has allowed the resolution of the M. tuberculosis DHQ2 loop containing residues 20–25 for the first time. These structures show the key interactions of the aromatic ring in the active site of both enzymes and additionally reveal an important change in the conformation and flexibility of the loop that closes over substrate binding. The loop conformation and the binding mode of compounds 4 b–d has been also studied by molecular dynamics simulations, which suggest that the benzyl group of inhibitors 4 prevent appropriate orientation of the catalytic tyrosine of the loop for proton abstraction and disrupts its basicity.
Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict. The benchmark and a simple target prediction method to use as a performance baseline are available at http://ballester.marseille.inserm.fr/TF-benchmark.tar.gz.
Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user‐friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.
The structural changes caused by the substitution of the aromatic moiety in (2S)-2-benzyl-3-dehydroquinic acids and its epimers in C2 by electron-withdrawing or electron-donating groups in type II dehydroquinase enzyme from M. tuberculosis and H. pylori has been investigated by structural and computational studies. Both compounds are reversible competitive inhibitors of this enzyme, which is essential in these pathogenic bacteria. The crystal structures of M. tuberculosis and H. pylori in complex with (2S)-2-(4-methoxy)benzyl- and (2S)-2-perfluorobenzyl-3-dehydroquinic acids have been solved at 2.0, 2.3, 2.0, and 1.9 Å, respectively. The crystal structure of M. tuberculosis in complex with (2R)-2-(benzothiophen-5-yl)methyl-3-dehydroquinic acid is also reported at 1.55 Å. These crystal structures reveal key differences in the conformation of the flexible loop of the two enzymes, a difference that depends on the presence of electron-withdrawing or electron-donating groups in the aromatic moiety of the inhibitors. This loop closes over the active site after substrate binding, and its flexibility is essential for the function of the enzyme. These differences have also been investigated by molecular dynamics simulations in an effort to understand the significant inhibition potency differences observed between some of these compounds and also to obtain more information about the possible movements of the loop. These computational studies have also allowed us to identify key structural factors of the H. pylori loop that could explain its reduced flexibility in comparison to the M. tuberculosis loop, specifically by the formation of a key salt bridge between the side chains of residues Asp18 and Arg20.
Several 3-alkylaryl mimics of the enol intermediate in the reaction catalyzed by type II dehydroquinase were synthesized to investigate the effect on the inhibition potency of replacing the oxygen atom in the side chain by a carbon atom. The length and the rigidity of the spacer was also studied. The inhibitory properties of the reported compounds against type II dehydroquinase from Mycobacterium tuberculosis and Helicobacter pylori are also reported. The binding modes of these analogs in the active site of both enzymes were studied by molecular docking using GOLD 5.0 and dynamic simulations studies.
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