Predicting the duration of action of ß2-adrenergic receptor agonists: Ligand and structure-based approaches.

Agonists of the ß2 adrenergic receptor (ADRB2) are an important class of medications used for the treatment of respiratory diseases. They can be classified as short acting (SABA) or long acting (LABA), with each class playing a different role in patient management. In this work we explored both ligand-based and structure-based high-throughput approaches to classify ß2-agonists based on their duration of action. A completely in-silico prediction pipeline using an AlphaFold generated structure was used for structure-based modelling. Our analysis identified the ligands' 3D structure and lipophilicity as the most relevant features for the prediction of the duration of action. Interaction-based methods were also able to select ligands with the desired duration of action, incorporating the bias directly in the structure-based drug discovery pipeline without the need for further processing.

Structural Insights into Molecular Recognition and Receptor Activation in Chemokine-Chemokine Receptor Complexes.

The chemokine system is a key player in the functioning of the immune system and a sought-after target for drug candidates. The number of experimental structures of chemokines in complex with chemokine receptors has increased rapidly over the past few years, providing essential information for rational development of chemokine receptor ligands. Here, we perform a comparative analysis of all chemokine-chemokine receptor structures, with the aim of characterizing the molecular recognition processes and highlighting the relationships between chemokine structures and functional processes. The structures show conserved interaction patterns between the chemokine core and the receptor N-terminus, while interactions near ECL2 display subfamily-specific features. Detailed analyses of the interactions of the chemokine N-terminal domain in the 7TM cavities reveal activation mechanisms for CCR5, CCR2, and CXCR2 and a mechanism for biased agonism in CCR1.

Mining the Protein Data Bank to inspire fragment library design.

The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).

One class classification for the detection of ß2 adrenergic receptor agonists using single-ligand dynamic interaction data.

G protein-coupled receptors are involved in many biological processes, relaying the extracellular signal inside the cell. Signaling is regulated by the interactions between receptors and their ligands, it can be stimulated by agonists, or inhibited by antagonists or inverse agonists. The development of a new drug targeting a member of this family requires to take into account the pharmacological profile of the designed ligands in order to elicit the desired response. The structure-based virtual screening of chemical libraries may prioritize a specific class of ligands by combining docking results and ligand binding information provided by crystallographic structures. The performance of the method depends on the relevance of the structural data, in particular the conformation of the targeted site, the binding mode of the reference ligand, and the approach used to compare the interactions formed by the docked ligand with those formed by the reference ligand in the crystallographic structure. Here, we propose a new method based on the conformational dynamics of a single protein-ligand reference complex to improve the biased selection of ligands with specific pharmacological properties in a structure-based virtual screening exercise. Interactions patterns between a reference agonist and the receptor, here exemplified on the ß2 adrenergic receptor, were extracted from molecular dynamics simulations of the agonist/receptor complex and encoded in graphs used to train a one-class machine learning classifier. Different conditions were tested: low to high affinity agonists, varying simulation duration, considering or ignoring hydrophobic contacts, and tuning of the classifier parametrization. The best models applied to post-process raw data from retrospective virtual screening obtained by docking of test libraries effectively filtered out irrelevant poses, discarding inactive and non-agonist ligands while identifying agonists. Taken together, our results suggest that consistency of the binding mode during the simulation is a key to the success of the method.

Comprehensive analysis of commercial fragment libraries.

Screening of fragment libraries is a valuable approach to the drug discovery process. The quality of the library is one of the keys to success, and more particularly the design or choice of a library has to meet the specificities of the research program. In this study, we made an inventory of the commercial fragment libraries and we established a methodology which allows any library to be positioned in relation to the complete offer currently on the market, by addressing the following questions: does this chemical library look like another chemical library? What is the coverage of the current chemical space by this chemical library? What are the characteristic structural features of the fragments of this chemical library? We based our analysis on 2D and 3D chemical descriptors, framework class generation and the generative topographic map. We identified 59 270 scaffolds, which can be searched in a dedicated web site (https://gtmfrag.drugdesign.unistra.fr) and developed a model which accounts for fragment diversity while being easy to interpret (download at 10.5281/zenodo.5534434).

Comparing transmembrane protein structures with ATOLL.

SUMMARY: The 3D structure of transmembrane helices plays a key role in the function of membrane proteins. While visual inspection can usually discern the distinctive features of a helix bundle, simply translating them into a 2D diagram can be difficult. ATOLL (Aligned Transmembrane dOmains Layout fLattening) projects the helix bundle onto the lipid bilayer plane, thereby facilitating the comparison of different structures of the same membrane protein or structures of different membrane proteins. AVAILABILITY AND IMPLEMENTATION: ATOLL is a program written in Python3. The source code is freely available on the web at https://github.com/LIT-CCM-lab/ATOLL. ATOLL is implemented into a web server (https://atoll.drugdesign.unistra.fr/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling of CCR5 Recognition by HIV-1 gp120: How the Viral Protein Exploits the Conformational Plasticity of the Coreceptor.

The chemokine receptor CCR5 is a key player in HIV-1 infection. The cryo-EM 3D structure of HIV-1 envelope glycoprotein (Env) subunit gp120 in complex with CD4 and CCR5 has provided important structural insights into HIV-1/host cell interaction, yet it has not explained the signaling properties of Env nor the fact that CCR5 exists in distinct forms that show distinct Env binding properties. We used classical molecular dynamics and site-directed mutagenesis to characterize the CCR5 conformations stabilized by four gp120s, from laboratory-adapted and primary HIV-1 strains, and which were previously shown to bind differentially to distinct CCR5 forms and to exhibit distinct cellular tropisms. The comparative analysis of the simulated structures reveals that the different gp120s do indeed stabilize CCR5 in different conformational ensembles. They differentially reorient extracellular loops 2 and 3 of CCR5 and thus accessibility to the transmembrane binding cavity. They also reshape this cavity differently and give rise to different positions of intracellular ends of transmembrane helices 5, 6 and 7 of the receptor and of its third intracellular loop, which may in turn influence the G protein binding region differently. These results suggest that the binding of gp120s to CCR5 may have different functional outcomes, which could result in different properties for viruses.

Inhibition of the NAD salvage pathway in schistosomes impairs metabolism, reproduction, and parasite survival.

NAD, a key co-enzyme required for cell metabolism, is synthesized via two pathways in most organisms. Since schistosomes apparently lack enzymes required for de novo NAD biosynthesis, we evaluated whether these parasites, which infect >200 million people worldwide, maintain NAD homeostasis via the NAD salvage biosynthetic pathway. We found that intracellular NAD levels decline in schistosomes treated with drugs that block production of nicotinamide or nicotinamide mononucleotide-known NAD precursors in the non-deamidating salvage pathway. Moreover, in vitro inhibition of the NAD salvage pathway in schistosomes impaired egg production, disrupted the outer membranes of both immature and mature parasites and caused loss of mobility and death. Inhibiting the NAD salvage pathway in schistosome-infected mice significantly decreased NAD levels in adult parasites, which correlated with reduced egg production, fewer liver granulomas and parasite death. Thus, schistosomes, unlike their mammalian hosts, appear limited to one metabolic pathway to maintain NAD-dependent metabolic processes.

Is it time for artificial intelligence to predict the function of natural products based on 2D-structure.

Currently, there is no established technique that allows the function of a compound produced by nature to be predicted by looking at its 2-dimensional chemical structure. One of chemistry's grand challenges: to find a function for every known metabolite. We explore the opportunity for Artificial Intelligence to provide rationale interrogation of metabolites to predict their function.

Unsupervised Classification of G-Protein Coupled Receptors and Their Conformational States Using IChem Intramolecular Interaction Patterns.

Over the past decade, the ever-growing structural information on G-protein coupled receptors (GPCRs) has revealed the three-dimensional (3D) characteristics of a receptor structure that is competent for G-protein binding. Structural markers are now commonly used to distinguish GPCR functional states, especially when analyzing molecular dynamics simulations. In particular, the position of the sixth helix within the seven transmembrane domains (TMs) is directly related to the coupling of the G-protein. Here, we show that the structural pattern defined by transmembrane intramolecular interactions (hydrogen bonds excluding backbone/backbone interactions, ionic bonds and aromatic interactions) is suitable for comparison of GPCR 3D structures and unsupervised distinction of the receptor states. First, we analyze a microsecond long molecular dynamic simulation of the human ß2-adrenergic receptor (ADRB2). Clustering of the 3D structures by pattern similarity identifies stable states which match the conformational classes defined by structural markers. Furthermore, the method directly spots the few state-specific interactions. Transforming pattern into graph, we extend the method to the comparison of different GPCRs. Clustering all GPCR experimentally determined structures by clique relative size first separates receptors, then their conformational states, thereby suggesting that the interaction patterns are specific of the receptor sequence and that the interaction signatures of conformational states are not shared across distant homologues.

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

Binding mode information improves fragment docking.

Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.

Structural Insights on Fragment Binding Mode Conservation.

Aiming at a deep understanding of fragment binding to ligandable targets, we performed a large scale analysis of the Protein Data Bank. Binding modes of 1832 drug-like ligands and 1079 fragments to 235 proteins were compared. We observed that the binding modes of fragments and their drug-like superstructures binding to the same protein are mostly conserved, thereby providing experimental evidence for the preservation of fragment binding modes during molecular growing. Furthermore, small chemical changes in the fragment are tolerated without alteration of the fragment binding mode. The exceptions to this observation generally involve conformational variability of the molecules. Our data analysis also suggests that, provided enough fragments have been crystallized within a protein, good interaction coverage of the binding pocket is achieved. Last, we extended our study to 126 crystallization additives and discuss in which cases they provide information relevant to structure-based drug design.

CCR5 adopts three homodimeric conformations that control cell surface delivery.

Biophysical methods and x-ray crystallography have revealed that class A G protein-coupled receptors (GPCRs) can form homodimers. We combined computational approaches with receptor cross-linking, energy transfer, and a newly developed functional export assay to characterize the residues involved in the dimerization interfaces of the chemokine receptor CCR5, the major co-receptor for HIV-1 entry into cells. We provide evidence of three distinct CCR5 dimeric organizations, involving residues of transmembrane helix 5. Two dimeric states corresponded to unliganded receptors, whereas the binding of the inverse agonist maraviroc stabilized a third state. We found that CCR5 dimerization was required for targeting the receptor to the plasma membrane. These data suggest that dimerization contributes to the conformational diversity of inactive class A GPCRs and may provide new opportunities to investigate the cellular entry of HIV-1 and mechanisms for its inhibition.

Structural Searching of Biosynthetic Enzymes to Predict Protein Targets of Natural Products.

Recently, we have demonstrated that site comparison methodology using flavonoid biosynthetic enzymes as the query could automatically identify structural features common to different flavonoid-binding proteins, allowing for the identification of flavonoid targets such as protein kinases. With the aim of further validating the hypothesis that biosynthetic enzymes and therapeutic targets can contain a similar natural product imprint, we collected a set of 159 crystallographic structures representing 38 natural product biosynthetic enzymes by searching the Protein Databank. Each enzyme structure was used as a query to screen a repository of approximately 10 000 ligandable sites by active site similarity. We report a full analysis of the screening results and highlight three retrospective examples where the natural product validates the method, thereby revealing novel structural relationships between natural product biosynthetic enzymes and putative protein targets of the natural product. From a prospective perspective, our work provides a list of up to 64 potential novel targets for 25 well-characterized natural products.

Multi-target Fragments Display Versatile Binding Modes.

Promiscuity is an interesting concept in fragment-based drug design as fragments with low specificity can be advantageous for finding many screening hits. We present a PDB-wide analysis of multi-target fragments and their binding mode conservation. Focussing on multi-target fragments, we found that the majority shows non-conserved binding modes, even if they bind in a similar conformation or similar protein targets. Surprisingly, fragment properties alone are not able to predict whether a fragment will exhibit a versatile or conserved binding mode, emphasizing the interplay between protein and fragment features during a binding event and the importance of structure-based modelling.

Efficient Inhibition of SmNACE by Coordination Complexes Is Abolished by S. mansoni Sequestration of Metal.

SmNACE is a NAD catabolizing enzyme expressed on the outer tegument of S. mansoni, a human parasite that is one of the major agents of the neglected tropical disease schistosomiasis. Recently, we identified aroylhydrazone derivatives capable of inhibiting the recombinant form of the enzyme with variable potency (IC50 ranging from 88 µM to 33 nM). In the present study, we investigated the mechanism of action of the least potent micromolar inhibitor (compound 1) and the most potent nanomolar inhibitor (compound 2) in the series on both the recombinant and native SmNACE enzymes. Using mass spectroscopy, spectrophotometry, and activity assays under different experimental conditions, we demonstrated that the >3 log gain in potency against recombinant SmNACE by this class of compounds is dependent on the formation of a coordination complex with metal cations, such as Ni(II), Zn(II), and Fe(II), that are loaded on the protein surface. Testing the compounds on live parasites, we observed that only the weak micromolar compound 1 was active on the native enzyme. We showed that S. mansoni effectively sequesters the metal from the coordination complex, resulting in the loss of inhibitory activity of the potent nanomolar compound 2. Importantly, the modeling of the transition complex between Zn(II) and compound 2 enabled the discovery of a new metal-independent aroylhydrazone analogue, which is now the most potent and selective inhibitor of native SmNACE known.

Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands?

The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW < 300 Da) with weak binding affinities to lead compounds with high affinity and selectivity. Usually, structural information from fragment-protein complexes is used to develop ideas about the binding mode of similar but drug-like molecules. In this regard, crystallization additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, ß-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.

Comparing atom-based with residue-based descriptors in predicting binding site similarity: do backbone atoms matter?

AIM: We question the level of detail required in protein 3D-representation to detect site similarity which is relevant for polypharmacology prediction. RESULTS: We modified the in-house program SiteAlign to replace generic pharmacophoric descriptors of cavity-lining amino acids by descriptors accounting for solvent exposure. Benchmarking the novel, atom-based, method (SiteAlign2) revealed no global improvement of performance. However, in the rare cases of no sequence or global structure similarities between the compared proteins, SiteAlign2 was more successful if backbone atoms are key determinants of ligand binding. CONCLUSION: SiteAlign suits the comparison of binding sites for close or distant homologs. SiteAlign2 provides a better insight into the physical model of site similarity between nonhomologs, but at the expense of an increased sensitivity to atomic coordinates.