Tracing binding modes in hit-to-lead optimization: chameleon-like poses of aspartic protease inhibitors

Expanding Automated Multiconformer Ligand Modeling to Macrocycles and Fragments

Small molecule ligands exhibit a diverse range of conformations in solution. Upon binding to a target protein, this conformational diversity is generally reduced. However, ligands can retain some degree of conformational flexibility even when bound to a receptor. In the Protein Data Bank (PDB), a small number of ligands have been modeled with distinct alternative conformations that are supported by X-ray crystallography density maps. However, the vast majority of structural models are fit to a single ligand conformation, potentially ignoring the underlying conformational heterogeneity present in the sample. We previously developed qFit-ligand to sample diverse ligand conformations and to select a parsimonious ensemble consistent with the density. While this approach indicated that many ligands populate alternative conformations, limitations in our sampling procedures often resulted in non-physical conformations and could not model complex ligands like macrocycles. Here, we introduce several improvements to qFit-ligand, including the use of routines within RDKit for stochastic conformational sampling. This new sampling method greatly enriches low energy conformations of small molecules and macrocycles. We further extended qFit-ligand to identify alternative conformations in PanDDA-modified density maps from high throughput X-ray fragment screening experiments. The new version of qFit-ligand improves fit to electron density and reduces torsional strain relative to deposited single conformer models and our previous version of qFit-ligand. These advances enhance the analysis of residual conformational heterogeneity present in ligand-bound structures, which can provide important insights for the rational design of therapeutic agents.

Molecular chameleons in drug discovery

Molecular chameleons possess a flexibility that allows them to dynamically shield or expose polar functionalities in response to the properties of the environment. Although the concept of molecular chameleons was introduced already in 1970, interest in them has grown considerably since the 2010s, when drug discovery has focused to an increased extent on new chemical modalities. Such modalities include cyclic peptides, macrocycles and proteolysis-targeting chimeras, all of which reside in a chemical space far from that of traditional small-molecule drugs. Both cell permeability and aqueous solubility are required for the oral absorption of drugs. Engineering these properties, and potent target binding, into the larger new modalities is a more daunting task than for traditional small-molecule drugs. The ability of chameleons to adapt to different environments may be essential for success. In this Review, we provide both general and theoretical insights into the realm of molecular chameleons. We discuss why chameleons have come into fashion and provide a do-it-yourself toolbox for their design; we then provide a glimpse of how advanced in silico methods can support molecular chameleon design.

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp3-Enriched Fragment Library

Fragment-based drug discovery has played an important role in medicinal chemistry and pharmaceutical research. Despite numerous demonstrated successes, the limited diversity and overrepresentation of planar, sp²-rich structures in commercial libraries often hamper the full potential of this approach. Hence, the thorough design of screening libraries inevitably determines the probability for meaningful hits and subsequent structural elaboration. Against this background, we present the generation of an exclusive fragment library based on iterative entry nomination by a specifically designed computational workflow: "Fragtory". Following a pharmacophore diversity-driven approach, we used Fragtory in an interdisciplinary academic setting to guide both tailored synthesis efforts and the implementation of in-house compounds to build a curated 288-member library of sp³-enriched fragments. Subsequent NMR screens against a model protein and hit validation by protein crystallography led to the identification of structurally novel ligands that were further characterized by isothermal titration calorimetry, demonstrating the applicability of our experimental approach.

Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules

Ever since coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic on March 11, 2020, by the WHO, a concerted effort has been made to find compounds capable of acting on the virus and preventing its replication. In this context, researchers have refocused part of their attention on certain natural compounds that have shown promising effects on the virus. Considering the importance of this topic in the current context, this study aimed to present a critical review and analysis of the main reports of plant-derived compounds as possible inhibitors of the two SARS-CoV-2 proteases: main protease (Mpro) and Papain-like protease (PLpro). From the search in the PubMed database, a total of 165 published articles were found that met the search patterns. A total of 590 unique molecules were identified from a total of 122 articles as potential protease inhibitors. At the same time, 114 molecules reported as natural products and with annotation of theoretical support and antiviral effects were extracted from the COVID-19 Help database. After combining the molecules extracted from articles and those obtained from the database, we identified 648 unique molecules predicted as potential inhibitors of Mpro and/or PLpro. According to our results, several of the predicted compounds with higher theoretical confidence are present in many plants used in traditional medicine and even food, such as flavonoids, carboxylic acids, phenolic acids, triterpenes, terpenes phytosterols, and triterpenoids. These are potential inhibitors of Mpro and PLpro. Although the predictions of several molecules against SARS-CoV-2 are promising, little experimental information was found regarding certain families of compounds. Only 45 out of the 648 unique molecules have experimental data validating them as inhibitors of Mpro or PLpro, with the most frequent scaffold present in these 45 compounds being the flavone. The novelty of this work lies in the analysis of the structural diversity of the chemical space among the molecules predicted as inhibitors of SARS-CoV-2 Mpro and PLpro proteases and the comparison to those molecules experimentally validated. This work emphasizes the need for experimental validation of certain families of compounds, preferentially combining classical enzymatic assays with interaction-based methods. Furthermore, we recommend checking the presence of Pan-Assay Interference Compounds (PAINS) and the presence of molecules previously reported as inhibitors of Mpro or PLpro to optimize resources and time in the discovery of new SARS-CoV-2 antivirals from plant-derived molecules.

fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation

Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.

Cytotoxic and Antifungal Amides Derived from Ferulic Acid: Molecular Docking and Mechanism of Action

Amides derived from ferulic acid have a wide spectrum of pharmacological activities, including antitumor and antifungal activity. In the present study, a series of ten amides were obtained by coupling reactions using the reagents (benzotriazol-1-yloxy) tripyrrolidinophosphonium hexafluorophosphate (PyBOP) and N,N′-dicyclohexylcarbodiimide (DCC). All the compounds were identified on the basis of their IR, ¹H- and ¹³C-NMR, HRMS data, and with yields ranging from 43.17% to 91.37%. The compounds were subjected to cytotoxic tests by the alamar blue technique and antifungal screening by the broth microdilution method to determine the minimum inhibitory concentration (MIC). The amides 10 and 11 displayed the best result in both biological evaluations, and compound 10 was the most potent and selective in HL-60 cancer cells, with no cytotoxicity on healthy cells. This amide had antifungal activity in all strains and had the lowest MIC against Candida albicans and Candida tropicalis. The possible mechanism of antifungal action occurs via the fungal cell wall. Molecular modeling suggested that compounds 10 and 11 interact with the enzymes GWT1 and GSC1, which are essential for the development of C. albicans. The findings of the present study demonstrated that compounds 10 and 11 may be used as a platform in drug development in the future.

Frag4Lead: growing crystallographic fragment hits by catalog using fragment-guided template docking

In recent years, crystallographic fragment screening has matured into an almost routine experiment at several modern synchrotron sites. The hits of the screening experiment, i.e. small molecules or fragments binding to the target protein, are revealed along with their 3D structural information. Therefore, they can serve as useful starting points for further structure-based hit-to-lead development. However, the progression of fragment hits to tool compounds or even leads is often hampered by a lack of chemical feasibility. As an attractive alternative, compound analogs that embed the fragment hit structurally may be obtained from commercial catalogs. Here, a workflow is reported based on filtering and assessing such potential follow-up compounds by template docking. This means that the crystallographic binding pose was integrated into the docking calculations as a central starting parameter. Subsequently, the candidates are scored on their interactions within the binding pocket. In an initial proof-of-concept study using five starting fragments known to bind to the aspartic protease endothiapepsin, 28 follow-up compounds were selected using the designed workflow and their binding was assessed by crystallography. Ten of these compounds bound to the active site and five of them showed significantly increased affinity in isothermal titration calorimetry of up to single-digit micromolar affinity. Taken together, this strategy is capable of efficiently evolving the initial fragment hits without major synthesis efforts and with full control by X-ray crystallography.

DeepFrag: a deep convolutional neural network for fragment-based lead optimization

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0.

A combination index and glycoproteomics-based approach revealed synergistic anticancer effects of curcuminoids of turmeric against prostate cancer PC3 cells

ETHNOPHARMACOLOGICAL RELEVANCE

Herbal medicines (HMs) often exert integration effects, including synergistic, additive and antagonistic effects, in such ways that they act on multiple targets and multiple pathways on account of their multiple components. Turmeric, made from the rhizome of Curcuma longa L., is a well-known HM prescribed in the polyherbal formulas for cancer treatment in traditional Chinese medicines (TCMs). However, neither the multiple anticancer compounds of turmeric nor the integration effects of these components are fully known.

AIM OF THE STUDY

This work aims to develop a systematic approach to reveal the integration effect mechanisms of multiple anticancer compounds in turmeric against prostate cancer PC3 cells.

MATERIALS AND METHODS

Combination index and omics technologies were applied to profile the integration effect mechanisms of bioactive compounds in proportions naturally found in turmeric. PC3 cell line (a prostate cancer cell line) fishing and high resolution mass spectrometry were employed to screen and identify the anticancer compounds from turmeric. The combinations which contain different cell-bound compounds in natural proportions were prepared for further evaluation of anti-cancer activity by using cell viability assays, and assessment of cell apoptosis and cell cycle analysis. Combination index analysis was applied to study the integration effects of the anticancer compounds in their natural proportions. Finally, quantitative glycoproteomics/proteomics and Western blot were implemented to reveal the potential synergistic effect mechanisms of the anticancer compounds based on their natural proportions in turmeric.

RESULTS

Three curcuminoids (curcumin, CUR; demethoxycurcumin, DMC; bisdemethoxycurcumin, BDMC) in turmeric were discovered and shown to possess significant synergistic anticancer activities. Combination index analysis revealed an additive effect of CUR combined with DMC or BDMC and a slight synergistic effect of DMC combined with BDMC in natural proportions in turmeric, while a combination of all three curcuminoids (CUR, DMC and BDMC) at a ratio of 1:1:1 yielded superior synergistic effects. Interestingly, the presence of BDMC and DMC are essential for synergistic effect. Glycoproteomics and proteomics demonstrated that different curcuminoids regulate various protein pathways, such as ribosome, glycolysis/gluconeogenesis, biosynthesis of amino acids, and combination of CUR + DMC + BDMC showed the most powerful effects on down-regulation of protein expression.

CONCLUSIONS

Our analytical approach provides a systematic understanding of the holistic activity and integration effects of the anti-cancer compounds in turmeric and three curcuminoids of turmeric showed a synergistic effect on PC3 cells.

Toward G protein-coupled receptor structure-based drug design using X-ray lasers

Rational structure-based drug design (SBDD) relies on the availability of a large number of co-crystal structures to map the ligand-binding pocket of the target protein and use this information for lead-compound optimization via an iterative process. While SBDD has proven successful for many drug-discovery projects, its application to G protein-coupled receptors (GPCRs) has been limited owing to extreme difficulties with their crystallization. Here, a method is presented for the rapid determination of multiple co-crystal structures for a target GPCR in complex with various ligands, taking advantage of the serial femtosecond crystallography approach, which obviates the need for large crystals and requires only submilligram quantities of purified protein. The method was applied to the human β2-adrenergic receptor, resulting in eight room-temperature co-crystal structures with six different ligands, including previously unreported structures with carvedilol and propranolol. The generality of the proposed method was tested with three other receptors. This approach has the potential to enable SBDD for GPCRs and other difficult-to-crystallize membrane proteins.

An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder

The predominant view in structure-based drug design is that small-molecule ligands, once bound to their target structures, display a well-defined binding mode. However, structural stability (robustness) is not necessary for thermodynamic stability (binding affinity). In fact, it entails an entropic penalty that counters complex formation. Surprisingly, little is known about the causes, consequences and real degree of robustness of protein-ligand complexes. Since hydrogen bonds have been described as essential for structural stability, here we investigate 469 such interactions across two diverse structure sets, comprising of 79 drug-like and 27 fragment ligands, respectively. Completely constricted protein-ligand complexes are rare and may fulfill a functional role. Most complexes balance order and disorder by combining a single anchoring point with looser regions. 25% do not contain any robust hydrogen bond and may form loose structures. Structural stability analysis reveals a hidden layer of complexity in protein-ligand complexes that should be considered in ligand design.

qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X-Ray Electron Density Maps

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.

Binding Pose Flip Explained via Enthalpic and Entropic Contributions

The anomalous binding modes of five highly similar fragments of TIE2 inhibitors, showing three distinct binding poses, are investigated. We report a quantitative rationalization for the changes in binding pose based on molecular dynamics simulations. We investigated five fragments in complex with the transforming growth factor β receptor type 1 kinase domain. Analyses of these simulations using Grid Inhomogeneous Solvation Theory (GIST), pK_A calculations, and a tool to investigate enthalpic differences upon binding unraveled the various thermodynamic contributions to the different binding modes. While one binding mode flip can be rationalized by steric repulsion, the second binding pose flip revealed a different protonation state for one of the ligands, leading to different enthalpic and entropic contributions to the binding free energy. One binding pose is stabilized by the displacement of entropically unfavored water molecules (binding pose determined by solvation entropy), ligands in the other binding pose are stabilized by strong enthalpic interactions, overcompensating the unfavorable water entropy in this pose (binding pose determined by enthalpic interactions). This analysis elucidates unprecedented details determining the flipping of the binding modes, which can elegantly explain the experimental findings for this system.

Metadynamics for Perspective Drug Design: Computationally Driven Synthesis of New Protein-Protein Interaction Inhibitors Targeting the EphA2 Receptor

Metadynamics (META-D) is emerging as a powerful method for the computation of the multidimensional free-energy surface (FES) describing the protein-ligand binding process. Herein, the FES of unbinding of the antagonist N-(3α-hydroxy-5β-cholan-24-oyl)-l-β-homotryptophan (UniPR129) from its EphA2 receptor was reconstructed by META-D simulations. The characterization of the free-energy minima identified on this FES proposes a binding mode fully consistent with previously reported and new structure-activity relationship data. To validate this binding mode, new N-(3α-hydroxy-5β-cholan-24-oyl)-l-β-homotryptophan derivatives were designed, synthesized, and tested for their ability to displace ephrin-A1 from the EphA2 receptor. Among them, two antagonists, namely compounds 21 and 22, displayed high affinity versus the EphA2 receptor and resulted endowed with better physicochemical and pharmacokinetic properties than the parent compound. These findings highlight the importance of free-energy calculations in drug design, confirming that META-D simulations can be used to successfully design novel bioactive compounds.

Active Site Mapping of an Aspartic Protease by Multiple Fragment Crystal Structures: Versatile Warheads To Address a Catalytic Dyad

Crystallography is frequently used as follow-up method to validate hits identified by biophysical screening cascades. The capacity of crystallography to directly screen fragment libraries is often underestimated, due to its supposed low-throughput and need for high-quality crystals. We applied crystallographic fragment screening to map the protein-binding site of the aspartic protease endothiapepsin by individual soaking experiments. Here, we report on 41 fragments binding to the catalytic dyad and adjacent specificity pockets. The analysis identifies already known warheads but also reveals hydrazide, pyrazole, or carboxylic acid fragments as novel functional groups binding to the dyad. A remarkable swapping of the S1 and S1' pocket between structurally related fragments is explained by either steric demand, required displacement of a well-bound water molecule, or changes of trigonal-planar to tetrahedral geometry of an oxygen functional group in a side chain. Some warheads simultaneously occupying both S1 and S1' are promising starting points for fragment-growing strategies.

Experimental Active-Site Mapping by Fragments: Hot Spots Remote from the Catalytic Center of Endothiapepsin

Successful optimization of a given lead scaffold requires thorough binding-site mapping of the target protein particular in regions remote from the catalytic center where high conservation across protein families is given. We screened a 361-entry fragment library for binding to the aspartic protease endothiapepsin by crystallography. This enzyme is frequently used as a surrogate for the design of renin and β-secretase inhibitors. A hit rate of 20% was achieved, providing 71 crystal structures. Here, we discuss 45 binding poses of fragments accommodated in pockets remote from the catalytic dyad. Three major hot spots are discovered in remote binding areas: Asp81, Asp119, and Phe291. Compared to the dyad binders, bulkier fragments occupy these regions. Many of the discovered fragments suggest an optimization concept on how to grow them into larger ligands occupying adjacent binding pockets that will possibly endow them with the desired selectivity for one given member of a protein family.

Six Biophysical Screening Methods Miss a Large Proportion of Crystallographically Discovered Fragment Hits: A Case Study

Fragment-based lead discovery (FBLD) has become a pillar in drug development. Typical applications of this method comprise at least two biophysical screens as prefilter and a follow-up crystallographic experiment on a subset of fragments. Clearly, structural information is pivotal in FBLD, but a key question is whether such a screening cascade strategy will retrieve the majority of fragment-bound structures. We therefore set out to screen 361 fragments for binding to endothiapepsin, a representative of the challenging group of aspartic proteases, employing six screening techniques and crystallography in parallel. Crystallography resulted in the very high number of 71 structures. Yet alarmingly, 44% of these hits were not detected by any biophysical screening approach. Moreover, any screening cascade, building on the results from two or more screening methods, would have failed to predict at least 73% of these hits. We thus conclude that, at least in the present case, the frequently applied biophysical prescreening filters deteriorate the number of possible X-ray hits while only the immediate use of crystallography enables exhaustive retrieval of a maximum of fragment structures, which represent a rich source guiding hit-to-lead-to-drug evolution.

Structures of endothiapepsin-fragment complexes from crystallographic fragment screening using a novel, diverse and affordable 96-compound fragment library

Crystallographic screening of the binding of small organic compounds (termed fragments) to proteins is increasingly important for medicinal chemistry-oriented drug discovery. To enable such experiments in a widespread manner, an affordable 96-compound library has been assembled for fragment screening in both academia and industry. The library is selected from already existing protein-ligand structures and is characterized by a broad ligand diversity, including buffer ingredients, carbohydrates, nucleotides, amino acids, peptide-like fragments and various drug-like organic compounds. When applied to the model protease endothiapepsin in a crystallographic screening experiment, a hit rate of nearly 10% was obtained. In comparison to other fragment libraries and considering that no pre-screening was performed, this hit rate is remarkably high. This demonstrates the general suitability of the selected compounds for an initial fragment-screening campaign. The library composition, experimental considerations and time requirements for a complete crystallographic fragment-screening campaign are discussed as well as the nine fully refined obtained endothiapepsin-fragment structures. While most of the fragments bind close to the catalytic centre of endothiapepsin in poses that have been observed previously, two fragments address new sites on the protein surface. ITC measurements show that the fragments bind to endothiapepsin with millimolar affinity.

Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts

Nonadditivity in protein–ligand affinity data represents highly instructive structure–activity relationship (SAR) features that indicate structural changes and have the potential to guide rational drug design. At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases. While structural causes for nonadditivity have been analyzed anecdotally, no systematic approaches to interpret and use nonadditivity prospectively have been developed yet. In this contribution, we lay the statistical framework for systematic analysis of nonadditivity in a SAR series. First, we develop a general metric to quantify nonadditivity. Then, we demonstrate the non-negligible impact of experimental uncertainty that creates apparent nonadditivity, and we introduce techniques to handle experimental uncertainty. Finally, we analyze public SAR data sets for strong nonadditivity and use recourse to the original publications and available X-ray structures to find structural explanations for the nonadditivity observed. We find that all cases of strong nonadditivity (ΔΔpK_i and ΔΔpIC₅₀ > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode. With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.