Acetylcholinesterase: A Versatile Template to Coin Potent Modulators of Multiple Therapeutic Targets.

ConspectusThe enzyme acetylcholinesterase (AChE) hydrolyzes the neurotransmitter acetylcholine (ACh) at cholinergic synapses of the peripheral and central nervous system. Thus, it is a prime therapeutic target for diseases that occur with a cholinergic deficit, prominently Alzheimer's disease (AD). Working at a rate near the diffusion limit, it is considered one of nature's most efficient enzymes. This is particularly meritorious considering that its catalytic site is buried at the bottom of a 20-Å-deep cavity, which is preceded by a bottleneck with a diameter shorter than that of the trimethylammonium group of ACh, which has to transit through it. Not only the particular architecture and amino acid composition of its active site gorge enable AChE to largely overcome this potential drawback, but it also offers plenty of possibilities for the design of novel inhibitor drug candidates.In this Account, we summarize our different approaches to colonize the vast territory of the AChE gorge in the pursuit of increased occupancy and hence of inhibitors with increased affinity. We pioneered the use of molecular hybridization to design inhibitors with extended binding at the CAS, reaching affinities among the highest reported so far. Further application of molecular hybridization to grow CAS extended binders by attaching a PAS-binding moiety through suitable linkers led to multisite inhibitors that span the whole length of the gorge, reaching the PAS and even interacting with midgorge residues. We show that multisite AChE inhibitors can also be successfully designed the other way around, by starting with an optimized PAS binder and then colonizing the gorge and CAS. Molecular hybridization from a multicomponent reaction-derived PAS binder afforded a single-digit picomolar multisite AChE inhibitor with more than 1.5 million-fold increased potency relative to the initial hit. This illustrates the powerful alliance between molecular hybridization and gorge occupancy for designing potent AChE inhibitors.Beyond AChE, we show that the stereoelectronic requirements imposed by the AChE gorge for multisite binding have a templating effect that leads to compounds that are active in other key biological targets in AD and other neurological and non-neurological diseases, such as BACE-1 and the aggregation of amyloidogenic proteins (ß-amyloid, tau, α-synuclein, prion protein, transthyretin, and human islet amyloid polypeptide). The use of known pharmacophores for other targets as the PAS-binding motif enables the rational design of multitarget agents with multisite binding within AChE and activity against a variety of targets or pathological events, such as oxidative stress and the neuroinflammation-modulating enzyme soluble epoxide hydrolase, among others.We hope that our results can contribute to the development of drug candidates that can modify the course of neurodegeneration and may inspire future works that exploit the power of molecular hybridization in other proteins featuring large cavities.

Binding of Cholesterol to the N-Terminal Domain of the NPC1L1 Transporter: Analysis of the Epimerization-Related Binding Selectivity and Loop Mutations.

Cholesterol is a fat-like substance with a pivotal physiological relevance in humans, and its homeostasis is tightly regulated by various cellular processes, including the import in the small intestine and the reabsorption in the biliary ducts by the Niemann-Pick C1 Like 1 (NPC1L1) importer. NPC1L1 can mediate the absorption of a variety of sterols but strikingly exhibits a large sensitivity to cholesterol epimerization. This study examines the molecular basis of the epimerization-related selective binding of cholesterol by combining extended unbiased molecular dynamics simulations of the apo and holo species of the N-terminal domain of wild-type NPC1L1, in conjunction with relative binding free energy, umbrella sampling, and well-tempered metadynamics calculations. The analysis of the results discloses the existence of two distinct binding modes for cholesterol and epi-cholesterol. The former binds deeper in the cavity, forming key hydrogen-bond interactions with Q95, S56, and a water molecule. In contrast, epi-cholesterol is shifted ca. 3 Å to the mouth of the cavity and the transition to the Q95 site is prevented by an energetic barrier of 4.1 kcal·mol-1. Thus, the configuration of the hydroxyl group of cholesterol, together with the presence of a structural water molecule, is a key feature for effective absorption. Finally, whereas these findings may seemingly be challenged by single-point mutations that impair cholesterol transport but have a mild impact on the binding of cholesterol to the Q95 binding site, our results reveal that they have a drastic influence on the conformational landscape of the α8/ß7 loop in the apo species compared to the wild-type protein. Overall, the results give support to the functional role played by the α8/ß7 loop in regulating the access of ligands to NPC1L1, and hence to interpreting the impact of these mutations on diseases related to disruption of sterol absorption, paving the way to understanding certain physiological dysfunctions.

On the relevance of query definition in the performance of 3D ligand-based virtual screening.

Ligand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E+ database and a Morgan Fingerprint filtered version (denoted DUD-E+-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS ), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.

Screening and Biological Evaluation of Soluble Epoxide Hydrolase Inhibitors: Assessing the Role of Hydrophobicity in the Pharmacophore-Guided Search of Novel Hits.

The human soluble epoxide hydrolase (sEH) is a bifunctional enzyme that modulates the levels of regulatory epoxy lipids. The hydrolase activity is carried out by a catalytic triad located at the center of a wide L-shaped binding site, which contains two hydrophobic subpockets at both sides. On the basis of these structural features, it can be assumed that desolvation is a major factor in determining the maximal achievable affinity that can be attained for this pocket. Accordingly, hydrophobic descriptors may be better suited to the search of novel hits targeting this enzyme. This study examines the suitability of quantum mechanically derived hydrophobic descriptors in the discovery of novel sEH inhibitors. To this end, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophores were generated by combining electrostatic and steric or alternatively hydrophobic and hydrogen-bond parameters in conjunction with a tailored list of 76 known sEH inhibitors. The pharmacophore models were then validated by using two external sets chosen (i) to rank the potency of four distinct series of compounds and (ii) to discriminate actives from decoys, using in both cases datasets taken from the literature. Finally, a prospective study was performed including a virtual screening of two chemical libraries to identify new potential hits, which were subsequently experimentally tested for their inhibitory activity on human, rat, and mouse sEH. The use of hydrophobic-based descriptors led to the identification of six compounds as inhibitors of the human enzyme with IC50 < 20 nM, including two with IC50 values of 0.4 and 0.7 nM. The results support the use of hydrophobic descriptors as a valuable tool in the search of novel scaffolds that encode a proper hydrophilic/hydrophobic distribution complementary to the target's binding site.

Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models.

In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between n-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, this study reports the experimental toluene/water partition coefficients (log Ptol/w) for a series of 16 drugs that were selected as an external test set in the framework of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) blind challenge. This external set has been used by the computational community to calibrate their methods in the current edition (SAMPL9) of this contest. Furthermore, the study also investigates the performance of two computational strategies for the prediction of log Ptol/w. The first relies on the development of two machine learning (ML) models, which are built up by combining the selection of 11 molecular descriptors in conjunction with either the multiple linear regression (MLR) or the random forest regression (RFR) model to target a dataset of 252 experimental log Ptol/w values. The second consists of the parametrization of the IEF-PCM/MST continuum solvation model from B3LYP/6-31G(d) calculations to predict the solvation free energies of 163 compounds in toluene and benzene. The performance of the ML and IEF-PCM/MST models has been calibrated against external test sets, including the compounds that define the SAMPL9 log Ptol/w challenge. The results are used to discuss the merits and weaknesses of the two computational approaches.

Molecular Basis for Non-Covalent, Non-Competitive FAAH Inhibition.

Fatty acid amide hydrolase (FAAH) plays a key role in the control of cannabinoid signaling and it represents a promising therapeutic strategy for the treatment of a wide range of diseases, including neuropathic pain and chronic inflammation. Starting from kinetics experiments carried out in our previous work for the most potent inhibitor 2-amino-3-chloropyridine amide (TPA14), we have investigated its non-competitive mechanism of action using molecular dynamics, thermodynamic integration and QM-MM/GBSA calculations. The computational studies highlighted the impact of mutations on the receptor binding pockets and elucidated the molecular basis of the non-competitive inhibition mechanism of TPA14, which prevents the endocannabinoid anandamide (AEA) from reaching its pro-active conformation. Our study provides a rationale for the design of non-competitive potent FAAH inhibitors for the treatment of neuropathic pain and chronic inflammation.

Azobioisosteres of Curcumin with Pronounced Activity against Amyloid Aggregation, Intracellular Oxidative Stress, and Neuroinflammation.

Many (poly-)phenolic natural products, for example, curcumin and taxifolin, have been studied for their activity against specific hallmarks of neurodegeneration, such as amyloid-ß 42 (Aß42) aggregation and neuroinflammation. Due to their drawbacks, arising from poor pharmacokinetics, rapid metabolism, and even instability in aqueous medium, the biological activity of azobenzene compounds carrying a pharmacophoric catechol group, which have been designed as bioisoteres of curcumin has been examined. Molecular simulations reveal the ability of these compounds to form a hydrophobic cluster with Aß42, which adopts different folds, affecting the propensity to populate fibril-like conformations. Furthermore, the curcumin bioisosteres exceeded the parent compound in activity against Aß42 aggregation inhibition, glutamate-induced intracellular oxidative stress in HT22 cells, and neuroinflammation in microglial BV-2 cells. The most active compound prevented apoptosis of HT22 cells at a concentration of 2.5 µm (83 % cell survival), whereas curcumin only showed very low protection at 10 µm (21 % cell survival).

Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations.

Covalent inhibitors are assuming central importance in drug discovery projects, especially in this pandemic scenario. Many research groups have focused their attention on inhibiting viral proteases or human proteases such as cathepsin L (hCatL). The inhibition of these critical enzymes may impair viral replication. However, molecular modeling of covalent ligands is challenging since covalent and noncovalent ligand-bound states must be considered in the binding process. In this work, we evaluated the suitability of free energy perturbation (FEP) calculations as a tool for predicting the binding affinity of reversible covalent inhibitors of hCatL. Our strategy relies on the relative free energy calculated for both covalent and noncovalent complexes and the free energy changes have been compared with experimental data for eight nitrile-based inhibitors, including three new inhibitors of hCatL. Our results demonstrate that the covalent complex can be employed to properly rank the inhibitors. Nevertheless, a comparison of the free energy changes in both noncovalent and covalent states is valuable to interpret the effect triggered by the formation of the covalent bond on the interactions played by functional groups distant from the warhead. Overall, FEP can be employed as a powerful predictor tool in developing and understanding the activity of reversible covalent inhibitors.

Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model.

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pKa were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pKa values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method.

Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening.

The accuracy of structure-based (SB) virtual screening (VS) is heavily affected by the scoring function used to rank a library of screened compounds. Even in cases where the docked pose agrees with the experimental binding mode of the ligand, the limitations of current scoring functions may lead to sensible inaccuracies in the ability to discriminate between actives and inactives. In this context, the combination of SB and ligand-based (LB) molecular similarity may be a promising strategy to increase the hit rates in VS. This study explores different strategies that aim to exploit the synergy between LB and SB methods in order to mitigate the limitations of these techniques, and to enhance the performance of VS studies by means of a balanced combination between docking scores and three-dimensional (3D) similarity. Particularly, attention is focused to the use of measurements of molecular similarity with PharmScreen, which exploits the 3D distribution of atomic lipophilicity determined from quantum mechanical-based continuum solvation calculations performed with the MST model, in conjunction with three docking programs: Glide, rDock, and GOLD. Different strategies have been explored to combine the information provided by docking and similarity measurements for re-ranking the screened ligands. For a benchmarking of 44 datasets, including 41 targets, the hybrid methods increase the identification of active compounds, according to the early (ROCe%) and total (AUC) enrichment metrics of VS, compared to pure LB and SB methods. Finally, the hybrid approaches are also more effective in enhancing the chemical diversity of active compounds. The datasets employed in this work are available in https://github.com/Pharmacelera/Molecular-Similarity-and-Docking.

Prediction of the n-octanol/water partition coefficients in the SAMPL6 blind challenge from MST continuum solvation calculations.

The IEFPCM/MST continuum solvation model is used for the blind prediction of n-octanol/water partition of a set of 11 fragment-like small molecules within the SAMPL6 Part II Partition Coefficient Challenge. The partition coefficient of the neutral species (log P) was determined using an extended parametrization of the B3LYP/6-31G(d) version of the Miertus-Scrocco-Tomasi continuum solvation model in n-octanol. Comparison with the experimental data provided for partition coefficients yielded a root-mean square error (rmse) of 0.78 (log P units), which agrees with the accuracy reported for our method (rmse = 0.80) for nitrogen-containing heterocyclic compounds. Out of the 91 sets of log P values submitted by the participants, our submission is within those with an rmse < 1 and among the four best ranked physical methods. The largest errors involve three compounds: two with the largest positive deviations (SM13 and SM08), and one with the largest negative deviations (SM15). Here we report the potentiometric determination of the log P for SM13, leading to a value of 3.62 ± 0.02, which is in better agreement with most empirical predictions than the experimental value reported in SAMPL6. In addition, further inclusion of several conformations for SM08 significantly improved our results. Inclusion of these refinements led to an overall error of 0.51 (log P units), which supports the reliability of the IEFPCM/MST model for predicting the partitioning of neutral compounds.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches.

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

On the Binding of Congo Red to Amyloid Fibrils.

Amyloids are characterized by their capacity to bind Congo red (CR), one of the most used amyloid-specific dyes. The structural features of CR binding were unknown for years, mainly because of the lack of amyloid structures solved at high resolution. In the last few years, solid-state NMR spectroscopy enabled the determination of the structural features of amyloids, such as the HET-s prion forming domain (HET-s PFD), which also has recently been used to determine the amyloid-CR interface at atomic resolution. Herein, we combine spectroscopic data with molecular docking, molecular dynamics, and excitonic quantum/molecular mechanics calculations to examine and rationalize CR binding to amyloids. In contrast to a previous assumption on the binding mode, our results suggest that CR binding to the HET-s PFD involves a cooperative process entailing the formation of a complex with 1:1 stoichiometry. This provides a molecular basis to explain the bathochromic shift in the maximal absorbance wavelength when CR is bound to amyloids.

Ligand Binding Rate Constants in Heme Proteins Using Markov State Models and Molecular Dynamics Simulations.

Computer simulation studies of the molecular basis for ligand migration in proteins allow the description of key events such as the transition between docking sites, displacement of existing ligands and solvent molecules, and open/closure of specific "gates", among others. In heme proteins, ligand migration from the solvent to the active site preludes the binding to the heme iron and triggers different functions. In this work, molecular dynamics simulations, a Markov State Model of migration and empirical kinetic equations are combined to study the migration of O2 and NO in two truncated hemoglobins of Mycobacterium tuberculosis (Mt-TrHbN and Mt-TrHbO). For Mt-TrHbN, we show that the difference in the association constant in the oxy and deoxy states relies mainly in the displacement of water molecules anchored in the distal cavity in the deoxy form. The results here provide a valuable approach to study ligand migration in globins.

Understanding the Mechanism of Direct Activation of AMP-Kinase: Toward a Fine Allosteric Tuning of the Kinase Activity.

Mammalian AMP-activated protein kinase (AMPK) is a Ser/Thr protein kinase with a key role as a sensor in cellular energy homeostasis. It has a major role in numerous metabolic disorders, such as type 2 diabetes, obesity, and cancer, and hence it has gained progressive interest as a potential therapeutic target. AMPK is a heterotrimeric enzyme composed by an α-catalytic subunit and two regulatory subunits, ß and γ. It is regulated by several mechanisms, including indirect activators such as metformin and direct activators such as compound A-769662. The crystal structure of AMPK bound to A-769662 has been recently reported, suggesting a hypothetical allosteric mechanism of AMPK activation assisted by phosphorylated Ser108 at the ß-subunit. Here, we have studied the direct activation mechanism of A-769662 by means of molecular dynamics simulations, suggesting that the activator may act as a glue, coupling the dynamical motion of the ß-subunit and the N-terminal domain of the α-subunit, and assisting the preorganization of the ATP-binding site. This is achieved through the formation of an allosteric network that connects the activator and ATP-binding sites, particularly through key interactions formed between αAsp88 and ßArg83 and between ßpSer108 and αLys29. Overall, these studies shed light into key mechanistic determinants of the allosteric regulation of this cellular energy sensor, and pave the way for the fine-tuning of the rational design of direct activators of this cellular energy sensor.

Thermal Stability of Globins: Implications of Flexibility and Heme Coordination Studied by Molecular Dynamics Simulations.

Proteins are sensitive to temperature, and abrupt changes in the normal temperature conditions can have a profound impact on both structure and function, leading to protein unfolding. However, the adaptation of certain organisms to extreme conditions raises questions about the structural features that permit the structure and function of proteins to be preserved under these adverse conditions. To gain insight into the molecular basis of protein thermostability in the globin family, we have examined three representative examples: human neuroglobin, horse heart myoglobin, and Drosophila hemoglobin, which differ in their melting temperatures and coordination states of the heme iron in the absence of external ligands. In order to elucidate the possible mechanisms that govern the thermostability of these proteins, microsecond-scale classical molecular dynamics simulations were performed at different temperatures. Structural fluctuations and essential dynamics were analyzed, indicating that the flexibility of the CD region, which includes the two short C and D helixes and the connecting CD loop, is directly related to the thermostability. We observed that a larger inherent flexibility of the protein produces higher thermostability, probably concentrating the thermal fluctuations observed at high temperature in flexible regions, preventing unfolding. Globally, the results of this work improve our understanding of thermostability in the globin family.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes-5.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes is a series of Editorials which is published on a biannual basis by the Editorial Board of the Medicinal Chemistry section of the journal Molecules [...].

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes-6.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes is a series of Editorials that is published on a biannual basis by the Editorial Board of the Medicinal Chemistry section of the journal Molecules [...].

Computational Study of the Aza-Michael Addition of the Flavonoid (+)-Taxifolin in the Inhibition of ß-Amyloid Fibril Aggregation.

Inhibition of abnormal protein self-aggregation is an attractive strategy against amyloidogenic diseases, but has found limited success due to the complexity of protein self-assembly, the absence of fully reproducible aggregation assays, and the scarce knowledge of the inhibition mechanisms by small molecules. In this context, catechol-containing compounds may lead to covalent adducts with amyloid fibrils that interfere with the aggregation process. In particular, the covalent adduct formed between the oxidized form of (+)-taxifolin and an ß-amyloid (Aß42) suggests the involvement of a specific recognition motif that enables the chemical reaction with Aß42. In this study, we have examined the mechanisms implicated in the aza-Michael addition of the o-quinone species of (+)-taxifolin with Aß42 fibrils. The results support the binding of (+)-taxifolin to the hydrophobic groove delimited by the edges defined by Lys16 and Glu22 residues in the fibril. The chemical reaction proceeds through the nucleophilic attack of the deprotonated amino group of a Lys16 residue in a process activated by the interaction between the o-quinone ring with a vicinal Lys16 residue, as well as by a water-assisted proton transfer, which is the rate-limiting step of the reaction. This specific inhibition mechanism, which may explain the enhanced anti-aggregating activity of oxidized flavonoids compared to fresh compounds, holds promise for developing disease-modifying therapies.

Multiple Multicomponent Reactions: Unexplored Substrates, Selective Processes, and Versatile Chemotypes in Biomedicine.

Multiple multicomponent reactions rapidly assemble complex structures. Despite being very productive, the lack of selectivity and the reduced number of viable transformations restrict their general application in synthesis. Hereby, we describe a rationale for a selective version of these processes based in the preferential generation of intermediates which are less reactive than the initial substrates. In this way, applying the Groebke-Blackburn-Bienaymé reaction on a range of α-polyamino-polyazines, we prepared a family compact heterocyclic scaffolds with relevant applications in medicinal and biological chemistry (live cell imaging probes, selective binders for DNA quadruplexes, and antiviral agents against human adenoviruses). The approach has general character and yields complex molecular targets in a selective, tunable and direct manner.