Structure based design of iminohydantoin BACE1 inhibitors: identification of an orally available, centrally active BACE1 inhibitor

Impact of protein conformations on binding free energy calculations in the beta-secretase 1 system

In binding free energy calculations, simulations must sample all relevant conformations of the system in order to obtain unbiased results. For instance, different ligands can bind to different metastable states of a protein, and if these protein conformational changes are not sampled in relative binding free energy calculations, the contribution of these states to binding is not accounted for and thus calculated binding free energies are inaccurate. In this work, we investigate the impact of different beta-sectretase 1 (BACE1) protein conformations obtained from x-ray crystallography on the binding of BACE1 inhibitors. We highlight how these conformational changes are not adequately sampled in typical molecular dynamics simulations. Furthermore, we show that insufficient sampling of relevant conformations induces substantial error in relative binding free energy calculations, as judged by a variation in calculated relative binding free energies up to 2 kcal/mol depending on the starting protein conformation. These results emphasize the importance of protein conformational sampling and pose this BACE1 system as a challenge case for further method development in the area of enhanced protein conformational sampling, either in combination with binding calculations or as an endpoint correction.

Improvements in Precision of Relative Binding Free Energy Calculations Afforded by the Alchemical Enhanced Sampling (ACES) Approach

Accurate in silico predictions of how strongly small molecules bind to proteins, such as those afforded by relative binding free energy (RBFE) calculations, can greatly increase the efficiency of the hit-to-lead and lead optimization stages of the drug discovery process. The success of such calculations, however, relies heavily on their precision. Here, we show that a recently developed alchemical enhanced sampling (ACES) approach can consistently improve the precision of RBFE calculations on a large and diverse set of proteins and small molecule ligands. The addition of ACES to conventional RBFE calculations lowered the average hysteresis by over 35% (0.3-0.4 kcal/mol) and the average replicate spread by over 25% (0.2-0.3 kcal/mol) across a set of 10 protein targets and 213 small molecules while maintaining similar or improved accuracy. We show in atomic detail how ACES improved convergence of several representative RBFE calculations through enhancing the sampling of important slowly transitioning ligand degrees of freedom.

Palladium-Catalyzed Cross-Coupling of Aryl Bromides and Chlorides with Trimethylsilylalkynes under Mild Conditions

Herein, we disclose a palladium-catalyzed cross-coupling of aryl bromides and chlorides with trimethylsilylalkynes under mild reaction conditions. This method utilizes commercially available and air stable palladium precatalysts and avoids the use of copper cocatalysts. Moreover, it allows for the synthesis of a wide range of disubstituted alkynes in high yields with excellent functional group tolerance. The utility of the developed method was further demonstrated via the late-stage alkynylation of pharmaceuticals and natural bioactive compounds.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

ADME profiling, molecular docking, DFT, and MEP analysis reveal cissamaline, cissamanine, and cissamdine from Cissampelos capensis L.f. as potential anti-Alzheimer's agents

The current pharmacotherapies for Alzheimer's disease (AD) demonstrate limited efficacy and are associated with various side effects, highlighting the need for novel therapeutic agents. Natural products, particularly from medicinal plants, have emerged as a significant source of potential neuroprotective compounds. In this context, Cissampelos capensis L.f., renowned for its medicinal properties, has recently yielded three new proaporphine alkaloids; cissamaline, cissamanine, and cissamdine. Despite their promising bioactive profiles, the biological targets of these alkaloids in the context of AD have remained unexplored. This study undertakes a comprehensive in silico examination of the binding affinity and molecular interactions of these alkaloids with human protein targets implicated in AD. The drug likeness and ADME analyses indicate favorable pharmacokinetic profiles for these compounds, suggesting their potential efficacy in targeting the central nervous system. Molecular docking studies indicate that cissamaline, cissamanine, and cissamdine interact with key AD-associated proteins. These interactions are comparable to, or in some aspects slightly less potent than, those observed with established AD drugs, highlighting their potential as novel therapeutic agents for Alzheimer's disease. Crucially, Density Functional Theory (DFT) calculations offer deep insights into the electronic and energetic characteristics of these alkaloids. These calculations reveal distinct electronic properties, with differences in total energy, binding energy, HOMO-LUMO gaps, dipole moments, and electrophilicity indices. Such variations suggest unique reactivity profiles and molecular stability, pertinent to their pharmacological potential. Moreover, Molecular Electrostatic Potential (MEP) analyses provide visual representations of the electrostatic characteristics of these alkaloids. The analyses highlight areas prone to electrophilic and nucleophilic attacks, indicating their potential for specific biochemical interactions. This combination of DFT and MEP results elucidates the intricate electronic, energetic, and electrostatic properties of these compounds, underpinning their promise as AD therapeutic agents. The in silico findings of this study shed light on the promising potential of cissamaline, cissamanine, and cissamdine as agents for AD treatment. However, further in vitro and in vivo studies are necessary to validate these theoretical predictions and to understand the precise mechanisms through which these alkaloids may exert their therapeutic effects.

Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization

Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.

Accelerated Enveloping Distribution Sampling to Probe the Presence of Water Molecules

Determining the presence of water molecules at protein-ligand interfaces is still a challenging task in free-energy calculations. The inappropriate placement of water molecules results in the stabilization of wrong conformational orientations of the ligand. With classical alchemical perturbation methods, such as thermodynamic integration (TI), it is essential to know the amount of water molecules in the active site of the respective ligands. However, the resolution of the crystal structure and the correct assignment of the electron density do not always lead to a clear placement of water molecules. In this work, we apply the one-step perturbation method named accelerated enveloping distribution sampling (AEDS) to determine the presence of water molecules in the active site by probing them in a fast and straightforward way. Based on these results, we combined the AEDS method with standard TI to calculate accurate binding free energies in the presence of buried water molecules. The main idea is to perturb the water molecules with AEDS such that they are allowed to alternate between regular water molecules and non-interacting dummy particles while treating the ligand with TI over an alchemical pathway. We demonstrate the use of AEDS to probe the presence of water molecules for six different test systems. For one of these, previous calculations showed difficulties to reproduce the experimental binding free energies, and here, we use the combined TI-AEDS approach to tackle these issues.

Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations

In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free-energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could, in principle, greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies correlate with experimental results with a weighted average of R² = 0.55 for the entire dataset. We also observe an overall root-mean-square error (RMSE) of 1.1 kcal/mol after shifting the zero-point of the simulation data to match the average experimental values. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The good performance of ABFEP in rank ordering compounds demonstrated in this work confirms it as a useful tool to improve the hit rates in virtual screening, thus facilitating hit discovery.

Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy

We developed an effective binding free energy prediction protocol which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations to substitute the specified atomic charges of force fields with quantum-mechanically recalculated ones at a proposed pose using a mining minima approach with the VeraChem mining minima engine. We tested this protocol using seven well-known targets with 147 different ligands and compared it with classical mining minima and the most popular binding free energy (BFE) methods using different metrics. Our new protocol, dubbed Qcharge-VM2, yielded an overall Pearson correlation of 0.86, which was better than all the methods examined. Qcharge-VM2 performed significantly better than implicit solvent-based methods, such as MM-GBSA and MM-PBSA, but not as good as explicit water-based free energy perturbation methods, such as FEP+, in terms of root-mean-square error, RMSE (1.75 kcal/mol) and mean unsigned error, MUE (1.39 kcal/mol) on a limited set of targets. However, our protocol is substantially less computationally demanding compared with FEP+. The combined accuracy and efficiency of our method can be valuable in drug discovery campaigns.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Computational Studies Applied to Linalool and Citronellal Derivatives Against Alzheimer's and Parkinson's Disorders: A Review with Experimental Approach

Alzheimer's and Parkinson's are neurodegenerative disorders that affect a great number of people around the world, seriously compromising the quality of life of individuals, due to motor and cognitive damage. In these diseases, pharmacological treatment is used only to alleviate symptoms. This emphasizes the need to discover alternative molecules for use in prevention. Using Molecular Docking, this review aimed to evaluate the anti-Alzheimer's and anti-Parkinson's activity of linalool and citronellal, as well as their derivatives. Before performing Molecular Docking simulations, the compounds' pharmacokinetic characteristics were evaluated. For Molecular Docking, 7 chemical compounds derived from citronellal, and 10 compounds derived from linalool, and molecular targets involved in Alzheimer's and Parkinson's pathophysiology were selected. According to the Lipinski rules, the compounds under study presented good oral absorption and bioavailability. For toxicity, some tissue irritability was observed. For Parkinson-related targets, the citronellal and linalool derived compounds revealed excellent energetic affinity for α-Synuclein, Adenosine Receptors, Monoamine Oxidase (MAO), and Dopamine D1 receptor proteins. For Alzheimer disease targets, only linalool and its derivatives presented promise against BACE enzyme activity. The compounds studied presented high probability of modulatory activity against the disease targets under study, and are potential candidates for future drugs.

The Invention of WM382, a Highly Potent PMIX/X Dual Inhibitor toward the Treatment of Malaria

Drug resistance to first-line antimalarials-including artemisinin-is increasing, resulting in a critical need for the discovery of new agents with novel mechanisms of action. In collaboration with the Walter and Eliza Hall Institute and with funding from the Wellcome Trust, a phenotypic screen of Merck's aspartyl protease inhibitor library identified a series of plasmepsin X (PMX) hits that were more potent than chloroquine. Inspired by a PMX homology model, efforts to optimize the potency resulted in the discovery of leads that, in addition to potently inhibiting PMX, also inhibit another essential aspartic protease, plasmepsin IX (PMIX). Further potency and pharmacokinetic profile optimization efforts culminated in the discovery of WM382, a very potent dual PMIX/X inhibitor with robust in vivo efficacy at multiple stages of the malaria parasite life cycle and an excellent resistance profile.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

Dual Inhibitors of AChE and BACE-1 for Reducing Aβ in Alzheimer's Disease: From In Silico to In Vivo

Alzheimer's disease (AD) is a complex and widespread condition, still not fully understood and with no cure yet. Amyloid beta (Aβ) peptide is suspected to be a major cause of AD, and therefore, simultaneously blocking its formation and aggregation by inhibition of the enzymes BACE-1 (β-secretase) and AChE (acetylcholinesterase) by a single inhibitor may be an effective therapeutic approach, as compared to blocking one of these targets or by combining two drugs, one for each of these targets. We used our ISE algorithm to model each of the AChE peripheral site inhibitors and BACE-1 inhibitors, on the basis of published data, and constructed classification models for each. Subsequently, we screened large molecular databases with both models. Top scored molecules were docked into AChE and BACE-1 crystal structures, and 36 Molecules with the best weighted scores (based on ISE indexes and docking results) were sent for inhibition studies on the two enzymes. Two of them inhibited both AChE (IC₅₀ between 4-7 μM) and BACE-1 (IC₅₀ between 50-65 μM). Two additional molecules inhibited only AChE, and another two molecules inhibited only BACE-1. Preliminary testing of inhibition by F681-0222 (molecule 2) on APPswe/PS1dE9 transgenic mice shows a reduction in brain tissue of soluble Aβ42.

Assessing the effect of forcefield parameter sets on the accuracy of relative binding free energy calculations

Software for accurate prediction of protein-ligand binding affinity can be a key enabling tool for small molecule drug discovery. Free energy perturbation (FEP) is a computational technique that can be used to compute binding affinity differences between molecules in a congeneric series. It has shown promise in reliably generating accurate predictions and is now widely used in the pharmaceutical industry. However, the high computational cost and use of commercial software, together with the technical challenges to setup, run, and analyze the simulations, limits the usage of FEP. Here, we use an automated FEP workflow which uses the open-source OpenMM package. To enable effective application of FEP, we compared the performance of different water models, partial charge assignments, and AMBER protein forcefields in eight benchmark test cases previously assembled for FEP validation studies.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.

BACE1: A Key Regulator in Alzheimer's Disease Progression and Current Development of its Inhibitors

BACKGROUND

Alzheimer's disease (AD) is a chronic neurodegenerative disease with no specific disease-modifying treatment. β-secretase (BACE1) is considered the potential and rationale target because it is involved in the rate-limiting step, which produces toxic Aβ₄₂ peptides that leads to deposits in the form of amyloid plaques extracellularly, resulting in AD.

OBJECTIVE

This study aims to discuss the role and implications of BACE1 and its inhibitors in the management of AD.

METHODS

We have searched and collected the relevant quality work from PubMed using the following keywords "BACE1", BACE2", "inhibitors", and "Alzheimer's disease". In addition, we included the work which discusses the role of BACE1 in AD and the recent work on its inhibitors.

RESULTS

In this review, we have discussed the importance of BACE1 in regulating AD progression and the current development of BACE1 inhibitors. However, the development of a BACE1 inhibitor is very challenging due to the large active site of BACE1. Nevertheless, some of the BACE1 inhibitors have managed to enter advanced phases of clinical trials, such as MK-8931 (Verubecestat), E2609 (Elenbecestat), AZD3293 (Lanabecestat), and JNJ-54861911 (Atabecestat). This review also sheds light on the prospect of BACE1 inhibitors as the most effective therapeutic approach in delaying or preventing AD progression.

CONCLUSION

BACE1 is involved in the progression of AD. The current ongoing or failed clinical trials may help understand the role of BACE1 inhibition in regulating the Aβ load and cognitive status of AD patients.

Bioisosteres of the Phenyl Ring: Recent Strategic Applications in Lead Optimization and Drug Design

The benzene moiety is the most prevalent ring system in marketed drugs, underscoring its historic popularity in drug design either as a pharmacophore or as a scaffold that projects pharmacophoric elements. However, introspective analyses of medicinal chemistry practices at the beginning of the 21st century highlighted the indiscriminate deployment of phenyl rings as an important contributor to the poor physicochemical properties of advanced molecules, which limited their prospects of being developed into effective drugs. This Perspective deliberates on the design and applications of bioisosteric replacements for a phenyl ring that have provided practical solutions to a range of developability problems frequently encountered in lead optimization campaigns. While the effect of phenyl ring replacements on compound properties is contextual in nature, bioisosteric substitution can lead to enhanced potency, solubility, and metabolic stability while reducing lipophilicity, plasma protein binding, phospholipidosis potential, and inhibition of cytochrome P450 enzymes and the hERG channel.

CHARMM-GUI Free Energy Calculator for Practical Ligand Binding Free Energy Simulations with AMBER

Alchemical free energy methods, such as free energy perturbation (FEP) and thermodynamic integration (TI), become increasingly popular and crucial for drug design and discovery. However, the system preparation of alchemical free energy simulation is an error-prone, time-consuming, and tedious process for a large number of ligands. To address this issue, we have recently presented CHARMM-GUI Free Energy Calculator that can provide input and postprocessing scripts for NAMD and GENESIS FEP molecular dynamics systems. In this work, we extended three submodules of Free Energy Calculator to work with the full suite of GPU-accelerated alchemical free energy methods and tools in AMBER, including input and postprocessing scripts. The BACE1 (β-secretase 1) benchmark set was used to validate the AMBER-TI simulation systems and scripts generated by Free Energy Calculator. The overall results of relatively large and diverse systems are almost equivalent with different protocols (unified and split) and with different timesteps (1, 2, and 4 fs), with R² > 0.9. More importantly, the average free energy differences between two protocols are small and reliable with four independent runs, with a mean unsigned error (MUE) below 0.4 kcal/mol. Running at least four independent runs for each pair with AMBER20 (and FF19SB/GAFF2.1/OPC force fields), we obtained a MUE of 0.99 kcal/mol and root-mean-square error of 1.31 kcal/mol for 58 alchemical transformations in comparison with experimental data. In addition, a set of ligands for T4-lysozyme was used to further validate our free energy calculation protocol whose results are close to experimental data (within 1 kcal/mol). In summary, Free Energy Calculator provides a user-friendly web-based tool to generate the AMBER-TI system and input files for high-throughput binding free energy calculations with access to the full set of GPU-accelerated alchemical free energy, enhanced sampling, and analysis methods in AMBER.

Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.

Visible-Light-Induced Photocatalytic Oxidative Decarboxylation of Cinnamic Acids to 1,2-Diketones

A concerted metallophotoredox catalysis has been realized for the efficient decarboxylative functionalization of α,β-unsaturated carboxylic acids with aryl iodides in the presence of perylene bisimide dye to afford 1,2-diketones.

RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback of a large number of poses that must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast pre-filtering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance that is better than that of the original RASPD method and traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

Hydantoin-bridged medium ring scaffolds by migratory insertion of urea-tethered nitrile anions into aromatic C-N bonds

Bicyclic or tricyclic nitrogen-containing heterocyclic scaffolds were constructed rapidly by intramolecular nucleophilic aromatic substitution of metallated nitriles tethered by a urea linkage to a series of electronically unactivated heterocyclic precursors. The substitution reaction constitutes a ring expansion, enabled by the conformationally constrained tether between the nitrile and the heterocycle. Attack of the metallated urea leaving group on the nitrile generates a hydantoin that bridges the polycyclic products. X-ray crystallography reveals ring-dependant strain within the hydantoin.

aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.

How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations?

With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (R), predictive index (PI), and Kendall's τ. The mean values of PI, R, and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.

Scope of β-Secretase (BACE1)-Targeted Therapy in Alzheimer's Disease: Emphasizing the Flavonoid Based Natural Scaffold for BACE1 Inhibition

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common form of dementia in the world. Studies report the presence of extracellular amyloid plaques consisting of β-amyloid peptide and intracellular tangles consisting of hyperphosphorylated tau proteins as the histopathological indicators of AD. The process of β-amyloid peptide generation by sequential cleavage of amyloid precursor protein by β-secretase (BACE1) and γ-secretase, followed by its aggregation to form amyloid plaques, is the mechanistic basis of the amyloid hypothesis. Other popular hypotheses related to the pathogenesis of AD include the tau hypothesis and the oxidative stress hypothesis. Various targets of the amyloid cascade are now in prime focus to develop drugs for AD. Many BACE1 inhibitors, β-amyloid aggregation inhibitors, and Aβ clearance strategies using monoclonal antibodies are in various stages of clinical trials. This review provides an in-depth evaluation of the role of BACE1 in disease pathogenesis and also highlights the therapeutic approaches developed to find more potent but less toxic inhibitors for BACE1, particularly emphasizing the natural scaffold as a nontoxic lead for BACE1 inhibition. Cellular targets and signaling cascades involving BACE1 have been highlighted to understand the physiological role of BACE1. This knowledge is extremely crucial to understand the toxicity evaluations for BACE1-targeted therapy. We have particularly highlighted the scope of flavonoids as a new generation of nontoxic BACE1 inhibitory scaffolds. The structure-activity relationship of BACE1 inhibition for this group of compounds has been highlighted to provide a guideline to design more selective highly potent inhibitors. The review aims to provide a holistic overview of BACE1-targeted therapy for AD that paves the way for future drug development.

Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF

Accurate prediction of the absolute or relative protein-ligand binding affinity is one of the major tasks in computer-aided drug design projects, especially in the stage of lead optimization. In principle, the alchemical free energy (AFE) methods such as thermodynamic integration (TI) or free-energy perturbation (FEP) can fulfill this task, but in practice, a lot of hurdles prevent them from being routinely applied in daily drug design projects, such as the demanding computing resources, slow computing processes, unavailable or inaccurate force field parameters, and difficult and unfriendly setting up and post-analysis procedures. In this study, we have exploited practical protocols of applying the CPU (central processing unit)-TI and newly developed GPU (graphic processing unit)-TI modules and other tools in the AMBER software package, combined with ff14SB/GAFF1.8 force fields, to conduct efficient and accurate AFE calculations on protein-ligand binding free energies. We have tested 134 protein-ligand complexes in total for four target proteins (BACE, CDK2, MCL1, and PTP1B) and obtained overall comparable performance with the commercial Schrodinger FEP+ program (WangJ. Am. Chem. Soc.2015, 137, 2695-2703). The achieved accuracy fits within the requirements for computations to generate effective guidance for experimental work in drug lead optimization, and the needed wall time is short enough for practical application. Our verified protocol provides a practical solution for routine AFE calculations in real drug design projects.

Acetylene Group, Friend or Foe in Medicinal Chemistry

The use of an acetylene (ethynyl) group in medicinal chemistry coincides with the launch of the Journal of Medicinal Chemistry in 1959. Since then, the acetylene group has been broadly exploited in drug discovery and development. As a result, it has become recognized as a privileged structural feature for targeting a wide range of therapeutic target proteins, including MAO, tyrosine kinases, BACE1, steroid receptors, mGlu5 receptors, FFA1/GPR40, and HIV-1 RT. Furthermore, a terminal alkyne functionality is frequently introduced in chemical biology probes as a click handle to identify molecular targets and to assess target engagement. This Perspective is divided into three parts encompassing: (1) the physicochemical properties of the ethynyl group, (2) the advantages and disadvantages of the ethynyl group in medicinal chemistry, and (3) the impact of the ethynyl group on chemical biology approaches.

Plasmepsin Inhibitors in Antimalarial Drug Discovery: Medicinal Chemistry and Target Validation (2000 to Present)

Plasmepsins represent novel antimalarial drug targets. However, plasmepsin-based antimalarial drug discovery efforts in the past 2 decades have generally suffered some drawbacks including lack of translatability of target inhibition to potent parasite inhibition in vitro and in vivo as well as poor selectivity over the related human aspartic proteases. Most studies reported in this period have over-relied on the use of hemoglobinase plasmepsins I-IV (particularly I and II) as targets for the new inhibitors even though these are known to be nonessential at the asexual stage of parasite development. Therefore, future antimalarial drug discovery efforts seeking to identify plasmepsin inhibitors should focus on incorporating non-hemoglobinase plasmepsins such as V, IX, and X in their screening in order to maximize chances of success. Additionally, there is need to go beyond just target enzymatic activity profiling to establishing cellular activity, physicochemical as well as drug metabolism and pharmacokinetics properties and finally in vivo proof-of-concept while ensuring selectivity over related human host proteases.

AD "Statin": Alzheimer's Disorder is a "Fast" Disease Preventable by Therapeutic Intervention Initiated Even Late in Life and Reversible at the Early Stages

The present study posits that Alzheimer's disorder is a "fast" disease. This is in sharp contrast to a view, prevailing until now, that Alzheimer's Disease (AD) is a quintessential "slow" disease that develops throughout the life as one prolonged process. According to this view, beta-amyloid (Aβ) is produced and secreted solely by the beta-amyloid precursor protein (βAPP) proteolytic/secretory pathway. As its extracellular levels increase, it triggers neurodegeneration starting relatively early in life. Damages accumulate and manifest, late in life in sporadic Alzheimer's Disease (SAD) cases, as AD symptoms. In familial AD (FAD) cases, where mutations in βAPP gene or in presenilins increase production of either common Aβ isoform or of its more toxic isoforms, neurodegeneration reaches critical threshold sooner and AD symptoms occur earlier in life, mostly in late 40s and 50s. There are currently no preventive AD therapies but if they were available, according to this viewpoint it would be largely futile to intervene late in life in case of potential SAD or at mid-age in cases of FAD because, although AD symptoms have not yet manifested, the damage has already occurred during the preceding decades. In this paradigm, to be effective, preventive therapeutic intervention should be initiated early in life. The outlook suggested by the present study is radically different. According to it, Alzheimer's disease evolves in two stages. The first stage is a slow process of intracellular beta-amyloid accumulation. It occurs via βAPP proteolytic/secretory pathway and cellular uptake of secreted Aβ common to Homo sapiens, including healthy humans, and to non-human mammals, and results neither in significant damage, nor in manifestation of the disease. The second stage occurs exclusively in humans, commences shortly before symptomatic onset of the disease, sharply accelerates the production and increases intracellular levels of Aβ that is not secreted but is retained intracellularly, generates significant damages, triggers AD symptoms, and is fast. It is driven by an Aβ generation pathway qualitatively and quantitatively different from βAPP proteolytic process and entirely independent of beta-amyloid precursor protein, and results in rapid and substantial intracellular accumulation of Aβ, consequent significant neurodegeneration, and symptomatic AD. In this paradigm, a preventive therapy for AD, an AD "statin", would be effective when initiated at any time prior to commencement of the second stage. Moreover, there are good reasons to believe that with a drug blocking βAPP-independent Aβ production pathway in the second stage, it would be possible not only to preempt the disease but also to stop and to reverse it even when early AD symptoms have already manifested. The present study posits a notion of AD as a Fast Disease, offers evidence for the occurrence of the AD-specific Aβ production pathway, describes cellular and molecular processes constituting an engine that drives Alzheimer's disease, and explains why non-human mammals are not susceptible to AD and why only a subset of humans develop the disease. It establishes that Alzheimer's disease is preventable by therapeutic intervention initiated even late in life, details a powerful mechanism underlying the disease, suggests that Aβ produced in the βAPP-independent pathway is retained intracellularly, elaborates why neither BACE inhibition nor Aβ immunotherapy are effective in treatment of AD and why intracellularly retained beta-amyloid could be the primary agent of neuronal death in Alzheimer's disease, necessitates generation of a novel animal AD model capable of producing Aβ via βAPP-independent pathway, proposes therapeutic targets profoundly different from previously pursued components of the βAPP proteolytic pathway, and provides conceptual rationale for design of drugs that could be used not only preemptively but also for treatment and reversal of the early stages of the disease.

Alzheimer's Disease is Driven by Intraneuronally Retained Beta-Amyloid Produced in the AD-Specific, βAPP-Independent Pathway: Current Perspective and Experimental Models for Tomorrow

A view of the origin and progression of Alzheimer's disease, AD, prevailing until now and formalized as the Amyloid Cascade Hypothesis theory, maintains that the disease is initiated by overproduction of beta-amyloid, Aβ, which is generated solely by the Aβ precursor protein, βAPP, proteolytic pathway and secreted from the cell. Consequent extracellular accumulation of Aβ triggers a cascade of molecular and cellular events leading to neurodegeneration that starts early in life, progresses as one prolonged process, builds up for decades, and culminates in symptomatic manifestations of the disease late in life. In this paradigm, a time window for commencement of therapeutic intervention is small and accessible only early in life. The outlook introduced in the present study is fundamentally different. It posits that the βAPP proteolytic/secretory pathway of Aβ production causes AD in humans no more than it does in either short- or long-lived non-human mammals that share this pathway with humans, accumulate beta-amyloid as they age, but do not develop the disease. Alzheimer's disease, according to this outlook, is driven by an additional powerful AD-specific pathway of Aβ production that operates in affected humans, is completely independent of the βAPP precursor, and is not available in non-human mammals. The role of the βAPP proteolytic pathway in the disease in humans is activation of this additional AD-specific Aβ production pathway. This occurs through accumulation of intracellular Aβ, primarily via ApoE-assisted cellular uptake of secreted beta-amyloid, but also through retention of a fraction of Aβ produced in the βAPP proteolytic pathway. With time, accumulated intracellular Aβ triggers mitochondrial dysfunction. In turn, cellular stresses associated with mitochondrial dysfunction, including ER stress, activate a second, AD-specific, Aβ production pathway: Asymmetric RNA-dependent βAPP mRNA amplification; animal βAPP mRNA is ineligible for this process. In this pathway, every conventionally produced βAPP mRNA molecule serves potentially as a template for production of severely 5'-truncated mRNA encoding not the βAPP but its C99 fragment (hence "asymmetric"), the immediate precursor of Aβ. Thus produced, N-terminal signal peptide-lacking C99 is processed not in the secretory pathway on the plasma membrane, but at the intracellular membrane sites, apparently in a neuron-specific manner. The resulting Aβ is, therefore, not secreted but is retained intraneuronally and accumulates rapidly within the cell. Increased levels of intracellular Aβ augment mitochondrial dysfunction, which, in turn, sustains the activity of the βAPP mRNA amplification pathway. These self-propagating mutual Aβ overproduction/mitochondrial dysfunction feedback cycles constitute a formidable two-stroke engine, an engine that drives Alzheimer's disease. The present outlook envisions Alzheimer's disorder as a two-stage disease. The first stage is a slow process of intracellular beta-amyloid accumulation. It results neither in significant neurodegenerative damage, nor in manifestation of the disease. The second stage commences with the activation of the βAPP mRNA amplification pathway shortly before symptomatic onset of the disease, sharply increases the rate of Aβ generation and the extent of its intraneuronal accumulation, produces significant damages, triggers AD symptoms, and is fast. In this paradigm, the time window of therapeutic intervention is wide open, and preventive treatment can be initiated any time, even late in life, prior to commencement of the second stage of the disease. Moreover, there are good reasons to believe that with a drug blocking the βAPP mRNA amplification pathway, it would be possible not only to preempt the disease but also to stop and to reverse it even when early AD symptoms have already manifested. There are numerous experimental models of AD, all based on a notion of the exceptionality of βAPP proteolytic/secretory pathway in Aβ production in the disease. However, with no drug even remotely effective in Alzheimer's disease, a long list of candidate drugs that succeeded remarkably in animal models, yet failed utterly in human clinical trials of potential AD drugs, attests to the inadequacy of currently employed AD models. The concept of a renewable supply of beta-amyloid, produced in the βAPP mRNA amplification pathway and retained intraneuronally in Alzheimer's disease, explains spectacular failures of both BACE inhibition and Aβ-immunotherapy in human clinical trials. This concept also forms the basis of a new generation of animal and cell-based experimental models of AD, described in the present study. These models incorporate Aβ- or C99-encoding mRNA amplification pathways of Aβ production, as well as intracellular retention of their product, and can support not only further investigation of molecular mechanisms of AD but also screening for and testing of candidate drugs aimed at therapeutic targets suggested by the present study.

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.

How Significant Are Unusual Protein-Ligand Interactions? Insights from Database Mining

We present a new approach to derive interaction propensities of protein-ligand atom pairs from mining of the Protein Data Bank. To ensure solid statistics, we use a line-of-sight contact filter and normalize the observed frequency of hits by a statistical null model based on exposed surface areas of atom types in the protein-ligand binding site. This allows us to investigate which intermolecular interactions and geometries are found more often than expected by chance in protein-ligand complexes. We focus our study on some of the unusual interactions that were postulated to be favorable, including σ-hole bonding of halogen and sulfur atoms, weak hydrogen bonding with fluorine as acceptor, and different types of dipolar interactions. Our results confirm some and challenge other common assumptions on these interactions and highlight other contact types that are yet underexplored in structure-based drug design.

Water in protein hydration and ligand recognition

This review describes selected basics of water in biomolecular recognition. We focus on a qualitative understanding of the most important physical aspects, how these change in magnitude between bulk water and protein environment, and how the roles that water plays for proteins arise from them. These roles include mechanical support, thermal coupling, dielectric screening, mass and charge transport, and the competition with a ligand for the occupation of a binding site. The presence or absence of water has ramifications that range from the thermodynamic binding signature of a single ligand up to cellular survival. The large inhomogeneity in water density, polarity and mobility around a solute is hard to assess in experiment. This is a source of many difficulties in the solvation of protein models and computational studies that attempt to elucidate or predict ligand recognition. The influence of water in a protein binding site on the experimental enthalpic and entropic signature of ligand binding is still a point of much debate. The strong water‐water interaction in enthalpic terms is counteracted by a water molecule's high mobility in entropic terms. The complete arrest of a water molecule's mobility sets a limit on the entropic contribution of a water displacement process, while the solvent environment sets limits on ligand reactivity.

BACE1 inhibitors: Current status and future directions in treating Alzheimer's disease

Alzheimer's disease (AD) is an irreversible, progressive neurodegenerative brain disorder with no current cure. One of the important therapeutic approaches of AD is the inhibition of β-site APP cleaving enzyme-1 (BACE1), which is involved in the rate-limiting step of the cleavage process of the amyloid precursor protein (APP) leading to the generation of the neurotoxic amyloid β (Aβ) protein after the γ-secretase completes its function. The produced insoluble Aβ aggregates lead to plaques deposition and neurodegeneration. BACE1 is, therefore, one of the attractive targets for the treatment of AD. This approach led to the development of potent BACE1 inhibitors, many of which were advanced to late stages in clinical trials. Nonetheless, the high failure rate of lead drug candidates targeting BACE1 brought to the forefront the need for finding new targets to uncover the mystery behind AD. In this review, we aim to discuss the most promising classes of BACE1 inhibitors with a description and analysis of their pharmacodynamic and pharmacokinetic parameters, with more focus on the lead drug candidates that reached late stages of clinical trials, such as MK8931, AZD-3293, JNJ-54861911, E2609, and CNP520. In addition, the manuscript discusses the safety concerns and insignificant physiological effects, which were highlighted for the most successful BACE1 inhibitors. Furthermore, the review demonstrates with increasing evidence that despite tremendous efforts and promising results conceived with BACE1 inhibitors, the latest studies suggest that their clinical use for treating Alzheimer's disease should be reconsidered. Finally, the review sheds light on alternative therapeutic options for targeting AD.

Protein-Protein Interactions and Aggregation Inhibitors in Alzheimer's Disease

BACKGROUND

Alzheimer's Disease (AD), a multifaceted disorder, involves complex pathophysiology and plethora of protein-protein interactions. Thus such interactions can be exploited to develop anti-AD drugs.

OBJECTIVE

The interaction of dynamin-related protein 1, cellular prion protein, phosphoprotein phosphatase 2A and Mint 2 with amyloid β, etc., studied recently, may have critical role in progression of the disease. Our objective has been to review such studies and their implications in design and development of drugs against the Alzheimer's disease.

METHODS

Such studies have been reviewed and critically assessed.

RESULTS

Review has led to show how such studies are useful to develop anti-AD drugs.

CONCLUSION

There are several PPIs which are current topics of research including Drp1, Aβ interactions with various targets including PrPC, Fyn kinase, NMDAR and mGluR5 and interaction of Mint2 with PDZ domain, etc., and thus have potential role in neurodegeneration and AD. Finally, the multi-targeted approach in AD may be fruitful and opens a new vista for identification and targeting of PPIs in various cellular pathways to find a cure for the disease.

Hybrid approach to sieve out natural compounds against dual targets in Alzheimer's Disease

Excess Aβ production by the key protease BACE1, results in Aβ aggregation, forming amyloid plaques, all of which contribute to the pathogenesis of Alzheimer’s disease. Besides the multi-factorial nature of the disease, the diversity in the size and shape of known ligands that bind to the active site of BACE1, that is the flexibility of the enzyme, pose a serious challenge for the identification of drug candidates. To address the issue of receptor flexibility we have carried out ensemble docking with multiple receptor conformations. Therein, two representative structures each from closed and semi-open BACE1 conformations were selected for virtual screening to identify compounds that bind to the active site of both the conformations. These outperformed compounds were ranked using pharmacophore models generated by a ligand-based approach, for the identification of BACE1 inhibitors. The inhibitors were further predicted for anti-amyloidogenic activity using a QSAR model already established by our group thus enlisting compounds with dual potency. BACE1 inhibitory and anti-amyloidogenic activity for the commercially available compounds were validated using in vitro studies. Thus, incorporation of receptor flexibility in BACE1 through ensemble docking in conjunction with structure and ligand-based approach for screening might act as an effective protocol for obtaining promising scaffolds against AD.

Bennett acceptance ratio method to calculate the binding free energy of BACE1 inhibitors: Theoretical model and design of new ligands of the enzyme

In recent years, the design, development, and evaluation of several inhibitors of the BACE1 enzyme, as part of Alzheimer's treatment, have gathered the scientific community's interest. Here, a linear regression model was built using binding free energy calculations through the Bennett acceptance ratio method for 20 known inhibitors of the BACE1 enzyme, with a Pearson coefficient of R = 0.88 and R²  = 0.78. The validation of this model was verified employing eight additional random inhibitors, which also gave a linear correlation with R = 0.97 and R²  = 0.93. Furthermore, this linear regression model was also used for proposing the structure of four potential BACE1 inhibitors, and the most active of them gave a theoretical K_d  = 10 nM. However, these molecules have not been synthesized yet. Our team used a total time of more than 800 ns for the Molecular Dynamics to carry out this study, and all the software used were freely available.

Computational Studies Applied to Flavonoids against Alzheimer's and Parkinson's Diseases

Neurodegenerative diseases, such as Parkinson's and Alzheimer's, are understood as occurring through genetic, cellular, and multifactor pathophysiological mechanisms. Several natural products such as flavonoids have been reported in the literature for having the capacity to cross the blood-brain barrier and slow the progression of such diseases. The present article reports on in silico enzymatic target studies and natural products as inhibitors for the treatment of Parkinson's and Alzheimer's diseases. In this study we evaluated 39 flavonoids using prediction of molecular properties and in silico docking studies, while comparing against 7 standard reference compounds: 4 for Parkinson's and 3 for Alzheimer's. Osiris analysis revealed that most of the flavonoids presented no toxicity and good absorption parameters. The Parkinson's docking results using selected flavonoids as compared to the standards with four proteins revealed similar binding energies, indicating that the compounds 8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, capensinidin, and rosinidin are potential leads with the necessary pharmacological and structural properties to be drug candidates. The Alzheimer's docking results suggested that seven of the 39 flavonoids studied, being those with the best molecular docking results, presenting no toxicity risks, and having good absorption rates (8-prenylnaringenin, europinidin, epicatechin gallate, homoeriodictyol, aspalathin, butin, and norartocarpetin) for the targets analyzed, are the flavonoids which possess the most adequate pharmacological profiles.