Free Energy Calculations Reveal the Origin of Binding Preference for Aminoadamantane Blockers of Influenza A/M2TM Pore

Conformational heterogeneity and structural features for function of the prototype viroporin influenza AM2

The 97-residue influenza A matrix 2 (ΑM2) protein, a prototype for viroporins, transports protons through water molecules and His37. We discuss structural biology and molecular biophysics experiments and some functional assays that have transformed over 40 years our understanding of the structure and function of AM2. The structural studies on ΑM2 have been performed with different conditions (pH, temperature, lipid, constructs) and using various protein constructs, e.g., AM2 transmembrane (AM2TM) domain, AM2 conductance domain (AM2CD), ectodomain-containing or ectodomain-truncated, AM2 full length (AM2FL) and aimed to describe the different conformations and structural details that are necessary for the stability and function of AM2. However, the conclusions from these experiments appeared sometimes ambiguous and caused exciting debates. This was not due to inaccurate measurements, but instead because of the different membrane mimetic environment used, e.g., detergent, micelles or phospholipid bilayer, the method (e.g., X-ray crystallography, solid state NMR, solution NMR, native mass spectrometry), the used protein construct (e.g., AM2TM or AM2CD), or the amino acids residues to follow observables (e.g., NMR chemical shifts). We present these results according to the different used biophysical methods, the research groups and often by keeping a chronological order for presenting the progress in the research. We discuss ideas for additional research on structural details of AM2 and how the present findings can be useful to explore new routes of influenza A inhibition. The AM2 research can provide inspiration to study other viroporins as drug targets.

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

Accurate calculation of affinity changes to the close state of influenza A M2 transmembrane domain in response to subtle structural changes of adamantyl amines using free energy perturbation methods in different lipid bilayers

Experimental binding free energies of 27 adamantyl amines against the influenza M2(22-46) WT tetramer, in its closed form at pH 8, were measured by ITC in DPC micelles. The measured Kd's range is ~44 while the antiviral potencies (IC50) range is ~750 with a good correlation between binding free energies computed with Kd and IC50 values (r = 0.76). We explored with MD simulations (ff19sb, CHARMM36m) the binding profile of complexes with strong, moderate and weak binders embedded in DMPC, DPPC, POPC or a viral mimetic membrane and using different experimental starting structures of M2. To predict accurately differences in binding free energy in response to subtle changes in the structure of the ligands, we performed 18 alchemical perturbative single topology FEP/MD NPT simulations (OPLS2005) using the BAR estimator (Desmond software) and 20 dual topology calculations TI/MD NVT simulations (ff19sb) using the MBAR estimator (Amber software) for adamantyl amines in complex with M2(22-46) WT in DMPC, DPPC, POPC. We observed that both methods with all lipids show a very good correlation between the experimental and calculated relative binding free energies (r = 0.77-0.87, mue = 0.36-0.92 kcal mol-1) with the highest performance achieved with TI/MBAR and lowest performance with FEP/BAR in DMPC bilayers. When antiviral potencies are used instead of the Kd values for computing the experimental binding free energies we obtained also good performance with both FEP/BAR (r = 0.83, mue = 0.75 kcal mol-1) and TI/MBAR (r = 0.69, mue = 0.77 kcal mol-1).

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Comparative Interaction Studies of Quercetin with 2-Hydroxyl-propyl-β-cyclodextrin and 2,6-Methylated-β-cyclodextrin

Quercetin (QUE) is a well-known natural product that can exert beneficial properties on human health. However, due to its low solubility its bioavailability is limited. In the present study, we examine whether its formulation with two cyclodextrins (CDs) may enhance its pharmacological profile. Comparative interaction studies of quercetin with 2-hydroxyl-propyl-β-cyclodextrin (2HP-β-CD) and 2,6-methylated cyclodextrin (2,6Me-β-CD) were performed using NMR spectroscopy, DFT calculations, and in silico molecular dynamics (MD) simulations. Using T1 relaxation experiments and 2D DOSY it was illustrated that both cyclodextrin vehicles can host quercetin. Quantum mechanical calculations showed the formation of hydrogen bonds between QUE with 2HP-β-CD and 2,6Μe-β-CD. Six hydrogen bonds are formed ranging between 2 to 2.8 Å with 2HP-β-CD and four hydrogen bonds within 2.8 Å with 2,6Μe-β-CD. Calculations of absolute binding free energies show that quercetin binds favorably to both 2,6Me-β-CD and 2HP-β-CD. MM/GBSA results show equally favorable binding of quercetin in the two CDs. Fluorescence spectroscopy shows moderate binding of quercetin in 2HP-β-CD (520 M-1) and 2,6Me-β-CD (770 M-1). Thus, we propose that both formulations (2HP-β-CD:quercetin, 2,6Me-β-CD:quercetin) could be further explored and exploited as small molecule carriers in biological studies.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Development of hetero-triaryls as a new chemotype for subtype-selective and potent Sirt5 inhibition

In contrast to other sirtuins (NAD⁺-dependent class III lysine deacylases), inhibition of Sirt5 is poorly investigated, yet. Our present work is based on the recently identified Sirt5 inhibitor balsalazide, an approved drug with negligible bioavailability after oral administration. After gaining first insights into its structure-activity relationship in previous work, we were able to now develop heteroaryl-triaryls as a novel chemotype of drug-like, potent and subtype-selective Sirt5 inhibitors. The unfavourable azo group of the lead structure was modified in a systematic and comprehensive manner, leading us to a few open-chained and, most importantly, five-membered heteroaromatic substitutes (isoxazole CG_209, triazole CG_220, pyrazole CG_232) with very encouraging in vitro activities (IC₅₀ on Sirt5 in the low micromolar range, <10 μM). These advanced inhibitors were free of cytotoxicity and showed favourable pharmacokinetic properties, as confirmed by permeability into mitochondria using live cell imaging experiments. Furthermore, results from calculations of the relative free binding affinities of the analogues compared to balsalazide as reference compound agreed well with the trends for inhibitory activities obtained in the in vitro experiments. Therefore, this method can be used to predict the affinity of closely related future potential Sirt5 inhibitors. Encouraged by our findings, we employed chemoproteomic selectivity profiling to confirm Sirt5 as main target of balsalazide and one of its improved analogues. An immobilised balsalazide-analogue specifically pulled down Sirt5 from whole cell lysates and competition experiments identified glutaryl-CoA dehydrogenase (GCDH) and nucleotide diphosphate kinase (NME4) as potential off-targets, once again confirming the selectivity of the novel balsalazide-derived Sirt5 inhibitors. In summary, a combination of targeted chemical synthesis, biological work, and computational studies led to a new generation of tailored Sirt5 inhibitors, which represent valuable chemical tools for the investigation of the physiological role of Sirt5, but could also serve as advanced lead structures for drug candidates for systemic use.

From Acid Activation Mechanisms of Proton Conduction to Design of Inhibitors of the M2 Proton Channel of Influenza A Virus

With an estimated 1 billion people affected across the globe, influenza is one of the most serious health concerns worldwide. Therapeutic treatments have encompassed a number of key functional viral proteins, mainly focused on the M2 proton channel and neuraminidase. This review highlights the efforts spent in targeting the M2 proton channel, which mediates the proton transport toward the interior of the viral particle as a preliminary step leading to the release of the fusion peptide in hemagglutinin and the fusion of the viral and endosomal membranes. Besides the structural and mechanistic aspects of the M2 proton channel, attention is paid to the challenges posed by the development of efficient small molecule inhibitors and the evolution toward novel ligands and scaffolds motivated by the emergence of resistant strains.

Inside and Out of the Pore: Comparing Interactions and Molecular Dynamics of Influenza A M2 Viroporin Complexes in Standard Lipid Bilayers

Ion channels located at viral envelopes (viroporins) have a critical function for the replication of infectious viruses and are important drug targets. Over the last decade, the number and duration of molecular dynamics (MD) simulations of the influenza A M2 ion channel owing to the increased computational efficiency. Here, we aimed to define the system setup and simulation conditions for the correct description of the protein-pore and the protein-lipid interactions for influenza A M2 in comparison with experimental data. We performed numerous MD simulations of the influenza A M2 protein in complex with adamantane blockers in standard lipid bilayers using OPLS2005 and CHARMM36 (C36) force fields. We explored the effect of varying the M2 construct (M2(22-46) and M2(22-62)), the lipid buffer size and type (stiffer DMPC or softer POPC with or without 20% cholesterol), the simulation time, the H37 protonation site (N_δ or Ν_ε), the conformational state of the W41 channel gate, and M2's cholesterol binding sites (BSs). We report that the 200 ns MD with M2(22-62) (having N_ε Η37) in the 20 Å lipid buffer with the C36 force field accurately describe: (a) the M2 pore structure and interactions inside the pore, that is, adamantane channel blocker location, water clathrate structure, and water or chloride anion blockage/passage from the M2 pore in the presence of a channel blocker and (b) interactions between M2 and the membrane environment as reflected by the calculation of the M2 bundle tilt, folding of amphipathic helices, and cholesterol BSs. Strikingly, we also observed that the C36 1 μs MD simulations using M2(22-62) embedded in a 20 Å POPC:cholesterol (5:1) scrambled membrane produced frequent interactions with cholesterol, which when combined with computational kinetic analysis, revealed the experimentally observed BSs of cholesterol and suggested three similarly long-interacting positions in the top leaflet that have previously not been observed experimentally. These findings promise to be useful for other viroporin systems.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

Rimantadine Binds to and Inhibits the Influenza A M2 Proton Channel without Enantiomeric Specificity

The influenza A M2 wild-type (WT) proton channel is the target of the anti-influenza drug rimantadine. Rimantadine has two enantiomers, though most investigations into drug binding and inhibition have used a racemic mixture. Solid-state NMR experiments using the full length-M2 WT have shown significant spectral differences that were interpreted to indicate tighter binding for (R)- vs (S)-rimantadine. However, it was unclear if this correlates with a functional difference in drug binding and inhibition. Using X-ray crystallography, we have determined that both (R)- and (S)-rimantadine bind to the M2 WT pore with slight differences in the hydration of each enantiomer. However, this does not result in a difference in potency or binding kinetics, as shown by similar values for kon, koff, and Kd in electrophysiological assays and for EC50 values in cellular assays. We concluded that the slight differences in hydration for the (R)- and (S)-rimantadine enantiomers are not relevant to drug binding or channel inhibition. To further explore the effect of the hydration of the M2 pore on binding affinity, the water structure was evaluated by grand canonical ensemble molecular dynamics simulations as a function of the chemical potential of the water. Initially, the two layers of ordered water molecules between the bound drug and the channel's gating His37 residues mask the drug's chirality. As the chemical potential becomes more unfavorable, the drug translocates down to the lower water layer, and the interaction becomes more sensitive to chirality. These studies suggest the feasibility of displacing the upper water layer and specifically recognizing the lower water layers in novel drugs.

In Silico Analysis Revealed a Unique Binding but Ineffective Mode of Amantadine to Influenza Virus B M2 Channel

The M2 proton channel of influenza A (AM2) and B (BM2) have a highly conserved function motif, considered as the effective target. As yet, there is no effective drug against BM2. Research showed that AM2 channel blocker, amantadine (AMT), was able to bind to BM2 channel, but AMT lacked inhibition against BM2. Nevertheless, the study of the binding but ineffective mode of AMT to BM2 is challenging. To resolve the challenge and obtain more information for drug design of inhibitors targeting BM2, multiple molecular dynamics simulations were performed. We discovered AMT mainly adopted up binding mode in BM2, involved in a transition flipping from down mode to up mode. Furthermore, we discovered a new key factor to explain ineffective inhibition of AMT to BM2 because of the unmatched spatial geometry between AMT and BM2. Our work could enrich structural feature information on BM2 and provide a new perspective for rational drug design of anti-influenza B.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Machine Learning and Scoring Functions (SFs) for Molecular Drug Discovery: Prediction and Characterisation of Druggable Drugs and Targets

Predicting druggability and prioritising disease-modifying targets is critical in drug discovery. In this chapter, we describe the testing of a druggability rule based on 9 molecular parameters, which uses cutpoints for each molecular parameter and targets based on mixture clustering discriminant analysis. We demonstrate that principal component constructs and score functions of violations can be used to identify the hidden pattern of druggable molecules and disease targets. Random Forest and Artificial Neural Network rules to classify the high-score target from the low-score molecular violators, based both on molecular parameters and the principal component constructs, have confirmed the value of logD's inclusion in the scoring function. Our scoring functions of counts of violations and novel principal component analytic molecular and target-based constructs partitioned chemospace well, identifying both good and poor druggable molecules and targets. Viable molecules and targets were located in both the beyond Rule of 5 and expanded Rule of 5 regions. Random Forest and Artificial Neural Networks showed different variable importance profiles, with Artificial Neural Networks models performing better than Random Forests. The most important molecular descriptors that influence classification, by the Random Forest methods, were MW, NATOM, logD, and PSA. The optimal Artificial Neural Networks target models indicated that PSA and logD were more important than the traditional parameter MW. Overall, our score 4 partitions using logD were optimal at classification as shown in all Random Forests and Artificial Neural Networks analyses.

Molecular Mechanism for Attractant Signaling to DHMA by E. coli Tsr

The attractant chemotaxis response of Escherichia coli to norepinephrine requires that it be converted to 3,4-dihydroxymandelic acid (DHMA) by the monoamine oxidase TynA and the aromatic aldehyde dehydrogenase FeaB. DHMA is sensed by the serine chemoreceptor Tsr, and the attractant response requires that at least one subunit of the periplasmic domain of the Tsr homodimer (pTsr) has an intact serine-binding site. DHMA that is generated in vivo by E. coli is expected to be a racemic mixture of the (R) and (S) enantiomers, so it has been unclear whether one or both chiral forms are active. Here, we used a combination of state-of-the-art tools in molecular docking and simulations, including an in-house simulation-based docking protocol, to investigate the binding properties of (R)-DHMA and (S)-DHMA to E. coli pTsr. Our studies computationally predicted that (R)-DHMA should promote a stronger attractant response than (S)-DHMA because of a consistently greater-magnitude piston-like pushdown of the pTsr α-helix 4 toward the membrane upon binding of (R)-DHMA than upon binding of (S)-DHMA. This displacement is caused primarily by interaction of DHMA with Tsr residue Thr156, which has been shown by genetic studies to be critical for the attractant response to L-serine and DHMA. These findings led us to separate the two chiral species and test their effectiveness as chemoattractants. Both the tethered cell and motility migration coefficient assays validated the prediction that (R)-DHMA is a stronger attractant than (S)-DHMA. Our study demonstrates that refined computational docking and simulation studies combined with experiments can be used to investigate situations in which subtle differences between ligands may lead to diverse chemotactic responses.

The L46P mutant confers a novel allosteric mechanism of resistance toward the influenza A virus M2 S31N proton channel blockers

The Food and Drug Administration-approved influenza A antiviral amantadine inhibits the wild-type (WT) AM2 channel but not the S31N mutant predominantly found in circulating strains. In this study, serial viral passages were applied to select resistance against a newly developed isoxazole-conjugated adamantane inhibitor that targets the AM2 S31N channel. This led to the identification of the novel drug-resistant mutation L46P located outside the drug-binding site, which suggests an allosteric resistance mechanism. Intriguingly, when the L46P mutant was introduced to AM2 WT, the channel remained sensitive toward amantadine inhibition. To elucidate the molecular mechanism, molecular dynamics simulations and binding free energy molecular mechanics-generalized born surface area (MM-GBSA) calculations were performed on WT and mutant channels. It was found that the L46P mutation caused a conformational change in the N terminus of transmembrane residues 22-31 that ultimately broadened the drug-binding site of AM2 S31N inhibitor 4, which spans residues 26-34, but not of AM2 WT inhibitor amantadine, which spans residues 31-34. The MM-GBSA calculations showed stronger binding stability for 4 in complex with AM2 S31N compared with 4 in complex with AM2 S31N/L46P, and equal binding free energies of amantadine in complex with AM2 WT and AM2 L46P. Overall, these results demonstrate a unique allosteric resistance mechanism toward AM2 S31N channel blockers, and the L46P mutant represents the first experimentally confirmed drug-resistant AM2 mutant that is located outside of the pore where drug binds. SIGNIFICANCE STATEMENT: AM2 S31N is a high-profile antiviral drug target, as more than 95% of currently circulating influenza A viruses carry this mutation. Understanding the mechanism of drug resistance is critical in designing the next generation of AM2 S31N channel blockers. Using a previously developed AM2 S31N channel blocker as a chemical probe, this study was the first to identify a novel resistant mutant, L46P. The L46P mutant is located outside of the drug-binding site. Molecular dynamics simulations showed that L46P causes a dilation of drug-binding site between residues 22 and 31, which affects the binding of AM2 S31N channel blockers, but not the AM2 WT inhibitor amantadine.

Comparative Perturbation Effects Exerted by the Influenza A M2 WT Protein Inhibitors Amantadine and the Spiro[pyrrolidine-2,2'-adamantane] Variant AK13 to Membrane Bilayers Studied Using Biophysical Experiments and Molecular Dynamics Simulations

Aminoadamantane drugs are lipophilic amines that block the membrane-embedded influenza A M2 WT (wild type) ion channel protein. The comparative effects of amantadine ( Amt) and its synthetic spiro[pyrrolidine-2,2'-adamantane] (AK13) analogue in dimyristoylphosphatidylcholine (DMPC) bilayers were studied using a combination of experimental biophysical methods, differential scanning calorimetry (DSC), X-ray diffraction, solid-state NMR (ssNMR) spectroscopy, and molecular dynamics (MD) simulations. All three experimental methods pointed out that the two analogues perturbed drastically the DMPC bilayers with AK13 to be more effective at high concentrations. AK13 was tolerated in lipid bilayers at very high concentrations, while Amt was crystallized. This is an important consideration in the formulations of drugs as it designates a limitation of Amt incorporation. MD simulations verify provided details about the strong interactions of the drugs in the interface region between phosphoglycerol backbone and lipophilic segments. The two drugs form hydrogen bonding with both water and sn-2 carbonyls in their amine form or water and phosphate oxygens in their ammonium form. Such localization of the drugs explains the DMPC bilayers reorientation and their strong perturbing effect evidenced by all biophysical methodologies applied.

Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding Mode

An intense effort is made by pharmaceutical and academic research laboratories to identify and develop selective antagonists for each adenosine receptor (AR) subtype as potential clinical candidates for "soft" treatment of various diseases. Crystal structures of subtypes A_2A and A₁ARs offer exciting opportunities for structure-based drug design. In the first part of the present work, Maybridge HitFinder library of 14400 compounds was utilized to apply a combination of structure-based against the crystal structure of A_2AAR and ligand-based methodologies. The docking poses were rescored by CHARMM energy minimization and calculation of the desolvation energy using Poisson-Boltzmann equation electrostatics. Out of the eight selected and tested compounds, five were found positive hits (63% success). Although the project was initially focused on targeting A_2AAR, the identified antagonists exhibited low micromolar or micromolar affinity against A_2A/A₃, ARs, or A₃AR, respectively. Based on these results, 19 compounds characterized by novel chemotypes were purchased and tested. Sixteen of them were identified as AR antagonists with affinity toward combinations of the AR family isoforms (A_2A/A₃, A₁/A₃, A₁/A_2A/A₃, and A₃). The second part of this work involves the performance of hundreds of molecular dynamics (MD) simulations of complexes between the ARs and a total of 27 ligands to resolve the binding interactions of the active compounds, which were not achieved by docking calculations alone. This computational work allowed the prediction of stable and unstable complexes which agree with the experimental results of potent and inactive compounds, respectively. Of particular interest is that the 2-amino-thiophene-3-carboxamides, 3-acylamino-5-aryl-thiophene-2-carboxamides, and carbonyloxycarboximidamide derivatives were found to be selective and possess a micromolar to low micromolar affinity for the A₃ receptor.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Affinity of Rimantadine Enantiomers against Influenza A/M2 Protein Revisited

Recent findings from solid state NMR (ssNMR) studies suggested that the (R)-enantiomer of rimantadine binds to the full M2 protein with higher affinity than the (S)-enantiomer. Intrigued by these findings, we applied functional assays, such as antiviral assay and electrophysiology (EP), to evaluate the binding affinity of rimantadine enantiomers to the M2 protein channel. Unexpectedly, no significant difference was found between the two enantiomers. Our experimental data based on the full M2 protein function were further supported by alchemical free energy calculations and isothermal titration calorimetry (ITC) allowing an evaluation of the binding affinity of rimantadine enantiomers to the M2TM pore. Both enantiomers have similar channel blockage, affinity, and antiviral potency.

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

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

On the role of water density fluctuations in the inhibition of a proton channel

Hv1 is a transmembrane four-helix bundle that transports protons in a voltage-controlled manner. Its crucial role in many pathological conditions, including cancer and ischemic brain damage, makes Hv1 a promising drug target. Starting from the recently solved crystal structure of Hv1, we used structural modeling and molecular dynamics simulations to characterize the channel's most relevant conformations along the activation cycle. We then performed computational docking of known Hv1 inhibitors, 2-guanidinobenzimidazole (2GBI) and analogs. Although salt-bridge patterns and electrostatic potential profiles are well-defined and distinctive features of activated versus nonactivated states, the water distribution along the channel lumen is dynamic and reflects a conformational heterogeneity inherent to each state. In fact, pore waters assemble into intermittent hydrogen-bonded clusters that are replaced by the inhibitor moieties upon ligand binding. The entropic gain resulting from releasing these conformationally restrained waters to the bulk solvent is likely a major contributor to the binding free energy. Accordingly, we mapped the water density fluctuations inside the pore of the channel and identified the regions of maximum fluctuation within putative binding sites. Two sites appear as outstanding: One is the already known binding pocket of 2GBI, which is accessible to ligands from the intracellular side; the other is a site located at the exit of the proton permeation pathway. Our analysis of the waters confined in the hydrophobic cavities of Hv1 suggests a general strategy for drug discovery that can be applied to any ion channel.

Viral channel forming proteins--How to assemble and depolarize lipid membranes in silico

Viral channel forming proteins (VCPs) have been discovered in the late 70s and are found in many viruses to date. Usually they are small and have to assemble to form channels which depolarize the lipid membrane of the host cells. Structural information is just about to emerge for just some of them. Thus, computational methods play a pivotal role in generating plausible structures which can be used in the drug development process. In this review the accumulation of structural data is introduced from a historical perspective. Computational performances and their predictive power are reported guided by biological questions such as the assembly, mechanism of function and drug–protein interaction of VCPs. An outlook of how coarse grained simulations can contribute to yet unexplored issues of these proteins is given. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

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Early references about the discovery of viral channel forming proteins.

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Latest structural information about the class of proteins.

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Identification of structural motifs, assembly mechanism of function and drug action using computational methods.

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Outlook for the use of coarse grained techniques to address assembly and integration into cellular processes.

Interpreting Thermodynamic Profiles of Aminoadamantane Compounds Inhibiting the M2 Proton Channel of Influenza A by Free Energy Calculations

The development of novel anti-influenza drugs is of great importance because of the capability of influenza viruses to occasionally cross interspecies barriers and to rapidly mutate. One class of anti-influenza agents, aminoadamantanes, including the drugs amantadine and rimantadine now widely abandoned due to virus resistance, bind to and block the pore of the transmembrane domain of the M2 proton channel (M2TM) of influenza A. Here, we present one of the still rare studies that interprets thermodynamic profiles from isothermal titration calorimetry (ITC) experiments in terms of individual energy contributions to binding, calculated by the computationally inexpensive implicit solvent/implicit membrane molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach, for aminoadamantane compounds binding to M2TM of the avian "Weybridge" strain. For all eight pairs of aminoadamantane compounds considered, the trend of the predicted relative binding free energies and their individual components, effective binding energies and changes in the configurational entropy, agrees with experimental measures (ΔΔG, ΔΔH, TΔΔS) in 88, 88, and 50% of the cases. In addition, information yielded by the MM-PBSA approach about determinants of binding goes beyond that available in component quantities (ΔH, ΔS) from ITC measurements. We demonstrate how one can make use of such information to link thermodynamic profiles from ITC with structural causes on the ligand side and, ultimately, to guide decision making in lead optimization in a prospective manner, which results in an aminoadamantane derivative with improved binding affinity against M2TM(Weybridge).

Inexpensive Method for Selecting Receptor Structures for Virtual Screening

This article introduces a screening performance index (SPI) to help select from a number of experimental structures one or a few that are more likely to identify more actives among its top hits from virtual screening of a compound library. It achieved this by docking only known actives to the experimental structures without considering a large number of decoys to reduce computational costs. The SPI is calculated by using the docking energies of the actives to all the receptor structures. We evaluated the performance of the SPI by applying it to study eight protein systems: fatty acid binding protein adipocyte FABP4, serine/threonine-protein kinase BRAF, beta-1 adrenergic receptor ADRB1, TGF-beta receptor type I TGFR1, adenosylhomocysteinase SAHH, thyroid hormone receptor beta-1 THB, phospholipase A2 group IIA PA2GA, and cytochrome P450 3a4 CP3A4. We found that the SPI agreed with the results from other popular performance metrics such as Boltzmann-Enhanced Discrimination Receiver Operator Characteristics (BEDROC), Robust Initial Enhancement (RIE), Area Under Accumulation Curve (AUAC), and Enrichment Factor (EF) but is less expensive to calculate. SPI also performed better than the best docking energy, the molecular volume of the bound ligand, and the resolution of crystal structure in selecting good receptor structures for virtual screening. The implications of these findings were further discussed in the context of ensemble docking, in situations when no experimental structure for the targeted protein was available, or under circumstances when quick choices of receptor structures need to be made before quantitative indexes such as the SPI and BEDROC can be calculated.

Focusing on shared subpockets - new developments in fragment-based drug discovery

INTRODUCTION

Protein-protein interactions (PPIs) are important targets for understanding fundamental biology and for the development of therapeutic agents. Based on different physicochemical properties, numerous pieces of software (e.g., POCKETQUERY, ANCHORQUERY and FTMap) have been reported to find pockets on protein surfaces and have applications in facilitating the design and discovery of small-molecular-weight compounds that bind to these pockets.

AREAS COVERED

The authors discuss a pocket-centric method of analyzing PPI interfaces, which prioritize their pockets for small-molecule drug discovery and the importance of multicomponent reaction chemistry as starting points for undruggable targets. The authors also provide their perspectives on the field.

EXPERT OPINION

Only the tight interplay of efficient computational methods capable of screening a large chemical space and fast synthetic chemistry will lead to progress in the rational design of PPI antagonists in the future. Early drug discovery platforms will also benefit from efficient rapid feedback loops from early clinical research back to molecular design and the medicinal chemistry bench.

Resistance-Mutation (N31) Effects on Drug Orientation and Channel Hydration in Amantadine-Bound Influenza A M2

The mechanism of amantadine binding to the S31 variant of the M2 protein of Influenza A is well understood, but the reasons behind N31 M2 amantadine insensitivity remain under investigation. Many molecular dynamics studies have evaluated the influence of amantadine position within the channel pore on its ability to inhibit proton conductance in M2, but little is known about the influence of amantadine rotational orientation. Replica-exchange umbrella sampling, steered, and classic molecular dynamics simulations were performed on amantadine in the solid-state NMR structure of S31 M2 and an N31 M2 homologue, both in the homotetramer configuration, to explore the effects of the position and tilt angle of amantadine on inhibition of the M2 channel. Steered simulations show that amantadine rotates with the amine toward the bulk water as it passes into the hydrophobic entryway lined by Val27 side chains. Results from all simulation types performed indicate that amantadine has a strong, specific orientation with the amine turned inward toward the central cavity in the S31 M2 pore but has variable orientation and a strong propensity to remain outward pointing in N31 M2. Free energy profiles from umbrella sampling, measured relative to bulk water, show amantadine binds more strongly to the S31 M2 pore by 8 kcal/mol in comparison to amantadine in the N31 pore, suggesting that it can escape more readily from the N31 channel through the Val27 secondary gate, whereas it is captured by the S31 channel in the same region. Lower water density and distribution near amantadine in S31 M2 reveal that the drug inhibits proton conductance in S31 M2 because of its inward-pointing configuration, whereas in N31 M2, amantadine forms hydrogen bonds with an N31 side chain and does not widely occlude water occupancy in any configuration. Both amantadine's weaker binding to and weaker water occlusion in N31 M2 might contribute to its inefficacy as an inhibitor of the mutant protein.

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

The 2013/2014 Community Structure-Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor-small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R(2) = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.

Membrane Protein Structure, Function, and Dynamics: a Perspective from Experiments and Theory

Membrane proteins mediate processes that are fundamental for the flourishing of biological cells. Membrane-embedded transporters move ions and larger solutes across membranes; receptors mediate communication between the cell and its environment and membrane-embedded enzymes catalyze chemical reactions. Understanding these mechanisms of action requires knowledge of how the proteins couple to their fluid, hydrated lipid membrane environment. We present here current studies in computational and experimental membrane protein biophysics, and show how they address outstanding challenges in understanding the complex environmental effects on the structure, function, and dynamics of membrane proteins.

Structure-based virtual screening for drug discovery: principles, applications and recent advances

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

Structure-based predictions of activity cliffs

In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods.

A virtual screening approach for identifying plants with anti H5N1 neuraminidase activity

Recent outbreaks of highly pathogenic and occasional drug-resistant influenza strains have highlighted the need to develop novel anti-influenza therapeutics. Here, we report computational and experimental efforts to identify influenza neuraminidase inhibitors from among the 3000 natural compounds in the Malaysian-Plants Natural-Product (NADI) database. These 3000 compounds were first docked into the neuraminidase active site. The five plants with the largest number of top predicted ligands were selected for experimental evaluation. Twelve specific compounds isolated from these five plants were shown to inhibit neuraminidase, including two compounds with IC50 values less than 92 μM. Furthermore, four of the 12 isolated compounds had also been identified in the top 100 compounds from the virtual screen. Together, these results suggest an effective new approach for identifying bioactive plant species that will further the identification of new pharmacologically active compounds from diverse natural-product resources.

Why bound amantadine fails to inhibit proton conductance according to simulations of the drug-resistant influenza A M2 (S31N)

The mechanisms responsible for drug resistance in the Asn31 variant of the M2 protein of influenza A are not well understood. Molecular dynamics simulations were performed on wild-type (Ser31) and S31N influenza A M2 in the homotetramer configuration. After evaluation of 13 published M2 structures, a solid-state NMR structure with amantadine bound was selected for simulations, an S31N mutant structure was developed and equilibrated, and the native and mutant structures were used to determine the binding behavior of amantadine and the dynamics of water in the two channels. Amantadine is stable in the plugging region of wild-type M2, with the adamantane in contact with the Val27 side chains, while amantadine in S31N M2 has more variable movement and orientation, and spontaneously moves lower into the central cavity of the channel. Free energy profiles from umbrella sampling support this observation. In this configuration, water surrounds the drug and can easily transport protons past it, so the drug binds without blocking proton transport in the S31N M2 channel.

Hydrogen-bonded water molecules in the M2 channel of the influenza A virus guide the binding preferences of ammonium-based inhibitors

The tetrameric M2 proton channel of influenza A virus is an integral membrane protein responsible for the acidification of the viral interior. Drugs such as amantadine target the transmembrane region of wild type M2 by acting as pore blockers. However, a number of mutations affecting this domain confer drug resistance, prompting the need for alternative inhibitors. The availability of high-resolution structures of drug-bound M2, paired with computational investigations, revealed that inhibitors can bind at different sites, and provided useful insights in understanding the principles governing proton conduction. Here, we investigated by computation the energetic and geometric factors determining the relative stability of pore blockers at individual sites of different M2 strains. We found that local free energy minima along the translocation pathway of positively charged chemical species correspond to experimentally determined binding sites of inhibitors. Then, by examining the structure of water clusters hydrating each site, as well as of those displaced by binding of hydrophobic scaffolds, we predicted the binding preferences of M2 ligands. This information can be used to guide the identification of novel classes of inhibitors.

Structural and energetic analysis of drug inhibition of the influenza A M2 proton channel

The type A influenza virus matrix protein 2 (M2) is a highly selective proton channel in the viral envelope. Because of its crucial role in viral infection and replication, the M2 channel has been a target of anti-influenza drugs. Due to the occurrence of drug-resistant mutations in the M2 channel, existing anti-influenza drugs that block the M2 channel, such as amantadine and rimantadine, have lost their efficacy against these mutant channels. Recent experimental and computational efforts have made great progress in understanding the drug resistance mechanisms of these mutations as well as designing novel drug candidates to block the mutant M2 channels. In this review, we briefly summarize the structural characteristics of the M2 channel, and then we discuss these recent studies on drug resistance and drug design of the mutant channels, focusing on the structures and energetics. We show that structural biology experiments and molecular modeling have led to the successful design of novel drugs targeting mutant M2 channels.