Improving Accuracy, Diversity, and Speed with Prime Macrocycle Conformational Sampling

Isolation, Structure Elucidation, and Biological Activity of the Selective TACR2 Antagonist Tumonolide and its Aldehyde from a Marine Cyanobacterium

The macrocyclic tumonolide (1) with enamide functionality and the linear tumonolide aldehyde (2) are new interconverting natural products from a marine cyanobacterium with a peptide-polyketide skeleton, representing a hybrid of apratoxins and palmyrolides or laingolides. The planar structures were established by NMR and mass spectrometry. The relative configuration of the stereogenically-rich apratoxin-like polyketide portion was determined using J-based configuration analysis. The absolute configuration of tumonolide (1) was determined by chiral analysis of the amino acid units and computational methods, followed by NMR chemical shift and ECD spectrum prediction, indicating all-R configuration for the polyketide portion, as in palmyrolide A and contrary to the all-S configuration in apratoxins. Functional screening against a panel of 168 GPCR targets revealed tumonolide (1) as a selective antagonist of TACR2 with an IC50 of 7.0 μM, closely correlating with binding affinity. Molecular docking studies established the binding mode and rationalized the selectivity for TACR2 over TACR1 and TACR3. RNA sequencing upon treatment of HCT116 colorectal cancer cells demonstrated activation of the pulmonary fibrosis idiopathic signaling pathway and the insulin secretion signaling pathway at 20 μM, indicating its potential to modulate these pathways.

From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product

Scaffold replacement as part of an optimization process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge. Here, we consider a set of over 1000 time-stamped compounds, beginning with a macrocyclic natural-product lead and ending with a broad-spectrum crop anti-fungal. We demonstrate the application of the QuanSA 3D-QSAR method employing an active learning procedure that combines two types of molecular selection. The first identifies compounds predicted to be most active of those most likely to be well-covered by the model. The second identifies compounds predicted to be most informative based on exhibiting low predicted activity but showing high 3D similarity to a highly active nearest-neighbor training molecule. Beginning with just 100 compounds, using a deterministic and automatic procedure, five rounds of 20-compound selection and model refinement identifies the binding metabolic form of florylpicoxamid. We show how iterative refinement broadens the domain of applicability of the successive models while also enhancing predictive accuracy. We also demonstrate how a simple method requiring very sparse data can be used to generate relevant ideas for synthetic candidates.

A novel aminopeptidase N/CD13 inhibitor selectively targets an endothelial form of CD13 after coupling to proteins

Aminopeptidase N/CD13, a membrane-bound enzyme upregulated in tumor vasculature and involved in angiogenesis, can be used as a receptor for the targeted delivery of drugs to tumors through ligand-directed targeting approaches. We describe a novel peptide ligand (VGCARRYCS, called "G4") that recognizes CD13 with high affinity and selectivity. Enzymological and computational studies showed that G4 is a competitive inhibitor that binds to the catalytic pocket of CD13 through its N-terminal region. Fusing the peptide C-terminus to tumor necrosis factor-alpha (TNF) or coupling it to a biotin/avidin complex causes loss of binding and inhibitory activity against different forms of CD13, including natural or recombinant ectoenzyme and a membrane form expressed by HL60 promyelocytic leukemia cells (likely due to steric hindrance), but not binding to a membrane form of CD13 expressed by endothelial cells (ECs). Furthermore, G4-TNF systemically administered to tumor-bearing mice exerted anticancer effects through a CD13-targeting mechanism, indicating the presence of a CD13 form in tumor vessels with an accessible binding site. Biochemical studies showed that most CD13 molecules expressed on the surface of ECs are catalytically inactive. Other functional assays showed that these molecules can promote endothelial cell adhesion to plates coated with G4-avidin complexes, suggesting that the endothelial form of CD13 can exert catalytically independent biological functions. In conclusion, ECs express a catalytically inactive form of CD13 characterized by an accessible conformation that can be selectively targeted by G4-protein conjugates. This form of CD13 may represent a specific target receptor for ligand-directed targeted delivery of therapeutics to tumors.

Addressing Challenges of Macrocyclic Conformational Sampling in Polar and Apolar Solvents: Lessons for Chameleonicity

We evaluated a workflow to reliably sample the conformational space of a set of 47 peptidic macrocycles. Starting from SMILES strings, we use accelerated molecular dynamics simulations to overcome high energy barriers, in particular, the cis-trans isomerization of peptide bonds. We find that our approach performs very well in polar solvents like water and dimethyl sulfoxide. Interestingly, the protonation state of a secondary amine in the ring only slightly influences the conformational ensembles of our test systems. For several of the macrocycles, determining the conformational distribution in chloroform turns out to be considerably more challenging. Especially, the choice of partial charges crucially influences the ensembles in chloroform. We address these challenges by modifying initial structures and the choice of partial charges. Our results suggest that special care has to be taken to understand the configurational distribution in apolar solvents, which is a key step toward a reliable prediction of membrane permeation of macrocycles and their chameleonic properties.

Cyclic Peptide Linker Design and Optimization by Molecular Dynamics Simulations

Cyclic peptides are an emerging therapeutic modality that can target protein-protein interaction sites with high affinity and selectivity. A common medicinal chemistry strategy for the optimization of peptide hits is conformational stabilization through macrocyclization. We present a method based on explicit solvent enhanced sampling molecular dynamics simulations for estimating the impact of varying linker lengths and chemistry on the conformational stability of a peptide. The method is demonstrated on three cyclic peptide series that bind to proteins PCSK9, trypsin, and MDM2 adopting loop, β-sheet, and helical secondary structures. In general, the simulations show greater solution stability of the receptor-bound conformation for the higher-affinity peptides, consistent with the idea that preorganizing a ligand for binding can enhance binding affinity. The impact of the force field and sampling is discussed for one series that does not follow this trend. We have successfully applied this method to internal discovery programs to design peptides with increased potency and chemical stability.

Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design

Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

Determining the Predominant Conformations of Mortiamides A-D in Solution Using NMR Data and Molecular Modeling Tools

Macrocyclic peptidomimetics have been seriously contributing to our arsenal of drugs to combat diseases. The search for nature's discoveries led us to mortiamides A-D (found in a novel fungus from Northern Canada), which is a family of cyclic peptides that clearly have demonstrated impressive pharmaceutical potential. This prompted us to learn more about their solution-state properties as these are central for binding to target molecules. Here, we secured and isolated mortiamide D, and then acquired high-resolution nuclear magnetic resonance (NMR) data to learn more about its structure and dynamics attributes. Sets of two-dimensional NMR experiments provided atomic-level (through-bond and through-space) data to confirm the primary structure, and NMR-driven molecular dynamics (MD) simulations suggested that more than one predominant three-dimensional (3D) structure exist in solution. Further steps of MD simulations are consistent with the finding that the backbones of mortiamides A-C also have at least two prominent macrocyclic shapes, but the side-chain structures and dynamics differed significantly. Knowledge of these solution properties can be exploited for drug design and discovery.

Beyond 20 in the 21st Century: Prospects and Challenges of Non-canonical Amino Acids in Peptide Drug Discovery

Life is constructed primarily using a toolbox of 20 canonical amino acids-relying upon these building blocks for the assembly of proteins and peptides that regulate nearly every cellular task, including cell structure, function, and maintenance. While Nature continues to be a source of inspiration for drug discovery, medicinal chemists are not beholden to only 20 canonical amino acids and have begun to explore non-canonical amino acids (ncAAs) for the construction of designer peptides with improved drug-like properties. However, as our toolbox of ncAAs expands, drug hunters are encountering new challenges in approaching the iterative peptide design-make-test-analyze cycle with a seemingly boundless set of building blocks. This Microperspective focuses on new technologies that are accelerating ncAA interrogation in peptide drug discovery (including HELM notation, late-stage functionalization, and biocatalysis) while shedding light on areas where further investment could not only accelerate the discovery of new medicines but also improve downstream development.

Identification of small-molecule protein-protein interaction inhibitors for NKG2D

NKG2D (natural-killer group 2, member D) is a homodimeric transmembrane receptor that plays an important role in NK, γδ+, and CD8+ T cell-mediated immune responses to environmental stressors such as viral or bacterial infections and oxidative stress. However, aberrant NKG2D signaling has also been associated with chronic inflammatory and autoimmune diseases, and as such NKG2D is thought to be an attractive target for immune intervention. Here, we describe a comprehensive small-molecule hit identification strategy and two distinct series of protein-protein interaction inhibitors of NKG2D. Although the hits are chemically distinct, they share a unique allosteric mechanism of disrupting ligand binding by accessing a cryptic pocket and causing the two monomers of the NKG2D dimer to open apart and twist relative to one another. Leveraging a suite of biochemical and cell-based assays coupled with structure-based drug design, we established tractable structure-activity relationships with one of the chemical series and successfully improved both the potency and physicochemical properties. Together, we demonstrate that it is possible, albeit challenging, to disrupt the interaction between NKG2D and multiple protein ligands with a single molecule through allosteric modulation of the NKG2D receptor dimer/ligand interface.

Potent antitumor activity of ensartinib in MET exon 14 skipping-mutated non-small cell lung cancer

Met proto-oncogene exon 14 skipping (METex14) mutations are targetable driver genes in approximately 3% of non-small-cell lung cancers (NSCLCs). Ensartinib, a type Ia MET inhibitor, is a multi-kinase inhibitor that has been approved for ALK-positive NSCLCs. Ensartinib was administered for compassionate use (cohort 1) and in a phase II clinical trial (cohort 2) to patients with METex14 mutant NSCLCs, with ORR as a primary endpoint. Molecular simulation was conducted to evaluate ensartinib c-MET interaction, and cell lines, patient-derived organoids (PDOs), and xenograft models were used to test the effectiveness of ensartinib. Among 29 evaluable patients, the ORR and DCR of ensartinib were 67% and 94% in cohort 1, and 73% and 91% in cohort 2. The median DoR was 6.8 months and median PFS was 6.1 months in the total population. Rash was the most common drug-related adverse event, and peripheral edema of any grade was reported in only 9% patients. Molecular simulations indicated favorable binding of ensartinib to c-MET. The kinase assay demonstrated an IC₅₀ of 7.9 nM of ensartinib against METex14 protein. In vitro, Hs746T (METex14 mutation) and EBC-1 (MET amplification) cells were sensitive to ensartinib, with IC₅₀ of 31 and 44 nM, respectively. Ensartinib exhibited comparable inhibitory effects on cell migration as crizotinib and tepotinib in both cell types. In vivo, ensartinib suppressed the growth of Hs746T cells. Ensartinib also potently inhibited the viability of PDOs. Overall, Ensartinib exhibited substantial antitumor effects against METex14 mutant NSCLCs in preclinical and clinical trials, with relatively low peripheral edema rates.

Ensemble Docking Approach to Mitigate Pregnane X Receptor-Mediated CYP3A4 Induction Risk

Three structurally closely related dopamine D1 receptor positive allosteric modulators (D1 PAMs) based on a tetrahydroisoquinoline (THIQ) scaffold were profiled for their CYP3A4 induction potentials. It was found that the length of the linker at the C5 position greatly affected the potentials of these D1 PAMs as CYP3A4 inducers, and the level of induction correlated well with the activation of the pregnane X receptor (PXR). Based on the published PXR X-ray crystal structures, we built a binding model specifically for these THIQ-scaffold-based D1 PAMs in the PXR ligand-binding pocket via an ensemble docking approach and found the model could explain the observed CYP induction disparity. Combined with our previously reported D1 receptor homology model, which identified the C5 position as pointing toward the solvent-exposed space, our PXR-binding model coincidentally suggested that structural modifications at the C5 position could productively modulate the CYP induction potential while maintaining the D1 PAM potency of these THIQ-based PAMs.

Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS

The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is represented by the ensemble of its sampled conformations. Although some algorithms excel at creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations and often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, namely, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS ver. 4.6.7 and benchmarked on two small cyclic peptide model systems: a cyclized furin cleavage site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (cyclo-(CGPRRARSG)) and oxytocin (disulfide-bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. For Trpzip2, Res-REMD coupled with an umbrella-sampling-derived reservoir generated similar folded fractions as regular REMD but on a much faster time scale. For cyclic peptides, Res-REMD appeared to be marginally faster than REMD in ensemble generation. Finally, Res-REMD was more effective in sampling rare events such as trans to cis peptide bond isomerization. We provide a GitHub page with the modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD.

Evaluating the Conformations and Dynamics of Peptoid Macrocycles

Peptoid macrocycles are versatile and chemically diverse peptidomimetic oligomers. However, the conformations and dynamics of these macrocycles have not been evaluated comprehensively and require extensive further investigation. Recent studies indicate that two degrees of freedom, and four distinct conformations, adequately describe the behavior of each monomer backbone unit in most peptoid oligomers. On the basis of this insight, we conducted molecular dynamics simulations of model macrocycles using an exhaustive set of idealized possible starting conformations. Simulations of various sizes of peptoid macrocycles yielded a limited set of populated conformations. In addition to reproducing all relevant experimentally determined conformations, the simulations accurately predicted a cyclo-octamer conformation for which we now present the first experimental observation. Sets of three adjacent dihedral angles (ϕ_i, ψ_i, ω_i+1) exhibited correlated crankshaft motions over the course of simulation for peptoid macrocycles of six residues and larger. These correlated motions may occur in the form of an inversion of one amide bond and the concerted rotation of the preceding ϕ and ψ angles to their mirror-image conformation, a variation on "crankshaft flip" motions studied in polymers and peptides. The energy landscape of these peptoid macrocycles can be described as a network of conformations interconnected by transformations of individual crankshaft flips. For macrocycles of up to eight residues, our mapping of the landscape is essentially complete.

Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

Novel Macrocyclic Antagonists of the Calcitonin Gene-Related Peptide Receptor: Design, Realization, and Structural Characterization of Protein-Ligand Complexes

A series of macrocyclic calcitonin gene-related peptide (CGRP) receptor antagonists identified using structure-based design principles, exemplified by HTL0028016 (1) and HTL0028125 (2), is described. Structural characterization by X-ray crystallography of the interaction of two of the macrocycle antagonists with the CGRP receptor ectodomain is described, along with structure-activity relationships associated with point changes to the macrocyclic antagonists. The identification of non-peptidic/natural product-derived, macrocyclic ligands for a G protein coupled receptor (GPCR) is noteworthy.

Plasticity in ligand recognition at somatostatin receptors

Somatostatin is a signaling peptide that plays a pivotal role in physiologic processes relating to metabolism and growth through its actions at somatostatin receptors (SSTRs). Members of the SSTR subfamily, particularly SSTR2, are key drug targets for neuroendocrine neoplasms, with synthetic peptide agonists currently in clinical use. Here, we show the cryogenic-electron microscopy structures of active-state SSTR2 in complex with heterotrimeric Gi3 and either the endogenous ligand SST14 or the FDA-approved drug octreotide. Complemented by biochemical assays and molecular dynamics simulations, these structures reveal key details of ligand recognition and receptor activation at SSTRs. We find that SSTR ligand recognition is highly diverse, as demonstrated by ligand-induced conformational changes in ECL2 and substantial sequence divergence across subtypes in extracellular regions. Despite this complexity, we rationalize several known sources of SSTR subtype selectivity and identify an additional interaction for specific binding. These results provide valuable insights for structure-based drug discovery at SSTRs.

Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry

Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.

Enzymatic Synthesis of 3'-5', 3'-5' Cyclic Dinucleotides, Their Binding Properties to the Stimulator of Interferon Genes Adaptor Protein, and Structure/Activity Correlations

The 3'-5', 3'-5' cyclic dinucleotides (3'3'CDNs) are bacterial second messengers that can also bind to the stimulator of interferon genes (STING) adaptor protein in vertebrates and activate the host innate immunity. Here, we profiled the substrate specificity of four bacterial dinucleotide synthases from Vibrio cholerae (DncV), Bacillus thuringiensis (btDisA), Escherichia coli (dgcZ), and Thermotoga maritima (tDGC) using a library of 33 nucleoside-5'-triphosphate analogues and then employed these enzymes to synthesize 24 3'3'CDNs. The STING affinity of CDNs was evaluated in cell-based and biochemical assays, and their ability to induce cytokines was determined by employing human peripheral blood mononuclear cells. Interestingly, the prepared heterodimeric 3'3'CDNs bound to the STING much better than their homodimeric counterparts and showed similar or better potency than bacterial 3'3'CDNs. We also rationalized the experimental findings by in-depth STING-CDN structure-activity correlations by dissecting computed interaction free energies into a set of well-defined and intuitive terms. To this aim, we employed state-of-the-art methods of computational chemistry, such as quantum mechanics/molecular mechanics (QM/MM) calculations, and complemented the computed results with the {STING:3'3'c-di-ara-AMP} X-ray crystallographic structure. QM/MM identified three outliers (mostly homodimers) for which we have no clear explanation of their impaired binding with respect to their heterodimeric counterparts, whereas the R² = 0.7 correlation between the computed ΔG'_{int_rel} and experimental ΔT_m's for the remaining ligands has been very encouraging.

Computational studies of molecular pre-organization through macrocyclization: Conformational distribution analysis of closely related non-macrocyclic and macrocyclic analogs

Macrocycles form an important compound class in medicinal chemistry due to their interesting structural and biological properties. To help design macrocycles, it is important to understand how the conformational preferences are affected upon macrocyclization of a lead compound. To address this, we collected a unique data set of protein-ligand complexes containing "non-macrocyclic" ("linear") ligands matched with macrocyclic analogs binding to the same protein in a similar pose. Out of the 39 co-crystallized ligands considered, 10 were linear and 29 were macrocyclic. To enable a more general analysis, 128 additional ligands from the publications associated with these protein data bank entries were added to the data set. Using in total 167 collected ligands, we investigated if the conformers in the macrocyclic conformational ensembles were more similar to the bioactive conformation in comparison to the conformers of their linear counterparts. Unexpectedly, in most cases the macrocycle conformational ensemble distributions were not very different from those of the linear compounds. Thus, care should be taken when designing macrocycles with the aim to focus their conformational preference towards the bioactive conformation. We also set out to investigate potential conformational flexibility differences between the two compound classes, computational energy window settings and evaluate a literature metric for approximating the conformational focusing on the bioactive conformation.

Ensemble completeness in conformer sampling: the case of small macrocycles

In this study we compare the three algorithms for the generation of conformer ensembles Biovia BEST, Schrödinger Prime macrocycle sampling (PMM) and Conformator (CONF) form the University of Hamburg, with ensembles derived for exhaustive molecular dynamics simulations applied to a dataset of 7 small macrocycles in two charge states and three solvents. Ensemble completeness is a prerequisite to allow for the selection of relevant diverse conformers for many applications in computational chemistry. We apply conformation maps using principal component analysis based on ring torsions. Our major finding critical for all applications of conformer ensembles in any computational study is that maps derived from MD with explicit solvent are significantly distinct between macrocycles, charge states and solvents, whereas the maps for post-optimized conformers using implicit solvent models from all generator algorithms are very similar independent of the solvent. We apply three metrics for the quantification of the relative covered ensemble space, namely cluster overlap, variance statistics, and a novel metric, Mahalanobis distance, showing that post-optimized MD ensembles cover a significantly larger conformational space than the generator ensembles, with the ranking PMM > BEST >> CONF. Furthermore, we find that the distributions of 3D polar surface areas are very similar for all macrocycles independent of charge state and solvent, except for the smaller and more strained compound 7, and that there is also no obvious correlation between 3D PSA and intramolecular hydrogen bond count distributions.

Prediction of Chameleonic Efficiency

Chameleonic properties, i. e., the capacity of a molecule to hide polarity in non-polar environments and expose it in water, help achieving sufficient permeability and solubility for drug molecules with high MW. We present models of experimental measures of polarity for a set of 24 FDA approved drugs (MW 405-1113) and one PROTAC (MW 1034). Conformational ensembles in aqueous and non-polar environments were generated using molecular dynamics. A linear regression model that predicts chromatographic apparent polarity (EPSA) with a mean unsigned error of 10 Ų was derived based on separate terms for donor, acceptor, and total molecular SASA. A good correlation (R² =0.92) with an experimental measure of hydrogen bond donor potential, Δlog P_oct-tol , was found for the mean hydrogen bond donor SASA of the conformational ensemble scaled with Abraham's A hydrogen bond acidity. Two quantitative measures of chameleonic behaviour, the chameleonic efficiency indices, are introduced. We envision that the methods presented herein will be useful to triage designed molecules and prioritize those with the best chance of achieving acceptable permeability and solubility.

Synthesis and Biological Evaluation of Phosphoester and Phosphorothioate Prodrugs of STING Agonist 3',3'-c-Di(2'F,2'dAMP)

Cyclic dinucleotides (CDNs) are second messengers that bind to the stimulator of interferon genes (STING) and trigger the expression of type I interferons and proinflammatory cytokines. Here we evaluate the activity of 3',3'-c-di(2'F,2'dAMP) and its phosphorothioate analogues against five STING allelic forms in reporter-cell-based assays and rationalize our findings with X-ray crystallography and quantum mechanics/molecular mechanics calculations. We show that the presence of fluorine in the 2' position of 3',3'-c-di(2'F,2'dAMP) improves its activity not only against the wild type (WT) but also against REF and Q STING. Additionally, we describe the synthesis of the acyloxymethyl and isopropyloxycarbonyl phosphoester prodrugs of CDNs. Masking the negative charges of the CDNs results in an up to a 1000-fold improvement of the activities of the prodrugs relative to those of their parent CDNs. Finally, the uptake and intracellular cleavage of pivaloyloxymethyl prodrugs to the parent CDN is rapid, reaching a peak intracellular concentration within 2 h.

Development of a Highly Selective Plasmodium falciparum Proteasome Inhibitor with Anti-malaria Activity in Humanized Mice

Plasmodium falciparum proteasome (Pf20S) inhibitors are active against Plasmodium at multiple stages-erythrocytic, gametocyte, liver, and gamete activation stages-indicating that selective Pf20S inhibitors possess the potential to be therapeutic, prophylactic, and transmission-blocking antimalarials. Starting from a reported compound, we developed a noncovalent, macrocyclic peptide inhibitor of the malarial proteasome with high species selectivity and improved pharmacokinetic properties. The compound demonstrates specific, time-dependent inhibition of the β5 subunit of the Pf20S, kills artemisinin-sensitive and artemisinin-resistant P. falciparum isolates in vitro and reduces parasitemia in humanized, P. falciparum-infected mice.

Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation

Background

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples’ ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases.

Methods

We performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma–Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds.

Results

Over the 100 ns molecular dynamics simulations of 3CL-PRO’s crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO.

Conclusion

We identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.

Flexibility and Cell Permeability of Cyclic Ras-Inhibitor Peptides Revealed by the Coupled Nosé-Hoover Equation

Quantifying the cell permeability of cyclic peptides is crucial for their rational drug design. However, the reasons remain unclear why a minor chemical modification, such as the difference between Ras inhibitors cyclorasin 9A5 and 9A54, can substantially change a peptide's permeability. To address this question, we performed enhanced sampling simulations of these two 11-mer peptides using the coupled Nosé-Hoover equation (cNH) we recently developed. The present cNH simulations realized temperature fluctuations over a wide range (240-600 K) in a dynamic manner, allowing structural samplings that were well validated by nuclear Overhauser effect measurements. The derived structural ensembles were comprehensively analyzed by all-atom structural clustering, mapping the derived clusters onto principal components (PCs) that characterize the cyclic structure, and calculating cluster-dependent geometric and chemical properties. The planar-open conformation was dominant in aqueous solvent, owing to inclusion of the Trp side chain in the main-chain ring, while the compact-closed conformation, which favors cell permeation due to its compactness and high polarity, was also accessible. Conformation-dependent cell permeability was observed in one of the derived PCs, demonstrating that decreased cell permeability in 9A54 is due to the high free energy barrier separating the two conformations. The origin of the change in free energy surface was determined to be loss of flexibility in the modified residues 2-3, resulting from the increased bulkiness of their side chains. The derived molecular mechanism of cell permeability highlights the significance of complete structural dynamics surveys for accelerating drug development with cyclic peptides.

Conformational energies and equilibria of cyclic dinucleotides in vacuo and in solution: computational chemistry vs. NMR experiments

Performance of computational methods in modelling cyclic dinucleotides - an important and challenging class of compounds - has been evaluated by two different benchmarks: (1) gas-phase conformational energies and (2) qualitative agreement with NMR observations of the orientation of the χ-dihedral angle in solvent. In gas-phase benchmarks, where CCSD(T) and DLPNO-CCSD(T) methods have been used as the reference, most of the (dispersion corrected) density functional approximations are accurate enough to justify prioritizing computational cost and compatibility with other modelling options as the criterion of choice. NMR experiments of 3'3'-c-di-AMP, 3'3'-c-GAMP, and 3'3'-c-di-GMP show the overall prevalence of the anti-conformation of purine bases, but some population of syn-conformations is observed for guanines. Implicit solvation models combined with quantum-chemical methods struggle to reproduce this behaviour, probably due to a lack of dynamics and explicitly modelled solvent, leading to structures that are too compact. Molecular dynamics simulations overrepresent the syn-conformation of guanine due to the overestimation of an intramolecular hydrogen bond. Our combination of experimental and computational benchmarks provides "error bars" for modelling cyclic dinucleotides in solvent, where such information is generally difficult to obtain, and should help gauge the interpretability of studies dealing with binding of cyclic dinucleotides to important pharmaceutical targets. At the same time, the presented analysis calls for improvement in both implicit solvation models and force-field parameters.

Design of Thioether Cyclic Peptide Scaffolds with Passive Permeability and Oral Exposure

Advances in the design of permeable peptides and in the synthesis of large arrays of macrocyclic peptides with diverse amino acids have evolved on parallel but independent tracks. Less precedent combines their respective attributes, thereby limiting the potential to identify permeable peptide ligands for key targets. Herein, we present novel 6-, 7-, and 8-mer cyclic peptides (MW 774-1076 g·mol-1) with passive permeability and oral exposure that feature the amino acids and thioether ring-closing common to large array formats, including DNA- and RNA-templated synthesis. Each oral peptide herein, selected from virtual libraries of partially N-methylated peptides using in silico methods, reflects the subset consistent with low energy conformations, low desolvation penalties, and passive permeability. We envision that, by retaining the backbone N-methylation pattern and consequent bias toward permeability, one can generate large peptide arrays with sufficient side chain diversity to identify permeability-biased ligands to a variety of protein targets.

Protein-Ligand Interactions in the STING Binding Site Probed by Rationally Designed Single-Point Mutations: Experiment and Theory

STING protein (stimulator of interferon genes) plays an important role in the innate immune system. A number of potent compounds regulating its activity have been reported, mostly derivatives of cyclic dinucleotides (CDNs), natural STING agonists. Here, we aim to provide complementary information to large-scale "ligand-profiling" studies by probing the importance of STING-CDN protein-ligand interactions on the protein side. We examined in detail six typical CDNs each in complex with 13 rationally devised mutations in STING: S162A, S162T, Y167F, G230A, R232K, R232H, A233L, A233I, R238K, T263A, T263S, R293Q, and G230A/R293Q. The mutations switch on and off various types of protein-ligand interactions: π-π stacking, hydrogen bonding, ionic pairing, and nonpolar contacts. We correlated experimental data obtained by differential scanning fluorimetry, X-ray crystallography, and isothermal titration calorimetry with theoretical calculations. This enabled us to provide a mechanistic interpretation of the differences in the binding of representative CDNs to STING. We observed that the G230A mutation increased the thermal stability of the protein-ligand complex, indicating an increased level of ligand binding, whereas R238K and Y167F led to a complete loss of stabilization (ligand binding). The effects of the other mutations depended on the type of ligand (CDN) and varied, to some extent. A very good correlation (R² = 0.6) between the experimental binding affinities and interaction energies computed by quantum chemical methods enabled us to explain the effect of the studied mutations in detail and evaluate specific interactions quantitatively. Our work may inspire development of high-affinity ligands against the common STING haplotypes by targeting the key (sometimes non-intuitive) protein-ligand interactions.

Omipalisib inspired macrocycles as dual PI3K/mTOR inhibitors

Activation of the phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) signaling pathway occurs frequently in a wide range of human cancers and is a main driver of cell growth, proliferation, survival, and chemoresistance of cancer cells. Compounds targeting this pathway are under active development as anticancer therapeutics and some of them have reached advanced clinical trials or been approved by the FDA. Dual PI3K/mTOR inhibitors combine multiple therapeutic efficacies in a single molecule by inhibiting the pathway both upstream and downstream of AKT. Herein, we report our efforts on the exploration of novel small molecule macrocycles (MCXs) as dual PI3K/mTOR inhibitors. Macrocyclization is an attractive approach used in drug discovery, as the semi-rigid character of these structures could provide improved potency, selectivity and favorable pharmacokinetic properties. Importantly, this strategy allows access to new chemical space thus obtaining a better intellectual property position. A series of MCXs based on GSK-2126458, a known clinical PI3K/mTOR inhibitor is described. These molecules showed potent biochemical and cellular dual PI3K/mTOR inhibition, demonstrated strong antitumoral effects in human cancer cell lines, and displayed good drug-like properties. Among them, MCX 83 presented remarkable selectivity against a panel of 468 kinases, high in vitro metabolic stability, and favorable pharmacokinetic parameters without significant CYP450 and h-ERG binding inhibition. This profile qualified this compound as a suitable candidate for future in vivo PK-PD and efficacy studies in mouse cancer models.

Benchmark of Generic Shapes for Macrocycles

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional “basic knowledge” alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Computational generation of an annotated gigalibrary of synthesizable, composite peptidic macrocycles

Peptidomimetic macrocycles have the potential to regulate challenging therapeutic targets. Structures of this type having precise shapes and drug-like character are particularly coveted, but are relatively difficult to synthesize. Our laboratory has developed robust methods that integrate small-peptide units into designed scaffolds. These methods create macrocycles and embed condensed heterocycles to diversify outcomes and improve pharmacological properties. The hypothetical scope of the methodology is vast and far outpaces the capacity of our experimental format. We now describe a computational rendering of our methodology that creates an in silico three-dimensional library of composite peptidic macrocycles. Our open-source platform, CPMG (Composite Peptide Macrocycle Generator), has algorithmically generated a library of 2,020,794,198 macrocycles that can result from the multistep reaction sequences we have developed. Structures are generated based on predicted site reactivity and filtered on the basis of physical and three-dimensional properties to identify maximally diverse compounds for prioritization. For conformational analyses, we also introduce ConfBuster++, an RDKit port of the open-source software ConfBuster, which allows facile integration with CPMG and ready parallelization for better scalability. Our approach deeply probes ligand space accessible via our synthetic methodology and provides a resource for large-scale virtual screening.

Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins

Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein surfaces, and otherwise have favorable properties. Macrocyclization of a known binder may stabilize its bioactive conformation and improve its metabolic stability, cell permeability, and in certain cases oral bioavailability. Herein, we present implementation and application of an approach that automatically generates, evaluates, and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as the starting point, this approach identifies attachment points, generates linkers, evaluates their geometric compatibility, and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROteolysis TArgeting Chimeras (PROTACs) and proteins.

A Virtual Screening Platform Identifies Chloroethylagelastatin A as a Potential Ribosomal Inhibitor

Chloroethylagelastatin A (CEAA) is an analogue of agelastatin A (AA), a natural alkaloid derived from a marine sponge. It is under development for therapeutic use against brain tumors as it has excellent central nervous system (CNS) penetration and pre-clinical therapeutic activity against brain tumors. Recently, AA was shown to inhibit protein synthesis by binding to the ribosomal A-site. In this study, we developed a novel virtual screening platform to perform a comprehensive screening of various AA analogues showing that AA analogues with proven therapeutic activity including CEAA have significant ribosomal binding capacity whereas therapeutically inactive analogues show poor ribosomal binding and revealing structural fingerprint features essential for drug-ribosome interactions. In particular, CEAA was found to have greater ribosomal binding capacity than AA. Biological tests showed that CEAA binds the ribosome and contributes to protein synthesis inhibition. Our findings suggest that CEAA may possess ribosomal inhibitor activity and that our virtual screening platform may be a useful tool in discovery and development of novel ribosomal inhibitors.

Synthetic group A streptogramin antibiotics that overcome Vat resistance

Natural products serve as chemical blueprints for the majority of antibiotics in our clinical arsenal. The evolutionary process by which these molecules arise is inherently accompanied by the co-evolution of resistance mechanisms that shorten the clinical lifetime of any given class¹. Virginiamycin acetyltransferases (Vats) are resistance proteins that provide protection against streptogramins², potent Gram-positive antibiotics that inhibit the bacterial ribosome³. Due to the challenge of selectively modifying the chemically complex, 23-membered macrocyclic scaffold of group A streptogramins, analogs that overcome Vat resistance have not been previously accessed². Here we report the design, synthesis, and antibacterial evaluation of group A streptogramin antibiotics with unprecedented structural variability. Using cryo-electron microscopy and forcefield-based refinement, we characterize the binding of eight analogs to the bacterial ribosome at high resolution, revealing new binding interactions that extend into the peptidyl tRNA binding site and towards synergistic binders that occupy the nascent peptide exit tunnel (NPET). One of these analogs has excellent activity against several streptogramin-resistant strains of S. aureus, exhibits decreased acetylation rates in vitro, and is effective at lowering bacterial load in a mouse model of infection. Our results demonstrate that the combination of rational design and modular chemical synthesis can revitalize classes of antibiotics that are limited by naturally arising resistance mechanisms.

Macrocycle Cell Permeability Measured by Solvation Free Energies in Polar and Apolar Environments

The relation of surface polarity and conformational preferences is decisive for cell permeability and thus bioavailability of macrocyclic drugs. Here, we employ grid inhomogeneous solvation theory (GIST) to calculate solvation free energies for a series of six macrocycles in water and chloroform as a measure of passive membrane permeability. We perform accelerated molecular dynamics simulations to capture a diverse structural ensemble in water and chloroform, allowing for a direct profiling of solvent-dependent conformational preferences. Subsequent GIST calculations facilitate a quantitative measure of solvent preference in the form of a transfer free energy, calculated from the ensemble-averaged solvation free energies in water and chloroform. Hence, the proposed method considers how the conformational diversity of macrocycles in polar and apolar solvents translates into transfer free energies. Following this strategy, we find a striking correlation of 0.92 between experimentally determined cell permeabilities and calculated transfer free energies. For the studied model systems, we find that the transfer free energy exceeds the purely water-based solvation free energies as a reliable estimate of cell permeability and that conformational sampling is imperative for a physically meaningful model. We thus recommend this purely physics-based approach as a computational tool to assess cell permeabilities of macrocyclic drug candidates.

Structure-Permeability Relationship of Semipeptidic Macrocycles-Understanding and Optimizing Passive Permeability and Efflux Ratio

We herein report the first thorough analysis of the structure-permeability relationship of semipeptidic macrocycles. In total, 47 macrocycles were synthesized using a hybrid solid-phase/solution strategy, and then their passive and cellular permeability was assessed using the parallel artificial membrane permeability assay (PAMPA) and Caco-2 assay, respectively. The results indicate that semipeptidic macrocycles generally possess high passive permeability based on the PAMPA, yet their cellular permeability is governed by efflux, as reported in the Caco-2 assay. Structural variations led to tractable structure-permeability and structure-efflux relationships, wherein the linker length, stereoinversion, N-methylation, and peptoids site-specifically impact the permeability and efflux. Extensive nuclear magnetic resonance, molecular dynamics, and ensemble-based three-dimensional polar surface area (3D-PSA) studies showed that ensemble-based 3D-PSA is a good predictor of passive permeability.

The emerging role of computational design in peptide macrocycle drug discovery

Drug discovery is a laborious process with rising cost per new drug. Peptide macrocycles are promising therapeutics, though conformational flexibility can reduce target affinity and specificity. Recent computational advancements address this problem by enabling rational design of rigidly folded peptide macrocycles.

AREAS COVERED

This review summarizes currently approved peptide macrocycle therapeutics and discusses advantages of mesoscale drugs over small molecules or protein therapeutics. It describes the history, rationale, and state of the art of computational tools, such as Rosetta, that allow the design of rigidly structured peptide macrocycles. The emerging pipeline for designing peptide macrocycle drugs is described, including current challenges in designing permeable molecules that can emulate the chameleonic behavior of natural macrocycles. Prospects for reducing computational cost and improving accuracy with emerging computational technologies are also discussed.

EXPERT OPINION

To embrace computational design of peptide macrocycle drugs, we must shift current attitudes regarding the role of computation in drug discovery, and move beyond Lipinski's rules. This technology has the potential to shift failures to earlier in silico stages of the drug discovery process, improving success rates in costly clinical trials. Given the available tools, now is the time for drug developers to incorporate peptide macrocycle design into drug discovery pipelines.

SIME: synthetic insight-based macrolide enumerator to generate the V1B library of 1 billion macrolides

We report on a new cheminformatics enumeration technology—SIME, synthetic insight-based macrolide enumerator—a new and improved software technology. SIME can enumerate fully assembled macrolides with synthetic feasibility by utilizing the constitutional and structural knowledge extracted from biosynthetic aspects of macrolides. Taken into account by the software are key information such as positions in macrolide structures at which chemical components can be inserted, and the types of structural motifs and sugars of interest that can be synthesized and incorporated at those positions. Additionally, we report on the chemical distribution analysis of the newly SIME-generated V1B (virtual 1 billion) library of macrolides. Those compounds were built based on the core of the Erythromycin structure, 13 structural motifs and a library of sugars derived from eighteen bioactive macrolides. This new enumeration technology can be coupled with cheminformatics approaches such as QSAR modeling and molecular docking to aid in drug discovery for rational designing of next generation macrolide therapeutics with desirable pharmacokinetic properties.

Improving Conformer Generation for Small Rings and Macrocycles Based on Distance Geometry and Experimental Torsional-Angle Preferences

The conformer generator ETKDG is a stochastic search method that utilizes distance geometry together with knowledge derived from experimental crystal structures. It has been shown to generate good conformers for acyclic, flexible molecules. This work builds on ETKDG to improve conformer generation of molecules containing small or large aliphatic (i.e., non-aromatic) rings. For one, we devise additional torsional-angle potentials to describe small aliphatic rings and adapt the previously developed potentials for acyclic bonds to facilitate the sampling of macrocycles. However, due to the larger number of degrees of freedom of macrocycles, the conformational space to sample is much broader than for small molecules, creating a challenge for conformer generators. We therefore introduce different heuristics to restrict the search space of macrocycles and bias the sampling toward more experimentally relevant structures. Specifically, we show the usage of elliptical geometry and customizable Coulombic interactions as heuristics. The performance of the improved ETKDG is demonstrated on test sets of diverse macrocycles and cyclic peptides. The code developed here will be incorporated into the 2020.03 release of the open-source cheminformatics library RDKit.

Adapting free energy perturbation simulations for large macrocyclic ligands: how to dissect contributions from direct binding and free ligand flexibility

Large and flexible ligands gain increasing interest in the development of bioactive agents. They challenge the applicability of computational ligand optimization strategies originally developed for small molecules. Free energy perturbation (FEP) is often used for predicting binding affinities of small molecule ligands, however, its use for more complex ligands remains limited. Herein, we report the structure-based design of peptide macrocycles targeting the protein binding site of human adaptor protein 14-3-3. We observe a surprisingly strong dependency of binding affinities on relatively small variations in substituent size. FEP was performed to rationalize observed trends. To account for insufficient convergence of FEP, restrained calculations were performed and complemented with extensive REST MD simulations of the free ligands. These calculations revealed that changes in affinity originate both from altered direct interactions and conformational changes of the free ligand. In addition, MD simulations provided the basis to rationalize unexpected trends in ligand lipophilicity. We also verified the anticipated interaction site and binding mode for one of the high affinity ligands by X-ray crystallography. The introduced fully-atomistic simulation protocol can be used to rationalize the development of structurally complex ligands which will support future ligand maturation efforts.

Conformational analysis of macrocycles: comparing general and specialized methods

Abstract

Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-ray_ppw), and (v) to the X-ray_ppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-ray_ppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-ray_ppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-ray_ppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima.

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s10822-020-00277-2) contains supplementary material, which is available to authorized users.

Conformation of the Macrocyclic Drug Lorlatinib in Polar and Nonpolar Environments: A MD Simulation and NMR Study

The replica exchange molecular dynamics (REMD) simulation is demonstrated to readily predict the conformations of the macrocyclic drug lorlatinib, as validated by solution NMR studies. In aqueous solution, lorlatinib adopts a conformer identical to its target bound structure. This conformer is stabilized by an extensive hydrogen bond network to the solvents. In chloroform, lorlatinib populates two conformers with the second one being less polar, which may contribute to lorlatinib's ability to cross cell membranes.

Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

Enzymatic Preparation of 2'-5',3'-5'-Cyclic Dinucleotides, Their Binding Properties to Stimulator of Interferon Genes Adaptor Protein, and Structure/Activity Correlations

Cyclic dinucleotides are second messengers in the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway, which plays an important role in recognizing tumor cells and viral or bacterial infections. They bind to the STING adaptor protein and trigger expression of cytokines via TANK binding kinase 1 (TBK1)/interferon regulatory factor 3 (IRF3) and inhibitor of nuclear factor-κB (IκB) kinase (IKK)/nuclear factor-κB (NFκB) signaling cascades. In this work, we describe an enzymatic preparation of 2'-5',3'-5'-cyclic dinucleotides (2'3'CDNs) with use of cyclic GMP-AMP synthases (cGAS) from human, mouse, and chicken. We profile substrate specificity of these enzymes by employing a small library of nucleotide-5'-triphosphate (NTP) analogues and use them to prepare 33 2'3'CDNs. We also determine affinity of these CDNs to five different STING haplotypes in cell-based and biochemical assays and describe properties needed for their optimal activity toward all STING haplotypes. Next, we study their effect on cytokine and chemokine induction by human peripheral blood mononuclear cells (PBMCs) and evaluate their cytotoxic effect on monocytes. Additionally, we report X-ray crystal structures of two new CDNs bound to STING protein and discuss structure-activity relationship by using quantum and molecular mechanical (QM/MM) computational modeling.

Modelling the binding mode of macrocycles: Docking and conformational sampling

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.