Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations.

Nicotinic acetylcholine receptor (nAChR) subtypes are key drug targets, but it is challenging to pharmacologically differentiate between them because of their highly similar sequence identities. Furthermore, α-conotoxins (α-CTXs) are naturally selective and competitive antagonists for nAChRs and hold great potential for treating nAChR disorders. Identifying selectivity-enhancing mutations is the chief aim of most α-CTX mutagenesis studies, although doing so with traditional docking methods is difficult due to the lack of α-CTX/nAChR crystal structures. Here, we use homology modeling to predict the structures of α-CTXs bound to two nearly identical nAChR subtypes, α3ß2 and α3ß4, and use free-energy perturbation (FEP) to re-predict the relative potency and selectivity of α-CTX mutants at these subtypes. First, we use three available crystal structures of the nAChR homologue, acetylcholine-binding protein (AChBP), and re-predict the relative affinities of twenty point mutations made to the α-CTXs LvIA, LsIA, and GIC, with an overall root mean square error (RMSE) of 1.08 ± 0.15 kcal/mol and an R2 of 0.62, equivalent to experimental uncertainty. We then use AChBP as a template for α3ß2 and α3ß4 nAChR homology models bound to the α-CTX LvIA and re-predict the potencies of eleven point mutations at both subtypes, with an overall RMSE of 0.85 ± 0.08 kcal/mol and an R2 of 0.49. This is significantly better than the widely used molecular mechanics-generalized born/surface area (MM-GB/SA) method, which gives an RMSE of 1.96 ± 0.24 kcal/mol and an R2 of 0.06 on the same test set. Next, we demonstrate that FEP accurately classifies α3ß2 nAChR selective LvIA mutants while MM-GB/SA does not. Finally, we use FEP to perform an exhaustive amino acid mutational scan of LvIA and predict fifty-two mutations of LvIA to have greater than 100X selectivity for the α3ß2 nAChR. Our results demonstrate the FEP is well-suited to accurately predict potency- and selectivity-enhancing mutations of α-CTXs for nAChRs and to identify alternative strategies for developing selective α-CTXs.

Evaluation of Free Energy Calculations for the Prioritization of Macrocycle Synthesis.

A tremendous research and development effort was exerted toward combating chronic hepatitis C, ultimately leading to curative oral treatments, all of which are targeting viral proteins. Despite the advantage of numerous targets allowing for broad hepatitis C virus (HCV) genotype coverage, the only host target inhibitors that advanced into clinical development were Cyclosporin A based cyclophilin inhibitors. While cyclosporin-based molecules typically require a fermentation process, Gilead successfully pursued a fully synthetic, oral program based on Sanglifehrin A. The drug discovery process, though greatly helped by facile crystallography, was still hampered by the limitations in the accuracy of predictive computational methods for prioritizing compound ideas. Recent advances in accuracy and speed of free energy perturbation (FEP) methods, however, are attractive for prioritizing and derisking synthetically challenging molecules and potentially could have had a significant impact on the speed of the development of this program. Here in our simulated prospective study, the binding free energies of 26 macrocyclic cyclophilin inhibitors were blindly predicted using FEP+ to test this hypothesis. The predictions had a low mean unsigned error (MUE) (1.1 kcal/mol) and accurately reproduced many design decisions from the program, suggesting that FEP+ has the potential to drive synthetic chemistry efforts by more accurately ranking compounds with nonintuitive structure-activity relationships (SARs).

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

A novel method for exploring macrocycle conformational space, Prime macrocycle conformational sampling (Prime-MCS), is introduced and evaluated in the context of other available algorithms (Molecular Dynamics, LowModeMD in MOE, and MacroModel Baseline Search). The algorithms were benchmarked on a data set of 208 macrocycles which was curated for diversity from the Cambridge Structural Database, the Protein Data Bank, and the Biologically Interesting Molecule Reference Dictionary. The algorithms were evaluated in terms of accuracy (ability to reproduce the crystal structure), diversity (coverage of conformational space), and computational speed. Prime-MCS most reliably reproduced crystallographic structures for RMSD thresholds >1.0 Å, most often produced the most diverse conformational ensemble, and was most often the fastest algorithm. Detailed analysis and examination of both typical and outlier cases were performed to reveal characteristics, shortcomings, expected performance, and complementarity of the methods.

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.

Potency-Enhancing Mutations of Gating Modifier Toxins for the Voltage-Gated Sodium Channel NaV1.7 Can Be Predicted Using Accurate Free-Energy Calculations.

Gating modifier toxins (GMTs) isolated from venomous organisms such as Protoxin-II (ProTx-II) and Huwentoxin-IV (HwTx-IV) that inhibit the voltage-gated sodium channel NaV1.7 by binding to its voltage-sensing domain II (VSDII) have been extensively investigated as non-opioid analgesics. However, reliably predicting how a mutation to a GMT will affect its potency for NaV1.7 has been challenging. Here, we hypothesize that structure-based computational methods can be used to predict such changes. We employ free-energy perturbation (FEP), a physics-based simulation method for predicting the relative binding free energy (RBFE) between molecules, and the cryo electron microscopy (cryo-EM) structures of ProTx-II and HwTx-IV bound to VSDII of NaV1.7 to re-predict the relative potencies of forty-seven point mutants of these GMTs for NaV1.7. First, FEP predicted these relative potencies with an overall root mean square error (RMSE) of 1.0 ± 0.1 kcal/mol and an R2 value of 0.66, equivalent to experimental uncertainty and an improvement over the widely used molecular-mechanics/generalized born-surface area (MM-GB/SA) RBFE method that had an RMSE of 3.9 ± 0.8 kcal/mol. Second, inclusion of an explicit membrane model was needed for the GMTs to maintain stable binding poses during the FEP simulations. Third, MM-GB/SA and FEP were used to identify fifteen non-standard tryptophan mutants at ProTx-II[W24] predicted in silico to have a at least a 1 kcal/mol gain in potency. These predicted potency gains are likely due to the displacement of high-energy waters as identified by the WaterMap algorithm for calculating the positions and thermodynamic properties of water molecules in protein binding sites. Our results expand the domain of applicability of FEP and set the stage for its prospective use in biologics drug discovery programs involving GMTs and NaV1.7.

Water Thermodynamics of Peptide Toxin Binding Sites on Ion Channels.

Peptide toxins isolated from venomous creatures, long prized as research tools due to their innate potency for ion channels, are emerging as drugs as well. However, it remains challenging to understand why peptide toxins bind with high potency to ion channels, to identify residues that are key for activity, and to improve their affinities via mutagenesis. We use WaterMap, a molecular dynamics simulation-based method, to gain computational insight into these three questions by calculating the locations and thermodynamic properties of water molecules in the peptide toxin binding sites of five ion channels. These include an acid-sensing ion channel, voltage-gated potassium channel, sodium channel in activated and deactivated states, transient-receptor potential channel, and a nicotinic receptor whose structures were recently determined by crystallography and cryo-electron microscopy (cryo-EM). All channels had water sites in the peptide toxin binding site, and an average of 75% of these sites were stable (low-energy), and 25% were unstable (medium or high energy). For the sodium channel, more unstable water sites were present in the deactivated state structure than the activated. Additionally, for each channel, unstable water sites coincided with the positions of peptide toxin residues that previous mutagenesis experiments had shown were important for activity. Finally, for the sodium channel in the deactivated state, unstable water sites were present in the peptide toxin binding pocket but did not overlap with the peptide toxin, suggesting that future experimental efforts could focus on targeting these sites to optimize potency.

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.

High throughput evaluation of macrocyclization strategies for conformer stabilization.

While macrocyclization of a linear compound to stabilize a known bioactive conformation can be a useful strategy to increase binding potency, the difficulty of macrocycle synthesis can limit the throughput of such strategies. Thus computational techniques may offer the higher throughput required to screen large numbers of compounds. Here we introduce a method for evaluating the propensity of a macrocyclic compound to adopt a conformation similar that of a known active linear compound in the binding site. This method can be used as a fast screening tool for prioritizing macrocycles by leveraging the assumption that the propensity for the known bioactive substructural conformation relates to the affinity. While this method cannot to identify new interactions not present in the known linear compound, it could quickly differentiate compounds where the three dimensional geometries imposed by the macrocyclization prevent adoption of conformations with the same contacts as the linear compound in their conserved region. Here we report the implementation of this method using an RMSD-based structural descriptor and a Boltzmann-weighted propensity calculation and apply it retrospectively to three macrocycle linker optimization design projects. We found the method performs well in terms of prioritizing more potent compounds.

Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.