Insights into How Cyclic Peptides Switch Conformations

The structural and functional impacts of rationally designed cyclic peptides on self-assembly-mediated functionality

Compared with their linear counterparts, cyclic peptides, characterized by their unique topologies, offer superior stability and enhanced functionality. In this review article, the rational design of cyclic peptide primary structures and their significant influence on self-assembly processes and functional capabilities are comprehensively reviewed. We emphasize how strategically modifying amino acid sequences and ring sizes critically dictate the formation and properties of peptide nanotubes (PNTs) and complex assemblies, such as rotaxanes. Adjusting the number of amino acid residues and side chains allows researchers to tailor the diameter, surface properties, and functions of PNTs precisely. In addition, we discuss the complex host-guest chemistry of cyclic peptides and their ability to form rotaxanes, highlighting their potential in the development of mechanically interlocked structures with novel functionalities. Moreover, the critical role of computational methods for accurately predicting the solution structures of cyclic peptides is also highlighted, as it enables the design of novel peptides with tailored properties for a range of applications. These insights set the stage for groundbreaking advances in nanotechnology, drug delivery, and materials science, driven by the strategic design of cyclic peptide primary structures.

Accurate Structure Prediction for Cyclic Peptides Containing Proline Residues with High-Temperature Molecular Dynamics

Cyclic peptides (CPs) are emerging as promising drug candidates. Numerous natural CPs and their analogs are effective therapeutics against various diseases. Notably, many of them contain peptidyl cis-prolyl bonds. Due to the high rotational barrier of peptide bonds, conventional molecular dynamics simulations struggle to effectively sample the cis/trans-isomerization of peptide bonds. Previous studies have highlighted the high accuracy of the residue-specific force field (RSFF) and the high sampling efficiency of high-temperature molecular dynamics (high-T MD). Herein, we propose a protocol that combines high-T MD with RSFF2C and a recently developed reweighting method based on probability densities for accurate structure prediction of proline-containing CPs. Our method successfully predicted 19 out of 23 CPs with the backbone rmsd < 1.0 Å compared to X-ray structures. Furthermore, we performed high-T MD and density reweighting on the sunflower trypsin inhibitor (SFTI-1)/trypsin complex to demonstrate its applicability in studying CP-complexes containing cis-prolines. Our results show that the conformation of SFTI-1 in aqueous solution is consistent with its bound conformation, potentially facilitating its binding.

Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides

Molecular dynamics simulation is a powerful tool for characterizing the solution structural ensembles of cyclic peptides. However, the ability of simulation to recapitulate experimental results and make accurate predictions largely depends on the force fields used. In our work here, we evaluate the performance of seven state-of-the-art force fields in recapitulating the experimental NMR results in water of 12 benchmark cyclic peptides, consisting of 6 cyclic pentapeptides, 4 cyclic hexapeptides, and 2 cyclic heptapeptides. The results show that RSFF2+TIP3P, RSFF2C+TIP3P, and Amber14SB+TIP3P exhibit similar and the best performance, all recapitulating the NMR-derived structure information on 10 cyclic peptides. Amber19SB+OPC successfully recapitulates the NMR-derived structure information on 8 cyclic peptides. In contrast, OPLS-AA/M+TIP4P, Amber03+TIP3P, and Amber14SBonlysc+GB-neck2 could only recapitulate the NMR-derived structure information on 5 cyclic peptides, the majority of which are not well-structured.

Gaussian accelerated molecular dynamics simulations facilitate prediction of the permeability of cyclic peptides

Despite their widespread use as therapeutics, clinical development of small molecule drugs remains challenging. Among the many parameters that undergo optimization during the drug development process, increasing passive cell permeability (i.e., log(P)) can have some of the largest impact on potency. Cyclic peptides (CPs) have emerged as a viable alternative to small molecules, as they retain many of the advantages of small molecules (oral availability, target specificity) while being highly effective at traversing the plasma membrane. However, the relationship between the dominant conformations that typify CPs in an aqueous versus a membrane environment and cell permeability remain poorly characterized. In this study, we have used Gaussian accelerated molecular dynamics (GaMD) simulations to characterize the effect of solvent on the free energy landscape of lariat peptides, a subset of CPs that have recently shown potential for drug development (Kelly et al., JACS 2021). Differences in the free energy of lariat peptides as a function of solvent can be used to predict permeability of these molecules, and our results show that permeability is most greatly influenced by N-methylation and exposure to solvent. Our approach lays the groundwork for using GaMD as a way to virtually screen large libraries of CPs and drive forward development of CP-based therapeutics.

Computational Prediction of Cyclic Peptide Structural Ensembles and Application to the Design of Keap1 Binders

The Nrf2 transcription factor is a master regulator of the cellular response to oxidative stress, and Keap1 is its primary negative regulator. Activating Nrf2 by inhibiting the Nrf2-Keap1 protein-protein interaction has shown promise for treating cancer and inflammatory diseases. A loop derived from Nrf2 has been shown to inhibit Keap1 selectively, especially when cyclized, but there are no reliable design methods for predicting an optimal macrocyclization strategy. In this work, we employed all-atom, explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the relative degree of preorganization for a series of peptides cyclized with a set of bis-thioether "staples". We then correlated these predictions to experimentally measured binding affinities for Keap1 and crystal structures of the cyclic peptides bound to Keap1. This work showcases a computational method for designing cyclic peptides by simulating and comparing their entire solution-phase ensembles, providing key insights into designing cyclic peptides as selective inhibitors of protein-protein interactions.

Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design

Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.

Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles

Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R² = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R² = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R² = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R² = 0.97 and R² = 0.91 for cyclic pentapeptides and hexapeptides, respectively.

Native and Engineered Cyclic Disulfide-Rich Peptides as Drug Leads

Bioactive peptides are a highly abundant and diverse group of molecules that exhibit a wide range of structural and functional variation. Despite their immense therapeutic potential, bioactive peptides have been traditionally perceived as poor drug candidates, largely due to intrinsic shortcomings that reflect their endogenous heritage, i.e., short biological half-lives and poor cell permeability. In this review, we examine the utility of molecular engineering to insert bioactive sequences into constrained scaffolds with desired pharmaceutical properties. Applying lessons learnt from nature, we focus on molecular grafting of cyclic disulfide-rich scaffolds (naturally derived or engineered), shown to be intrinsically stable and amenable to sequence modifications, and their utility as privileged frameworks in drug design.

Chiral discrimination of cyclodecapeptide to anti-COVID-19 clinical candidates: a theoretical study

Various undesirable side effects are frequently associated with isomers of chiral clinical agents. The separation of chiral medicines remains a challenging issue in the medicines research. In this work, we employed cyclic decapeptide as the host molecule and the M06-2X theoretical computational method for chiral recognition of four clinical candidate guests and their isomers, including bucillamine, molnupiravir, azvudine, and VV116, which are relevant for the treatment of COVID-19. The obtained results indicated that bucillamine and molnupiravir and their respective isomers may be distinguished by cyclic decapeptide and that some of the isomers of Azvudine and VV116 may be discriminated by cyclic decapeptide. The inclusion conformation, deformation analysis, and electrostatic potential analysis also visualized the binding modes and binding sites between cyclic peptides and medicine candidates. A series of weak interaction analyses suggest that hydrogen bonding and dispersion interactions may be the primary factors for the recognition and separation of the clinical candidates by cyclic decapeptides. Visualized analyses of noncovalent interaction, hydrogen bond interaction, and NBO, AIM topological demonstrated that the difference of dispersion interaction is not obvious between the complexes, while the type and number of hydrogen bonds are very different, hinting that hydrogen bonds might be crucial for the differentiation of molnupiravir and its isomers. These findings might provide a theoretical reference for the identification and separation of chiral compounds in host-guest interaction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-023-02149-5.

LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling

Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates for bioengineering and pharmaceutical applications. To enable high-throughput computational prediction and design of lasso peptides, we developed a software, LassoHTP, for automatic lasso peptide structure construction and modeling. LassoHTP consists of three modules, including the scaffold constructor, mutant generator, and molecular dynamics (MD) simulator. With a user-provided sequence and conformational annotation, LassoHTP can either generate the structure and conformational ensemble as is or conduct random mutagenesis. We used LassoHTP to construct eight known lasso peptide structures de novo and to simulate their conformational ensembles for 100 ns MD simulations. For benchmarking, we calculated the root mean square deviation (RMSD) of these ensembles with reference to their experimental crystal or NMR PDB structures; we also compared these RMSD values against those of the MD ensembles that are initiated from the PDB structures. Dihedral principal component analysis was also conducted. The results show that the LassoHTP-initiated ensembles are similar to those of the PDB-initiated ensembles. LassoHTP offers a computational platform to develop strategies for lasso peptide prediction and design.

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.

Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

Structure prediction of cyclic peptides by molecular dynamics + machine learning

Recent computational methods have made strides in discovering well-structured cyclic peptides that preferentially populate a single conformation. However, many successful cyclic-peptide therapeutics adopt multiple conformations in solution. In fact, the chameleonic properties of some cyclic peptides are likely responsible for their high cell membrane permeability. Thus, we require the ability to predict complete structural ensembles for cyclic peptides, including the majority of cyclic peptides that have broad structural ensembles, to significantly improve our ability to rationally design cyclic-peptide therapeutics. Here, we introduce the idea of using molecular dynamics simulation results to train machine learning models to enable efficient structure prediction for cyclic peptides. Using molecular dynamics simulation results for several hundred cyclic pentapeptides as the training datasets, we developed machine-learning models that can provide molecular dynamics simulation-quality predictions of structural ensembles for all the hundreds of thousands of sequences in the entire sequence space. The prediction for each individual cyclic peptide can be made using less than 1 second of computation time. Even for the most challenging classes of poorly structured cyclic peptides with broad conformational ensembles, our predictions were similar to those one would normally obtain only after running multiple days of explicit-solvent molecular dynamics simulations. The resulting method, termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), is the first technique capable of efficiently predicting complete structural ensembles of cyclic peptides without relying on additional molecular dynamics simulations, constituting a seven-order-of-magnitude improvement in speed while retaining the same accuracy as explicit-solvent simulations.

CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting

Molecular dynamics (MD) simulations are an exceedingly and increasingly potent tool for molecular behavior prediction and analysis. However, the enormous wealth of data generated by these simulations can be difficult to process and render in a human-readable fashion. Cluster analysis is a commonly used way to partition data into structurally distinct states. We present a method that improves on the state of the art by taking advantage of the temporal information of MD trajectories to enable more accurate clustering at a lower memory cost. To date, cluster analysis of MD simulations has generally treated simulation snapshots as a mere collection of independent data points and attempted to separate them into different clusters based on structural similarity. This new method, cluster analysis of trajectories based on segment splitting (CATBOSS), applies density-peak-based clustering to classify trajectory segments learned by change detection. Applying the method to a synthetic toy model as well as four real-life data sets–trajectories of MD simulations of alanine dipeptide and valine dipeptide as well as two fast-folding proteins–we find CATBOSS to be robust and highly performant, yielding natural-looking cluster boundaries and greatly improving clustering resolution. As the classification of points into segments emphasizes density gaps in the data by grouping them close to the state means, CATBOSS applied to the valine dipeptide system is even able to account for a degree of freedom deliberately omitted from the input data set. We also demonstrate the potential utility of CATBOSS in distinguishing metastable states from transition segments as well as promising application to cases where there is little or no advance knowledge of intrinsic coordinates, making for a highly versatile analysis tool.

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.

Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations

Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.

Understanding Ring Puckering in Small Molecules and Cyclic Peptides

The geometry of a molecule plays a significant role in determining its physical and chemical properties. Despite its importance, there are relatively few studies on ring puckering and conformations, often focused on small cycloalkanes, 5- and 6-membered carbohydrate rings, and specific macrocycle families. We lack a general understanding of the puckering preferences of medium-sized rings and macrocycles. To address this, we provide an extensive conformational analysis of a diverse set of rings. We used Cremer-Pople puckering coordinates to study the trends of the ring conformation across a set of 140 000 diverse small molecules, including small rings, macrocycles, and cyclic peptides. By standardizing using key atoms, we show that the ring conformations can be classified into relatively few conformational clusters, based on their canonical forms. The number of such canonical clusters increases slowly with ring size. Ring puckering motions, especially pseudo-rotations, are generally restricted and differ between clusters. More importantly, we propose models to map puckering preferences to torsion space, which allows us to understand the inter-related changes in torsion angles during pseudo-rotation and other puckering motions. Beyond ring puckers, our models also explain the change in substituent orientation upon puckering. We also present a novel knowledge-based sampling method using the puckering preferences and coupled substituent motion to generate ring conformations efficiently. In summary, this work provides an improved understanding of general ring puckering preferences, which will in turn accelerate the identification of low-energy ring conformations for applications from polymeric materials to drug binding.

Steering New Drug Discovery Campaigns: Permeability, Solubility, and Physicochemical Properties in the bRo5 Chemical Space

An increasing number of drug discovery programs concern compounds in the beyond rule of 5 (bRo5) chemical space, such as cyclic peptides, macrocycles, and degraders. Recent results show that common paradigms of property-based drug design need revision to be applied to larger and more flexible compounds. A virtual event entitled "Solubility, permeability and physico-chemical properties in the bRo5 chemical space" was organized to provide preliminary guidance on how to make the discovery of oral drugs in the bRo5 space more effective. The four speakers emphasized the importance of the bRo5 space as a source of new oral drugs and provided examples of experimental and computational methods specifically tailored for design and optimization in this chemical space.

Cyclic peptides: backbone rigidification and capability of mimicking motifs at protein-protein interfaces

Cyclization is commonly employed in efforts to improve the target binding affinity of peptide-based probes and therapeutics. Many structural motifs have been identified at protein-protein interfaces and provide promising targets for inhibitor design using cyclic peptides. Cyclized peptides are generally assumed to be rigidified relative to their linear counterparts. This rigidification potentially pre-organizes the molecules to interact properly with their targets. However, the actual impact of cyclization on, for example, peptide configurational entropy, is currently poorly understood in terms of both its magnitude and molecular-level origins. Moreover, even with thousands of desired structural motifs at hand, it is currently not possible to a priori identify the ones that are most promising to mimic using cyclic peptides nor to select the ideal linker length. Instead, labor-intensive chemical synthesis and experimental characterization of various cyclic peptide designs are required, in hopes of finding one with improved target affinity. Herein, using molecular dynamics simulations of polyglycines, we elucidated how head-to-tail cyclization impacts peptide backbone dihedral entropy and developed a simple strategy to rapidly screen for structures that can be reliably mimicked by preorganized cyclic peptides. As expected, cyclization generally led to a reduction in backbone dihedral entropy; notably, however, this effect was minimal when the length of polyglycines was >9 residues. We also found that the reduction in backbone dihedral entropy upon cyclization of small polyglycine peptides does not result from more restricted distributions of the dihedrals; rather, it was the correlations between specific dihedrals that caused the decrease in configurational entropy in the cyclic peptides. Using our comprehensive cyclo-Gn structural ensembles, we obtained a holistic picture of what conformations are accessible to cyclic peptides. Using "hot loops" recently identified at protein-protein interfaces as an example, we provide clear guidelines for choosing the "easiest" hot loops for cyclic peptides to mimic and for identifying appropriate cyclic peptide lengths. In conclusion, our results provide an understanding of the thermodynamics and structures of this interesting class of molecules. This information should prove particularly useful for designing cyclic peptide inhibitors of protein-protein interactions.

Membrane Permeability in Cyclic Peptides is Modulated by Core Conformations

Cyclic peptides have the potential to bind to challenging targets, which are undruggable with small molecules, but their application is limited by low membrane permeability. Here, using a series of cyclic pentapeptides, we showed that established physicochemical criteria of permeable peptides are heavily violated. We revealed that a dominant core conformation, stabilized by amides' shielding pattern, could guide the design of novel compounds. As a result, counter-intuitive strategies, such as incorporation of polar residues, can be beneficial for permeability. We further find that core globularity is a promising descriptor, which can extend the capability of standard predictive models.

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.

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.

Computational investigation of retro-isomer equilibrium structures: Intrinsically disordered, foldable, and cyclic peptides

We use all-atom modeling and advanced-sampling molecular dynamics simulations to investigate quantitatively the effect of peptide bond directionality on the equilibrium structures of four linear (two foldable, two disordered) and two cyclic peptides. We find that the retro forms of cyclic and foldable linear peptides adopt distinctively different conformations compared to their parents. While the retro form of a linear intrinsically disordered peptide with transient secondary structure fails to reproduce a secondary structure content similar to that of its parent, the retro form of a shorter disordered linear peptide shows only minor differences compared to its parent.

Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.

Conformation and Permeability: Cyclic Hexapeptide Diastereomers

Conformational ensembles of eight cyclic hexapeptide diastereomers in explicit cyclohexane, chloroform, and water were analyzed by multicanonical molecular dynamics (McMD) simulations. Free-energy landscapes (FELs) for each compound and solvent were obtained from the molecular shapes and principal component analysis at T = 300 K; detailed analysis of the conformational ensembles and flexibility of the FELs revealed that permeable compounds have different structural profiles even for a single stereoisomeric change. The average solvent-accessible surface area (SASA) in cyclohexane showed excellent correlation with the cell permeability, whereas this correlation was weaker in chloroform. The average SASA in water correlated with the aqueous solubility. The average polar surface area did not correlate with cell permeability in these solvents. A possible strategy for designing permeable cyclic peptides from FELs obtained from McMD simulations is proposed.

β-Branched Amino Acids Stabilize Specific Conformations of Cyclic Hexapeptides

Cyclic peptides (CPs) are a promising class of molecules for drug development, particularly as inhibitors of protein-protein interactions. Predicting low-energy structures and global structural ensembles of individual CPs is critical for the design of bioactive molecules, but these are challenging to predict and difficult to verify experimentally. In our previous work, we used explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the global structural ensembles of cyclic hexapeptides containing different permutations of glycine, alanine, and valine. One peptide, cyclo-(VVGGVG) or P7, was predicted to be unusually well structured. In this work, we synthesized P7, along with a less well-structured control peptide, cyclo-(VVGVGG) or P6, and characterized their global structural ensembles in water using NMR spectroscopy. The NMR data revealed a structural ensemble similar to the prediction for P7 and showed that P6 was indeed much less well-structured than P7. We then simulated and experimentally characterized the global structural ensembles of several P7 analogs and discovered that β-branching at one critical position within P7 is important for overall structural stability. The simulations allowed deconvolution of thermodynamic factors that underlie this structural stabilization. Overall, the excellent correlation between simulation and experimental data indicates that our simulation platform will be a promising approach for designing well-structured CPs and also for understanding the complex interactions that control the conformations of constrained peptides and other macrocycles.

Understanding and designing head-to-tail cyclic peptides

Cyclic peptides (CPs) are an exciting class of molecules with a variety of applications. However, design strategies for CP therapeutics, for example, are generally limited by a poor understanding of their sequence-structure relationships. This knowledge gap often leads to a trial-and-error approach for designing CPs for a specific purpose, which is both costly and time-consuming. Herein, we describe the current experimental and computational efforts in understanding and designing head-to-tail CPs along with their respective challenges. In addition, we provide several future directions in the field of computational CP design to improve its accuracy, efficiency and applicability. These advances, combined with experimental techniques, shall ultimately provide a better understanding of these interesting molecules and a reliable working platform to rationally design CPs with desired characteristics.

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.

Use of a Compact Tripodal Tris(bipyridine) Ligand to Stabilize a Single-Metal-Centered Chirality: Stereoselective Coordination of Iron(II) and Ruthenium(II) on a Semirigid Hexapeptide Macrocycle

Fe(II)-coordinating hexapeptides containing three 2,2'-bipyridine moieties as side chains were designed and synthesized. A cyclic hexapeptide having three [(2,2'-bipyridin)-5-yl]-d-alanine (d-Bpa5) residues, in which d-Bpa5 and Gly are alternately arranged with 3-fold rotational symmetry, coordinated with Fe(II) to form a 1:1 octahedral Fe(II)-peptide complex with a single facial-Λ configuration of the metal-centered chirality. NMR spectroscopy and molecular dynamics simulations revealed that the Fe(II)-peptide complex has an apparent C₃-symmetric conformations on the NMR time scale, while the peptide backbone is subject to dynamic conformational exchange between three asymmetric β/γ conformations and one C₃-symmetric γ/γ/γ conformation. The semirigid cyclic hexapeptide preferentially arranged these conformations of the small octahedral Fe(II)-bipyridine complex, as well as the Ru(II) congener, to underpin the single configuration of the metal-centered chirality.

Designing Well-Structured Cyclic Pentapeptides Based on Sequence-Structure Relationships

Cyclic peptides are a promising class of molecules for unique applications. Unfortunately, cyclic peptide design is severely limited by the difficulty in predicting the conformations they will adopt in solution. In this work, we use explicit-solvent molecular dynamics simulations to design well-structured cyclic peptides by studying their sequence-structure relationships. Critical to our approach is an enhanced sampling method that exploits the essential transitional motions of cyclic peptides to efficiently sample their conformational space. We simulated a range of cyclic pentapeptides from all-glycine to a library of cyclo-(X1X2AAA) peptides to map their conformational space and determine cooperative effects of neighboring residues. By combining the results from all cyclo-(X1X2AAA) peptides, we developed a scoring function to predict the structural preferences for X1-X2 residues within cyclic pentapeptides. Using this scoring function, we designed a cyclic pentapeptide, cyclo-(GNSRV), predicted to be well structured in aqueous solution. Subsequent circular dichroism and NMR spectroscopy revealed that this cyclic pentapeptide is indeed well structured in water, with a nuclear Overhauser effect and J-coupling values consistent with the predicted structure.

Mapping the sequence-structure relationships of simple cyclic hexapeptides

Cyclic peptides are promising protein-protein interaction modulators with high binding affinities and specificities, as well as enhanced stabilities and oral availabilities over linear analogs. Despite their relatively small size and cyclic architecture, it is currently difficult to predict the favored conformation(s) of most classes of cyclic peptides. An improved understanding of the sequence-structure relationships for cyclic peptides will offer an avenue for the rational design of cyclic peptides as possible therapeutics. In this work, we systematically explored the sequence-structure relationships for two cyclic hexapeptide systems using molecular dynamics simulation techniques. Starting with an all-glycine cyclic hexapeptide, cyclo-G₆, we systematically replaced glycine residues with alanines and characterized the structural ensembles of different variants. The same process was repeated with valines to investigate the effects of larger side chains. An analysis of the origin of structure preferences was performed using thermodynamics decomposition and several general observations are reported.

Toward accurately modeling N-methylated cyclic peptides

Cyclic peptides have unique properties and can target protein surfaces specifically and potently. N-Methylation provides a promising way to further optimize the pharmacokinetic and structural profiles of cyclic peptides. The capability to accurately model structures adopted by N-methylated cyclic peptides would facilitate rational design of this interesting and useful class of molecules. We apply molecular dynamics simulations with advanced enhanced sampling methods to efficiently characterize the structural ensembles of N-methylated cyclic peptides, while simultaneously evaluating the overall performance of several simulation force fields. We find that one of the residue-specific force fields, RSFF2, is able to recapitulate experimental structures of the N-methylated cyclic peptide benchmarks tested here when the correct amide isomers are used as initial configurations and enforced during the simulations. Thus, using our simulation approach, it is possible to accurately and efficiently predict the structures of N-methylated cyclic peptides if sufficient information is available to determine the correct amide cis/trans configuration. Moreover, our results suggest that, upon further optimization of RSFF2 to more reliably predict cis/trans isomers, molecular dynamics simulations will be able to de novo predict N-methylated cyclic peptides in the near future, strongly motivating such continued optimization.

Peptide ligands for targeting the extracellular domain of EGFR: Comparison between linear and cyclic peptides

Colorectal cancer (CRC) is the third most common solid internal malignancy among cancers. Early detection of cancer is key to increasing the survival rate of colorectal cancer patients. Overexpression of the EGFR protein is associated with CRC. We have designed a series of peptides that are highly specific for the extracellular domain of EGFR, based on our earlier studies on linear peptides. The previously reported linear peptide LARLLT, known to bind to EGFR, was modified with the goals of increasing its stability and its specificity toward EGFR. Peptide modifications, including D-amino acid substitution, cyclization, and chain reversal, were investigated. In addition, to facilitate labeling of the peptide with a fluorescent dye, an additional lysine residue was introduced onto the linear (KLARLLT) and cyclic peptides cyclo(KLARLLT) (Cyclo.L1). The lysine residue was also converted into an azide group in both a linear and reversed cyclic peptide sequences cyclo(K(N3)larllt) (Cyclo.L1.1) to allow for subsequent "click" conjugation. The cyclic peptides showed enhanced binding to EGFR by SPR. NMR and molecular modeling studies suggest that the peptides acquire a β-turn structure in solution. In vitro stability studies in human serum show that the cyclic peptide is more stable than the linear peptide.

Consequences of Depsipeptide Substitution on the ClpP Activation Activity of Antibacterial Acyldepsipeptides

The acyldepsipeptide (ADEP) antibiotics operate through a clinically unexploited mechanism of action and thus have attracted attention from several antibacterial development groups. The ADEP scaffold is synthetically tractable, and deep-seated modifications have produced extremely potent antibacterial leads against Gram-positive pathogens. Although newly identified ADEP analogs demonstrate remarkable antibacterial activity against bacterial isolates and in mouse models of bacterial infections, stability issues pertaining to the depsipeptide core remain. To date, no study has been reported on the natural ADEP scaffold that evaluates the sole importance of the macrocyclic linkage on target engagement, molecular conformation, and bioactivity. To address this gap in ADEP structure-activity relationships, we synthesized three ADEP analogs that only differ in the linkage motif (i.e., ester, amide, and N-methyl amide) and provide a side-by-side comparison of conformational behavior and biological activity. We demonstrate that while replacement of the naturally occurring ester linkage with a secondary amide maintains in vitro biochemical activity, this simple substitution results in a significant drop in whole-cell activity. This study provides direct evidence that ester to amide linkage substitution is unlikely to provide a reasonable solution for ADEP instability.

Analysis of Loops that Mediate Protein-Protein Interactions and Translation into Submicromolar Inhibitors

Effective strategies for mimicking α-helix and β-strand epitopes have been developed, producing valuable inhibitors for some classes of protein-protein interactions (PPIs). However, there are no general strategies for translating loop epitopes into useful PPI inhibitors. In this work, we use the LoopFinder program to identify diverse sets of "hot loops," which are loop epitopes that mediate PPIs. These include loops that are well-suited to mimicry with macrocyclic compounds, and loops that are most similar to variable loops on antibodies and ankyrin repeat proteins. We present data-driven criteria for scoring loop-mediated PPIs, uncovering a trove of potentially druggable interactions. We also use unbiased clustering to identify common structures among the hot loops. To translate these insights into real-world inhibitors, we describe a robust, diversity-oriented strategy for the rapid production and evaluation of cyclized loops. This method is applied to a computationally identified loop in the PPI between stonin2 and Eps15, producing submicromolar inhibitors. The most potent inhibitor is well-structured in water and successfully mimics the native epitope. Overall, these computational and experimental strategies provide new opportunities to design inhibitors for an otherwise intractable set of PPIs.

Computational methods to design cyclic peptides

Cyclic peptides (CPs) are promising modulators of protein-protein interactions (PPIs), but their application remains challenging. It is currently difficult to predict the structures and bioavailability of CPs. The ability to design CPs using computer modeling would greatly facilitate the development of CPs as potent PPI modulators for fundamental studies and as potential therapeutics. Herein, we describe computational methods to generate CP libraries for virtual screening, as well as current efforts to accurately predict the conformations adopted by CPs. These advances are making it possible to envision robust computational design of active CPs. However, unique properties of CPs pose significant challenges associated with sampling CP conformational space and accurately describing CP energetics. These major obstacles to structure prediction likely must be solved before robust design of active CPs can be reliably achieved.