Visualizing cyclic peptide hydration at the single-molecule level

Incorporation of phenylcarbonyl groups in the sidechain: A tool to induce ordered assembly of peptides on surfaces

The possibility of introducing various functionalities on peptides with relative ease allows them to be used for molecular applications. However, oligopeptides prepared entirely from proteinogenic amino acids seldom assemble as ordered structures on surfaces. Therefore, sidechain modifications of peptides that can increase the intermolecular interactions without altering the constitution of a given peptide become an attractive route to self-assembling them on surfaces. We find that replacing phenylalanine residues with unusual amino acids that have phenylcarbonyl sidechains in oligopeptides increases the formation of ordered self-assembly on a highly ordered pyrolytic graphite surface. Peptides containing the modified amino acids provided extended long-range ordered assemblies, while the analogous peptides containing phenylalanine residues failed to form long-range assemblies. X-ray crystallographic analysis of the bulk structures of these peptides and the analogous peptides containing phenylalanine residues reveal that such modifications do not alter the secondary structure in crystals. It also reveals that the secondary hydrogen bonding interaction through phenylcarbonyl sidechains facilitates extended growth of the peptides on graphite.

Accurate Mass Identification of an Interfering Water Adduct and Strategies in Development and Validation of an LC-MS/MS Method for Quantification of MPI8, a Potent SARS-CoV-2 Main Protease Inhibitor, in Rat Plasma in Pharmacokinetic Studies

MPI8, a peptidyl aldehyde, is a potent antiviral agent against coronavirus. Due to unique tri-peptide bonds and the formyl functional group, the bioassay of MPI8 in plasma was challenged by a strong interference from water MPI8. Using QTOF LC-MS/MS, we identified MPI8•H2O as the major interference form that co-existed with MPI8 in aqueous and biological media. To avoid the resolution of MPI8 and MPI8•H2O observed on reverse phase columns, we found that a Kinetex hydrophilic interaction liquid chromatography (HILIC) column provided co-elution of both MPI8 and MPI8•H2O with a good single chromatographic peak and column retention of MPI8 which is suitable for quantification. Thus, a sensitive, specific, and reproducible LC-MS/MS method for the quantification of MPI8 in rat plasma was developed and validated using a triple QUAD LC-MS/MS. The chromatographic separation was achieved on a Kinetex HILIC column with a flow rate of 0.4 mL/min under gradient elution. The calibration curves were linear (r2 > 0.99) over MPI8 concentrations from 0.5−500 ng/mL. The accuracy and precision are within acceptable guidance levels. The mean matrix effect and recovery were 139% and 73%, respectively. No significant degradation of MPI8 occurred under the experimental conditions. The method was successfully applied to a pharmacokinetic study of MPI8 after administration of MPI8 sulfonate in rats.

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.

Cosolvents Restrain Self-Assembly of a Fibroin-Like Peptide on Graphite

Controllable self-assembly of peptides on solid surfaces has been investigated for establishing functional bio/solid interfaces. In this work, we study the influence of organic solvents on the self-assembly of a fibroin-like peptide on a graphite surface. The peptide has been designed by mimicking fibroin proteins to have strong hydrogen bonds among peptides enabling their self-assembly. We have employed cosolvents of water and organic solvents with a wide range of dielectric constants to control peptide self-assembly on the surface. Atomic force microscopy has revealed that the peptides self-assemble into highly ordered monolayer-thick linear structures on graphite after incubation in pure water, where the coverage of peptides on the surface is more than 85%. When methanol is mixed, the peptide coverage becomes zero at a threshold concentration of 30% methanol on graphite and 25% methanol on MoS₂. The threshold concentration in ethanol, isopropanol, dimethyl sulfoxide, and acetone varies depending on the dielectric constant with restraining self-assembly of the peptides, and particularly low dielectric-constant protic solvents prevent the peptide self-assembly significantly. The observed phenomena are explained by competitive surface adsorption of the organic solvents and peptides and the solvation effect of the peptide assembly.

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.

Rethinking Ion Transport by Ionophores: Experimental and Computational Investigation of Single Water Hydration in Valinomycin-K+ Complexes

Valinomycin is a macrocyclic ionophore that transports K⁺ across hydrophobic membranes. Its function depends on selectivity, capture, transport, and release of the ion. While thermodynamics clearly indicate that valinomycin binds K⁺ preferentially over all other alkali ions, characterizing the capture/transport/release of K⁺ by valinomycin at the molecular level remains a challenge. The bracelet-like structure of valinomycin-K⁺ (K⁺VM) has the ion completely enveloped, facilitating transport through the cell membrane. We report that hydration by a single water molecule, (K⁺VM)(H₂O), produces three different conformers, identified by infrared spectroscopy and supporting computational studies. For two minor conformers, the water prevents the ionophore from closing, a conformation that would inhibit diffusion through the membrane. However, the dominant conformer encloses both the ion and the water, replicating the bracelet-like K⁺VM and arguably enhancing diffusion through the membrane. This potential for active participation of water in transport through the hydrophobic cellular membrane has never been previously considered.

Two-Dimensional Coordination Networks from Cyclic Dipeptides

Peptide-based biomimetic nanostructures and metal-organic coordination networks on surfaces are two promising classes of hybrid materials which have been explored recently. However, despite the great versatility and structural variability of natural and synthetic peptides, the two directions have so far not been merged in fabrication of metal-organic coordination networks using peptides as building blocks. Here we demonstrate that cyclic peptides can be used as ligands to form highly ordered, two-dimensional, peptide-based metal-organic coordination networks. The networks are formed on a Au(111) surface through coadsorption of cyclic dialanine with Cu-adatoms under Ultra-High Vacuum (UHV) conditions. Scanning Tunneling Microscopy (STM) in combination with X-ray Photoelectron spectroscopy (XPS) has been utilized to characterize the network structures at submolecular resolution and expound the chemical changes involved in network coordination. The networks involve a motif of three cyclic dialanine molecules coordinating to a central Cu-adatom. Interestingly the networks expose pores functionalized by the side chain of the cyclic peptide, suggesting a general method to form functionalized porous metal-organic networks on surfaces.

Supramolecular Assemblies on Surfaces: Nanopatterning, Functionality, and Reactivity

Understanding how molecules interact to form large-scale hierarchical structures on surfaces holds promise for building designer nanoscale constructs with defined chemical and physical properties. Here, we describe early advances in this field and highlight upcoming opportunities and challenges. Both direct intermolecular interactions and those that are mediated by coordinated metal centers or substrates are discussed. These interactions can be additive, but they can also interfere with each other, leading to new assemblies in which electrical potentials vary at distances much larger than those of typical chemical interactions. Earlier spectroscopic and surface measurements have provided partial information on such interfacial effects. In the interim, scanning probe microscopies have assumed defining roles in the field of molecular organization on surfaces, delivering deeper understanding of interactions, structures, and local potentials. Self-assembly is a key strategy to form extended structures on surfaces, advancing nanolithography into the chemical dimension and providing simultaneous control at multiple scales. In parallel, the emergence of graphene and the resulting impetus to explore 2D materials have broadened the field, as surface-confined reactions of molecular building blocks provide access to such materials as 2D polymers and graphene nanoribbons. In this Review, we describe recent advances and point out promising directions that will lead to even greater and more robust capabilities to exploit designer surfaces.

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.

Single-molecule insights into surface-mediated homochirality in hierarchical peptide assembly

Homochirality is very important in the formation of advanced biological structures, but the origin and evolution mechanisms of homochiral biological structures in complex hierarchical process is not clear at the single-molecule level. Here we demonstrate the single-molecule investigation of biological homochirality in the hierarchical peptide assembly, regarding symmetry break, chirality amplification, and chirality transmission. We find that homochirality can be triggered by the chirality unbalance of two adsorption configuration monomers. Co-assembly between these two adsorption configuration monomers is very critical for the formation of homochiral assemblies. The site-specific recognition is responsible for the subsequent homochirality amplification and transmission in their hierarchical assembly. These single-molecule insights open up inspired thoughts for understanding biological homochirality and have general implications for designing and fabricating artificial biomimetic hierarchical chiral materials.

Most chiral molecules and structures in living organisms exist as single enantiomers, but why? Here, the authors investigated surface-mediated homochirality on the single-molecule level and show that it can be triggered by the chirality unbalance of two adsorption configuration monomers.

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.

Imaging proteins at the single-molecule level

Imaging single proteins has been a long-standing ambition for advancing various fields in natural science, as for instance structural biology, biophysics, and molecular nanotechnology. In particular, revealing the distinct conformations of an individual protein is of utmost importance. Here, we show the imaging of individual proteins and protein complexes by low-energy electron holography. Samples of individual proteins and protein complexes on ultraclean freestanding graphene were prepared by soft-landing electrospray ion beam deposition, which allows chemical- and conformational-specific selection and gentle deposition. Low-energy electrons do not induce radiation damage, which enables acquiring subnanometer resolution images of individual proteins (cytochrome C and BSA) as well as of protein complexes (hemoglobin), which are not the result of an averaging process.

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.

Mass Spectrometry as a Preparative Tool for the Surface Science of Large Molecules

Measuring and understanding the complexity that arises when nanostructures interact with their environment are one of the major current challenges of nanoscale science and technology. High-resolution microscopy methods such as scanning probe microscopy have the capacity to investigate nanoscale systems with ultimate precision, for which, however, atomic scale precise preparation methods of surface science are a necessity. Preparative mass spectrometry (pMS), defined as the controlled deposition of m/z filtered ion beams, with soft ionization sources links the world of large, biological molecules and surface science, enabling atomic scale chemical control of molecular deposition in ultrahigh vacuum (UHV). Here we explore the application of high-resolution scanning probe microscopy and spectroscopy to the characterization of structure and properties of large molecules. We introduce the fundamental principles of the combined experiments electrospray ion beam deposition and scanning tunneling microscopy. Examples for the deposition and investigation of single particles, for layer and film growth, and for the investigation of electronic properties of individual nonvolatile molecules show that state-of-the-art pMS technology provides a platform analog to thermal evaporation in conventional molecular beam epitaxy. Additionally, it offers additional, unique features due to the use of charged polyatomic particles. This new field is an enormous sandbox for novel molecular materials research and demands the development of advanced molecular ion beam technology.