Potent and Protease Resistant Azapeptide Agonists of the GLP-1 and GIP Receptors.

The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play important physiological roles. Stabilized agonists of the GLP-1 receptor (GLP-1R) and the GIP receptor (GIPR) for the management of diabetes and obesity have generated widespread enthusiasm and have become blockbuster drugs. These therapeutics are refractory to the action of dipeptidyl peptidase-4 (DPP4), that catalyzes rapid removal of the two N-terminal residues of the native peptides, in turn severely diminishing their activity profiles.  Here we report that a single atom change from carbon to nitrogen in the backbone of the entire peptide make them refractory to DPP4 action while still retaining full potency and efficacy at their respective receptors.  This was accomplished by use of aza-amino acids, that are bioisosteric replacements for a-amino acids that perturb the structural backbone and local side chain conformations.  Molecular dynamics simulations reveal that aza-amino acid can populate the same conformational space that GLP-1 adopts when bound to the GLP-1R. The insertion of an aza-amino acid at the second position from the N-terminus in semaglutide and in a dual agonist of GLP-1R and GIPR further demonstrates its capability as a viable alternative to current DPP4 resistance strategies while offering additional structural variety.

A Boron-Dependent Antibiotic Derived from a Calcium-Dependent Antibiotic.

The prevalence of drug-resistant bacterial pathogens foreshadows a healthcare crisis. Calcium-dependent antibiotics (CDAs) are promising candidates to combat infectious diseases as many of them show modes of action (MOA) orthogonal to widespread resistance mechanisms. The calcium dependence is nonetheless one of the hurdles toward realizing their full potential. Using laspartomycin C (LspC) as a model, we explored the possibility of reducing, or even eliminating, its calcium dependence. We report herein a synthetic LspC analogue (B1) whose activity no longer depends on calcium and is instead induced by phenylboronic acid (PBA). In LspC, Asp1 and Asp7 coordinate to calcium to anchor it in the active conformation; these residues are replaced by serine in B1 and condense with PBA to form a boronic ester with the same anchoring effect. Using thin-layer chromatography, MS, NMR, and complementation assays, we demonstrate that B1 inhibits bacterial growth via the same MOA as LspC, i.e., sequestering the cell wall biosynthetic intermediate undecaprenyl phosphate. B1 is as potent and effective as LspC against several Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus. Our success in converting a CDA to a boron-dependent antibiotic opens a new avenue in the design and functional control of drug molecules.

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.

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.

Stapled ß-Hairpins Featuring 4-Mercaptoproline.

Peptides constrained by intramolecular cross-links, especially stapled α-helices, have emerged as versatile scaffolds for drug development. However, there are fewer examples of similarly constrained scaffolds for other secondary structures. Here, we used a novel computational strategy to identify an optimal staple for antiparallel ß-strands, and then we incorporated that staple within a ß-hairpin peptide. The hairpin uses 4-mercaptoproline as a novel staple component, which contributes to a unique, kinked structure. The stapled hairpins show a high degree of structure in aqueous solution, excellent resistance to degradation in cell lysates, and cytosolic penetration at micromolar concentrations. They also overlay with a unique subset of kinked hairpin motifs at protein-protein interaction interfaces. Thus, these scaffolds represent promising starting points for developing inhibitors of cellular protein-protein interactions.

Destabilized adaptive influenza variants critical for innate immune system escape are potentiated by host chaperones.

The threat of viral pandemics demands a comprehensive understanding of evolution at the host-pathogen interface. Here, we show that the accessibility of adaptive mutations in influenza nucleoprotein at fever-like temperatures is mediated by host chaperones. Particularly noteworthy, we observe that the Pro283 nucleoprotein variant, which (1) is conserved across human influenza strains, (2) confers resistance to the Myxovirus resistance protein A (MxA) restriction factor, and (3) critically contributed to adaptation to humans in the 1918 pandemic influenza strain, is rendered unfit by heat shock factor 1 inhibition-mediated host chaperone depletion at febrile temperatures. This fitness loss is due to biophysical defects that chaperones are unavailable to address when heat shock factor 1 is inhibited. Thus, influenza subverts host chaperones to uncouple the biophysically deleterious consequences of viral protein variants from the benefits of immune escape. In summary, host proteostasis plays a central role in shaping influenza adaptation, with implications for the evolution of other viruses, for viral host switching, and for antiviral drug development.

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.

ß-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 ß-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.

Generalized model for the diffusion of solvents in glassy polymers: From Fickian to Super Case II.

The diffusion of small solvent molecules in glassy polymers may take on a variety of different forms. Fickian, anomalous, Case II and Super Case II diffusion have all been observed, and theoretical models exist that describe each specific type of behavior. Here we present a single generalized kinetic model capable of yielding all these different types of diffusion on the basis of just two parameters. The principal determinant of the type of diffusion is observed to be a dimensionless parameter, γ, that describes the influence of solvent-induced swelling in lowering the potential barriers separating available solvent sites. A second parameter, η, which characterizes the effect of the solvent in reducing the potential energy of a solvent molecule when at rest at an available site, only influences the type of diffusion to a lesser extent. The theoretical analysis does not include any effects that are explicitly non-local in time, an example of which is the inclusion of polymer viscosity in the Thomas-Windle model; it thus represents a variant of Fick's second law utilizing a concentration-dependent diffusivity when η is small. To check the significance of time-delayed swelling, a simulation of a modified model was performed that contained a history-dependent term. The results were found to be very similar to those in the time-local model.

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.

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.

Genetically encoded discovery of perfluoroaryl macrocycles that bind to albumin and exhibit extended circulation in vivo.

Peptide-based therapeutics have gained attention as promising therapeutic modalities, however, their prevalent drawback is poor circulation half-life in vivo. In this paper, we report the selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine SNAr chemistry, with decafluoro-diphenylsulfone (DFS). Testing of the binding of the selected peptides to albumin identified SICRFFC as the lead sequence. We replaced DFS with isosteric pentafluorophenyl sulfide (PFS) and the PFS-SICRFFCGG exhibited KD = 4-6 µM towards human serum albumin. When injected in mice, the concentration of the PFS-SICRFFCGG in plasma was indistinguishable from the reference peptide, SA-21. More importantly, a conjugate of PFS-SICRFFCGG and peptide apelin-17 analogue (N3-PEG6-NMe17A2) showed retention in circulation similar to SA-21; in contrast, apelin-17 analogue was cleared from the circulation after 2 min. The PFS-SICRFFC is the smallest known peptide macrocycle with a significant affinity for human albumin and substantial in vivo circulation half-life. It is a productive starting point for future development of compact macrocycles with extended half-life in vivo.

A backbone-dependent rotamer library with high (ϕ, ψ) coverage using metadynamics simulations.

Backbone-dependent rotamer libraries are commonly used to assign the side chain dihedral angles of amino acids when modeling protein structures. Most rotamer libraries are created by curating protein crystal structure data and using various methods to extrapolate the existing data to cover all possible backbone conformations. However, these rotamer libraries may not be suitable for modeling the structures of cyclic peptides and other constrained peptides because these molecules frequently sample backbone conformations rarely seen in the crystal structures of linear proteins. To provide backbone-dependent side chain information beyond the α-helix, ß-sheet, and PPII regions, we used explicit-solvent metadynamics simulations of model dipeptides to create a new rotamer library that has high coverage in the (ϕ, ψ) space. Furthermore, this approach can be applied to build high-coverage rotamer libraries for noncanonical amino acids. The resulting Metadynamics of Dipeptides for Rotamer Distribution (MEDFORD) rotamer library predicts the side chain conformations of high-resolution protein crystal structures with similar accuracy (~80%) to a state-of-the-art rotamer library. Our ability to test the accuracy of MEDFORD at predicting the side chain dihedral angles of amino acids in noncanonical backbone conformation is restricted by the limited structural data available for cyclic peptides. For the cyclic peptide data that are currently available, MEDFORD and the state-of-the-art rotamer library perform comparably. However, the two rotamer libraries indeed make different rotamer predictions in noncanonical (ϕ, ψ) regions. For noncanonical amino acids, the MEDFORD rotamer library predicts the χ1 values with approximately 75% accuracy.

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.

N-Amination Converts Amyloidogenic Tau Peptides into Soluble Antagonists of Cellular Seeding.

The spread of neurofibrillary tangles composed of tau protein aggregates is a hallmark of Alzheimer's and related neurodegenerative diseases. Early oligomerization of tau involves conformational reorganization into parallel ß-sheet structures and supramolecular assembly into toxic fibrils. Despite the need for selective inhibitors of tau propagation, ß-rich protein assemblies are inherently difficult to target with small molecules. Here, we describe a minimalist approach to mimic the aggregation-prone modules within tau. We carried out a backbone residue scan and show that amide N-amination completely abolishes the tendency of these peptides to self-aggregate, rendering them soluble mimics of ordered ß-strands from the tau R2 and R3 domains. Several N-amino peptides (NAPs) inhibit tau fibril formation in vitro. We further demonstrate that NAPs 12 and 13 are effective at blocking the cellular seeding of endogenous tau by interacting with monomeric or fibrillar forms of extracellular tau. Peptidomimetic 12 is serum stable, non-toxic to neuronal cells, and selectivity inhibits the fibrilization of tau over Aß42. Structural analysis of our lead NAPs shows considerable conformational constraint imposed by the N-amino groups. The described backbone N-amination approach provides a rational basis for the mimicry of other aggregation-prone peptides that drive pathogenic protein assembly.

Genetically-encoded discovery of proteolytically stable bicyclic inhibitors for morphogen NODAL.

In this manuscript, we developed a two-fold symmetric linchpin (TSL) that converts readily available phage-displayed peptides libraries made of 20 common amino acids to genetically-encoded libraries of bicyclic peptides displayed on phage. TSL combines an aldehyde-reactive group and two thiol-reactive groups; it bridges two side chains of cysteine [C] with an N-terminal aldehyde group derived from the N-terminal serine [S], yielding a novel bicyclic topology that lacks a free N-terminus. Phage display libraries of SX1CX2X3X4X5X6X7C sequences, where X is any amino acid but Cys, were converted to a library of bicyclic TSL-[S]X1[C]X2X3X4X5X6X7[C] peptides in 45 ± 15% yield. Using this library and protein morphogen NODAL as a target, we discovered bicyclic macrocycles that specifically antagonize NODAL-induced signaling in cancer cells. At a 10 µM concentration, two discovered bicyclic peptides completely suppressed NODAL-induced phosphorylation of SMAD2 in P19 embryonic carcinoma cells. The TSL-[S]Y[C]KRAHKN[C] bicycle inhibited NODAL-induced proliferation of NODAL-TYK-nu ovarian carcinoma cells with apparent IC50 of 1 µM. The same bicycle at 10 µM concentration did not affect the growth of the control TYK-nu cells. TSL-bicycles remained stable over the course of the 72 hour-long assays in a serum-rich cell-culture medium. We further observed general stability in mouse serum and in a mixture of proteases (Pronase™) for 21 diverse bicyclic macrocycles of different ring sizes, amino acid sequences, and cross-linker geometries. TSL-constrained peptides to expand the previously reported repertoire of phage-displayed bicyclic architectures formed by cross-linking Cys side chains. We anticipate that it will aid the discovery of proteolytically stable bicyclic inhibitors for a variety of protein targets.

Polyvinylidene fluoride molecules in nanofibers, imaged at atomic scale by aberration corrected electron microscopy.

Atomic scale features of polyvinylidene fluoride molecules (PVDF) were observed with aberration corrected transmission electron microscopy. Thin, self-supporting PVDF nanofibers were used to create images that show conformations and relative locations of atoms in segments of polymer molecules, particularly segments near the surface of the nanofiber. Rows of CF2 atomic groups, at 0.25 nm intervals, which marked the paths of segments of the PVDF molecules, were seen. The fact that an electron microscope image of a segment of a PVDF molecule depended upon the particular azimuthal direction, along which the segment was viewed, enabled observation of twist around the molecular axis. The 0.2 nm side-by-side distance between the two fluorine atoms attached to the same carbon atom was clearly resolved. Morphological and chemical changes produced by energetic electrons, ranging from no change to fiber scission, over many orders of magnitude of electrons per unit area, promise quantitative new insights into radiation chemistry. Relative movements of segments of molecules were observed. Promising synergism between high resolution electron microscopy and molecular dynamic modeling was demonstrated. This paper is at the threshold of growing usefulness of electron microscopy to the science and engineering of polymer and other molecules.

[Effects of mulching patterns on soil water, broomcorn millet growth, photosynthetic charac- teristics and yield in the dryland of Loess Plateau in China].

The objective of this study was to explore the effects of mulching patterns on soil water, growth, photosynthetic characteristics, grain yield and water use efficiency (WUE) of broomcorn millet in the dryland of Loess Plateau in China. In a three-year field experiment from 2011 to 2013, we compared four different mulching patterns with traditional plat planting (no mulching) as the control (CK). The mulching patterns included W ridge covered with common plastic film + intredune covered with straw (SG), common ridge covered with common plastic film + intredune covered with straw (LM), double ridges covered with common plastic film + intredune covered with straw (QM), and the traditional plat planting covered with straw (JG). The results showed that the soil water storage in 0-100 cm layer was significantly higher in all mulching patterns than in CK, particularly in SG then followed by LM, QM and JG, and the differences among the mulching patterns reached a significant level at the different growth stages of broomcorn millet. Among all mulching patterns, SG had the greatest effect on the growth and photosynthesis of broomcorn millet, respectively increasing the yield and WUE by 55.9% and 64.9% over CK, and the differences among the mulching patterns also reached a significant level. Therefore, SG was recommended as an efficient planting pattern for broomcorn millet production in the dryland of Loess Plateau in China.