Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations

Anti-obesity and anti-diabetic bioactive peptides: A comprehensive review of their sources, properties, and techno-functional challenges

The scourge of obesity arising from obesogens and poor dieting still ravages our planet as half of the global population may be overweight and obese by 2035. This metabolic disorder is intertwined with type 2 diabetes (T2D), both of which warrant alternative therapeutic options other than clinically approved drugs like orlistat with their tendency of abuse and side effects. In this review, we comprehensively describe the global obesity problem and its connection to T2D. Obesity, overconsumption of fats, the mechanism of fat digestion, obesogenic gut microbiota, inhibition of fat digestion, and natural anti-obesity compounds are discussed. Similar discussions are made for diabetes with regard to glucose regulation, the diabetic gut microbiota, and insulinotropic compounds. The sources and production of anti-obesity bioactive peptides (AOBPs) and anti-diabetic bioactive peptides (ADBPs) are also described while explaining their structure-function relationships, gastrointestinal behaviors, and action mechanisms. Finally, the techno-functional applications of AOBPs and ADBPs are highlighted.

Synthesis of All-Peptide-Based Rotaxane from a Proline-Containing Cyclic Peptide

Rotaxane cross-linkers enhance the toughness of the resulting rotaxane cross-linked polymers through a stress dispersion effect, which is attributed to the mobility of the interlocked structure. To date, the compositional diversity of rotaxane cross-linkers has been limited, and the poor compatibility of these cross-linkers with peptides and proteins has made their use in such materials challenging. The synthesis of a rotaxane composed of peptides may result in a biodegradable cross-linker that is compatible with peptides and proteins, allowing the fortification of polypeptides and proteins and ultimately leading to the development of innovative materials that possess excellent mechanical properties and biodegradability. However, the chemical synthesis of all-peptide-based rotaxanes has remained elusive because of the absence of strong binding motifs in peptides, which prevents an axial peptide from penetrating a cyclic peptide. Here, we synthesized all-peptide-based rotaxanes using an active template method for proline-containing cyclic peptides. The results of molecular dynamics simulations suggested that cyclic peptides with an expansive inner cavity and carbonyl oxygens oriented toward the center are favorable for rotaxane synthesis. This rotaxane synthesis method is expected to accelerate the synthesis of peptides and proteins with mechanically interlocked structures, potentially leading to the development of peptide- and protein-based materials with unprecedented functionalities.

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.

BDNF- and VEGF-Responsive Stimulus to an NGF Mimic Cyclic Peptide with Copper Ionophore Capability and Ctr1/CCS-Driven Signaling

Neurotrophins are a family of growth factors that play a key role in the development and regulation of the functioning of the central nervous system. Their use as drugs is made difficult by their poor stability, cellular permeability, and side effects. Continuing our effort to use peptides that mimic the neurotrophic growth factor (NGF), the family model protein, and specifically the N-terminus of the protein, here we report on the spectroscopic characterization and resistance to hydrolysis of the 14-membered cyclic peptide reproducing the N-terminus sequence (SSSHPIFHRGEFSV (c-NGF(1-14)). Far-UV CD spectra and a computational study show that this peptide has a rigid conformation and left-handed chirality typical of polyproline II that favors its interaction with the D5 domain of the NGF receptor TrkA. c-NGF(1-14) is able to bind Cu2+ with good affinity; the resulting complexes have been characterized by potentiometric and spectroscopic measurements. Experiments on PC12 cells show that c-NGF(1-14) acts as an ionophore, influencing the degree and the localization of both the membrane transporter (Ctr1) and the copper intracellular transporter (CCS). c-NGF(1-14) induces PC12 differentiation, mimics the protein in TrkA phosphorylation, and activates the kinase cascade, inducing Erk1/2 phosphorylation. c-NGF(1-14) biological activities are enhanced when the peptide interacts with Cu2+ even with the submicromolar quantities present in the culture media as demonstrated by ICP-OES measurements. Finally, c-NGF(1-14) and Cu2+ concur to activate the cAMP response element-binding protein CREB that, in turn, induces the brain-derived neurotrophic factor (BDNF) and the vascular endothelial growth factor (VEGF) release.

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

Mechanism of RGD-conjugated nanodevice binding to its target protein integrin αVβ3 by atomistic molecular dynamics and machine learning

Active targeting strategies have been proposed to enhance the selective uptake of nanoparticles (NPs) by diseased cells, and recent experimental findings have proven the effectiveness of this approach. However, no mechanistic studies have yet revealed the atomistic details of the interactions between ligand-activated NPs and integrins. As a case study, here we investigate, by means of advanced molecular dynamics simulations (MD) and machine learning methods (namely equilibrium MD, binding free energy calculations and training of self-organized maps), the interaction of a cyclic-RGD-conjugated PEGylated TiO2 NP (the nanodevice) with the extracellular segment of integrin αVβ3 (the target), the latter experimentally well-known to be over-expressed in several solid tumors. Firstly, we proved that the cyclic-RGD ligand binding to the integrin pocket is established and kept stable even in the presence of the cumbersome realistic model of the nanodevice. In this respect, the unsupervised machine learning analysis allowed a detailed comparison of the ligand/integrin binding in the presence and in the absence of the nanodevice, which unveiled differences in the chemical features. Then, we discovered that unbound cyclic RGDs conjugated to the NP largely contribute to the interactions between the nanodevice and the integrin. Finally, by increasing the density of cyclic RGDs on the PEGylated TiO2 NP, we observed a proportional enhancement of the nanodevice/target binding. All these findings can be exploited to achieve an improved targeting selectivity and cellular uptake, and thus a more successful clinical outcome.

Recent advances in the exploration and discovery of SARS-CoV-2 inhibitory peptides from edible animal proteins

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), is spreading worldwide. Although the COVID-19 epidemic has passed its peak of transmission, the harm it has caused deserves our attention. Scientists are striving to develop medications that can effectively treat COVID-19 symptoms without causing any adverse reactions. SARS-CoV-2 inhibitory peptides derived from animal proteins have a wide range of functional activities in addition to safety. Identifying animal protein sources is crucial to obtaining SARS-CoV-2 inhibitory peptides from animal sources. This review aims to reveal the mechanisms of action of these peptides on SARS-CoV-2 and the possibility of animal proteins as a material source of SARS-CoV-2 inhibitory peptides. Also, it introduces the utilization of computer-aided design methods, phage display, and drug delivery strategies in the research on SARS-CoV-2 inhibitor peptides from animal proteins. In order to identify new antiviral peptides and boost their efficiency, we recommend investigating the interaction between SARS-CoV-2 inhibitory peptides from animal protein sources and non-structural proteins (Nsps) using a variety of technologies, including computer-aided drug approaches, phage display techniques, and drug delivery techniques. This article provides useful information for the development of novel anti-COVID-19 drugs.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.

Recent Advances on Antimicrobial Peptides from Milk: Molecular Properties, Mechanisms, and Applications

Traditional antibiotics are facing a tremendous challenge due to increased antimicrobial resistance; hence, there is an urgent need to find novel antibiotic alternatives. Milk protein-derived antimicrobial peptides (AMPs) are currently attracting substantial attention considering that they showcase an extensive spectrum of antimicrobial activities, with slower development of antimicrobial resistance and safety of raw materials. This review summarizes the molecular properties, and activity mechanisms and highlights the applications and limitations of AMPs derived from milk proteins comprehensively. Also the analytical technologies, especially bioinformatics methodologies, applied in the process of screening, identification, and mechanism illustration of AMPs were underlined. This review will give some ideas for further research and broadening of the applications of milk protein-derived AMPs in the food field.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Conformations of Macrocyclic Peptides Sampled by Nuclear Magnetic Resonance: Models for Cell-Permeability

The biological activities and pharmacological properties of peptides and peptide mimetics are determined by their conformational states. Therefore, a detailed understanding of the conformational landscape is crucial for rational drug design. Nuclear magnetic resonance (NMR) is the only method for structure determination in solution. However, it remains challenging to determine the structures of peptides using NMR because of very weak nuclear Overhauser effects (NOEs), the semiquantitative nature of the rotating frame Overhauser effect (ROE), and the low number of NOEs/ROEs in N-methylated peptides. In this study, we introduce a new approach to investigating the structures of modified macrocyclic peptides. We utilize exact NOEs (eNOEs) in viscous solvent mixtures to replicate various cellular environments. eNOEs provide detailed structural information for highly dynamic modified peptides. Structures of high precision were obtained for cyclosporin A, with a backbone atom rmsd of 0.10 Å. Distinct conformational states in different environments were identified for omphalotin A (OmphA), a fungal nematotoxic and multiple backbone N-methylated macrocyclic peptides. A model for cell-permeation is presented for OmphA, based on its structures in polar, apolar, and mixed polarity solvents. During the transition from a polar to an apolar environment, OmphA undergoes a rearrangement of its H-bonding network, accompanied by a cis to trans isomerization of the ω torsion angle within a type VIa β-turn. We hypothesize that the kinetics of these conformational transitions play a crucial role in determining the membrane-permeation capabilities of OmphA.

Stabilized cyclic peptides as modulators of protein-protein interactions: promising strategies and biological evaluation

Protein-protein interactions (PPIs) control many essential biological pathways which are often misregulated in disease. As such, selective PPI modulators are desirable to unravel complex functions of PPIs and thus expand the repertoire of therapeutic targets. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets for conventional drug modalities, rendering most PPIs "undruggable". Therefore, there is a growing need to discover innovative molecules that are able to modulate crucial PPIs. Peptides are ideal candidates to deliver such therapeutics attributed to their ability to closely mimic structural features of protein interfaces. However, their inherently poor proteolysis resistance and cell permeability inevitably hamper their biomedical applications. The introduction of a constraint (i.e., peptide cyclization) to stabilize peptides' secondary structure is a promising strategy to address this problem as witnessed by the rapid development of cyclic peptide drugs in the past two decades. Here, we comprehensively review the recent progress on stabilized cyclic peptides in targeting challenging PPIs. Technological advancements and emerging chemical approaches for stabilizing active peptide conformations are categorized in terms of α-helix stapling, β-hairpin mimetics and macrocyclization. To discover potent and selective ligands, cyclic peptide library technologies were updated based on genetic, biochemical or synthetic methodologies. Moreover, several advances to improve the permeability and oral bioavailability of biologically active cyclic peptides enable the de novo development of cyclic peptide ligands with pharmacological properties. In summary, the development of cyclic peptide-based PPI modulators carries tremendous promise for the next generation of therapeutic agents to target historically "intractable" PPI systems.

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

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

Cyclic Peptide Linker Design and Optimization by Molecular Dynamics Simulations

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

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.

Sequence controlled secondary structure is important for the site-selectivity of lanthipeptide cyclization

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides that are generated from precursor peptides through a dehydration and cyclization process. ProcM, a class II lanthipeptide synthetase, demonstrates high substrate tolerance. It is enigmatic that a single enzyme can catalyze the cyclization process of many substrates with high fidelity. Previous studies suggested that the site-selectivity of lanthionine formation is determined by substrate sequence rather than by the enzyme. However, exactly how substrate sequence contributes to site-selective lanthipeptide biosynthesis is not clear. In this study, we performed molecular dynamic simulations for ProcA3.3 variants to explore how the predicted solution structure of the substrate without enzyme correlates to the final product formation. Our simulation results support a model in which the secondary structure of the core peptide is important for the final product's ring pattern for the substrates investigated. We also demonstrate that the dehydration step in the biosynthesis pathway does not influence the site-selectivity of ring formation. In addition, we performed simulation for ProcA1.1 and 2.8, which are well-suited candidates to investigate the connection between order of ring formation and solution structure. Simulation results indicate that in both cases, C-terminal ring formation is more likely which was supported by experimental results. Our findings indicate that the substrate sequence and its solution structure can be used to predict the site-selectivity and order of ring formation, and that secondary structure is a crucial factor influencing the site-selectivity. Taken together, these findings will facilitate our understanding of the lanthipeptide biosynthetic mechanism and accelerate bioengineering efforts for lanthipeptide-derived products.

Synthesis of a Cyclooctapeptide, Cyclopurpuracin, and Evaluation of Its Antimicrobial Activity

Cyclopurpuracin is a cyclooctapeptide isolated from the methanol extract of Annona purpurea seeds with a sequence of cyclo-Gly-Phe-Ile-Gly-Ser-Pro-Val-Pro. In our previous study, the cyclisation of linear cyclopurpuracin was problematic; however, the reversed version was successfully cyclised even though the NMR spectra revealed the presence of a mixture of conformers. Herein, we report the successful synthesis of cyclopurpuracin using a combination of solid- and solution-phase synthetic methods. Initially, two precursors of cyclopurpuracin were prepared, precursor linear A (NH₂-Gly-Phe-Ile-Gly-Ser(t-Bu)-Pro-Val-Pro-OH) and precursor linear B (NH-Pro-Gly-Phe-Ile-Gly-Ser(t-Bu)-Pro-Val-OH, and various coupling reagents and solvents were trialled to achieve successful synthesis. The final product was obtained when precursors A and B were cyclised using the PyBOP/NaCl method, resulting in a cyclic product with overall yields of 3.2% and 3.6%, respectively. The synthetic products were characterised by HR-ToF-MS, ¹H-NMR, and ¹³C-NMR, showing similar NMR profiles to the isolated product from nature and no conformer mixture. The antimicrobial activity of cyclopurpuracin was also evaluated for the first time against S. aureus, E. coli, and C. albicans, showing weak activity with MIC values of 1000 µg/mL for both synthetic products, whereas the reversed cyclopurpuracin was more effective with an MIC of 500 µg/mL.

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.

Structure and Thermodynamics of Linear, Ring, and Catenane Polymers in Solutions and at Liquid-Liquid Interfaces

In recent decades, advances in the syntheses of mechanically interlocked macromolecules, such as catenanes, have led to much greater interest in the applications of these complexes, from molecular motors and actuators to nanoscale computational memory and nanoswitches. Much remains to be understood, however, regarding how catenated ring compounds behave as a result of the effects of different solvents as well as the effects of solvent/solvent interfaces. In this work, we have investigated, using molecular dynamics simulations, the effects of solvation of poly(ethylene oxide) chains of different topologies─linear, ring, and [2]catenane─in two solvents both considered favorable toward PEO (water, toluene) and at the water/toluene interface. Compared to ring and [2]catenane molecules, the linear PEO chain showed the largest increase in size at the water/toluene interface compared to bulk water or bulk toluene. Perhaps surprisingly, observations indicate that the tendency of all three topologies to extend at the water/toluene interface may have more to do with screening the interaction between the two solvents than with optimizing specific solvent-polymer contacts.

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.

Cytotoxic Cyclodepsipeptides and Cyclopentane Derivatives from a Plant-Associated Fungus Fusarium sp

In this work, four new cyclodepsipeptides, fusarihexins C-E (1-3) and enniatin Q (4), four new cyclopentane derivatives, fusarilins A-D (5-8), together with eight known compounds (9-16), were isolated from cultures of the endophytic fungus Fusarium sp. The structures of the isolated compounds were elucidated by analysis of HRMS and NMR spectroscopic data. The absolute configurations were determined using Marfey's method, a modified Mosher's method, single-crystal X-ray diffraction analysis, and ECD analysis. The antitumor activities of the isolated compounds in vitro were evaluated. Cyclodepsipeptides displayed cytotoxicities against the Huh-7, MRMT-1, and HepG-2 cell lines. Compounds 4, 9, 10, and 12 with IC₅₀ values of 1.0-9.1 μM exhibited the most potent cytotoxicities against the three cell lines as compared to the positive control-5-fluorouracil. Compounds 1-3 and 11 exhibited moderate cytotoxic activities (IC₅₀ values of 10.7-20.1 μM).

Self-Uptake Mechanism of Polymyxin-Based Lipopeptide against Gram-Negative Bacterial Membrane: Role of the First Adsorbed Lipopeptide

Research in the continuously increasing threat of polymyxin-resistant multidrug-resistant Pseudomonas aeruginosa, which causes severe infection in immunocompromised patients, has resulted in the development of several polymyxin-derived cyclic lipopeptides containing l-α-γ- diamino butyric acid-like FADDI-019 (F19). In this work, F19's insertion into a minimal model of the asymmetric outer membrane of the bacterium, which contained only penta-acylated lipid A (LipA) and lacked keto-d-octulosonic acid and O-antigens, in the top leaflet and phospholipids in the bottom leaflet, was studied. F19 exhibited all of the hallmarks of the self-uptake mechanism into the asymmetric bilayer. While a single monomer of the lipopeptide did not get partitioned into the inside of the bilayer, it competitively displaced Ca²⁺ from the membrane surface, observed as a decrease in Ca²⁺ coordination number with phosphate groups (1.89 vs 1.718), resulting in membrane destabilization. This resulted in an increment of the average defect size and the probability of interplay between lipid tails and hydrophobic residues of another F19. When more than one monomer was present in the system, the first monomer remained docked on the surface, while other monomers intercalated into the bilayer interior with their hydrophobic moieties "sleeved" by lipid acyl chains. The free energy barrier for partial insertion of the lipopeptide into a bilayer in the presence of surface-docked second F19 was recorded at ∼1.3 kcal/mol using two-dimensional (2D) well-tempered metadynamics, making it a low barrier process at 300 K. This study is an attempt to demonstrate the self-uptake mechanism of F19 during intercalation process into the bilayer interior, which may help in the design of better alternates for polymyxins to work against polymyxin resistance.

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

MOTIVATION

Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization.

RESULTS

Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/hpepdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Review of Performance Prediction Based on Machine Learning in Materials Science

With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.

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.

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems in the future. The liquid electrolyte is one of the most important parts of a battery and is extremely critical in stabilizing the electrode-electrolyte interfaces and constructing safe and long-life-span batteries. Tremendous efforts have been devoted to developing new electrolyte solvents, salts, additives, and recipes, where molecular dynamics (MD) simulations play an increasingly important role in exploring electrolyte structures, physicochemical properties such as ionic conductivity, and interfacial reaction mechanisms. This review affords an overview of applying MD simulations in the study of liquid electrolytes for rechargeable batteries. First, the fundamentals and recent theoretical progress in three-class MD simulations are summarized, including classical, ab initio, and machine-learning MD simulations (section 2). Next, the application of MD simulations to the exploration of liquid electrolytes, including probing bulk and interfacial structures (section 3), deriving macroscopic properties such as ionic conductivity and dielectric constant of electrolytes (section 4), and revealing the electrode-electrolyte interfacial reaction mechanisms (section 5), are sequentially presented. Finally, a general conclusion and an insightful perspective on current challenges and future directions in applying MD simulations to liquid electrolytes are provided. Machine-learning technologies are highlighted to figure out these challenging issues facing MD simulations and electrolyte research and promote the rational design of advanced electrolytes for next-generation rechargeable batteries.

Efficient 3D conformer generation of cyclic peptides formed by a disulfide bond

Cyclic peptides formed by disulfide bonds have been one large group of common drug candidates in drug development. Structural information of a peptide is essential to understand its interaction with its target. However, due to the high flexibility of peptides, it is difficult to sample the near-native conformations of a peptide. Here, we have developed an extended version of our MODPEP approach, named MODPEP2.0, to fast generate the conformations of cyclic peptides formed by a disulfide bond. MODPEP2.0 builds the three-dimensional (3D) structures of a cyclic peptide from scratch by assembling amino acids one by one onto the cyclic fragment based on the constructed rotamer and cyclic backbone libraries. Being tested on a data set of 193 diverse cyclic peptides, MODPEP2.0 obtained a considerable advantage in both accuracy and computational efficiency, compared with other sampling algorithms including PEP-FOLD, ETKDG, and modified ETKDG (mETKDG). MODPEP2.0 achieved a high sampling accuracy with an average C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α RMSD of 2.20 Å and 1.66 Å when 10 and 100 conformations were considered, respectively, compared with 3.41 Å and 2.62 Å for PEP-FOLD, 3.44 Å and 3.16 Å for ETKDG, 3.09 Å and 2.72 Å for mETKDG. MODPEP2.0 also reproduced experimental peptide structures for 81.35% of the test cases when an ensemble of 100 conformations were considered, compared with 54.95%, 37.50% and 50.00% for PEP-FOLD, ETKDG, and mETKDG. MODPEP2.0 is computationally efficient and can generate 100 peptide conformations in one second. MODPEP2.0 will be useful in sampling cyclic peptide structures and modeling related protein-peptide interactions, facilitating the development of cyclic peptide drugs.

Application of per-Residue Energy Decomposition to Design Peptide Inhibitors of PSD95 GK Domain

Specific interaction between the postsynaptic density protein 95 (PSD95) and synapse-associated protein 90/postsynaptic density 95–associated protein (SAPAP) is crucial for excitatory synaptic development and plasticity. Designing inhibitors that target the guanylate kinase (GK) domain of PSD95, which is responsible for the interaction, is a promising manipulation tool for the investigation of the function of PSD95 GK and the etiology of its related psychiatric disorders. Herein, we designed new peptide inhibitors of PSD95 GK/SAPAP with higher binding affinity by using molecular dynamics simulations. First, the interactions between PSD95 GK and their reported phosphorylated and unphosphorylated peptides were explored by molecular dynamics simulations. Besides the hydrogen bonding interactions mediated by the phospho-serine (p-Ser) or corresponding phosphomimic residue Asp/Glu, the hydrophobic interactions from the other amino acids also contribute to the PSD95 GK/SAPAP interaction. As an unphosphorylated synthetic peptide with moderate binding affinity and relatively lower molecular weight, the QSF inhibitory peptide was selected for further modification. Based on per-residue energy decomposition results of the PSD95 GK/QSF complex, ten peptides were designed to enhance the binding interactions, especially the hydrophobic interactions. The top-ranked five peptides with lower binding energy were eventually synthesized. The binding affinities of the synthesized peptides were determined using fluorescence polarization (FP) assay. As expected, all peptides have higher binding affinity than the QSF peptide (Ki = 5.64 ± 0.51 μM). Among them, F10W was the most potent inhibitor (Ki = 0.75 ± 0.25 μM), suggesting that enhancement of the hydrophobic interactions is an important strategy for the design of new inhibitory peptides targeting PSD95 GK.

Improved Recovery and Selectivity of Lanthanide-Ion-Binding Cyclic Peptide Hosts by Changing the Position of Acidic Amino Acids

The development of an effective host molecule to separate lanthanide (Ln) ions and a method for predicting its guest recognition/self-assembly behavior based on primary chemical structures are highly sought after in both academia and industry. Herein, we report the improvement of one-pot Ln ion recovery and a performance prediction method for four new cyclic peptide hosts that differ in the position of acidic amino acids. These cyclic peptide hosts could recognize Ln3+ directly through a 1:1 complexation–precipitation process and exhibited high Lu3+ selectivity in spite of similar ion size and electronegativity when the positions of the acidic amino acids were changed. This unpredictable selectivity was explained by considering the dipole moment, lowest unoccupied molecular orbital, and cohesion energy. In addition, a semi-empirical function using these parameters was proposed for screening the sequence and estimating the isolated yields without long-time molecular dynamics calculations. The insights obtained from this study can be employed for the development of high-performance peptides for the selective recovery of Ln and other metal ions, as well as for the construction of diverse supramolecular recognition systems.

Conformational preferences induced by cyclization in orbitides: a vibrational CD study

Orbitides are bioactive head-to-tail natural cyclic peptides from plant species. Their bioactivity is intrinsically related to the main conformations adopted in solution, whose correct characterization represents an important bottleneck for...

Conformational selection of vasopressin upon V1a receptor binding

The neuropeptide vasopressin (VP) and its three G protein-coupled receptors (V_1aR, V_1bR and V₂R) are of high interest in a wide array of drug discovery programs. V_1aR is of particular importance due to its cardiovascular functions and diverse roles in the central nervous system. The structure–activity relationships underpinning ligand-receptor interactions remain however largely unclear, hindering rational drug design. This is not least due to the high structural flexibility of VP in its free as well as receptor-bound states. In this work, we developed a novel approach to reveal features of conformational selectivity upon VP-V_1aR complex formation. We employed virtual screening strategies to probe VP’s conformational space for transiently adopted structures that favor binding to V_1aR. To this end, we dissected the VP conformational space into three sub-ensembles, each containing distinct structural sets for VP’s three-residue C-terminal tail. We validated the computational results with experimental nuclear magnetic resonance (NMR) data and docked each sub-ensemble to V_1aR. We observed that the conformation of VP’s three-residue tail significantly modulated the complex dissociation constants. Solvent-exposed and proline trans-configured VP tail conformations bound to the receptor with three-fold enhanced affinities compared to compacted or cis-configured conformations. The solvent-exposed and more flexible structures facilitated unique interaction patterns between VP and V_1aR transmembrane helices 3, 4, and 6 which led to high binding energies. The presented “virtual conformational space screening” approach, integrated with NMR spectroscopy, thus enabled identification and characterization of a conformational selection-type complex formation mechanism that confers novel perspectives on targeting the VP-V_1aR interactions at the level of the encounter complex – an aspect that opens novel research avenues for understanding the functionality of the evolutionary selected conformational properties of VP, as well as guidance for ligand design strategies to provide more potent and selective VP analogues.

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.

Rational Design of a Dual-Targeting Natural Toxin-Like Bicyclic Peptide for Selective Bioenergetic Blockage in Cancer Cells

Stapled α-helical peptides emerge as one of the attractive peptidomimetics which can efficiently penetrate the cell membrane to access intracellular targets. However, the incorporation of a highly lipophilic cross-link may lead to nonspecific membrane toxicity in certain cases. Here, we report a new class of thioether-tethered bicyclic α-helical peptide to mimic the highly constrained loop-helix structure of natural toxins with the dual-targeting ability for both cell-surface receptors and intracellular targets. The thioether cross-links are introduced to replace the redox-sensitive disulfide bonds in natural toxins via a photoinduced thiol-yne reaction followed by macrolactamization. As a proof of concept, α_Vβ₃ integrin targeting ligand was grafted into one of the macrocycles in the bicyclic scaffold, while a mitochondria-targeting proapoptotic motif was introduced into the other macrocycle stabilized by an i, i + 7 alkyl thioether cross-link to recapitulate its α-helical conformation. The obtained dual-targeting bicyclic α-helical BIRK peptides showed highly stable α-helical conformation in the presence of denaturants or under high temperature. Notably, BIRK peptides could induce selective cell death in α_Vβ₃ integrin-positive B16F10 cells by interfering with the bioenergetic functions of mitochondria. This work provides a new avenue to design and stabilize α-helical peptides in a highly constrained bicyclic loop-helix scaffold with dual functionality.

Solvent Effect on the Structure and Properties of RGD Peptide (1FUV) at Body Temperature (310 K) Using Ab Initio Molecular Dynamics

The structure and properties of the arginine-glycine-aspartate (RGD) sequence of the 1FUV peptide at 0 K and body temperature (310 K) are systematically investigated in a dry and aqueous environment using more accurate ab initio molecular dynamics and density functional theory calculations. The fundamental properties, such as electronic structure, interatomic bonding, partial charge distribution, and dielectric response function at 0 and 310 K are analyzed, comparing them in dry and solvated models. These accurate microscopic parameters determined from highly reliable quantum mechanical calculations are useful to define the range and strength of complex molecular interactions occurring between the RGD peptide and the integrin receptor. The in-depth bonding picture analyzed using a novel quantum mechanical metric, the total bond order (TBO), quantifies the role played by hydrogen bonds in the internal cohesion of the simulated structures. The TBO at 310 K decreases in the dry model but increases in the solvated model. These differences are small but extremely important in the context of conditions prevalent in the human body and relevant for health issues. Our results provide a new level of understanding of the structure and properties of the 1FUV peptide and help in advancing the study of RGD containing other peptides.

Effect of Flexibility, Lipophilicity, and the Location of Polar Residues on the Passive Membrane Permeability of a Series of Cyclic Decapeptides

Cyclic peptides have received increasing attention over the recent years as potential therapeutics for "undruggable" targets. One major obstacle is, however, their often relatively poor bioavailability. Here, we investigate the structure-permeability relationship of 24 cyclic decapeptides that share the same backbone N-methylation pattern but differ in their side chains. The peptides cover a large range of values for passive membrane permeability as well as lipophilicity and solubility. To rationalize the observed differences in permeability, we extracted for each peptide the population of the membrane-permeable conformation in water from extensive explicit-solvent molecular dynamics simulations and used this as a metric for conformational rigidity or "prefolding." The insights from the simulations together with lipophilicity measurements highlight the intricate interplay between polarity/lipophilicity and flexibility/rigidity and the possible compensating effects on permeability. The findings allow us to better understand the structure-permeability relationship of cyclic peptides and extract general guiding principles.