Uncovering the mechanisms of cyclic peptide self-assembly in membranes with the chirality-aware MA(R/S)TINI forcefield

CYCLOPEp Builder: Facilitating cyclic peptide and nanotube research through a user-friendly web platform

The study of cyclic peptides (CPs) and self-assembling cyclic peptide nanotubes (SCPNs) is pivotal in advancing applications in diverse fields such as biomedicine, nanoelectronics, and catalysis. Recognizing the limitations in the experimental study of these molecules, this article introduces CYCLOPEp Builder, a comprehensive web-based application designed to facilitate the design, simulation, and visualization of CPs and SCPNs. The tool is engineered to generate molecular topologies, essential for conducting Molecular Dynamics simulations that span All-Atom to Coarse-Grain resolutions. CYCLOPEp Builder's user-friendly interface simplifies the complex process of molecular modeling, providing researchers with the ability to readily construct CPs and SCPNs. The platform is versatile, equipped with various force fields, and capable of producing structures ranging from individual CPs to complex SCPNs with different sequences, offering parallel and antiparallel orientations among them. By enhancing the capacity for detailed visualization of molecular assemblies, CYCLOPEp Builder improves the understanding of CP and SCPN molecular interactions. This tool is a step forward in democratizing access to sophisticated simulations, offering an invaluable resource to the scientific community engaged in the exploration of supramolecular structures. CYCLOPEp is accessible at http://cyclopep.com/.

Dissipative Self-Assembly of Patchy Particles under Nonequilibrium Drive: A Computational Study

Inspired by biology and implemented using nanotechnology, the self-assembly of patchy particles has emerged as a pivotal mechanism for constructing complex structures that mimic natural systems with diverse functionalities. Here, we explore the dissipative self-assembly of patchy particles under nonequilibrium conditions, with the aim of overcoming the constraints imposed by equilibrium assembly. Utilizing extensive Monte Carlo (MC) and Molecular Dynamics (MD) simulations, we provide insight into the effects of external forces that mirror natural and chemical processes on the assembly rates and the stability of the resulting assemblies comprising 8, 10, and 13 patchy particles. Implemented by a favorable bond-promoting drive in MC or a pulsed square wave potential in MD, our simulations reveal the role these external drives play in accelerating assembly kinetics and enhancing structural stability, evidenced by a decrease in the time to first assembly and an increase in the duration the system remains in an assembled state. Through the analysis of an order parameter, entropy production, bond dynamics, and interparticle forces, we unravel the underlying mechanisms driving these advancements. We also validated our key findings by simulating a larger system of 100 patchy particles. Our comprehensive results not only shed light on the impact of external stimuli on self-assembly processes but also open a promising pathway for expanding the application by leveraging patchy particles for novel nanostructures.

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.