Elucidating Self-Assembling Peptide Aggregation via Morphoscanner: A New Tool for Protein-Peptide Structural Characterization

Aromatic group-induced self-assembly of short antimicrobial peptides: unveiling exceptionally potent antimicrobial efficacy

Self-assembled antimicrobial peptides (AMPs) have superior antimicrobial properties from their supramolecular nanostructures. A systematic study is needed to determine which AMPs can generate more significant self-assembled structures for antimicrobial activity. In this study, thirty-six self-assembled AMPs were systematically designed by combining six hexapeptide sequences with five aromatic groups, exploring the contributions of different sequences and groups to self-assembly and antimicrobial properties. Most peptides had broad-spectrum antimicrobial and self-assembly capabilities. Moderate self-assembly can endow these new AMPs with excellent antimicrobial properties. Candidate peptides Nap-f5f6 and Npx-f5f6 had outstanding antimicrobial activity, low toxicity, and high selectivity. This may be due to their self-assembly into nanofibers and nanospheres via π-π stacking provided by C- and N-terminal aromatic groups. Their membrane-disrupting bactericidal mechanism gives a rapid effect and prevents antibiotic resistance. Also, in infected mouse skin wounds, Nap-f5f6 and Npx-f5f6 had low toxicity, reduced bacterial load, and accelerated wound healing.

Tips and Tricks in the Modeling of Supramolecular Peptide Assemblies

Supramolecular peptide assemblies (SPAs) hold promise as materials for nanotechnology and biomedicine. Although their investigation often entails adapting experimental techniques from their protein counterparts, SPAs are fundamentally distinct from proteins, posing unique challenges for their study. Computational methods have emerged as indispensable tools for gaining deeper insights into SPA structures at the molecular level, surpassing the limitations of experimental techniques, and as screening tools to reduce the experimental search space. However, computational studies have grappled with issues stemming from the absence of standardized procedures and relevant crystal structures. Fundamental disparities between SPAs and protein simulations, such as the absence of experimentally validated initial structures and the importance of the simulation size, number of molecules, and concentration, have compounded these challenges. Understanding the roles of various parameters and the capabilities of different models and simulation setups remains an ongoing endeavor. In this review, we aim to provide readers with guidance on the parameters to consider when conducting SPA simulations, elucidating their potential impact on outcomes and validity.

Computational Models for the Study of Protein Aggregation

Protein aggregation has been studied by many groups around the world for many years because it can be the cause of a number of neurodegenerative diseases that have no effective treatment. Obtaining the structure of related fibrils and toxic oligomers, as well as describing the pathways and main factors that govern the self-organization process, is of paramount importance, but it is also very difficult. To solve this problem, experimental and computational methods are often combined to get the most out of each method. The effectiveness of the computational approach largely depends on the construction of a reasonable molecular model. Here we discussed different versions of the four most popular all-atom force fields AMBER, CHARMM, GROMOS, and OPLS, which have been developed for folded and intrinsically disordered proteins, or both. Continuous and discrete coarse-grained models, which were mainly used to study the kinetics of aggregation, are also summarized.

HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides

Self-assembling processes are ubiquitous phenomena that drive the organization and the hierarchical formation of complex molecular systems. The investigation of assembling dynamics, emerging from the interactions among biomolecules like amino-acids and polypeptides, is fundamental to determine how a mixture of simple objects can yield a complex structure at the nano-scale level. In this paper we present HyperBeta, a novel open-source software that exploits an innovative algorithm based on hyper-graphs to efficiently identify and graphically represent the dynamics of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β-sheets formation. Differently from the existing tools, HyperBeta directly manipulates data generated by means of coarse-grained molecular dynamics simulation tools (GROMACS), performed using the MARTINI force field. Coarse-grained molecular structures are visualized using HyperBeta ’s proprietary real-time high-quality 3D engine, which provides a plethora of analysis tools and statistical information, controlled by means of an intuitive event-based graphical user interface. The high-quality renderer relies on a variety of visual cues to improve the readability and interpretability of distance and depth relationships between peptides. We show that HyperBeta is able to track the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β-sheets formation in coarse-grained molecular dynamics simulations, and provides a completely new and efficient mean for the investigation of the kinetics of these nano-structures. HyperBeta will therefore facilitate biotechnological and medical research where these structural elements play a crucial role, such as the development of novel high-performance biomaterials in tissue engineering, or a better comprehension of the molecular mechanisms at the basis of complex pathologies like Alzheimer’s disease.

Probing mechanical properties and failure mechanisms of fibrils of self-assembling peptides

Self-assembling peptides (SAPs) are a promising class of biomaterials amenable to easy molecular design and functionalization. Despite their increasing usage in regenerative medicine, a detailed analysis of their biomechanics at the nanoscale level is still missing. In this work, we propose and validate, in all-atom dynamics, a coarse-grained model to elucidate strain distribution, failure mechanisms and biomechanical effects of functionalization of two SAPs when subjected to both axial stretching and bending forces. We highlight different failure mechanisms for fibril seeds and fibrils, as well as the negligible contribution of the chosen functional motif to the overall system rupture. This approach could lay the basis for the development of "more" coarse-grained models in the long pathway connecting SAP sequences and hydrogel mechanical properties.