Peptides in biology and materials: bridging simulation and experimental data

This Research Topic is organized in collaboration with the Second Workshop Peptides in Biology and Materials: Bridging Simulation and Experimental Data, which will be held in Sofia from April 28 to 30, 2026. Inspired by the themes of the workshop, the collection welcomes contributions from both participants and other researchers active in the field.Peptides are a key class of biomolecules due to their crucial role in numerous physiological processes and biological pathways. They also have broad applications in materials science, enabling the creation and design of novel materials with unprecedented properties. A comprehensive understanding of the physico-chemical characteristics of peptides and their interactions with other molecules is essential for developing groundbreaking drugs, therapies, and building blocks for advanced functional materials with tailored properties, including self-assembly and nanoparticle formation. Despite significant progress on both the computational and experimental sides, several challenges remain, including the prediction of peptide conformation, the reliable design of peptide inhibitors of protein-protein interactions, the modeling of peptide-induced pores in membranes, and peptide aggregation.Computational strategies for studying peptides differ from those developed for small molecules or proteins. Peptides display a high degree of structural flexibility that is strongly influenced by their environment, making the characterization of their conformational properties particularly challenging. Existing protein force fields can struggle to accurately describe peptide behavior in this context. Significant improvements, especially in coarse-grained models, are needed to better capture peptide interactions within aggregates and with other molecules. For instance, a more accurate description of hydrophobic effects could improve our understanding of peptide orientation and heterogeneity in membranes and their behavior in aggregated states.Experimental techniques such as X-ray crystallography, optical and NMR spectroscopy, and cryo-electron microscopy (cryo-EM) play a crucial role in validating and complementing computational simulations. Some of these methods provide high-resolution structural information, but they may encounter limitations when applied to peptide systems because of their conformational variability. In other cases, they provide only low-resolution structural information.Computational approaches can help overcome these limitations by offering complementary insights that enhance the interpretation of experimental data. At the interface between computation and experiments, machine learning approaches—including deep learning and generative models—are transforming peptide design. However, their success depends on the availability of large, high-quality datasets. A stronger integration of computational methods with experimental techniques, including high-throughput screening and machine learning analysis, will accelerate peptide discovery and optimization, reduce costs, and expand applications in drug development and materials science.We welcome original research articles, brief research reports, reviews, mini-reviews, and perspectives on topics including, but not limited to:• Peptide conformational landscapes and structure prediction across environments• Integration of simulations with NMR, cryo-EM, spectroscopy, and other experimental data• Development and benchmarking of force fields and coarse-grained models for peptides• Peptide–membrane interactions, including pore formation and membrane-active peptides• Peptide aggregation, self-assembly, and nanoparticle formation• Design of peptide inhibitors and other bioactive peptides targeting protein–protein interactions• Machine learning and data-driven approaches for peptide design, screening, and optimizationContributions must be within the mission and scope of Frontiers in Chemical Biology Topic editors: Gianfranco Bocchinfuso, Paolo A Calligari, Anela Ivanova, Manuel N. Melo Source: https://www.frontiersin.org/research-topics/79543/peptides-in-biology-and-materials-bridging-simulation-and-experimental-data