The primary objective of the workshop is to critically evaluate state-of-the-art in-silico methods applied to peptide systems and explore their integration with experimental data, covering topics including peptide design, bioactive peptides (membrane-active peptides and inhibitors of protein-protein interactions), and peptide-based materials.

This workshop is also an (independent) satellite event of the joint 37th European Peptide Symposium and 14th International Peptide Symposium (Florence on August 25th-29th 2024). Info here. 

SIBPA Bursiaries

The Italian Society of Pure and Applied Biophysics (SIBPA) will support the workshop by offering four bursaries of 125 €, to partially cover travel and lodging expenses of young SIBPA members. Applications for bursaries should be stated in the workshop's participation request form and must be accompanied by a CV and the abstract of the proposed contribution.

Deadline: 1st May *** NEW EXTENDED DEADLINE : 31ST MAY ***

The scholarships are reserved for SIBPA members (at the time of application) who are PhD students or Post-docs.

Introduction

Peptides are a key class of biomolecules due to their crucial role in numerous physiological processes and biological pathways. Furthermore, they possess extensive applicability 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 paramount for developing groundbreaking drugs, therapies, and building blocks for advanced functional materials with tailored properties, including self-assembly and nanoparticle formation.

State-of-the-art molecular simulation techniques, including enhanced sampling algorithms and coarse-grained models, enabled the exploration of peptide conformational landscapes, prediction of binding affinities, and unraveled various molecular aspects of peptide behavior and mechanisms of action. Machine learning approaches, such as deep learning and generative models, are revolutionizing peptide design by using large-scale datasets and predictive models to accelerate the discovery of novel peptides with desired properties. Recent advancements in algorithmic methods and force field descriptions have also improved the modeling of unconventional peptides sequences, extending simulation timescales and offering potential solutions to the challenges in peptide design.

Notwithstanding the great progress in the field, several aspects remain challenging, such as the prediction of peptide conformation, the reliable design of peptide inhibitors of protein-protein interactions, the modeling of peptide-induced pores in membranes, and of peptide aggregation, to name just a few.

Computational strategies for studying peptides must differ significantly from those developed for small compounds or proteins. Peptides exhibit a high degree of structural diversity, which is strongly influenced by their surrounding environment, making the characterization of their conformational properties rather challenging. Existing force fields, designed for proteins, have limitations in accurately describing this conformational heterogeneity. Substantial improvements, particularly in coarse-grained models, are also necessary to depict peptide interactions within aggregates and other compounds accurately. For example, a more precise understanding of hydrophobic effects could better capture the experimentally observed heterogeneity in the position and orientation of peptides in membranes, as well their behavior in aggregates.

On the other side, experimental techniques, including 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 about macromolecular systems, but they can sometimes face challenges when applied to systems involving peptides due to the mentioned heterogeneities in peptide conformations and interactions. In other cases, they can only provide “rough” pictures. Computational methods can thus play a crucial role in overcoming these limitations, by providing additional insights and complementing experimental data, thereby enhancing the overall understanding of peptide systems.

Thus, in this context more than in others, a deeper interconnection of computational methods with experimental techniques is fundamental. In the last decades, the integration of experimental data with in-silico methods has shown promising results in studying the structural and dynamic properties of proteins (SAXS- and FRET- guided molecular dynamics simulations, and metainference enhanced sampling approach, to cite a few). Extending this integration to the study of peptides requires specific efforts to address their unique properties.Furthermore, integrating experimental high-throughput screening methods for peptide design with computational analysis expedites the screening of peptide libraries, thus facilitating the identification of peptides with desired properties. This integration of computational and experimental methods enhances our understanding and empowers the design and optimization of peptides for diverse applications, encompassing drug development and materials engineering and helping to reduce the cost of design dramatically.