Biophysical Studies of Protein Structure-Function Relationships

Dear Colleagues,

Characterizing protein structures is essential in understanding the molecular mechanisms that govern biological systems. Proteins perform a vast array of functions in the cell, including biochemical catalysis (as enzymes), inter- and intra-cell communication, and metabolite transportation, to mention just a few. Their biological role is intimately tied to the flexibility, resiliency, and shape of their three-dimensional structure. Studying these properties provides crucial insights into the way in which they work and interact with other molecules, as well as suggesting how misfolding and/or mutations might affect their activity, leading to cell damage and human diseases.

However, not all proteins adopt a stable and well-defined structure. Unlike globular proteins, intrinsically disordered proteins (IDPs) do not fold into a single conformation under physiological conditions but rather exist as dynamic ensembles of conformations. This intrinsic flexibility allows them to engage in multiple, transient, and highly versatile interactions. Such plasticity is crucial for processes such as transcriptional regulation and signaling cascades. On the other hand, their structural instability also makes IDPs more prone to aberrant aggregation, a hallmark of several neurodegenerative diseases.

Traditionally, the structural characterization of proteins has relied on experimental techniques such as X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, absorption, and fluorescence. These spectroscopic and imaging methods have contributed significantly to our current understanding of protein architecture at the atomic level. Nonetheless, they are often time-consuming, costly, and technically challenging. In the case of IDPs, high conformational variability makes standard structure-based approaches particularly limited.

In recent years, computational methods have emerged as powerful complementary tools. Molecular modeling, molecular dynamics simulations, and AI-based structure prediction tools, such as AlphaFold, allow researchers to explore protein structures with increasing accuracy and efficiency. Importantly, new algorithms are being developed to better capture the conformational ensembles of IDPs and predict their interaction networks. When combined with experimental data, these in silico approaches offer a more complete picture of protein behavior, both for folded proteins and for those that remain intrinsically disordered.

This integrated understanding is vital not only in basic biological research but also in drug discovery. Knowledge of protein structure — including the dynamic and disordered nature of IDPs — allows for the rational design of molecules that can modulate protein function, paving the way for targeted therapies in numerous diseases.

Dr. Almerinda Di Venere
Prof. Dr. Giampiero Mei
Dr. Velia Minicozzi
Guest Editors

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