A little walk from physical to biological complexity: protein folding and stability

TL;DR

This paper explores the intersection of physics and biology through protein folding and stability, introducing thermodynamics-based bioinformatics tools that predict protein properties and their changes upon mutations, aiding disease understanding.

Contribution

It presents novel statistical potential-based methods for predicting protein stability and thermal resistance, linking physical principles with biological insights.

Findings

Predictors successfully estimate protein stability changes.

Application elucidates aggregation mechanisms in disease-related proteins.

Tools enhance understanding of protein folding and stability.

Abstract

As an example of topic where biology and physics meet, we present the issue of protein folding and stability, and the development of thermodynamics-based bioinformatics tools that predict the stability and thermal resistance of proteins and the change of these quantities upon amino acid substitutions. These methods are based on knowledge-driven statistical potentials, derived from experimental protein structures using the inverse Boltzmann law. We also describe an application of these predictors, which contributed to the understanding of the mechanisms of aggregation of a particular protein known to cause a neuronal disease.