Alpha helices are more evolutionarily robust to environmental perturbations than beta sheets: Bayesian learning and statistical mechanics for protein evolution
Tomoei Takahashi, George Chikenji, Kei Tokita, and Yoshiyuki Kabashima

TL;DR
This study models the evolutionary robustness of protein secondary structures to environmental changes, revealing alpha helices are more resilient than beta sheets, using Bayesian learning and statistical mechanics.
Contribution
It introduces a formal framework combining Bayesian learning and statistical mechanics to quantify protein structural robustness against environmental perturbations.
Findings
Alpha helices are more evolutionarily robust than beta sheets.
Higher stability against environmental perturbations correlates with increased alpha-helix content.
Robust proteins tend to have a steep reduction in structural space with increased stability.
Abstract
How typical elements that shape organisms, such as protein secondary structures, have evolved, or how evolutionarily susceptible/resistant they are to environmental changes, are significant issues in evolutionary biology, structural biology, and biophysics. According to Darwinian evolution, natural selection and genetic mutations are the primary drivers of biological evolution. However, the concept of ``robustness of the phenotype to environmental perturbations across successive generations," which seems crucial from the perspective of natural selection, has not been formalized or analyzed. In this study, through Bayesian learning and statistical mechanics we formalize the stability of the free energy in the space of amino acid sequences that can design particular protein structure against perturbations of the chemical potential of water surrounding a protein as such robustness. This…
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Taxonomy
TopicsProtein Structure and Dynamics · RNA and protein synthesis mechanisms
