Biology subjects

Marti-Aranda, A.

Publications and source records attributed to Marti-Aranda, A..

3 recordsLinked to original sources

bioRxiv2025

Allosteric and Energetic Remodeling by Protein Domain Extensions

Many functions of proteins are performed by independently folding structural units called domains. The structures of domains are conserved during evolution but they are not identical. For example, the >270 human PDZ domains vary in the number of secondary structure elements and in the length of loops. An important but largely unexplored question is the impact of these extensions on protein energy landscapes: beyond any immediate functional effects, do extensions also alter the consequences of perturbations elsewhere in the domain, altering the potential for regulation and evolvability? Here we perform massively parallel energetic measurements on a model human PDZ domain to directly and comprehensively answer this question. In total we quantify the binding to a ligand and abundance of [~]190,000 protein variants to quantify free energy changes for mutations throughout the canonical domain fold and [~]7,000 energetic couplings between these mutations and the two domain extensions, both alone and in combination. We find that both extensions--one structured and one more dynamic--substantially and specifically re-shape the energy landscape of the domain, with the removal of an [a]-helix altering the energetic consequences of 424 mutations in 54 sites on fold stability and 420 mutations in 56 sites on binding to a ligand. These changes to the energy landscape alter the effects of 330 allosteric mutations, including at solvent-accessible surface sites. Extending or pruning the domain therefore reshapes its energetic and allosteric landscape, adding and removing opportunities for the allosteric control of protein function.

biophysics↗

bioRxiv2025

The evolution of allostery in a protein family

Allosteric interactions in proteins are key to biological regulation and the efficacy of many drugs. The extent to which allostery is conserved in evolutionarily-related proteins is unknown, with implications for predicting, engineering and therapeutically targeting allostery. Here we directly address this question by constructing seven comprehensive allosteric maps for five homologous human proteins. The comparative maps reveal a modular allosteric architecture with conserved distant-dependent allosteric decay across the protein core connecting to protein-specific allosteric extensions to surfaces. These allosteric augmentations use both structurally-conserved residues and homolog-specific domain extensions. Our data provide the first comparative multidimensional protein energy landscapes and suggest that allostery evolves via the gain-and-loss of peripheral extensions to a conserved allosteric core.

genomics↗

bioRxiv2023

The genetic architecture of protein stability

There are more ways to synthesize a 100 amino acid protein (20100) than atoms in the universe. Only a miniscule fraction of such a vast sequence space can ever be experimentally or computationally surveyed. Deep neural networks are increasingly being used to navigate high-dimensional sequence spaces. However, these models are extremely complicated and provide little insight into the fundamental genetic architecture of proteins. Here, by experimentally exploring sequence spaces >1010, we show that the genetic architecture of at least some proteins is remarkably simple, allowing accurate genetic prediction in high-dimensional sequence spaces with fully interpretable biophysical models. These models capture the non-linear relationships between free energies and phenotypes but otherwise consist of additive free energy changes with a small contribution from pairwise energetic couplings. These energetic couplings are sparse and caused by structural contacts and backbone propagations. Our results suggest that artificial intelligence models may be vastly more complicated than the proteins that they are modeling and that protein genetics is actually both simple and intelligible.

biophysics↗