Biology subjects

Echave, J.

Publications and source records attributed to Echave, J..

5 recordsLinked to original sources

bioRxiv2025

Three biophysical constraints determine the variation of structural divergence among residues in enzyme evolution

AO_SCPLOWBSTRACTC_SCPLOWIn enzyme evolution, structural divergence varies among residues, forming residue-dependent structural divergence profiles. The evolutionary constraints that determine these profiles remain unclear. We build on the Mutation-Stability-Activity (MSA) model, a mechanistic mutation-selection model previously developed for sequence evolution. In the MSA model, mutations become fixed or are lost depending on their effects on stability and activity, with parameters aS and aA controlling selection on stability and activity, respectively. The Linearly Forced Elastic Network Model (LFENM) is used to calculate mutational effects on structure, stability, and activity. As substitutions accumulate, structural changes build up unevenly across residues, producing a structural divergence profile that depends on how each residue responds to mutation and on how strongly selection acts on stability and activity. Applied to 34 enzyme families, the MSA model recapitulates observed structural divergence profiles, and nested model comparisons show that mutation, stability, and activity constraints each contribute. However, the balance among these constraints varies widely across families: mutation always contributes substantially, but stability and activity contributions range from negligible to dominant, so any of the three can prevail in a given family. These variations have distinct origins: the mutation contribution depends on how unevenly the proteins flexibility is distributed across residues, while the stability and activity contributions depend on how strongly selection acts, as quantified by aS and aA. The MSA model thus recovers family-specific selection strengths from structural divergence profiles, suggesting these profiles encode information not only about enzyme architecture but also about the selective regime under which enzymes evolve.

biophysics↗

bioRxiv2024

On the variation of structural divergence among residues in enzyme evolution

AO_SCPLOWBSTRACTC_SCPLOWStructural divergence varies among protein residues, yet this variation has been largely overlooked compared with the well-studied case of sequence rate variation. Here we show that, in families of functionally conserved homologous enzymes, structural divergence increases with both residue flexibility and distance from the active site. Although these properties are correlated, modelling reveals that the pattern arises from two independent types of evolutionary constraints: non-functional and functional. The balance between these constraints varies widely across enzyme families, from non-functional to functional dominance. As functional constraints strengthen, structural divergence patterns are reshaped, becoming increasingly distinct from flexibility patterns and breaking the commonly assumed correspondence between evolutionary and dynamical structural ensembles. Active sites are more structurally conserved than average, but this conservation stems not only from functional constraints. Because active sites typically lie in rigid regions where non-functional constraints are high, both constraint types contribute comparably on average, with dominance shifting from one to the other depending on active-site rigidity. Together, these findings revise two long-standing assumptions: that evolutionary structural variation universally mirrors protein dynamics, and that active-site conservation reflects functional requirements alone. Both depend on the balance between non-functional and functional constraints that shape enzyme structural evolution.

evolutionary biology↗

bioRxiv2020

Evolutionary coupling range varies widely among enzymes

Recent studies proposed that enzyme active sites induce evolutionary constraints at long distances. The physical origin of such long-range evolutionary coupling is unknown. Here, I use a recent biophysical model of evolution to study the relationship between physical and evolutionary couplings on a diverse data set of monomeric enzymes. I show that evolutionary coupling is not universally long-range. Rather, range varies widely among enzymes, from 2[A] to 20[A]. Furthermore, the evolutionary coupling range of an enzyme does not inform on the underlying physical coupling, which is short-range for all enzymes. Rather, evolutionary coupling range is determined by functional selection pressure. SIGNIFICANCEUntil recently, only residues near enzyme active sites were thought to be evolutionarily constrained. However, recent studies proposed that active sites induce long-range evolutionary constraints. This seems to conflict with the common finding that physical couplings in proteins are short-range. This raises the question of how short-range physical couplings may cause long-range evolutionary couplings. Here, I show that the function that maps physical coupling into evolutionary coupling depends on functional selection pressure. Under weak selection, both couplings are similarly short-range; under strong selection, short-range physical coupling is non-linearly turned into long-range evolutionary coupling. Thus, due to a huge variation of selection pressure, evolutionary coupling range varies widely among enzymes, from very short (2 [A]) to very long (20 [A]).

evolutionary biology↗

bioRxiv2020

Fast and exact single and double mutation-response scanning of proteins

Studying the effect of perturbations on protein structure is a basic approach in protein research. Important problems, such as predicting pathological mutations and understanding patterns structural evolution, have been addressed by computational simulations based on modelling mutations as forces and predicting deformations using the Linear Response Approximation. In single mutation-response scanning simulations, a sensitivity matrix is obtained by averaging deformations over point mutations. In double mutation-response scanning simulations, a compensation matrix is obtained by minimizing deformations over pairs of mutations. These very useful simulation-based methods may be too slow to deal with large supra-molecular complexes, such as a ribosome or a virus capsid, or large number of proteins, such as the human proteome, which limits their applicability. To address this issue, I derived analytical closed formulas to calculate the sensitivity and compensation matrices directly, without simulations. Here, I present these derivations and show that the resulting analytical methods are much faster than their simulation counterparts, and that where the simulation methods are approximate, the analytical methods are exact by design.

biophysics↗

bioRxiv2018

Beyond stability constraints: a biophysical model of enzyme evolution with selection for stability and activity

Proteins trace trajectories in sequence space as their amino acids become substituted by other amino acids. The number of substitutions per unit time, the rate of evolution, varies among sites because of biophysical constraints. Several properties that characterize sites local environments have been proposed as biophysical determinants of site-specific evolutionary rates. Thus, rate increases with increasing solvent exposure, increasing flexibility, and decreasing local packing density. For enzymes, rate increases also with increasing distance from the proteins active residues, presumably due to functional constraints. The dependence of rates on solvent accessibility, packing density, and flexibility has been mechanistically explained in terms of selection for stability. However, as I show here, a stability-based model fails to reproduce the observed rate-distance dependence, overestimating rates close to the active residues and underestimating rates of distant sites. Here, I pose a new biophysical model of enzyme evolution with selection for stability and activity (MSA) and compare it with a stability-based counterpart (MS). Testing these models on a structurally and functionally diverse dataset of monomeric enzymes, I found that MSA fits observed rates better than MS for most proteins. While both models reproduce the observed dependence of rates on solvent accessibility, packing, and flexibility, MSA fits these dependencies somewhat better. Importantly, while MS fails to reproduce the dependence of rates on distance from the active residues, MSA accounts for the rate-distance dependence quantitatively. Thus, the variation of evolutionary rate among enzyme sites is mechanistically underpinned by natural selection for both stability and activity.

biophysics↗