Julián Echave

Gamma carbonic anhydrases in plant mitochondria

Three genes from Arabidopsis thaliana with high sequence similarity to gamma carbonic anhydrase (γCA), a Zn containing enzyme from Methanosarcina thermophila(CAM), were identified and characterized. Evolutionary and structural analyses predict that these genes code for active forms of γCA. Phylogenetic analyses reveal that these Arabidopsis gene products cluster together with CAM and related sequences from α and γ proteobacteria, organisms proposed as the mitochondrial endosymbiont ancestor. Indeed, in vitro and in vivo experiments indicate that these gene products are transported into the mitochondria as occurs with several mitochondrial protein genes transferred, during evolution, from the endosymbiotic bacteria to the host genome. Moreover, putative CAM orthologous genes are detected in other plants and green algae and were predicted to be imported to mitochondria. Structural modeling and sequence analysis performed in more than a hundred homologous sequences show a high conservation of functionally important active site residues. Thus, the three histidine residues involved in Zn coordination (His 81, 117 and 122), Arg 59, Asp 61, Gin 75, and Asp 76 of CAM are conserved and properly arranged in the active site cavity of the models. Two other functionally important residues (Glu 62 and Glu 84 of CAM) are lacking, but alternative amino acids that might serve to their roles are postulated. Accordingly, we propose that photosynthetic eukaryotic organisms (green algae and plants) contain γCAs and that these enzymes codified by nuclear genes are imported into mitochondria to accomplish their biological function.

Quantum theory of four-atom reactions using arrangement channel hyperspherical coordinates: formulation and application to OH + H2 H2O + H

An arrangement channel hyperspherical coordinate method for performing quantum scattering calculations on four-atom reactions is formulated. This method treats the vibrational and rotational states in different arrangement channels by a close-coupling expansion in nonorthogonal functions. The method is applied to the calculation of state-to-state probabilities for the OH+H2→H2O+H reaction. Good agreement is found with cumulative and state-selected reaction probabilities previously calculated by other methods. The major advantage of this general approach is that the whole S matrix can be obtained in a single calculation.

Evolutionary conservation of protein vibrational dynamics

The aim of the present work is to study the evolutionary divergence of vibrational protein dynamics. To this end, we used the Gaussian Network Model to perform a systematic analysis of normal mode conservation on a large dataset of proteins classified into homologous sets of family pairs and superfamily pairs. We found that the lowest most collective normal modes are the most conserved ones. More precisely, there is, on average, a linear correlation between normal mode conservation and mode collectivity. These results imply that the previously observed conservation of backbone flexibility (B-factor) profiles is due to the conservation of the most collective modes, which contribute the most to such profiles. We discuss the possible roles of normal mode robustness and natural selection in the determination of the observed behavior. Finally, we draw some practical implications for dynamics-based protein alignment and classification and discuss possible caveats of the present approach.

Evolutionary Conservation of Protein Backbone Flexibility

Internal protein dynamics is essential for biological function. During evolution, protein divergence is functionally constrained: properties more relevant for function vary more slowly than less important properties. Thus, if protein dynamics is relevant for function, it should be evolutionary conserved. In contrast with the well-studied evolution of protein structure, the evolutionary divergence of protein dynamics has not been addressed systematically before, apart from a few case studies. X-Ray diffraction analysis gives information not only on protein structure but also on B-factors, which characterize the flexibility that results from protein dynamics. Here we study the evolutionary divergence of protein backbone dynamics by comparing the Cα flexibility (B-factor) profiles for a large dataset of homologous proteins classified into families and superfamilies. We show that Cα flexibility profiles diverge slowly, so that they are conserved at family and superfamily levels, even for pairs of proteins with nonsignificant sequence similarity. We also analyze and discuss the correlations among the divergences of flexibility, sequence, and structure.

Gamma carbonic anhydrase like complex interact with plant mitochondrial complex I

We report the identification by two hybrid screens of two novel similar proteins, called Arabidopsis thaliana gamma carbonic anhydrase like1 and 2 (AtγCAL1 and AtγCAL2), that interact specifically with putative Arabidopsis thaliana gamma Carbonic Anhydrase (AtγCA) proteins in plant mitochondria. The interaction region that was located in the N-terminal 150 amino acids of mature AtγCA and AtγCA like proteins represents a new interaction domain. In vitro experiments indicate that these proteins are imported into mitochondria and are associated with mitochondrial complex I as AtγCAs. All plant species analyzed contain both AtγCA and AtγCAL sequences indicating that these genes were conserved throughout plant evolution. Structural modeling of AtγCAL sequences show a deviation of functionally important active site residues with respect to γCAs but could form active interfaces in the interaction with AtγCAs. We postulate a CA complex tightly associated to plant mitochondrial complex.

Site-Specific Structural Constraints on Protein Sequence Evolutionary Divergence: Local Packing Density versus Solvent Exposure

Protein sequences evolve under selection pressures imposed by functional and biophysical requirements, resulting in site-dependent rates of amino acid substitution. Relative solvent accessibility (RSA) and local packing density (LPD) have emerged as the best candidates to quantify structural constraint. Recent research assumes that RSA is the main determinant of sequence divergence. However, it is not yet clear which is the best predictor of substitution rates. To address this issue, we compared RSA and LPD with site-specific rates of evolution for a diverse data set of enzymes. In contrast with recent studies, we found that LPD measures correlate better than RSA with evolutionary rate. Moreover, the independent contribution of RSA is minor. Taking into account that LPD is related to backbone flexibility, we put forward the possibility that the rate of evolution of a site is determined by the ease with which the backbone deforms to accommodate mutations.

A mechanistic stress model of protein evolution accounts for site-specific evolutionary rates and their relationship with packing density and flexibility

BackgroundProtein sites evolve at different rates due to functional and biophysical constraints. It is usually considered that the main structural determinant of a site’s rate of evolution is its Relative Solvent Accessibility (RSA). However, a recent comparative study has shown that the main structural determinant is the site’s Local Packing Density (LPD). LPD is related with dynamical flexibility, which has also been shown to correlate with sequence variability. Our purpose is to investigate the mechanism that connects a site’s LPD with its rate of evolution.ResultsWe consider two models: an empirical Flexibility Model and a mechanistic Stress Model. The Flexibility Model postulates a linear increase of site-specific rate of evolution with dynamical flexibility. The Stress Model, introduced here, models mutations as random perturbations of the protein’s potential energy landscape, for which we use simple Elastic Network Models (ENMs). To account for natural selection we assume a single active conformation and use basic statistical physics to derive a linear relationship between site-specific evolutionary rates and the local stress of the mutant’s active conformation.We compare both models on a large and diverse dataset of enzymes. In a protein-by-protein study we found that the Stress Model outperforms the Flexibility Model for most proteins. Pooling all proteins together we show that the Stress Model is strongly supported by the total weight of evidence. Moreover, it accounts for the observed nonlinear dependence of sequence variability on flexibility. Finally, when mutational stress is controlled for, there is very little remaining correlation between sequence variability and dynamical flexibility.ConclusionsWe developed a mechanistic Stress Model of evolution according to which the rate of evolution of a site is predicted to depend linearly on the local mutational stress of the active conformation. Such local stress is proportional to LPD, so that this model explains the relationship between LPD and evolutionary rate. Moreover, the model also accounts for the nonlinear dependence between evolutionary rate and dynamical flexibility.

A perturbative view of protein structural variation

It was recently found that the lowest‐energy collective normal modes dominate the evolutionary divergence of protein structures. This was attributed to a presumed functional importance of such motions, i.e., to natural selection. In contrast to this selectionist explanation, we proposed that the observed behavior could be just the expected physical response of proteins to random mutations. This proposal was based on the success of a linearly forced elastic network model (LFENM) of mutational effects on structure to account for the observed pattern of structural divergence. Here, to further test the mutational explanation and the LFENM, we analyze the structural differences observed not only in homologous (globin‐like) proteins but also in unselected experimentally engineered myoglobin mutants and in wild‐type variants subject to other perturbations such as ligand‐binding and pH changes. We show that the lowest normal modes dominate structural change in all the cases considered and that the LFENM reproduces this behavior quantitatively. The collective nature of the lowest normal modes results in global conformational changes that depend little on the exact nature or location of the perturbation. Significantly, the evolutionarily conserved structural core matches the regions observed to be more robust with respect to mutations, so that the core would be more conserved even under unselected random mutations. In a word, the observed patterns of structural variation can be seen as the natural response of proteins to perturbations and can be adequately modeled using the LFENM, which serves as a common framework to relate a priori different phenomena. Proteins 2010.

Quantum scattering and quasi-classical trajectory calculations for the H2+OH⇌H2O+H reaction on a new potential surface

Six-dimensional (6D) quantum scattering calculations of reaction probabilities are reported for theOH+H2⇌H2O+H reaction. An arrangement channel hyperspherical coordinate method is used. A new potential energy surface due to Ochoa and Clary is employed. The results agree well with those calculated using the rotating bond approximation (RBA) and the quasi-classical trajectory (QCT) method. 6D quantum, RBA and QCT calculations of rate constants for the OH+H2 reaction agree well with experiment. In addition, RBA calculations of differential cross sections for the OH+D2→HOD+D reaction and the photodetachment spectrum for H3O− also agree well with experiment. These results suggest that the new potential surface is reliable for this reason.

Local packing density is the main structural determinant of the rate of protein sequence evolution at site level.

Functional and biophysical constraints result in site-dependent patterns of protein sequence variability. It is commonly assumed that the key structural determinant of site-specific rates of evolution is the Relative Solvent Accessibility (RSA). However, a recent study found that amino acid substitution rates correlate better with two Local Packing Density (LPD) measures, the Weighted Contact Number (WCN) and the Contact Number (CN), than with RSA. This work aims at a more thorough assessment. To this end, in addition to substitution rates, we considered four other sequence variability scores, four measures of solvent accessibility (SA), and other CN measures. We compared all properties for each protein of a structurally and functionally diverse representative dataset of monomeric enzymes. We show that the best sequence variability measures take into account phylogenetic tree topology. More importantly, we show that both LPD measures (WCN and CN) correlate better than all of the SA measures, regardless of the sequence variability score used. Moreover, the independent contribution of the best LPD measure is approximately four times larger than that of the best SA measure. This study strongly supports the conclusion that a sites packing density rather than its solvent accessibility is the main structural determinant of its rate of evolution.