Optimality of the genetic code with respect to protein stability and amino acid frequencies

How robust is the natural genetic code with respect to mistranslation errors? It has long been known that the genetic code is very efficient in limiting the effect of point mutation. A misread codon will commonly code either for the same amino acid or for a similar one in terms of its biochemical properties, so the structure and function of the coded protein remain relatively unaltered. Previous studies have attempted to address this question more quantitatively, namely by statistically estimating the fraction of randomly generated codes that do better than the genetic code regarding its overall robustness. In this paper, we extend these results by investigating the role of amino acid frequencies in the optimality of the genetic code. When measuring the relative fitness of the natural code with respect to a random code, it is indeed natural to assume that a translation error affecting a frequent amino acid is less favorable than that of a rare one, at equal mutation cost. We find that taking the amino acid frequency into account accordingly decreases the fraction of random codes that beat the natural code, making the latter comparatively even more robust. This effect is particularly pronounced when more refined measures of the amino acid substitution cost are used than hydrophobicity. To show this, we devise a new cost function by evaluating with computer experiments the change in folding free energy caused by all possible single-site mutations in a set of known protein structures. With this cost function, we estimate that of the order of one random code out of 100 millions is more fit than the natural code when taking amino acid frequencies into account. The genetic code seems therefore structured so as to minimize the consequences of translation errors on the 3D structure and stability of proteins.Comment: 31 pages, 2 figures, postscript fil

The first peptides: the evolutionary transition between prebiotic amino acids and early proteins

The issues we attempt to tackle here are what the first peptides did look like when they emerged on the primitive earth, and what simple catalytic activities they fulfilled. We conjecture that the early functional peptides were short (3 to 8 amino acids long), were made of those amino acids, Gly, Ala, Val and Asp, that are abundantly produced in many prebiotic synthesis experiments and observed in meteorites, and that the neutralization of Asp's negative charge is achieved by metal ions. We further assume that some traces of these prebiotic peptides still exist, in the form of active sites in present-day proteins. Searching these proteins for prebiotic peptide candidates led us to identify three main classes of motifs, bound mainly to Mg^{2+} ions: D(F/Y)DGD corresponding to the active site in RNA polymerases, DGD(G/A)D present in some kinds of mutases, and DAKVGDGD in dihydroxyacetone kinase. All three motifs contain a DGD submotif, which is suggested to be the common ancestor of all active peptides. Moreover, all three manipulate phosphate groups, which was probably a very important biological function in the very first stages of life. The statistical significance of our results is supported by the frequency of these motifs in today's proteins, which is three times higher than expected by chance, with a P-value of 3 10^{-2}. The implications of our findings in the context of the appearance of life and the possibility of an experimental validation are discussed.Comment: 22 pages, 2 figures, J. Theor. Biol. (2009) in pres

SODa: An Mn/Fe superoxide dismutase prediction and design server

Background: Superoxide dismutases (SODs) are ubiquitous metalloenzymes that play an important role in the defense of aerobic organisms against oxidative stress, by converting reactive oxygen species into nontoxic molecules. We focus here on the SOD family that uses Fe or Mn as cofactor. Results: The SODa webtool http://babylone.ulb.ac.be/soda predicts if a target sequence corresponds to an Fe/Mn SOD. If so, it predicts the metal ion specificity (Fe, Mn or cambialistic) and the oligomerization mode (dimer or tetramer) of the target. In addition, SODa proposes a list of residue substitutions likely to improve the predicted preferences for the metal cofactor and oligomerization mode. The method is based on residue fingerprints, consisting of residues conserved in SOD sequences or typical of SOD subgroups, and of interaction fingerprints, containing residue pairs that are in contact in SOD structures. Conclusion: SODa is shown to outperform and to be more discriminative than traditional techniques based on pairwise sequence alignments. Moreover, the fact that it proposes selected mutations makes it a valuable tool for rational protein design. © 2008 Kwasigroch et al; licensee BioMed Central Ltd.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

ABSTRACT:Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Protein decoy sets for evaluating energy functions.

Energy functions are crucial ingredients of protein tertiary structure prediction methods. Assessing the quality of energy functions is therefore of prime importance. It requires the elaboration of a standard evaluation scheme, whose key elements are: i). sets that contain the native and several non-native structures of proteins (decoys) in order to test whether the energy functions display the expected quality features and ii). measures to evaluate the reliability of energy functions. We present here a survey of the recent advances in these two related fields. In a first part, we analyze and review the large number of decoy sets that are available on the web, and we summarize the characteristics of a challenging decoy set. We then discuss how to define the quality of energy functions and review the measures related to it.Journal ArticleResearch Support, Non-U.S. Gov'tReviewinfo:eu-repo/semantics/publishe

In silico analysis of the thermodynamic stability changes of psychrophilic and mesophilic alpha-amylases upon exhaustive single-site mutations.

Identifying sequence modifications that distinguish psychrophilic from mesophilic proteins is important for designing enzymes with different thermodynamic stabilities and to understand the underlying mechanisms. The PoPMuSiC algorithm is used to introduce, in silico, all the single-site mutations in four mesophilic and one psychrophilic chloride-dependent alpha-amylases and to evaluate the changes in thermodynamic stability. The analysis of the distribution of the sequence positions that could be stabilized upon mutation shows a clear difference between the three domains of psychrophilic and mesophilic alpha-amylases. Most of the mutations stabilizing the psychrophilic enzyme are found in domains B and C, contrary to the mesophilic proteins where they are preferentially situated in the catalytic domain A. Moreover, the calculations show that the environment of some residues responsible for the activity of the psychrophilic protein has evolved to reinforce favorable interactions with these residues. In the second part, these results are exploited to propose rationally designed mutations that are predicted to confer to the psychrophilic enzyme mesophilic-like thermodynamic properties. Interestingly, most of the mutations found in domain C strengthen the interactions with domain A, in agreement with suggestions made on the basis of structural analyses. Although this study focuses on single-site mutations, the thermodynamic effects of the recommended mutations should be additive if the mutated residues are not close in space.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

Prédiction du repliement et de la stabilité de protéines par des potentiels dérivés de structures connues

Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Ab initio structure predictions using a hierarchic approach applied to 434 CRO and Drosophila homeodomain

A discrete-state ab initio protein structure prediction procedure is presented, based on the assumption that some protein fold in an hierarchical way, where the early folding of independent units precedes and helps complete structure formation. It involves a first step predicting, by means of threading algorithms and local structure prediction methods, the location of autonomous protein subunits presenting favorable local and tertiary interactions. The second step consists of predicting the structure of these units by Monte Carlo simulated annealing using several database-derived potentials. In a last step, these predicted structures are used as starting conformations of additional simulations, keeping these structures frozen and including the complete protein sequence. This procedure is applied to two small DNA-binding proteins, 434 cro and the Drosophila melanogaster homeodomain that contain 65 and 47 residues, respectively, and is compared to the nonhierarchical procedure where the whole protein is predicted in a single run. The best predicted structures were found to present root-mean-square deviation relative to the native conformation of 2.7 A in the case of the homeodomain and of 3.9 A for 434 cro; these structures thus represent low-resolution models of the native structures. Strikingly, not only the helices were correctly predicted but also intervening turn motifs.info:eu-repo/semantics/publishe

Stability changes upon mutation of solvent-accessible residues in proteins evaluated by database-derived potentials.

The stability changes in peptides and proteins caused by the substitution of a single amino acid, which can be measured experimentally by the change in folding free energy, are evaluated here using effective potentials derived from known protein structures. The analysis is focused on mutations of residues that are accessible to the solvent. These represent in total 106 mutations, introduced at different sites in barnase, bacteriophage T4 lysozyme and chymotrypsin inhibitor 2, and in a synthetic helical peptide. Assuming that the mutations do not modify the backbone structure, the changes in folding free energies are computed using various types of database-derived potentials and are compared with the measured ones. Distance-dependent residue-residue potentials are found to be inadequate for estimating the stability changes caused by these mutations, as they are dominated by hydrophobic interactions, which do not play an essential role at the protein surface. On the contrary, the potentials based on backbone torsion angle propensities yield quite good results. Indeed, for a subset of 96 out of the 106 mutations, the computed and measured changes in folding free energy correlate with a linear correlation coefficient of 0.87. Moreover, the ten mutations that are excluded from the correlation either seem to cause modifications of the backbone structure or to involve strong hydrophobic interactions, which are atypical for solvent-accessible residues. We find furthermore that raising the ionic strength of the solvent used for measuring the changes in folding free energies improves the correlation, as it tends to mask the electrostatic interactions. When adding to these 106 mutations 44 mutations performed in staphylococcal nuclease and chemotactic protein, which were first discarded because some of them were suspected to affect the backbone conformation or the denatured state, the correlation between measured and computed folding free energy changes remains quite good: the correlation coefficient is 0.86 for 135 out of the 150 mutations. The success of the backbone torsion potentials in predicting stability changes indicates that the approximations made for deriving these potentials are adequate. It suggests moreover that the local interactions along the chain dominate at the protein surface.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

PoPMuSiC, an algorithm for predicting protein mutant stability changes: application to prion proteins.

A novel tool for computer-aided design of single-site mutations in proteins and peptides is presented. It proceeds by performing in silico all possible point mutations in a given protein or protein region and estimating the stability changes with linear combinations of database-derived potentials, whose coefficients depend on the solvent accessibility of the mutated residues. Upon completion, it yields a list of the most stabilizing, destabilizing or neutral mutations. This tool is applied to mouse, hamster and human prion proteins to identify the point mutations that are the most likely to stabilize their cellular form. The selected mutations are essentially located in the second helix, which presents an intrinsic preference to form beta-structures, with the best mutations being T183-->F, T192-->A and Q186-->A. The T183 mutation is predicted to be by far the most stabilizing one, but should be considered with care as it blocks the glycosylation of N181 and this blockade is known to favor the cellular to scrapie conversion. Furthermore, following the hypothesis that the first helix might induce the formation of hydrophilic beta-aggregates, several mutations that are neutral with respect to the structure's stability but improve the helix hydrophobicity are selected, among which is E146-->L. These mutations are intended as good candidates to undergo experimental tests.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe