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PubMed2005

Looking at structure, stability, and evolution of proteins through the principal eigenvector of contact matrices and hydrophobicity profiles.

We review and further develop an analytical model that describes how thermodynamic constraints on the stability of the native state influence protein evolution in a site-specific manner. To this end, we represent both protein sequences and protein structures as vectors: structures are represented by the principal eigenvector (PE) of the protein contact matrix, a quantity that resembles closely the effective connectivity of each site; sequences are represented through the "interactivity" of each amino acid type, using novel parameters that are correlated with hydropathy scales. These interactivity parameters are more strongly correlated than the other hydropathy scales that we examine with: (1) the change upon mutations of the unfolding free energy of proteins with two-states thermodynamics; (2) genomic properties as the genome-size and the genome-wide GC content; (3) the main eigenvectors of the substitution matrices. The evolutionary average of the interactivity vector correlates very strongly with the PE of a protein structure. Using this result, we derive an analytic expression for site-specific distributions of amino acids across protein families in the form of Boltzmann distributions whose "inverse temperature" is a function of the PE component. We show that our predictions are in agreement with site-specific amino acid distributions obtained from the Protein Data Bank, and we determine the mutational model that best fits the observed site-specific amino acid distributions. Interestingly, the optimal model almost minimizes the rate at which deleterious mutations are eliminated by natural selection.

Amino Acid Motifs↗
PubMed2003

Identification of functional similarities between proteins using directed evolution.

Protein sequences are often highly redundant and evolution can change them beyond recognition. It can therefore be difficult to identify proteins with functional or structural similarities by inspection of their sequences. Here we have used an experimental evolutionary approach to detect hidden similarities between the antisense RNA-binding protein Rop and other proteins. We created an envelope of functional Rop mutants by combinatorial mutagenesis, used the compilation of mutant sequences to search a database of protein structures, and thereby identified a segment of the enzyme valyl-tRNA-synthetase (ValRS). Further inspection revealed that the structures of the RNA-binding sites of both proteins are highly related, as indeed are the RNA ligands. From the known 3D structure of the ValRS in complex with tRNA, we were able to build a model of an RNA hairpin pair in complex with Rop that has proved to be consistent with the biochemical and NMR data for the interaction between Rop and RNA hairpins. We suggest that this approach (mutational envelope scanning), by generating sequence information de novo, can help uncover hidden similarities between proteins.

Amino Acids↗
PubMed1986

Genetic code redundancy and its differential influence on the evolution of protein interiors versus exteriors.

The distinctive amino acid compositions of protein exteriors and interiors were compared to the composition bias imposed by genetic code redundancy. It transpired that the synonym allocation is biased more in favour of those residues which are preferred in interiors, and this leads to an average interior residue being more probable and less mutable compared to an exterior residue. The general implications for protein evolution are discussed in association with the known evolutionary behavior of particular protein families. It is suggested that some proteins may have their structural history "fossilised" in their interiors and that the "amino acid" code is in reality a "protein" code.

Amino Acid Sequence↗
PubMed2005

Universal sharing patterns in proteomes and evolution of protein fold architecture and life.

Protein evolution is imprinted in both the sequence and the structure of evolutionary building blocks known as protein domains. These domains share a common ancestry and can be unified into a comparatively small set of folding architectures, the protein folds. We have traced the distribution of protein folds between and within proteomes belonging to Eukarya, Archaea, and Bacteria along the branches of a universal phylogeny of protein architecture. This tree was reconstructed from global fold-usage statistics derived from a structural census of proteomes. We found that folds shared by the three organismal domains were placed almost exclusively at the base of the rooted tree and that there were marked heterogeneities in fold distribution and clear evolutionary patterns related to protein architecture and organismal diversification. These include a relative timing for the emergence of prokaryotes, congruent episodes of architectural loss and diversification in Archaea and Bacteria, and a late and quite massive rise of architectural novelties in Eukarya perhaps linked to multicellularity.

Evolution, Molecular↗
PubMed2006

Evolution of reproductive proteins from animals and plants.

Sexual reproduction is a fundamental biological process common among eukaryotes. Because of the significance of reproductive proteins to fitness, the diversity and rapid divergence of proteins acting at many stages of reproduction is surprising and suggests a role of adaptive diversification in reproductive protein evolution. Here we review the evolution of reproductive proteins acting at different stages of reproduction among animals and plants, emphasizing common patterns. Although we are just beginning to understand these patterns, by making comparisons among stages of reproduction for diverse organisms we can begin to understand the selective forces driving reproductive protein diversity and the functional consequences of reproductive protein evolution.

Animals↗
PubMed1991

Brave new proteins: what evolution reveals about protein structure.

Synthetic proteins provide important information about the principles of protein structure. They illuminate the processes of natural protein evaluation, but are not limited by these processes. Here, we review studies of several mutant proteins and discuss the general principles that can be derived from them.

Animals↗
PubMed1997

Strategies for the in vitro evolution of protein function: enzyme evolution by random recombination of improved sequences.

Sets of genes improved by directed evolution can be recombined in vitro to produce further improvements in protein function. Recombination is particularly useful when improved sequences are available; costs of generating such sequences, however, must be weighed against the costs of further evolution by sequential random mutagenesis. Four genes encoding para-nitrobenzyl (pNB) esterase variants exhibiting enhanced activity were recombined in two cycles of high-fidelity DNA shuffling and screening. Genes encoding enzymes exhibiting further improvements in activity were analyzed in order to elucidate evolutionary processes at the DNA level and begin to provide an experimental basis for choosing in vitro evolution strategies and setting key parameters for recombination. DNA sequencing of improved variants from the two rounds of DNA shuffling confirmed important features of the recombination process: rapid fixation and accumulation of beneficial mutations from multiple parent sequences as well as removal of silent and deleterious mutations. The five to sixfold further enhancement of total activity towards the para-nitrophenyl (pNP) ester of loracarbef was obtained through recombination of mutations from several parent sequences as well as new point mutations. Computer simulations of recombination and screening illustrate the trade-offs between recombining fewer parent sequences (in order to reduce screening requirements) and lowering the potential for further evolution. Search strategies which may substantially reduce screening requirements in certain situations are described.

Carboxylic Ester Hydrolases↗
PubMed2000

Episodic evolution of protein hormones: molecular evolution of pituitary prolactin.

Previous studies have shown that pituitary growth hormone displays an episodic pattern of evolution, with a slow underlying evolutionary rate and occasional sustained bursts of rapid change. The present study establishes that pituitary prolactin shows a similar pattern. During much of tetrapod evolution the sequence of prolactin has been strongly conserved, showing a slow basal rate of change (approx 0.27x10(9) substitutions/amino acid site/year). This rate has increased substantially ( approximately 12- to 38-fold) on at least four occasions during eutherian evolution, during the evolution of primates, artiodactyls, rodents, and elephants. That these increases are real and not a consequence of inadvertant comparison of paralogous genes is shown (for at least the first three groups) by the fact that they are confined to mature protein coding sequence and not apparent in sequences coding for signal peptides or when synonymous substitutions are examined. Sequences of teleost prolactins differ markedly from those of tetrapods and lungfish, but during the course of teleost evolution the rate of change of prolactin has been less variable than that of growth hormone. It is concluded that the evolutionary pattern seen for prolactin shows long periods of near-stasis interrupted by occasional bursts of rapid change, resembling the pattern seen for growth hormone in general but not in detail. The most likely basis for these bursts appears to be adaptive evolution though the biological changes involved are relatively small.

Animals↗
PubMed2001

Computationally focusing the directed evolution of proteins.

Directed evolution has proven to be a successful strategy for the modification of enzyme properties. To date, the preferred experimental procedure has been to apply mutations or crossovers randomly throughout the gene. With the emergence of powerful computational methods, it has become possible to develop focused combinatorial searches, guided by computer algorithms. Here, we describe several computational methods that have emerged to aid the optimization of mutant libraries, the targeting of specific residues for mutagenesis, and the design of recombination experiments.

Algorithms↗
PubMed2005

Downhill protein folding: evolution meets physics.

Proteins can be redesigned to fold downhill on a free energy surface characterized by only a few coordinates, confirming a principal prediction of the 'energy-landscape' model. Nonetheless, natural proteins have small but significant barriers. Spectroscopy and kinetics reveal potential biological causes for activation barriers during protein folding: evolution against protein aggregation and for protein function.

Animals↗
PubMed2005

Conservation of cis prolyl bonds in proteins during evolution.

In proteins and peptides, the vast majority of peptide bonds occurs in trans conformation, but a considerable fraction (about 5%) of X-Pro bonds adopts the cis conformation. Here we study the conservation of cis prolyl residues in evolutionary related proteins. We find that overall, in contrast to local, protein sequence similarity is a clear indicator for the conformation of prolyl residues. We observe that cis prolyl residues are more often conserved than trans prolyl residues, and both are more conserved than the surrounding amino acids, which show the same extent of conservation as the whole protein. The pattern of amino acid exchanges differs between cis and trans prolyl residues. Also, the cis prolyl bond is maintained in proteins with sequence identity as low as 20%. This finding emphasizes the importance of cis peptide bonds in protein structure and function.

Amino Acids↗
PubMed2003

Exploiting the co-evolution of interacting proteins to discover interaction specificity.

Protein interactions are fundamental to the functioning of cells, and high throughput experimental and computational strategies are sought to map interactions. Predicting interaction specificity, such as matching members of a ligand family to specific members of a receptor family, is largely an unsolved problem. Here we show that by using evolutionary relationships within such families, it is possible to predict their physical interaction specificities. We introduce the computational method of matrix alignment for finding the optimal alignment between protein family similarity matrices. A second method, 3D embedding, allows visualization of interacting partners via spatial representation of the protein families. These methods essentially align phylogenetic trees of interacting protein families to define specific interaction partners. Prediction accuracy depends strongly on phylogenetic tree complexity, as measured with information theoretic methods. These results, along with simulations of protein evolution, suggest a model for the evolution of interacting protein families in which interaction partners are duplicated in coupled processes. Using these methods, it is possible to successfully find protein interaction specificities, as demonstrated for >18 protein families.

Amino Acid Sequence↗
PubMed2002

Studying genomes through the aeons: protein families, pseudogenes and proteome evolution.

Protein families can be used to understand many aspects of genomes, both their "live" and their "dead" parts (i.e. genes and pseudogenes). Surveys of genomes have revealed that, in every organism, there are always a few large families and many small ones, with the overall distribution following a power-law. This commonality is equally true for both genes and pseudogenes, and exists despite the fact that the specific families that are enlarged differ greatly between organisms. Furthermore, because of family structure there is great redundancy in proteomes, a fact linked to the large number of dispensable genes for each organism and the small size of the minimal, indispensable sub-proteome. Pseudogenes in prokaryotes represent families that are in the process of being dispensed with. In particular, the genome sequences of certain pathogenic bacteria (Mycobacterium leprae, Yersinia pestis and Rickettsia prowazekii) show how an organism can undergo reductive evolution on a large scale (i.e. the dying out of families) as a result of niche change. There appears to be less pressure to delete pseudogenes in eukaryotes. These can be divided into two varieties, duplicated and processed, where the latter involves reverse transcription from an mRNA intermediate. We discuss these collectively in yeast, worm, fly, and human. The fly has few pseudogenes apparently because of its high rate of genomic DNA deletion. In the other three organisms, the distribution of pseudogenes on the chromosome and amongst different families is highly non-uniform. Pseudogenes tend not to occur in the middle of chromosome arms, and tend to be associated with lineage-specific (as opposed to highly conserved) families that have environmental-response functions. This may be because, rather than being dead, they may form a reservoir of diverse "extra parts" that can be resurrected to help an organism adapt to its surroundings. In yeast, there may be a novel mechanism involving the [PSI+] prion that potentially enables this resurrection. In worm, the pseudogenes tend to arise out of families (e.g. chemoreceptors) that are greatly expanded in it compared to the fly. The human genome stands out in having many processed pseudogenes. These have a character very different from those of the duplicated variety, to a large extent just representing random insertions. Thus, their occurrence tends to be roughly in proportion to the amount of mRNA for a particular protein and to reflect the extent of the intergenic sequences. Further information about pseudogenes is available at http://genecensus.org/pseudogene

Animals↗
PubMed1996

[Calculation of specific evolution rate of proteins based on phylogenetic].

A method of calculating specific protein evolution distance and evolution rate is presented. This is based on the reconstruction of the phylogenetic tree and on the deduction of ancestral sequences according to temporally homologous protein sequences. The evolution distance and evolution rate are calculated from the difference percentage of the sequence between the temporal and deduced ancestral sequences. A comparison with Dayoff simulation method and an example of using it to calculate the specific evolution rates of mammalian erythropoietin. The method presented here would be specially useful under certain circumstances.

Amino Acid Sequence↗
PubMed2006

Disorder and sequence repeats in hub proteins and their implications for network evolution.

Protein interaction networks display approximate scale-free topology, in which hub proteins that interact with a large number of other proteins determine the overall organization of the network. In this study, we aim to determine whether hubs are distinguishable from other networked proteins by specific sequence features. Proteins of different connectednesses were compared in the interaction networks of Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Homo sapienswith respect to the distribution of predicted structural disorder, sequence repeats, low complexity regions, and chain length. Highly connected proteins ("hub proteins") contained significantly more of, and greater proportion of, these sequence features and tended to be longer overall as compared to less connected proteins. These sequence features provide two different functional means for realizing multiple interactions: (1) extended interaction surface and (2) flexibility and adaptability, providing a mechanism for the same region to bind distinct partners. Our view contradicts the prevailing view that scaling in protein interactomes arose from gene duplication and preferential attachment of equivalent proteins. We propose an alternative evolutionary network specialization process, in which certain components of the protein interactome improved their fitness for binding by becoming longer or accruing regions of disorder and/or internal repeats and have therefore become specialized in network organization.

Amino Acid Sequence↗
PubMed1998

How far divergent evolution goes in proteins.

In theory, mutations of protein sequences may eventually generate different functions as well as different structures. The observation of such records of protein evolution have been obscured by the dissipation of memory about the ancestors. In the past year, new advances in our understanding of divergent evolution were allowed by new protein structure determinations, including the ClpP proteases, steroid delta-isomerase, carboxypeptidase G2, the thrombin inhibitor triabin and the chloroplast Rieske protein. There is strong evidence for their distant homology with proteins of known structure despite significant functional or structural differences.

Adenosine Triphosphatases↗
PubMed2006

Understanding relationship between sequence and functional evolution in yeast proteins.

The underlying relationship between functional variables and sequence evolutionary rates is often assessed by partial correlation analysis. However, this strategy is impeded by the difficulty of conducting meaningful statistical analysis using noisy biological data. A recent study suggested that the partial correlation analysis is misleading when data is noisy and that the principal component regression analysis is a better tool to analyze biological data. In this paper, we evaluate how these two statistical tools (partial correlation and principal component regression) perform when data are noisy. Contrary to the earlier conclusion, we found that these two tools perform comparably in most cases. Furthermore, when there is more than one 'true' independent variable, partial correlation analysis delivers a better representation of the data. Employing both tools may provide a more complete and complementary representation of the real data. In this light, and with new analyses, we suggest that protein length and gene dispensability play significant, independent roles in yeast protein evolution.

Evolution, Molecular↗