Species-specific protein sequence and fold optimizations

Evolution of complete proteomes: guanine-cytosine pressure, phylogeny and environmental influences blend the proteomic architecture

Background

Guanine-cytosine (GC) composition is an important feature of genomes. Likewise, amino acid composition is a distinct, but less valued, feature of proteomes. A major concern is that it is not clear what valuable information can be acquired from amino acid composition data. To address this concern, in-depth analyses of the amino acid composition of the complete proteomes from 63 archaea, 270 bacteria, and 128 eukaryotes were performed.

Results

Principal component analysis of the amino acid matrices showed that the main contributors to proteomic architecture were genomic GC variation, phylogeny, and environmental influences. GC pressure drove positive selection on Ala, Arg, Gly, Pro, Trp, and Val, and adverse selection on Asn, Lys, Ile, Phe, and Tyr. The physico-chemical framework of the complete proteomes withstood GC pressure by frequency complementation of GC-dependent amino acid pairs with similar physico-chemical properties. Gln, His, Ser, and Val were responsible for phylogeny and their constituted components could differentiate archaea, bacteria, and eukaryotes. Environmental niche was also a significant factor in determining proteomic architecture, especially for archaea for which the main amino acids were Cys, Leu, and Thr. In archaea, hyperthermophiles, acidophiles, mesophiles, psychrophiles, and halophiles gathered successively along the environment-based principal component. Concordance between proteomic architecture and the genetic code was also related closely to genomic GC content, phylogeny, and lifestyles.

Conclusions

Large-scale analyses of the complete proteomes of a wide range of organisms suggested that amino acid composition retained the trace of GC variation, phylogeny, and environmental influences during evolution. The findings from this study will help in the development of a global understanding of proteome evolution, and even biological evolution.

Development of constraint-based system-level models of microbial metabolism

Genome-scale models of metabolism are valuable tools for using genomic information to predict microbial phenotypes. System-level mathematical models of metabolic networks have been developed for a number of microbes and have been used to gain new insights into the biochemical conversions that occur within organisms and permit their survival and proliferation. Utilizing these models, computational biologists can (1) examine network structures, (2) predict metabolic capabilities and resolve unexplained experimental observations, (3) generate and test new hypotheses, (4) assess the nutritional requirements of the organism and approximate its environmental niche, (5) identify missing enzymatic functions in the annotated genome, and (6) engineer desired metabolic capabilities in model organisms. This chapter details the protocol for developing genome-scale models of metabolism in microbes as well as tips for accelerating the model building process.

A legume specific protein database (LegProt) improves the number of identified peptides, confidence scores and overall protein identification success rates for legume proteomics

A legume specific protein database (LegProt) has been created containing sequences from seven legume species, i.e., Glycine max, Lotus japonicus, Medicago sativa, Medicago truncatula, Lupinusalbus, Phaseolus vulgaris, and Pisum sativum. The database consists of amino acid sequences translated from predicted gene models and 6-frame translations of tentative consensus (TC) sequences assembled from expressed sequence tags (ESTs) and singleton ESTs. This database was queried using mass spectral data for protein identification and identification success rates were compared to the NCBI nr database. Specifically, Mascot MS/MS ion searches of tandem nano-LC Q-TOFMS/MS mass spectral data showed that relative to the NCBI nr protein database, the LegProt database yielded a 54% increase in the average protein score (i.e., from NCBI nr 480 to LegProt 739) and a 50% increase in the average number of matched peptides (i.e., from NCBI nr 8 to LegProt 12). The overall identification success rate also increased from 88% (NCBI nr) to 93% (LegProt). Mascot peptide mass fingerprinting (PMF) searches of the LegProt database using MALDI-TOFMS data yielded a significant increase in the identification success rate from 19% (NCBI nr) to 34% (LegProt) while the average scores and average number of matched peptides showed insignificant changes. The results demonstrate that the LegProt database significantly increases legume protein identification success rates and the confidence levels compared to the commonly used NCBI nr. These improvements are primarily due to the presence of a large number of legume specific TC sequences in the LegProt database that were not found in NCBI nr. The LegProt database is freely available for download (http://bioinfo.noble.org/manuscript-support/legumedb) and will serve as a valuable resource for legume proteomics.

Synonymous codon usage and its potential link with optimal growth temperature in prokaryotes

The relationship between codon usage in prokaryotes and their ability to grow at extreme temperatures has been given much attention over the past years. Previous studies have suggested that the difference in synonymous codon usage between (hyper)thermophiles and mesophiles is a consequence of a selective pressure linked to growth temperature. Here, we performed an updated analysis of the variation in synonymous codon usage with growth temperature; our study includes a large number of species from a wide taxonomic and growth temperature range. The presence of psychrophilic species in our study allowed us to test whether the same selective pressure acts on synonymous codon usage at very low growth temperature. Our results show that the synonymous codon usage for Arg (through the AGG, AGA and CGT codons) is the most discriminating factor between (hyper)thermophilic and non-thermophilic species, thus confirming previous studies. We report the unusual clustering of an Archaeal psychrophile with the thermophilic and hyperthermophilic species on the synonymous codon usage factorial map; the other psychrophiles in our study cluster with the mesophilic species. Our conclusion is that the difference in synonymous codon usage between (hyper)thermophilic and non-thermophilic species cannot be clearly attributed to a selective pressure linked to growth at high temperatures.

Amino acid variation in cellular processes in 108 bacterial proteomes

We have analysed 108 bacterial proteomes in the KEGG database to explore the variation of amino acid composition with respect to protein function. The ratio between the observed amino acid composition and that predicted based on mononucleotide composition was calculated for each functional category. This indicated whether the compositional variation arose from mutation or selection pressure. The results showed that charged amino acids (Lys, Arg and Glu), were found more frequently than expected in proteins involved in genetic information processing (i.e. transcription, translation, etc.) Similarly, in the proteins involved in processing environmental information (e.g. signal transduction), the hydrophobic amino acid Leu was found in excess of values expected from the base composition in the genes.

Armadillo: domain boundary prediction by amino acid composition

The identification and annotation of protein domains provides a critical step in the accurate determination of molecular function. Both computational and experimental methods of protein structure determination may be deterred by large multi-domain proteins or flexible linker regions. Knowledge of domains and their boundaries may reduce the experimental cost of protein structure determination by allowing researchers to work on a set of smaller and possibly more successful alternatives. Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins. We present here a simple computational method to identify protein domain linkers and their boundaries from sequence information alone. Our domain predictor, Armadillo (http://armadillo.blueprint.org), uses any amino acid index to convert a protein sequence to a smoothed numeric profile from which domains and domain boundaries may be predicted. We derived an amino acid index called the domain linker propensity index (DLI) from the amino acid composition of domain linkers using a non-redundant structure dataset. The index indicates that Pro and Gly show a propensity for linker residues while small hydrophobic residues do not. Armadillo predicts domain linker boundaries from Z-score distributions and obtains 35% sensitivity with DLI in a two-domain, single-linker dataset (within +/-20 residues from linker). The combination of DLI and an entropy-based amino acid index increases the overall Armadillo sensitivity to 56% for two domain proteins. Moreover, Armadillo achieves 37% sensitivity for multi-domain proteins, surpassing most other prediction methods. Armadillo provides a simple, but effective method by which prediction of domain boundaries can be obtained with reasonable sensitivity. Armadillo should prove to be a valuable tool for rapidly delineating protein domains in poorly conserved proteins or those with no sequence neighbors. As a first-line predictor, domain meta-predictors could yield improved results with Armadillo predictions.

Global analysis of predicted proteomes: functional adaptation of physical properties

The physical characteristics of proteins are fundamentally important in organismal function. We used the complete predicted proteomes of >100 organisms spanning the three domains of life to investigate the comparative biology and evolution of proteomes. Theoretical 2D gels were constructed with axes of protein mass and charge (pI) and converted to density estimates comparable across all types and sizes of proteome. We asked whether we could detect general patterns of proteome conservation and variation. The overall pattern of theoretical 2D gels was strongly conserved across all life forms. Nevertheless, coevolved replicons from the same organism (different chromosomes or plasmid and host chromosomes) encode proteomes more similar to each other than those from different organisms. Furthermore, there was disparity between the membrane and nonmembrane subproteomes within organisms (proteins of membrane proteomes are on the average more basic and heavier) and their variation across organisms, suggesting that membrane proteomes evolve most rapidly. Experimentally, a significant positive relationship independent of phylogeny was found between the predicted proteome and Biolog profile, a measure associated with the ecological niche. Finally, we show that, for the smallest and most alkaline proteomes, there is a negative relationship between proteome size and basicity. This relationship is not adequately explained by AT bias at the DNA sequence level. Together, these data provide evidence of functional adaptation in the properties of complete proteomes.

Thermophilic prokaryotes have characteristic patterns of codon usage, amino acid composition and nucleotide content

A number of recent studies have shown that thermophilic prokaryotes have distinguishable patterns of both synonymous codon usage and amino acid composition, indicating the action of natural selection related to thermophily. On the other hand, several other studies of whole genomes have illustrated that nucleotide bias can have dramatic effects on synonymous codon usage and also on the amino acid composition of the encoded proteins. This raises the possibility that the thermophile-specific patterns observed at both the codon and protein levels are merely reflections of a single underlying effect at the level of nucleotide composition. Moreover, such an effect at the nucleotide level might be due entirely to mutational bias. In this study, we have compared the genomes of thermophiles and mesophiles at three levels: nucleotide content, codon usage and amino acid composition. Our results indicate that the genomes of thermophiles are distinguishable from mesophiles at all three levels and that the codon and amino acid frequency differences cannot be explained simply by the patterns of nucleotide composition. At the nucleotide level, we see a consistent tendency for the frequency of adenine to increase at all codon positions within the thermophiles. Thermophiles are also distinguished by their pattern of synonymous codon usage for several amino acids, particularly arginine and isoleucine. At the protein level, the most dramatic effect is a two-fold decrease in the frequency of glutamine residues among thermophiles. These results indicate that adaptation to growth at high temperature requires a coordinated set of evolutionary changes affecting (i) mRNA thermostability, (ii) stability of codon-anticodon interactions and (iii) increased thermostability of the protein products. We conclude that elevated growth temperature imposes selective constraints at all three molecular levels: nucleotide content, codon usage and amino acid composition. In addition to these multiple selective effects, however, the genomes of both thermophiles and mesophiles are often subject to superimposed large changes in composition due to mutational bias.