Amino Acid Biases in the N- and C-termini of Proteins are Evolutionarily Conserved and are Conserved Between Functionally Related Proteins

The Influence of Codon Usage, Protein Abundance, and Protein Stability on Protein Evolution Vary by Evolutionary Distance and the Type of Protein

In general, the evolutionary rate of proteins is not primarily related to protein and amino acid functions, and factors such as protein abundance, codon usage, and the protein's T_M are more important. To better understand the factors that affect protein evolution, E. coli MG1655 orthologs were compared to those in closely related bacteria and to more distantly related prokaryotes, eukaryotes, and archaea. Also, the evolution of different types of proteins was studied. The analyses indicate that the amino acid conservation of enzymes that do not use macromolecules (e.g. DNA, RNA, and proteins) as substrates and that carry out metabolic processes involving small molecules (i.e. small molecule enzymes) is different than other enzymes. For example, the small molecule enzymes have a lower percent identity than other enzymes when sequences from closely related bacteria are compared. Analyses indicate the lower percent identity is not a result of the amino acid or codon usage of the small molecule enzymes. The small molecule enzymes also don't have a significantly lower protein abundance indicating that is also not likely an important factor driving differences in amino acid conservation. Analyses indicate different methods to measure the T_M of proteins have different relationships between amino acid conservation over different evolutionary distances. In totality, the results demonstrate that the relationship between the factors thought to affect protein evolution (protein abundance, codon usage, and proteins T_Ms) and protein evolution are complex and depend on the factor, the organisms, and the type of proteins being analyzed.

Impact of C-terminal amino acid composition on protein expression in bacteria

The C-terminal sequence of a protein is involved in processes such as efficiency of translation termination and protein degradation. However, the general relationship between features of this C-terminal sequence and levels of protein expression remains unknown. Here, we identified C-terminal amino acid biases that are ubiquitous across the bacterial taxonomy (1,582 genomes). We showed that the frequency is higher for positively charged amino acids (lysine, arginine), while hydrophobic amino acids and threonine are lower. We then studied the impact of C-terminal composition on protein levels in a library of Mycoplasma pneumoniae mutants, covering all possible combinations of the two last codons. We found that charged and polar residues, in particular lysine, led to higher expression, while hydrophobic and aromatic residues led to lower expression, with a difference in protein levels up to fourfold. We further showed that modulation of protein degradation rate could be one of the main mechanisms driving these differences. Our results demonstrate that the identity of the last amino acids has a strong influence on protein expression levels.

REPARATION: ribosome profiling assisted (re-)annotation of bacterial genomes

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods depend heavily on sequence composition and often underestimate the complexity of the proteome. We developed RibosomeE Profiling Assisted (re-)AnnotaTION (REPARATION), a de novo machine learning algorithm that takes advantage of experimental protein synthesis evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation (https://github.com/Biobix/REPARATION). REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel (small) ORFs including variants of previously annotated ORFs and >70% of all (variants of) annotated protein coding ORFs were predicted by REPARATION to be translated. Our predictions are supported by matching mass spectrometry proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to intrinsically perform a de novo ORF delineation in bacterial genomes irrespective of the sequence features linked to open reading frames.

Protein alpha-N-acetylation studied by N-terminomics

Cotranslational protein N-terminal modifications, including proteolytic maturation such as initiator methionine excision by methionine aminopeptidases and N-terminal blocking, occur universally. Protein alpha-N-acetylation, or the transfer of the acetyl moiety of acetyl-coenzyme A to nascent protein N-termini, catalysed by multisubunit N-terminal acetyltransferase complexes, generally takes place during protein translation. Nearly all protein modifications are known to influence different protein aspects such as folding, stability, activity and localization, and several studies have indicated similar functions for protein alpha-N-acetylation. However, until recently, protein alpha-N-acetylation remained poorly explored, mainly due to the absence of targeted proteomics technologies. The recent emergence of N-terminomics technologies that allow isolation of protein N-terminal peptides, together with proteogenomics efforts combining experimental and informational content have greatly boosted the field of alpha-N-acetylation. In this review, we report on such emerging technologies as well as on breakthroughs in our understanding of protein N-terminal biology.

Profiling of N-acetylated protein termini provides in-depth insights into the N-terminal nature of the proteome

N-terminal processing of proteins is a process affecting a large part of the eukaryotic proteome. Although N-terminal processing is an essential process, not many large inventories are available, in particular not for human proteins. Here we show that by using dedicated mass spectrometry-based proteomics techniques it is possible to unravel N-terminal processing in a semicomprehensive way. Our multiprotease approach led to the identification of 1391 acetylated human protein N termini in HEK293 cells and revealed that the role of the penultimate position on the cleavage efficiency by the methionine aminopeptidases is essentially conserved from Escherichia coli to human. Sequence analysis and comparisons of amino acid frequencies in the data sets of experimentally derived N-acetylated peptides from Drosophila melanogaster, Saccharomyces cerevisiae, and Halobacterium salinarum showed an exceptionally higher frequency of alanine residues at the penultimate position of human proteins, whereas the penultimate position in S. cerevisiae and H. salinarum is predominantly a serine. Genome-wide comparisons revealed that this effect is not related to protein N-terminal processing but can be traced back to characteristics of the genome.

Prediction of catalytic residues using the variation of stereochemical properties

In this paper, we investigate a simple protein sequence conservation measure which takes amino acid similarity into account. Instead of grouping 20 amino acids into disjoint sets in previous methods, we consider ten overlapping classes. The method is based on the assumption that a column in a multiple sequence alignment is evolved from an identical column in the evolutionary history. Two ten-dimensional vectors are constructed for each position to denote frequencies of ten classes in a column and the corresponding hypothetical identical column. Then the cosine function of the angle between these two vectors is considered as a measure of divergence of stereochemical properties at this position. This divergence, combining with other conservation scores, is used as conservation measure of the column. Finally, we evaluate our methods by identifying catalytic sites, using rank analysis criterion and receiver operator characteristic analysis criterion.