A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays.

Post-translational modifications (PTMs) are important steps in the biosynthesis of proteins. Aside from their integral contributions to protein development, i.e. perform specialized proteolytic cleavage of regulatory subunits, the covalent addition of functional groups of proteins or the degradation of entire proteins, PTMs are also involved in enabling proteins to withstand and recover from temporary environmental stresses (heat shock, microgravity and many others). The literature supports evidence of thousands of recently discovered PTMs, many of which may likely contribute similarly (perhaps, even, interchangeably) to protein stress response. Although there are many PTM actors on the biological stage, our study determines that these PTMs are generally cast into organism-specific, preferential roles. In this work, we study the PTM compositions across the mitochondrial (Mt) and non-Mt proteomes of 11 diverse organisms to illustrate that each organism appears to have a unique list of PTMs, and an equally unique list of PTM-associated residue reaction sites (RSs), where PTMs interact with protein. Despite the present limitation of available PTM data across different species, we apply existing and current protein data to illustrate particular organismal biases. We explore the relative frequencies of observed PTMs, the RSs and general amino-acid compositions of Mt and non-Mt proteomes. We apply these data to create networks and heatmaps to illustrate the evidence of bias. We show that the number of PTMs and RSs appears to grow along with organismal complexity, which may imply that environmental stress could play a role in this bias.

A content and structural assessment of oxidative motifs across a diverse set of life forms.

Exposure to weightlessness (microgravity) or other protein stresses are detrimental to animal and human protein tissue health. Protein damage has been associated with stress and is linked to aging and the onset of diseases such as Alzheimer׳s, Parkinson׳s, sepsis, and others. Protein stresses may cause alterations to physical protein structure, altering its functional identity. Alterations from stresses such as microgravity may be responsible for forms of muscle atrophy (as noted in returning astronauts), however, protein stresses come from other sources as well. Oxidative carbonylation is a protein stress which is a driving force behind protein decay and is attracted to protein segments enriched in R, K, P, T, E and S residues. Since mitochondria apply oxidative processes to produce ATP, their proteins may be placed in the same danger as those that are exposed to stresses. However, they do not appear to be impacted in the same way. Across 14 diverse organisms, we evaluate the coverage of motifs which are high in the amino acids thought to be affected by protein stresses such as oxidation. For this study, we study RKPT and PEST motifs which are both responsible for attracting forms of oxidation across mitochondrial and non-mitochondrial proteins. We show that mitochondrial proteins have fewer of these oxidative sites compared to non-mitochondrial proteins. Additionally, we analyze the oxidative regions to determine that their motifs preferentially tend to make up the connection points between the four kinds of structures of folded proteins (helices, turns, sheets, and coils).

Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis.

Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base-base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel-Ziv techniques from data compression.

A base composition analysis of natural patterns for the preprocessing of metagenome sequences.

BACKGROUND: On the pretext that sequence reads and contigs often exhibit the same kinds of base usage that is also observed in the sequences from which they are derived, we offer a base composition analysis tool. Our tool uses these natural patterns to determine relatedness across sequence data. We introduce spectrum sets (sets of motifs) which are permutations of bacterial restriction sites and the base composition analysis framework to measure their proportional content in sequence data. We suggest that this framework will increase the efficiency during the pre-processing stages of metagenome sequencing and assembly projects. RESULTS: Our method is able to differentiate organisms and their reads or contigs. The framework shows how to successfully determine the relatedness between these reads or contigs by comparison of base composition. In particular, we show that two types of organismal-sequence data are fundamentally different by analyzing their spectrum set motif proportions (coverage). By the application of one of the four possible spectrum sets, encompassing all known restriction sites, we provide the evidence to claim that each set has a different ability to differentiate sequence data. Furthermore, we show that the spectrum set selection having relevance to one organism, but not to the others of the data set, will greatly improve performance of sequence differentiation even if the fragment size of the read, contig or sequence is not lengthy. CONCLUSIONS: We show the proof of concept of our method by its application to ten trials of two or three freshly selected sequence fragments (reads and contigs) for each experiment across the six organisms of our set. Here we describe a novel and computationally effective pre-processing step for metagenome sequencing and assembly tasks. Furthermore, our base composition method has applications in phylogeny where it can be used to infer evolutionary distances between organisms based on the notion that related organisms often have much conserved code.