Patterns and Signals of Biology: An Emphasis On The Role of Post Translational Modifications in Proteomes for Function and Evolutionary Progression

After synthesis, a protein is still immature until it has been customized for a specific task. Post-translational modifications (PTMs) are steps in biosynthesis to perform this customization of protein for unique functionalities. PTMs are also important to protein survival because they rapidly enable protein adaptation to environmental stress factors by conformation change. The overarching contribution of this thesis is the construction of a computational profiling framework for the study of biological signals stemming from PTMs associated with stressed proteins. In particular, this work has been developed to predict and detect the biological mechanisms involved in types of stress response with PTMs in mitochondrial (Mt) and non-Mt protein. Before any mechanism can be studied, there must first be some evidence of its existence. This evidence takes the form of signals such as biases of biological actors and types of protein interaction. Our framework has been developed to locate these signals, distilled from “Big Data” resources such as public databases and the the entire PubMed literature corpus. We apply this framework to study the signals to learn about protein stress responses involving PTMs, modification sites (MSs). We developed of this framework, and its approach to analysis, according to three main facets: (1) by statistical evaluation to determine patterns of signal dominance throughout large volumes of data, (2) by signal location to track down the regions where the mechanisms must be found according to the types and numbers of associated actors at relevant regions in protein, and (3) by text mining to determine how these signals have been previously investigated by researchers. The results gained from our framework enable us to uncover the PTM actors, MSs and protein domains which are the major components of particular stress response mechanisms and may play roles in protein malfunction and disease

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

Man of Letters, Literary Lady, Journalist or Reporter?

The enormous changes wrought in the British newspaper industry during the late nineteenth and early twentieth centuries brought about a revolution in newspaper reading habits, financing and influence, all aspects of which have been well-documented by historians of the press. But what of the contributor, particularly the freelance whose millions of words formed, mostly anonymously, the content of the new mass market press? How did writers negotiate changes in the literary marketplace during this time as editors demanded more ‘news’ and less in the way of whimsical paragraphing, and sketches, the traditional newspaper output of the professional man, or woman, of letters? Through the study of memoirs, correspondence and the fictional output of contributors to the press during this time, it is possible to discern the often fraught relations between writers and their most lucrative market

Post-translational modification bias and organism complexity

A protein post-translational modification (PTM) is a cellular mechanism that enables certain proteins to perform specialized tasks in a cell. There are many different types of natural PTM and they preferentially affect one amino acid over other in a protein. In our work, we are particularly interested in the protein response to various types of stress conditions in cells. It is our hypothesis that environmental stresses influence PTM-bias and may suggest a preference for PTM activity across the proteins of a given organism. To test this hypothesis, we analyzed the protein content from phylogenetically distinct organisms for the presence of PTM, its type and the amino acid target of this PTM. Our result suggests, PTM bias exists and it is unique to each organism. Across the mitochondrial and non-mitochondrial proteins of 11 organisms, the result indicates a strong bias, which is pronounced with the increasing complexity of organization of the living organism. Our work suggests that PTM bias and diversification may likely have been directed by an organism\u27s environmental stress conditions

A Meta-genome Sequencing and Assembly Preprocessing Algorithm Inspired by Restriction Site Base Composition

In meta-genome sequencing and assembly projects, where there are different types of contigs mixed together in a single pool, the task of assembling its different organisms is a complex and challenging problem. It is therefore desirable to sort the contigs by origins into separate bins from which to work. We propose a framework of using the base compositions of bacterial restriction sites to generate sets of motifs which work to differentiate organismal groups, including the contigs from those groups. We introduce spectrum sets and show how to strategically select them for use in binning contigs from different organisms. We suggest that this framework can save time during a meta-genome sequencing and assembly project.Our method is able to differentiate organisms and to successfully determine the association of the contigs which were derived from an organism. In particular, we show that two genera are fundamentally different by analyzing their motif proportions. Using one of the four total spectrum sets, which encompass all known restriction sites, we show that different sets have different abilities to distinguish sequences. In addition, we show that the selection of a spectrum set which is relevant to one organism, but not the other, greatly improves performance of differentiation, even when the contig size is short (1000bps). Using ten trials of newly selected contigs to confirm our premise, our study provides a proof of concept for a novel and computationally effective method for a preprocessing step in meta-genome sequencing and assembly tasks

A text mining application for linking functionally stressed-proteins to their post-translational modifications

In the proteome, stresses may work against optimal protein function and PTMs play roles in protein stress responses. Many peer-reviewed articles are available to bioinformatics research in the literature, however, the details of stress, protein and their PTM interactions have been scattered throughout the literature and these concepts are mentioned amongst the other details of respective studies. In each publication, for instance, there are many small pieces of knowledge which could be combined to build a better understanding. Since it is impossible to harvest all of its available knowledge using manual means, text mining methods are an attractive approach to assemble ideas from articles where these concepts may not have been a main focus. We present a text mining method to harvest and assemble a knowledge base relating to the relationships of stresses, proteins and PTMs from the literature. Although we also studied the stresses, proteins and PTMs which were associated with apoptosis, diabetes and Parkinson’s diseases in the literature, to introduce our method, we address these concepts as they are related to Alzheimer’s. We use the results from our text mining tool to process article abstracts to build networks which suggest how functional proteins may be linked to environmental stresses and their PTMs. We discuss how networks of biologically relevant keywords may eventually be used to describe directions in research which could be further explored to forecast new trends of studies. We also show how our method may help to predict stress, protein and PTM associations which may be included in the future