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

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