Pattern Discovery from Biosequences

In this thesis we have developed novel methods for analyzing biological data, the primary sequences of the DNA and proteins, the microarray based gene expression data, and other functional genomics data. The main contribution is the development of the pattern discovery algorithm SPEXS, accompanied by several practical applications for analyzing real biological problems. For performing these biological studies that integrate different types of biological data we have developed a comprehensive web-based biological data analysis environment Expression Profiler (http://ep.ebi.ac.uk/)

A structural classification of protein-protein interactions for detection of convergently evolved motifs and for prediction of protein binding sites on sequence level

BACKGROUND: A long-standing challenge in the post-genomic era of Bioinformatics is the prediction of protein-protein interactions, and ultimately the prediction of protein functions. The problem is intrinsically harder, when only amino acid sequences are available, but a solution is more universally applicable. So far, the problem of uncovering protein-protein interactions has been addressed in a variety of ways, both experimentally and computationally. MOTIVATION: The central problem is: How can protein complexes with solved threedimensional structure be utilized to identify and classify protein binding sites and how can knowledge be inferred from this classification such that protein interactions can be predicted for proteins without solved structure? The underlying hypothesis is that protein binding sites are often restricted to a small number of residues, which additionally often are well-conserved in order to maintain an interaction. Therefore, the signal-to-noise ratio in binding sites is expected to be higher than in other parts of the surface. This enables binding site detection in unknown proteins, when homology based annotation transfer fails. APPROACH: The problem is addressed by first investigating how geometrical aspects of domain-domain associations can lead to a rigorous structural classification of the multitude of protein interface types. The interface types are explored with respect to two aspects: First, how do interface types with one-sided homology reveal convergently evolved motifs? Second, how can sequential descriptors for local structural features be derived from the interface type classification? Then, the use of sequential representations for binding sites in order to predict protein interactions is investigated. The underlying algorithms are based on machine learning techniques, in particular Hidden Markov Models. RESULTS: This work includes a novel approach to a comprehensive geometrical classification of domain interfaces. Alternative structural domain associations are found for 40% of all family-family interactions. Evaluation of the classification algorithm on a hand-curated set of interfaces yielded a precision of 83% and a recall of 95%. For the first time, a systematic screen of convergently evolved motifs in 102.000 protein-protein interactions with structural information is derived. With respect to this dataset, all cases related to viral mimicry of human interface bindings are identified. Finally, a library of 740 motif descriptors for binding site recognition - encoded as Hidden Markov Models - is generated and cross-validated. Tests for the significance of motifs are provided. The usefulness of descriptors for protein-ligand binding sites is demonstrated for the case of "ATP-binding", where a precision of 89% is achieved, thus outperforming comparable motifs from PROSITE. In particular, a novel descriptor for a P-loop variant has been used to identify ATP-binding sites in 60 protein sequences that have not been annotated before by existing motif databases

Motif discovery in sequential data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (v. 2, leaves [435]-467).In this thesis, I discuss the application and development of methods for the automated discovery of motifs in sequential data. These data include DNA sequences, protein sequences, and real-valued sequential data such as protein structures and timeseries of arbitrary dimension. As more genomes are sequenced and annotated, the need for automated, computational methods for analyzing biological data is increasing rapidly. In broad terms, the goal of this thesis is to treat sequential data sets as unknown languages and to develop tools for interpreting an understanding these languages. The first chapter of this thesis is an introduction to the fundamentals of motif discovery, which establishes a common mode of thought and vocabulary for the subsequent chapters. One of the central themes of this work is the use of grammatical models, which are more commonly associated with the field of computational linguistics. In the second chapter, I use grammatical models to design novel antimicrobial peptides (AmPs). AmPs are small proteins used by the innate immune system to combat bacterial infection in multicellular eukaryotes. There is mounting evidence that these peptides are less susceptible to bacterial resistance than traditional antibiotics and may form the basis for a novel class of therapeutics.(cont.) In this thesis, I described the rational design of novel AmPs that show limited homology to naturally-occurring proteins but have strong bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis. These peptides were designed using a linguistic model of natural AmPs by treating the amino acid sequences of natural AmPs as a formal language and building a set of regular grammars to describe this language. is set of grammars was used to create novel, unnatural AmP sequences that conform to the formal syntax of natural antimicrobial peptides but populate a previously unexplored region of protein sequence space. The third chapter describes a novel, GEneric MOtif DIscovery Algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As I show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. These motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices, or any other model for sequential data.(cont.) I demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids and DNA sequences, and the discovery of conserved protein sub-structures. The final chapter is devoted to a series of smaller projects, employing tool methods indirectly related to motif discovery in sequential data. I describe the construction of a software tool, Biogrep that is designed to match large pattern sets against large biosequence databases in a parallel fashion. is makes biogrep well-suited to annotating sets of sequences using biologically significant patterns. In addition, I show that the BLOSUM series of amino acid substitution matrices, which are commonly used in motif discovery and sequence alignment problems, have changed drastically over time.The fidelity of amino acid sequence alignment and motif discovery tools depends strongly on the target frequencies implied by these underlying matrices. us, these results suggest that further optimization of these matrices is possible. The final chapter also contains two projects wherein I apply statistical motif discovery tools instead of grammatical tools.(cont.) In the first of these two, I develop three different physiochemical representations for a set of roughly 700 HIV-I protease substrates and use these representations for sequence classification and annotation. In the second of these two projects, I develop a simple statistical method for parsing out the phenotypic contribution of a single mutation from libraries of functional diversity that contain a multitude of mutations and varied phenotypes. I show that this new method successfully elucidates the effects of single nucleotide polymorphisms on the strength of a promoter placed upstream of a reporter gene. The central theme, present throughout this work, is the development and application of novel approaches to finding motifs in sequential data. The work on the design of AmPs is very applied and relies heavily on existing literature. In contrast, the work on Gemoda is the greatest contribution of this thesis and contains many new ideas.by Kyle L. Jensen.Ph.D

Study and analysis of knowledgebase of molecular systems and to develop model for prediction of molecular structure

Not availabl

Inference of biomolecular interactions from sequence data

This thesis describes our work on the inference of biomolecular interactions from sequence data. In particular, the first part of the thesis focuses on proteins and describes computational methods that we have developed for the inference of both intra- and inter-protein interactions from genomic data. The second part of the thesis centers around protein-RNA interactions and describes a method for the inference of binding motifs of RNA-binding proteins from high-throughput sequencing data. The thesis is organized as follows. In the first part, we start by introducing a novel mathematical model for the characterization of protein sequences (chapter 1). We then show how, using genomic data, this model can be successfully applied to two different problems, namely to the inference of interacting amino acid residues in the tertiary structure of protein domains (chapter 2) and to the prediction of protein-protein interactions in large paralogous protein families (chapters 3 and 4). We conclude the first part by a discussion of potential extensions and generalizations of the methods presented (chapter 5). In the second part of this thesis, we first give a general introduction about RNA- binding proteins (chapter 6). We then describe a novel experimental method for the genome-wide identification of target RNAs of RNA-binding proteins and show how this method can be used to infer the binding motifs of RNA-binding proteins (chapter 7). Finally, we discuss a potential mechanism by which KH domain-containing RNA- binding proteins could achieve the specificity of interaction with their target RNAs and conclude the second part of the thesis by proposing a novel type of motif finding algorithm tailored for the inference of their recognition elements (chapter 8)

Undecidability and novelty generation in RNA automata

As today, the evolution of the earliest life was an exploration of adaptive forms. The earliest life also undertook great leaps in the overall complexity of the molecular dynamic system. A theoretical framework for this form of evolution has not been resolved. This thesis builds upon the discoveries of early life chemistry and seeks to move towards understanding the organising principles that allowed life to evolve. In computer science novelty generation is often linked to universal computation, as the boundaries of complexity are found at the edge of undecidability where self-referential incomputable statements can be generated. At the intersection of early life chemistry and computer science, this thesis draws from the dominant RNA-world model and incorporates this into the constructions of automata theory to investigate the computational properties of a system of single-stranded RNA molecules. Limited to the plausible RNA-world operations of ligation and cleavage, RNA automata are constructed of increasing complexity; from the Finite Automaton (RNA-FA) to the Turing machine equivalent 2-stack Pushdown Automaton (RNA-2PDA) and ultimately a universal RNA-UPDA with the capacity to generate undecidability. A path forward from undecidable computation in RNA automata to novelty generation is mapped. The coupled phenotype-environment space is presented as a framework for biological system expansion. The framework draws on the discoveries of Alan Turing and Emil Post on the continual expansion of computational systems overcoming their undecidable boundaries. An analogue of these extensions, from the perspective of ecological developmental biology, offers a self-referential model of the organism coupled with its environment, which is capable of novelty generation. This thesis concludes by outlining future avenues of research to develop the coupled phenotype-environment space framework as well as identifying biological computational constructions in extant organisms