Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

Applications of Evolutionary Bioinformatics in Basic and Biomedical Research

With the revolutionary progress in sequencing technologies, computational biology emerged as a game-changing field which is applied in understanding molecular events of life for not only complementary but also exploratory purposes. Bioinformatics resources and tools significantly help in data generation, organization and analysis. However, there is still a need for developing new approaches built based on a biologist’s point of view. In protein bioinformatics, there are several fundamental problems such as (i) determining protein function; (ii) identifying protein-protein interactions; (iii) predicting the effect of amino acid variants. Here, I present three chapters addressing these problems from an evolutionary perspective. Firstly, I describe a novel search pipeline for protein domain identification. The algorithm chain provides sensitive domain assignments with the highest possible specificity. Secondly, I present a tool enabling large-scale visualization of presences and absences of proteins in hierarchically clustered genomes. This tool visualizes multi-layer information of any kind of genome-linked data with a special focus on domain architectures, enabling identification of coevolving domains/proteins, which can eventually help in identifying functionally interacting proteins. And finally, I propose an approach for distinguishing between benign and damaging missense mutations in a human disease by establishing the precise evolutionary history of the associated gene. This part introduces new criteria on how to determine functional orthologs via phylogenetic analysis. All three parts use comparative genomics and/or sequence analyses. Taken together, this study addresses important problems in protein bioinformatics and as a whole it can be utilized to describe proteins by their domains, coevolving partners and functionally important residues

Network biology methods for functional characterization and integrative prioritization of disease genes and proteins

Nowadays, large amounts of experimental data have been produced by high-throughput techniques, in order to provide more insight into complex phenotypes and cellular processes. The development of a variety of computational and, in particular, network-based approaches to analyze these data have already shed light on previously unknown mechanisms. However, we are still far from a comprehensive understanding of human diseases and their causes as well as appropriate preventive measures and successful therapies. This thesis describes the development of methods and user-friendly software tools for the integrative analysis and interactive visualization of biological networks as well as their application to biomedical data for understanding diseases. We design an integrative phenotype-specific framework for prioritizing candidate disease genes and functionally characterizing similar phenotypes. It is applied to the identification of several disease-relevant genes and processes for inflammatory bowel diseases and primary sclerosing cholangitis as well as for Parkinson's disease. Since finding the causative disease genes does often not suffice to understand diseases, we also concentrate on the molecular characterization of sequence mutations and their effect on protein structure and function. We develop a software suite to support the interactive, multi-layered visual analysis of molecular interaction mechanisms such as protein binding, allostery and drug resistance. To capture the dynamic nature of proteins, we also devise an approach to visualizing and analyzing ensembles of protein structures as, for example, generated by molecular dynamics simulations.In den letzten Jahren wurde mittels Hochdurchsatzverfahren eine große Menge experimenteller Daten generiert, um einen Einblick in komplexe Phänotypen und zelluläre Prozesse zu ermöglichen. Die Entwicklung von verschiedenen bioinformatischen und insbesondere netzwerkbasierten Ansätzen zur Analyse dieser Daten konnte bereits Aufschluss über bisher unbekannte Mechanismen geben. Dennoch sind wir weit entfernt von einem umfassenden Verständnis menschlicher Krankheiten und ihrer Ursachen sowie geeigneter präventiver Maßnahmen und erfolgreicher Therapien. Diese Dissertation beschreibt die Entwicklung von Methoden und benutzerfreundlichen Softwarewerkzeugen für die integrative Analyse und interaktive Visualisierung biologischer Netzwerke sowie ihre Anwendung auf biomedizinische Daten zum Verständnis von http://scidok.sulb.uni-saarland.de/volltexte/incoming/2016/6595/Krankheiten. Wir entwerfen ein integratives, phänotypspezifisches Framework für die Priorisierung potentiell krankheitserregender Gene und die funktionelle Charakterisierung ähnlicher Phänotypen. Es wird angewandt, um mehrere krankheitsspezifische Gene und Prozesse von chronisch-entzündlichen Darmerkrankungen und primär sklerosierender Cholangitis sowie von Parkinson zu bestimmen. Da es für das Verständnis von Krankheiten oft nicht genügt, die krankheitserregenden Gene zu entdecken, konzentrieren wir uns auch auf die molekulare Charakterisierung von Sequenzmutationen und ihren Effekt auf die Proteinstruktur und -funktion. Wir entwickeln eine Software, um die interaktive, vielschichtige visuelle Analyse von molekularen Mechanismen wie Proteinfaltung, Allosterie und Arzneimittelresistenz zu unterstützen. Um den dynamischen Charakter von Proteinen zu erfassen, ersinnen wir auch eine Methode für die Visualisierung und Analyse von Proteinstrukturen, welche sich zum Beispiel während Molekulardynamiksimulationen ergeben

Potential application of network descriptions for understanding conformational changes and protonation states of ABC transporters.

The ABC (ATP Binding Cassette) transporter protein superfamily comprises a large number of ubiquitous and functionally versatile proteins conserved from archaea to humans. ABC transporters have a key role in many human diseases and also in the development of multidrug resistance in cancer and in parasites. Although a dramatic progress has been achieved in ABC protein studies in the last decades, we are still far from a detailed understanding of their molecular functions. Several aspects of pharmacological ABC transporter targeting also remain unclear. Here we summarize the conformational and protonation changes of ABC transporters and the potential use of this information in pharmacological design. Network related methods, which recently became useful tools to describe protein structure and dynamics, have not been applied to study allosteric coupling in ABC proteins as yet. A detailed description of the strengths and limitations of these methods is given, and their potential use in describing ABC transporter dynamics is outlined. Finally, we highlight possible future aspects of pharmacological utilization of network methods and outline the future trends of this exciting field

Exploring combined verses single mode of inhibition of Mycobacterium Tuberculosis RNA polymerase as a therapeutic intervention to overcome drug resistance challenges: atomistic perspectives.

Masters Degree. University of KwaZulu-Natal, Westville.The impact of Rifampin resistance on the overall global epidemic of antimicrobial resistance has become very prominent in recent years and is eventually stifling current efforts being made to control tuberculosis drug resistance. Rifampin resistance has significantly contributed to making TB the leading cause of morbidity from an infectious disease globally. The RNA polymerase of Mycobacterium tuberculosis has been extensively explored as a therapeutic target for Rifampin resistance with recent studies exploring synergistic inhibition as an effective approach, by combining Rifampin and other drugs in the TB drug resistance. Apart from the paucity of data elucidating the structural mechanism of action of the synergistic interaction between Rifampin and DAAPI, previous studies did not also utilize the X-ray crystal structure of Mtb RNAP due its unavailability. This thesis used advanced computational tools to unravel molecular insights into the suppression of the emergence of resistance to Rifampin by a novel Nα-aroyl-N-aryl-phenylalaninamides (AAPI) prototype inhibitor, DAAPI, co-bound to Mtb RNAP with Rifampin. Our studies revealed co-binding induced a stable Mtb RNAP protein structure, increased the degree of compactness of binding site residues around Rifampin and subsequently improved the binding affinity of Rifampin. Studies in this thesis further provide an atomistic mechanism behind Rifampin resistance when the recently resolved crystal structure of Mycobacterium tuberculosis RNA polymerase is subjected to a single active site mutation. We also identified and rationalized the structural interplay of this single active site mutation upon co-binding of Rifampin with the novel inhibitor, DAAPI. Our findings report that the mutation distorted the overall conformational landscape of Mycobacterium tuberculosis RNA polymerase, resulting in a reduction of binding affinity of Rifampin and an overall shift in the residue interaction network of Mycobacterium tuberculosis RNA polymerase and upon single binding. Interestingly, co-binding with DAAPI, though impacted by the mutation exhibited improved Rifampin binding interactions amidst a distorted residue interaction network. Findings establish a structural mechanism by which the novel inhibitor DAAPI stabilizes Mycobacterium tuberculosis RNA polymerase upon co-binding with Rifampin, thus suppressing Rifampin resistance. We also provide vital conformational dynamics and structural mechanisms of mutant enzyme-single ligand and mutant enzyme-dual ligand interactions which could potentially shift the current therapeutic protocol of TB infections, thus aiding in the design of novel Mycobacterium tuberculosis RNA polymerase inhibitors with improved therapeutic features against the mutant proteins

Robust Algorithms for Detecting Hidden Structure in Biological Data

Biological data, such as molecular abundance measurements and protein sequences, harbor complex hidden structure that reflects its underlying biological mechanisms. For example, high-throughput abundance measurements provide a snapshot the global state of a living cell, while homologous protein sequences encode the residue-level logic of the proteins\u27 function and provide a snapshot of the evolutionary trajectory of the protein family. In this work I describe algorithmic approaches and analysis software I developed for uncovering hidden structure in both kinds of data. Clustering is an unsurpervised machine learning technique commonly used to map the structure of data collected in high-throughput experiments, such as quantification of gene expression by DNA microarrays or short-read sequencing. Clustering algorithms always yield a partitioning of the data, but relying on a single partitioning solution can lead to spurious conclusions. In particular, noise in the data can cause objects to fall into the same cluster by chance rather than due to meaningful association. In the first part of this thesis I demonstrate approaches to clustering data robustly in the presence of noise and apply robust clustering to analyze the transcriptional response to injury in a neuron cell. In the second part of this thesis I describe identifying hidden specificity determining residues (SDPs) from alignments of protein sequences descended through gene duplication from a common ancestor (paralogs) and apply the approach to identify numerous putative SDPs in bacterial transcription factors in the LacI family. Finally, I describe and demonstrate a new algorithm for reconstructing the history of duplications by which paralogs descended from their common ancestor. This algorithm addresses the complexity of such reconstruction due to indeterminate or erroneous homology assignments made by sequence alignment algorithms and to the vast prevalence of divergence through speciation over divergence through gene duplication in protein evolution