KCNQ1 is a homotetrameric voltage-gated potassium channel expressed in cardiomyocytes and epithelial tissues. However, currents arising from KCNQ1 have never been physiologically observed. KCNQ1 is able to provide the diverse potassium conductances required by these distinct cell types through coassembly with and modulation by type I transmembrane β-subunits of the KCNE gene family. KCNQ1-KCNE K+ channels play important physiological roles. In cardiac tissues the association of KCNQ1 with KCNE1 gives rise to IKs, the slow delayed outwardly rectifying potassium current. IKs is in part responsible for repolarizing heart muscle, and is therefore crucial in maintaining normal heart rhymicity. IKschannels help terminate each action potential and provide cardiac repolarization reserve. As such, mutations in either subunit can lead to Romano-Ward Syndrome or Jervell and Lange-Nielsen Syndrome, two forms of Q-T prolongation. In epithelial cells, KCNQ1-KCNE1, KCNQ1-KCNE2 and KCNQ1-KCNE3 give rise to potassium currents required for potassium recycling and secretion. These functions arise because the biophysical properties of KCNQ1 are always dramatically altered by KCNE co-expression. We wanted to understand how KCNE peptides are able to modulate KCNQ1. In Chapter II, we produce partial truncations of KCNE3 and demonstrate the transmembrane domain is necessary and sufficient for both assembly with and modulation of KCNQ1. Comparing these results with published results obtained from chimeric KCNE peptides and partial deletion mutants of KCNE1, we propose a bipartite modulation residing in KCNE peptides. Transmembrane modulation is either active (KCNE3) or permissive (KCNE1). Active transmembrane KCNE modulation masks juxtamembranous C-terminal modulation of KCNQ1, while permissive modulation allows C-terminal modulation of KCNQ1 to express. We test our hypothesis, and demonstrate C-terminal Long QT point mutants in KCNE1 can be masked by active trasnsmembrane modulation. Having confirmed the importance the C-terminus of KCNE1, we continue with two projects designed to elucidate KCNE1 C-terminal structure. In Chapter III we conduct an alanine-perturbation scan within the C-terminus. C-terminal KCNE1 alanine point mutations result in changes in the free energy for the KCNQ1-KCNE1 channel complex. High-impact point mutants cluster in an arrangement consistent with an alphahelical secondary structure, "kinked" by a single proline residue. In Chapter IV, we use oxidant-mediated disulfide bond formation between non-native cysteine residues to demonstrate amino acid side chains residing within the C-terminal domain of KCNE1 are close and juxtaposed to amino acid side chains on the cytoplasmic face of the KCNQ1 pore domain. Many of the amino acids identified as high impact through alanine perturbation correspond with residues identified as able to form disulfide bonds with KCNQ1. Taken together, we demonstrate that the interaction between the C-terminus of KCNE1 and the pore domain of KCNQ1 is required for the proper modulation of KCNQ1 by KCNE1, and by extension, normal IKs function and heart rhymicity.

Indiana University-Purdue University Indianapolis (IUPUI) / Intrinsically disordered proteins (IDPs) are abundant in nature, being more prevalent in the proteomes of eukaryotes than those of bacteria or archaea. As introduced in Chapter I, these proteins, or portions of these proteins, lack stable equilibrium structures and instead have dynamic conformations that vary over time and population. Despite the lack of preformed structure, IDPs carry out many and varied molecular functions and participate in vital biological pathways. In particular, IDPs play important roles in cellular signaling that is, in part, enabled by the ability of IDPs to mediate molecular recognition. In Chapter II, the role of intrinsic disorder in molecular recognition is examined through two example IDPs: p53 and 14-3-3. The p53 protein uses intrinsically disordered regions at its N- and C-termini to interact with a large number of partners, often using the same residues. The 14-3-3 protein is a structured domain that uses the same binding site to recognize multiple intrinsically disordered partners. Examination of the structural details of these interactions highlights the importance of intrinsic disorder and induced fit in molecular recognition. More generally, many intrinsically disordered regions that mediate interactions share similar features that are identifiable from protein sequence. Chapter IV reviews several models of IDP mediated protein-protein interactions that use completely different parameterizations. Each model has its relative strengths in identifying novel interaction regions, and all suggest that IDP mediated interactions are common in nature. In addition to the biologic importance of IDPs, they are also practically important in the structural study of proteins. The presence of intrinsic disordered regions can inhibit crystallization and solution NMR studies of otherwise well-structured proteins. This problem is compounded in the context of high throughput structure determination. In Chapter III, the effect of IDPs on structure determination by X-ray crystallography is examined. It is found that protein crystals are intolerant of intrinsic disorder by examining existing crystal structures from the PDB. A retrospective analysis of Protein Structure Initiative data indicates that prediction of intrinsic disorder may be useful in the prioritization and improvement of targets for structure determination.

Proteins adopt three-dimensional structures which serve as a starting point to understand protein function and their evolutionary ancestry. It is unclear how proteins fold in vivo and how this process can be recreated in silico in order to predict protein structure from sequence. Contact maps are a possibility to describe whether two residues are in spatial proximity and structures can be derived from this simplified representation. Coevolution or supervised machine learning techniques can compute contact maps from sequence: however, these approaches only predict sparse subsets of the actual contact map. It is shown that the composition of these subsets substantially influences the achievable reconstruction quality because most information in a contact map is redundant. No strategy was proposed which identifies unique contacts for which no redundant backup exists. The StructureDistiller algorithm quantifies the structural relevance of individual contacts and identifies crucial contacts in protein structures. It is demonstrated that using this information the reconstruction performance on a sparse subset of a contact map is increased by 0.4 A, which constitutes a substantial performance gain. The set of the most relevant contacts in a map is also more resilient to false positively predicted contacts: up to 6% of false positives are compensated before reconstruction quality matches a naive selection of contacts without any false positive contacts. This information is invaluable for the training to new structure prediction methods and provides insights into how robustness and information content of contact maps can be improved. In literature, the relevance of two types of residues for in vivo folding has been described. Early folding residues initiate the folding process, whereas highly stable residues prevent spontaneous unfolding events. The structural relevance score proposed by this thesis is employed to characterize both types of residues. Early folding residues form pivotal secondary structure elements, but their structural relevance is average. In contrast, highly stable residues exhibit significantly increased structural relevance. This implies that residues crucial for the folding process are not relevant for structural integrity and vice versa. The position of early folding residues is preserved over the course of evolution as demonstrated for two ancient regions shared by all aminoacyl-tRNA synthetases. One arrangement of folding initiation sites resembles an ancient and widely distributed structural packing motif and captures how reverberations of the earliest periods of life can still be observed in contemporary protein structures.

Proteine spielen auf der zellulären Ebene eines Organismus eine fundamentale Rolle. Sie sind quasi die „Maschinen“ der Zelle. Ihre Bedeutung wird nicht zuletzt in ihrem Namen deutlich, welcher 1838 erstmals von J. Berzelius verwendet wurde und „das Erste“, „das Wichtigste“ bedeutet. Proteine sind aus Aminosäuren aufgebaute Moleküle. Unter physiologischen Bedingungen besitzen sie eine definierte dreidimensionale Gestalt, welche für ihre biologische Funktion bestimmend ist. Es wird heutzutage davon ausgegangen, dass diese dreidimensionale, stabile Struktur von Proteinen eindeutig durch die Abfolge der einzelnen Aminosäuren, der Sequenz, bestimmt ist. Diese Abfolge ist für jedes Protein in der Desoxyribonukleinsäure (DNS) gespeichert. Es ist allerdings eines der größten ungelösten Probleme der letzten Jahrzehnte, wie die Beziehung zwischen Sequenz und 3D-Struktur tatsächlich aussieht. Die Beantwortung dieser Fragestellung erfordert interdisziplinäre Ansätze aus Biologie, Informatik und Physik. In dieser Arbeit werden mit Hilfe von Methoden der theoretischen (Bio-) Physik einige der damit verbundenen Aspekte untersucht. Das Hauptaugenmerk liegt dabei auf Wechselwirkungen der einzelnen Aminosäuren eines Proteins untereinander, wofür in dieser Arbeit ein entsprechendes Energiemodell entwickelt wurde. Es werden Grundzustände sowie Energielandschaften untersucht und mit experimentellen Daten verglichen. Die Stärke der Wechselwirkung einzelner Aminosäuren erlaubt zusätzlich Aussagen über die Stabilität von Proteinen bezüglich mechanischer Kräfte. Die vorliegende Arbeit unterteilt sich wie folgt: Kapitel 2 dient der Einleitung und stellt Proteine und ihre Funktionen dar. Kapitel 3 stellt die Modellierung der Proteinstrukturen in zwei verschiedenen Modellen vor, welche in dieser Arbeit entwickelt wurden, um 3D-Strukturen von Proteinen zu beschreiben. Anschließend wird in Kapitel 4 ein Algorithmus zum Auffinden des exakten Energieminimums dargestellt. Kapitel 5 beschäftigt sich mit der Frage, wie eine geeignete diskrete Energiefunktion aus experimentellen Daten gewonnen werden kann. In Kapitel 6 werden erste Ergebnisse dieses Modells dargestellt. Der Frage, ob der experimentell bestimmte Zustand dem energetischen Grundzustand eines Proteins entspricht, wird in Kapitel 7 nachgegangen. Die beiden Kapitel 8 und 9 zeigen die Anwendung des Modells an zwei Proteinen, dem Tryptophan cage protein als dem kleinsten, stabilen Protein und Kinesin, einem Motorprotein, für welches 2007 aufschlussreiche Experimente zur mechanischen Stabilität durchgeführt wurden. Kapitel 10 bis 12 widmen sich Membranproteinen. Dabei beschäftigt sich Kapitel 10 mit der Vorhersage von stabilen Bereichen (sog. Entfaltungsbarrieren) unter externer Krafteinwirkung. Zu Beginn wird eine kurze Einleitung zu Membranproteinen gegeben. Im folgenden Kapitel 11 wird die Entfaltung mit Hilfe des Modells und Monte-Carlo-Techniken simuliert. Mit dem an Membranproteine angepassten Wechselwirkungsmodell ist es möglich, den Einfluss von Mutationen auch ohne explizite strukturelle Informationen vorherzusagen. Dieses Thema wird in Kapitel 12 diskutiert. Die Beziehung zwischen Primär- und Tertiärstruktur eines Proteins wird in Kapitel 13 behandelt. Es wird ein Ansatz skizziert, welcher in der Lage ist, Strukturbeziehungen zwischen Proteinen zu detektieren, die mit herkömmlichen Methoden der Bioinformatik nicht gefunden werden können. Die letzten beiden Kapitel schließlich geben eine Zusammenfassung bzw. einen Ausblick auf künftige Entwicklungen und Anwendungen des Modells.

Molecular dynamics (MD) is a powerful tool that can be applied to protein folding and protein structure. MD allows for the calculation of movement, and final position, of atoms in a biomolecule. These movements can be used to investigate the pathways that allow proteins to fold into energetically favorable structures. While MD is very useful, it still has its limitations. Most notable, computing power and time are of constant concern. Protein structure is inherently important due to the direct link between the structure of a protein and its function. One of the four levels of protein structure, the secondary structure, is the first level to accommodate for the three-dimensional shape of a protein. The main driving force behind secondary structure is hydrogen bonding, which occurs between the carboxyl oxygen and the amine hydrogen of the backbone of a peptide. Determining a greater link between hydrogen bond patterns and types of secondary structure can provide more insight on how proteins fold. Because molecular dynamics allows for an atomic level view of the dynamics behind protein folding/unfolding, it becomes very useful in observing the effects of particular hydrogen bond patterns on the folding pathway and final structure formed of a protein. Using molecular dynamic simulations, a series of experiments in an attempt to alter structure, hydrogen bonding, and folding patterns, can be performed. This information can be used to better understand the driving force of secondary structure, and use the knowledge gained to manipulate these simulations to force folding events, and with that, desired secondary structure features.

Indiana University-Purdue University Indianapolis (IUPUI) / Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations. The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein. Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions. Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs. Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins. Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.

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