Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package ROSETTA for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with ROSETTA. For each of these six tasks, we provide a tutorial that illustrates a basic ROSETTA protocol. The ROSETTA method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 A ˚. More impressively, there are several cases in which ROSETTA has been used to predict structures with atomic level accuracy better than 2.5 A ˚. In addition to de novo structure prediction, ROSETTA also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. ROSETTA has been used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes. Most recently, ROSETTA has been used to solve the X-ray crystallographic phase problem. ROSETTA is a unified software package for protein structure prediction and functional design. It has been used to predic

Towards Automating Protein Structure Determination from NMR Data

Nuclear magnetic resonance (NMR) spectroscopy technique is becoming exceedingly significant due to its capability of studying protein structures in solution. However, NMR protein structure determination has remained a laborious and costly process until now, even with the help of currently available computer programs. After the NMR spectra are collected, the main road blocks to the fully automated NMR protein structure determination are peak picking from noisy spectra, resonance assignment from imperfect peak lists, and structure calculation from incomplete assignment and ambiguous nuclear Overhauser enhancements (NOE) constraints. The goal of this dissertation is to propose error-tolerant and highly-efficient methods that work well on real and noisy data sets of NMR protein structure determination and the closely related protein structure prediction problems. One major contribution of this dissertation is to propose a fully automated NMR protein structure determination system, AMR, with emphasis on the parts that I contributed. AMR only requires an input set with six NMR spectra. We develop a novel peak picking method, PICKY, to solve the crucial but tricky peak picking problem. PICKY consists of a noise level estimation step, a component forming step, a singular value decomposition-based initial peak picking step, and a peak refinement step. The first systematic study on peak picking problem is conducted to test the performance of PICKY. An integer linear programming (ILP)-based resonance assignment method, IPASS, is then developed to handle the imperfect peak lists generated by PICKY. IPASS contains an error-tolerant spin system forming method and an ILP-based assignment method. The assignment generated by IPASS is fed into the structure calculation step, FALCON-NMR. FALCON-NMR has a threading module, an ab initio module, an all-atom refinement module, and an NOE constraints-based decoy selection module. The entire system, AMR, is successfully tested on four out of five real proteins with practical NMR spectra, and generates 1.25A, 1.49A, 0.67A, and 0.88A to the native reference structures, respectively. Another contribution of this dissertation is to propose novel ideas and methods to solve three protein structure prediction problems which are closely related to NMR protein structure determination. We develop a novel consensus contact prediction method, which is able to eliminate server correlations, to solve the protein inter-residue contact prediction problem. We also propose an ultra-fast side chain packing method, which only uses local backbone information, to solve the protein side chain packing problem. Finally, two complementary local quality assessment methods are proposed to solve the local quality prediction problem for comparative modeling-based protein structure prediction methods

Synergistic Applications of MD and NMR for the Study of Biological Systems

Modern biological sciences are becoming more and more multidisciplinary. At the same time, theoretical and computational approaches gain in reliability and their field of application widens. In this short paper, we discuss recent advances in the areas of solution nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations that were made possible by the combination of both methods, that is, through their synergistic use. We present the main NMR observables and parameters that can be computed from simulations, and how they are used in a variety of complementary applications, including dynamics studies, model-free analysis, force field validation, and structural studies

New approaches to protein docking

In the first part of this work, we propose new methods for protein docking. First, we present two approaches to protein docking with flexible side chains. The first approach is a fast greedy heuristic, while the second is a branch -&-cut algorithm that yields optimal solutions. For a test set of protease-inhibitor complexes, both approaches correctly predict the true complex structure. Another problem in protein docking is the prediction of the binding free energy, which is the the final step of many protein docking algorithms. Therefore, we propose a new approach that avoids the expensive and difficult calculation of the binding free energy and, instead, employs a scoring function that is based on the similarity of the proton nuclear magnetic resonance spectra of the tentative complexes with the experimental spectrum. Using this method, we could even predict the structure of a very difficult protein-peptide complex that could not be solved using any energy-based scoring functions. The second part of this work presents BALL (Biochemical ALgorithms Library), a framework for Rapid Application Development in the field of Molecular Modeling. BALL provides an extensive set of data structures as well as classes for Molecular Mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, NMR shift prediction, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility, which results from an object-oriented and generic programming approach, distinguishes it from other software packages.Der erste Teil dieser Arbeit beschäftigt sich mit neuen Ansätzen zum Proteindocking. Zunächst stellen wir zwei Ansätze zum Proteindocking mit flexiblen Seitenketten vor. Der erste Ansatz beruht auf einer schnellen, gierigen Heuristik, während der zweite Ansatz auf branch-&-cut-Techniken beruht und das Problem optimal lösen kann. Beide Ansätze sind in der Lage die korrekte Komplexstruktur für einen Satz von Testbeispielen (bestehend aus Protease-Inhibitor-Komplexen) vorherzusagen. Ein weiteres, grösstenteils ungelöstes, Problem ist der letzte Schritt vieler Protein-Docking-Algorithmen, die Vorhersage der freien Bindungsenthalpie. Daher schlagen wir eine neue Methode vor, die die schwierige und aufwändige Berechnung der freien Bindungsenthalpie vermeidet. Statt dessen wird eine Bewertungsfunktion eingesetzt, die auf der Ähnlichkeit der Protonen-Kernresonanzspektren der potentiellen Komplexstrukturen mit dem experimentellen Spektrum beruht. Mit dieser Methode konnten wir sogar die korrekte Struktur eines Protein-Peptid-Komplexes vorhersagen, an dessen Vorhersage energiebasierte Bewertungsfunktionen scheitern. Der zweite Teil der Arbeit stellt BALL (Biochemical ALgorithms Library) vor, ein Rahmenwerk zur schnellen Anwendungsentwicklung im Bereich MolecularModeling. BALL stellt eine Vielzahl von Datenstrukturen und Algorithmen für die FelderMolekülmechanik,Vergleich und Analyse von Proteinstrukturen, Datei-Import und -Export, NMR-Shiftvorhersage und Visualisierung zur Verfügung. Beim Entwurf von BALL wurde auf Robustheit, einfache Benutzbarkeit und Erweiterbarkeit Wert gelegt. Von existierenden Software-Paketen hebt es sich vor allem durch seine Erweiterbarkeit ab, die auf der konsequenten Anwendung von objektorientierter und generischer Programmierung beruht

New approaches to protein docking

In the first part of this work, we propose new methods for protein docking. First, we present two approaches to protein docking with flexible side chains. The first approach is a fast greedy heuristic, while the second is a branch -&-cut algorithm that yields optimal solutions. For a test set of protease-inhibitor complexes, both approaches correctly predict the true complex structure. Another problem in protein docking is the prediction of the binding free energy, which is the the final step of many protein docking algorithms. Therefore, we propose a new approach that avoids the expensive and difficult calculation of the binding free energy and, instead, employs a scoring function that is based on the similarity of the proton nuclear magnetic resonance spectra of the tentative complexes with the experimental spectrum. Using this method, we could even predict the structure of a very difficult protein-peptide complex that could not be solved using any energy-based scoring functions. The second part of this work presents BALL (Biochemical ALgorithms Library), a framework for Rapid Application Development in the field of Molecular Modeling. BALL provides an extensive set of data structures as well as classes for Molecular Mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, NMR shift prediction, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility, which results from an object-oriented and generic programming approach, distinguishes it from other software packages.Der erste Teil dieser Arbeit beschäftigt sich mit neuen Ansätzen zum Proteindocking. Zunächst stellen wir zwei Ansätze zum Proteindocking mit flexiblen Seitenketten vor. Der erste Ansatz beruht auf einer schnellen, gierigen Heuristik, während der zweite Ansatz auf branch-&-cut-Techniken beruht und das Problem optimal lösen kann. Beide Ansätze sind in der Lage die korrekte Komplexstruktur für einen Satz von Testbeispielen (bestehend aus Protease-Inhibitor-Komplexen) vorherzusagen. Ein weiteres, grösstenteils ungelöstes, Problem ist der letzte Schritt vieler Protein-Docking-Algorithmen, die Vorhersage der freien Bindungsenthalpie. Daher schlagen wir eine neue Methode vor, die die schwierige und aufwändige Berechnung der freien Bindungsenthalpie vermeidet. Statt dessen wird eine Bewertungsfunktion eingesetzt, die auf der Ähnlichkeit der Protonen-Kernresonanzspektren der potentiellen Komplexstrukturen mit dem experimentellen Spektrum beruht. Mit dieser Methode konnten wir sogar die korrekte Struktur eines Protein-Peptid-Komplexes vorhersagen, an dessen Vorhersage energiebasierte Bewertungsfunktionen scheitern. Der zweite Teil der Arbeit stellt BALL (Biochemical ALgorithms Library) vor, ein Rahmenwerk zur schnellen Anwendungsentwicklung im Bereich MolecularModeling. BALL stellt eine Vielzahl von Datenstrukturen und Algorithmen für die FelderMolekülmechanik,Vergleich und Analyse von Proteinstrukturen, Datei-Import und -Export, NMR-Shiftvorhersage und Visualisierung zur Verfügung. Beim Entwurf von BALL wurde auf Robustheit, einfache Benutzbarkeit und Erweiterbarkeit Wert gelegt. Von existierenden Software-Paketen hebt es sich vor allem durch seine Erweiterbarkeit ab, die auf der konsequenten Anwendung von objektorientierter und generischer Programmierung beruht

Structural investigation of the Bacillus subtilis morphogenic factor RodZ

A thesis to obtain a Master degree in Structural and Functional BiochemistryRodZ is a protein widely conserved in bacteria and a core component of the morphogenic apparatus of the cell. It is known to be required for assembly of the bacterial actin homologue, MreB, that controls cell wall synthesis and cell shape. The domain organization of RodZ consists of a well-conserved N-terminal (RodZn) with helix-turn-helix motif (HTH), a conserved transmembrane domain, and a conserved C-terminal domain (RodZc). RodZn, located in the cytoplasm, has been shown to interact with MreB actin-homologue by x-ray studies in T. maritima. However, the structure of RodZn from gram-positive B. subtilis showed low homology with the published one from gram-negative T. maritima. Here we present the solution structure of RodZn from B. subtilis determined for the first time, by NMR spectroscopy. Compared to previous structural data obtained from the crystallized RodZn from T. maritima and more recently from S. aureus, several differences could be observed, namely the length of the alpha-helices and the presence of an extended coil. Interaction studies were preformed between RodZn domain and MreB from which no significant results could be extrapolated. Since HTH motif is frequently associated with DNA interaction, the involvement of RodZn in DNA organization is being investigated. At the same time, RodZc domain, which structure has never been reported, was subject of study. Bioinformatic, biophysical and biochemical methodologies were employed to study this domain. A model based in a pseudo-ab initio methodology was built, revealing an Ig-like fold. The Ig superfamily is a large group of cell surface and soluble proteins that are involved in the recognition, binding, or adhesion processes of cells. Therefore, RodZ is thought to be a protein that establishes a link between the inner side of the cell membrane and the outer side, promoting spatiotemporal coordination between peptidoglycan synthesis and cell division

Structure and dynamics of the NO sensing domain of the human soluble Guanylate Cyclase

Soluble guanylate cyclase (sGC) is a heterodimeric heme-protein composed of two subunits called α and β [1-5]. The most common heterodimeric form is the combination of α1 with β1 subunits [1]. The α1 (80KDa) and β1 (70 KDa) subunits are 690 and 619 amino acids in length respectively, and are encoded by the genes, GUCY1A2 and GUCY1A3 respectively [3].(...

Novel approaches for bond order assignment and NMR shift prediction

Molecular modelling is one of the cornerstones of modern biological and pharmaceutical research. Accurate modelling approaches easily become computationally overwhelming and thus, different levels of approximations are typically employed. In this work, we develop such approximation approaches for problems arising in structural bioinformatics. A fundamental approximation of molecular physics is the classification of chemical bonds, usually in the form of integer bond orders. Many input data sets lack this information, but several problems render an automated bond order assignment highly challenging. For this task, we develop the BOA Constructor method which accounts for the non-uniqueness of solutions and allows simple extensibility. Testing our method on large evaluation sets, we demonstrate how it improves on the state of the art. Besides traditional applications, bond orders yield valuable input for the approximation of molecular quantities by statistical means. One such problem is the prediction of NMR chemical shifts of protein atoms. We present our pipeline NightShift for automated model generation, use it to create a new prediction model called Spinster, and demonstrate that it outperforms established, manually developed approaches. Combining Spinster and BOA Constructor, we create the Liops-model that for the first time allows to efficiently include the influence of non-protein atoms. Finally, we describe our work on manual modelling techniques, including molecular visualization and novel input paradigms.Methoden des molekularen Modellierens gehören zu den Grundpfeilern moderner biologischer und pharmazeutischer Forschung. Akkurate Modelling-Methoden erfordern jedoch enormen Rechenaufwand, weshalb üblicherweise verschiedene Näherungsverfahren eingesetzt werden. Im Promotionsvortrag werden solche im Rahmen der Promotion entwickelten Näherungen für verschiedene Probleme aus der strukturbasierten Bioinformatik vorgestellt. Eine fundamentale Näherung der molekularen Physik ist die Einteilung chemischer Bindungen in wenige Klassen, meist in Form ganzzahliger Bindungsordnungen. In vielen Datensätzen ist diese Information nicht enthalten und eine automatische Zuweisung ist hochgradig schwierig. Für diese Problemstellung wird die BOA Constructor-Methode vorgestellt, die sowohl mit uneindeutigen Lösungen umgehen kann als auch vom Benutzer leicht erweitert werden kann. In umfangreichen Tests zeigen wir, dass unsere Methode dem bisherigen Stand der Forschung überlegen ist. Neben klassischen Anwendungen liefern Bindungsordnungen wertvolle Informationen für die statistische Vorhersage molekularer Eigenschaften wie z.B. der chemischen Verschiebung von Proteinatomen. Mit der von uns entwickelten NightShift-Pipeline wird ein Verfahren zur automatischen Generierung von Vorhersagemodellen präsentiert, wie z.B. dem Spinster-Modell, das den bisherigen manuell entwickelten Verfahren überlegen ist. Die Kombination mit BOA Constructor führt zum sogenannten Liops-Modell, welches als erstes Modell die effiziente Berücksichtigung des Einflusses von nicht-Proteinatomen erlaubt

Improving the resolution of interaction maps: A middleground between high-resolution complexes and genome-wide interactomes

Protein-protein interactions are ubiquitous in Biology and therefore central to understand living organisms. In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps or interactomes for a number of relevant organisms including human. Although the analysis of interaction networks is proving useful, current interactomes provide a blurry and granular picture of the molecular machinery, i.e. unless the structure of the protein complex is known the molecular details of the interaction are missing and sometime is even not possible to know if the interaction between the proteins is direct, i.e. physical interaction or part of functional, not necessary, direct association. Unfortunately, the determination of the structure of protein complexes cannot keep pace with the discovery of new protein-protein interactions resulting in a large, and increasing, gap between the number of complexes that are thought to exist and the number for which 3D structures are available. The aim of the thesis was to tackle this problem by implementing computational approaches to derive structural models of protein complexes and thus reduce this existing gap. Over the course of the thesis, a novel modelling algorithm to predict the structure of protein complexes, V-D2OCK, was implemented. This new algorithm combines structure-based prediction of protein binding sites by means of a novel algorithm developed over the course of the thesis: VORFFIP and M-VORFFIP, data-driven docking and energy minimization. This algorithm was used to improve the coverage and structural content of the human interactome compiled from different sources of interactomic data to ensure the most comprehensive interactome. Finally, the human interactome and structural models were compiled in a database, V-D2OCK DB, that offers an easy and user-friendly access to the human interactome including a bespoken graphical molecular viewer to facilitate the analysis of the structural models of protein complexes. Furthermore, new organisms, in addition to human, were included providing a useful resource for the study of all known interactomes