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

BALL - biochemical algorithms library 1.3

<p>Abstract</p> <p>Background</p> <p>The Biochemical Algorithms Library (BALL) is a comprehensive rapid application development framework for structural bioinformatics. It provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers. In the ten years since its original publication, BALL has seen a substantial increase in functionality and numerous other improvements.</p> <p>Results</p> <p>Here, we discuss BALL's current functionality and highlight the key additions and improvements: support for additional file formats, molecular edit-functionality, new molecular mechanics force fields, novel energy minimization techniques, docking algorithms, and support for cheminformatics.</p> <p>Conclusions</p> <p>BALL is available for all major operating systems, including Linux, Windows, and MacOS X. It is available free of charge under the Lesser GNU Public License (LPGL). Parts of the code are distributed under the GNU Public License (GPL). BALL is available as source code and binary packages from the project web site at <url>http://www.ball-project.org</url>. Recently, it has been accepted into the debian project; integration into further distributions is currently pursued.</p

BALL-SNP: combining genetic and structural information to identify candidate non-synonymous single nucleotide polymorphisms

Background: High-throughput genetic testing is increasingly applied in clinics. Next-Generation Sequencing (NGS) data analysis however still remains a great challenge. The interpretation of pathogenicity of single variants or combinations of variants is crucial to provide accurate diagnostic information or guide therapies. Methods: To facilitate the interpretation of variants and the selection of candidate non-synonymous polymorphisms (nsSNPs) for further clinical studies, we developed BALL-SNP. Starting from genetic variants in variant call format (VCF) files or tabular input, our tool, first, visualizes the three-dimensional (3D) structure of the respective proteins from the Protein Data Bank (PDB) and highlights mutated residues, automatically. Second, a hierarchical bottom up clustering on the nsSNPs within the 3D structure is performed to identify nsSNPs, which are close to each other. The modular and flexible implementation allows for straightforward integration of different databases for pathogenic and benign variants, but also enables the integration of pathogenicity prediction tools. The collected background information of all variants is presented below the 3D structure in an easily interpretable table format. Results: First, we integrated different data resources into BALL-SNP, including databases containing information on genetic variants such as ClinVar or HUMSAVAR; third party tools that predict stability or pathogenicity in silico such as I-Mutant2.0; and additional information derived from the 3D structure such as a prediction of binding pockets. We then explored the applicability of BALL-SNP on the example of patients suffering from cardiomyopathies. Here, the analysis highlighted accumulation of variations in the genes JUP, VCL, and SMYD2. Conclusion: Software solutions for analyzing high-throughput genomics data are important to support diagnosis and therapy selection. Our tool BALL-SNP, which is freely available at http://www.ccb.uni-saarland.de/BALL-SNP , combines genetic information with an easily interpretable and interactive, graphical representation of amino acid changes in proteins. Thereby relevant information from databases and computational tools is presented. Beyond this, proximity to functional sites or accumulations of mutations with a potential collective effect can be discovered

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

BALLView : a molecular viewer and modeling tool

Over the last ten years, many molecular modeling software were developed, but most of them offer only limited capabilities or are rather difficult to use. This motivated us to create our own molecular viewer and modeling tool BALLView, based on our biochemical algorithms library BALL. Through its flexible and intuitive interface, BALLView provides a wide range of features in fields of electrostatic potentials, molecular mechanics, and molecular editing. In addition, BALLView is also a powerful molecular viewer with state-of-the-art graphics: it provides a variety of different models for biomolecular visualization, e.g. ball-and-stick models, molecular surfaces, or ribbon models. Since BALLView features a very intuitive graphical user interface, even inexperienced users have direct access to the full functionality. This makes BALLView particularly useful for teaching. For more advanced users, BALLView is extensible in different ways. First, extension on the level of C++ code is very convenient, since the the underlying code was designed as a modular development framework. Second, an interface to the scripting language Python allows the interactive rapid prototyping of new methods. BALLView is portable and runs on all major platforms (Windows, MacOS X, Linux, most Unix flavors). It is available free of charge under the GNU Public License (GPL) from our website (www.ballview.org).Im Laufe der letzten zehn Jahre wurden viele verschiedene Molecular Modeling Programme geschrieben, aber die meisten bieten nur eingeschränkte Funktionalität, oder sind sehr unintuiv zu bedienen. Dies impliziert, dass viele Forscher Probleme mit diesen Programmen haben und benutzerfreundlichere Software vorziehen würden. Dies inspirierte uns dazu,mit BALLView ein neuartiges Modellierungsprogramm zu entwickeln, basierend auf unserer biochemischen Algorithmenbibliothek BALL. Durch seine flexible Oberfläche bietet BALLView eine reiche Palette an Funktionen in den Bereichen Elektrostatik, Molekularmechanik und dem Edititieren von Molekülen an. Darüberhinaus ist BALLView auch ein leistungsfähiges Programm zur Visualisierung von Molekülen, das über Grafikfähigkeiten verfügt, die dem neuesten Stand der Technik entsprechen. BALLView unterstützt neben allen Standard-Molekülmodellen wie bspw. Stick, Cartoon, Ribbon und Oberflächen auch die Visualisierung von elektrostatischen Feldern. Alle aufgeführten Funktionen können auch von unerfahrenen Benutzern verwendet werden, da BALLView eine sehr intuitive Benutzeroberfläche besitzt. Dadurch ist es hervorragend geeignet zum Einsatz in der Lehre. Für fortgeschrittene Benutzer ist BALLView erweiterbar auf zwei unterschiedlichen Wegen: Durch das Design der zugrundeliegenden Klassenhierarchie sind Erweiterungen auf der Ebene des C++ Programmcodes sehr einfach zu realisieren. Desweiteren bietet BALLView ein Interface zur Skriptsprache Python, die interaktives Rapid-Prototyping von neuen Funktionen erlaubt. BALLView ist portierbar und kann auf allen verbreiteten Plattformen (Windows, MacOS X, Linux, die meisten Unix-Derivate) verwendet werden. Es ist frei verfügbar unter der LGPL Lizenz und kann von unserer Webseite heruntergeladen werden (www.ballview.org)

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Interactive Web-based Visualization of Atomic Position-time Series Data

Extracting and interpreting the information contained in large sets of time-varying three dimensional positional data for the constituent atoms of simulated material system is a challenging task. This thesis work reports our initial implementation of a web-based visualization system and its use-case study. The system allows the users to perform the desired visualization task on a web browser for the position-time series data extracted from the local or remote hosts. It involves a pre-processing step for data reduction, which involves skipping uninteresting parts of the data uniformly (at full atomic configuration level) or non-uniformly (at atomic species level or individual atom level). Atomic configuration at a given time step (snapshot) is rendered using the ball-stick representation and can be animated by rendering successive configurations. The entire atomic dynamics can be captured as the trajectories by rendering the atomic positions at all time steps together as points. The trajectories can be manipulated at both species and atomic levels so that we can focus on one or more trajectories of interest. They can be color-coded according to the additional information including the time elapsed and the distance traveled. The instantaneous atomic structure and the complete trajectories can be superimposed to help assess the 3D geometries and extents of the selected trajectories. The implementation was done using WebGL and Three.js for graphical rendering, HTML5 and Javascript for GUI, and Elasticsearch and JSON for data storage and retrieval within the Grails Framework. We have demonstrated the usefulness of our visualization system by analyzing the simulated position-time series for proton-bearing forsterite (Mg2SiO4) system – an abundant mineral of Earths upper mantle. Visualization reveals that protons (hydrogen ions) incorporated as interstitials are much more mobile than protons substituting the host Mg and Si cation sites. The proton diffusion appears to be anisotropic with high mobility along the x-direction, showing limited discrete jumps in other two directions. Our work at the present represents a simplistic (direct) web-based rendering of large atomic data sets. While the atomic structure can be animated at an interactive rate, the trajectory processing is slow, taking several minutes. We anticipate to further improve the system and use it in gaining useful structural and dynamical information from more materials simulation data