PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment

Background  

The Monte Carlo simulation of sequence evolution is routinely used to assess the performance of phylogenetic inference methods and sequence alignment algorithms. Progress in the field of molecular evolution fuels the need for more realistic and hence more complex simulations, adapted to particular situations, yet current software makes unreasonable assumptions such as homogeneous substitution dynamics or a uniform distribution of indels across the simulated sequences. This calls for an extensible simulation framework written in a high-level functional language, offering new functionality and making it easy to incorporate further complexity.     

MathSBML: a package for manipulating SBML-based biological models

MathSBML is a Mathematica package designed for manipulating Systems Biology Markup Language (SBML) models. It converts SBML models into Mathematica data structures and provides a platform for manipulating and evaluating these models. Once a model is read by MathSBML, it is fully compatible with standard Mathematica functions such as NDSolve (a differential-algebraic equations solver). MathSBML also provides an application programming interface for viewing, manipulating, running numerical simulations; exporting SBML models; and converting SBML models in to other formats, such as XPP, HTML and FORTRAN. By accessing the full breadth of Mathematica functionality, MathSBML is fully extensible to SBML models of any size or complexity. AVAILABILITY: Open Source (LGPL) at http://www.sbml.org and http://www.sf.net/projects/sbml     

FitDockApp：基于模板的分子对接PyMOL插件图形化计算软件

目的 分子对接在预测分子之间的结合模式和亲和力方面起着至关重要的作用,是计算结构生物学和计算机辅助药物设计研究的重要方法。本研究团队近期开发了一款基于模板的新型对接方法FitDock,当存在近似的蛋白质配体模板时,它在准确性和速度方面都超过了业界常用的分子对接方法。为了增强FitDock方法的可用性,使其在分子模拟领域得到更广泛的应用,很有必要发展图像化的软件工具。方法 基于Python图像化编程,本文开发了FitDockApp,这是分子可视化软件PyMOL的插件软件。结果 FitDockApp能够通过操作窗口界面,实现基于模板的分子对接和配体结构比对,实时显示预测三维结构,并提供将对接文件上传到实验室服务器获取最优模板的便利。此外,FitDockApp还具备批量对接功能。结论 FitDockApp通过用户友好的界面简化了对接过程,并提供丰富的功能,帮助研究人员获得精确的对接结果。FitDockApp是一款免费软件,兼容Windows和Linux系统,可在http://cao.labshare.cn/fitdock/下载。     

The Structure Superposition Database

The need for new tools for investigating biological systems on a large scale is becoming acute, particularly with respect to computationally intensive analyses such as comparisons of many three-dimensional protein structures. Structure superposition is a valuable approach for understanding evolutionary relationships and for the prediction of function. But while available tools are adequate for generating and viewing superpositions of single pairs of protein structures, these tools are generally too cumbersome and time-consuming for examining multiple superpositions. To address this need, we have created the Structure Superposition Database (SSD) for accessing, viewing and understanding large sets of structure superposition data. The initial implementation of the SSD contains the results of pairwise, all-by-all superpositions of a representative set of 115 (beta/alpha)8 barrel structures (TIM barrels). Future plans call for extending the database to include representative structure superpositions for many additional folds. The SSD can be browsed with a user interface module developed as an extension to Chimera, an extensible molecular modeling program. Features of the user interface module facilitate viewing multiple superpositions together. The SSD interface module can be downloaded from http://ssd.rbvi.ucsf.edu.     

BEAST 2: A Software Platform for Bayesian Evolutionary Analysis

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.

This is a PLOS Computational Biology Software Article.

     

MODELER4SIMCOAL2: a user-friendly, extensible modeler of demography and linked loci for coalescent simulations

MODELER4SIMCOAL2 (M4S2) is an extensible graphical tool to model linked loci and population demographies. M4S2 is easy to use, allowing for the modeling of complicated scenarios, making coalescent simulation modeling accessible to biologists with limited computer skills. The software includes an extension system allowing for new models to be created, published and downloaded from the Internet. Availability: M4S2 is available from http://popgen.eu/soft/m4s2 under a GPL license. The web site also contains guides, screen shots and tutorials.     

MollDE: a homology modeling framework you can click with

SUMMARY: Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDE's user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE. AVAILABILITY: http://dunbrack.fccc.edu/molide/molide.php CONTACT: rl_dunbrack@fccc.edu.     

The PyRosetta Toolkit: A Graphical User Interface for the Rosetta Software Suite

The Rosetta Molecular Modeling suite is a command-line-only collection of applications that enable high-resolution modeling and design of proteins and other molecules. Although extremely useful, Rosetta can be difficult to learn for scientists with little computational or programming experience. To that end, we have created a Graphical User Interface (GUI) for Rosetta, called the PyRosetta Toolkit, for creating and running protocols in Rosetta for common molecular modeling and protein design tasks and for analyzing the results of Rosetta calculations. The program is highly extensible so that developers can add new protocols and analysis tools to the PyRosetta Toolkit GUI.     

Genome-tools: a flexible package for genome sequence analysis

Genome-tools is a Perl module, a set of programs, and a user interface that facilitates access to genome sequence information. The package is flexible, extensible, and designed to be accessible and useful to both nonprogrammers and programmers. Any relatively well-annotated genome available with standard GenBank genome files may be used with genome-tools. A simple Web-based front end permits searching any available genome with an intuitive interface. Flexible design choices also make it simple to handle revised versions of genome annotation files as they change. In addition, programmers can develop cross-genomic tools and analyses with minimal additional overhead by combining genome-tools modules with newly written modules. Genome-tools runs on any computer platform for which Perl is available, including Unix, Microsoft Windows, and Mac OS. By simplifying the access to large amounts of genomic data, genome-tools may be especially useful for molecular biologists looking at newly sequenced genomes, for which few informatics tools are available. The genome-tools Web interface is accessible at http://genome-tools.sourceforge.net, and the source code is available at http://sourceforge.net/projects/genome-tools.     

Computer-aided design of biological circuits using TinkerCell

Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze, and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field.     

Deciphering the molecular organization of GET pathway chaperones through native mass spectrometry

Get3/4/5 chaperone complex is responsible for targeting C-terminal tail-anchored membrane proteins to the endoplasmic reticulum. Despite the availability of several crystal structures of independent proteins and partial structures of subcomplexes, different models of oligomeric states and structural organization have been proposed for the protein complexes involved. Here, using native mass spectrometry (Native-MS), coupled with intact dissociation, we show that Get4/5 exclusively forms a tetramer using both Get5/5 and a novel Get4/4 dimerization interface. Addition of Get3 to this leads to a hexameric (Get3)₂-(Get4)₂-(Get5)₂ complex with closed-ring cyclic architecture. We further validate our claims through molecular modeling and mutational abrogation of the proposed interfaces. Native-MS has become a principal tool to determine the state of oligomeric organization of proteins. The work demonstrates that for multiprotein complexes, native-MS, coupled with molecular modeling and mutational perturbation, can provide an alternative route to render a detailed view of both the oligomeric states as well as the molecular interfaces involved. This is especially useful for large multiprotein complexes with large unstructured domains that make it recalcitrant to conventional structure determination approaches.     

Fast and accurate modeling of protein-protein interactions by combining template-interface-based docking with flexible refinement

The similarity between folding and binding led us to posit the concept that the number of protein-protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein-protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge-based combinatorial method on the Docking Benchmark and show that it efficiently finds high-quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to "classical" docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large-scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high-quality models of protein-protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge-based methods will be increasingly used by the broad proteomics community.     

Detailed comparison of the protein-ligand docking efficiencies of GOLD, a commercial package and ArgusLab, a licensable freeware

Molecular docking and virtual screening based on molecular docking have become an integral part of many modern structure-based drug discovery efforts. Hence, it becomes a useful endeavor to evaluate existing docking programs, which can assist in the choice of the most suitable docking algorithm for any particular study. The objective of the current study was to evaluate the ability of ArgusLab 4.0, a relatively new molecular modeling package in which molecular docking is implemented, to reproduce crystallographic binding orientations and to compare its accuracy with that of a well established commercial package, GOLD. The study also aimed to evaluate the effect of the nature of the binding site and ligand properties on docking accuracy. The three dimensional structures of a carefully chosen set of 75 pharmaceutically relevant protein-ligand complexes were used for the comparative study. The study revealed that the commercial package outperforms the freely available docking engine in almost all the parameters tested. However, the study also revealed that although lagging behind in accuracy, results from ArgusLab are biologically meaningful. This taken together with the fact that ArgusLab has an easy to use graphical user interface, means that it can be employed as an effective teaching tool to demonstrate molecular docking to beginners in this area.     

Using Sculptor and Situs for simultaneous assembly of atomic components into low-resolution shapes

We describe an integrated software system called Sculptor that combines visualization capabilities with molecular modeling algorithms for the analysis of multi-scale data sets. Sculptor features extensive special purpose visualization techniques that are based on modern GPU programming and are capable of representing complex molecular assemblies in real-time. The integration of graphics and modeling offers several advantages. The user interface not only eases the usually steep learning curve of pure algorithmic techniques, but it also permits instant analysis and post-processing of results, as well as the integration of results from external software. Here, we implemented an interactive peak-selection strategy that enables the user to explore a preliminary score landscape generated by the colors tool of Situs. The interactive placement of components, one at a time, is advantageous for low-resolution or ambiguously shaped maps, which are sometimes difficult to interpret by the fully automatic peak selection of colors. For the subsequent refinement of the preliminary models resulting from both interactive and automatic peak selection, we have implemented a novel simultaneous multi-body docking in Sculptor and Situs that softly enforces shape complementarities between components using the normalization of the cross-correlation coefficient. The proposed techniques are freely available in Situs version 2.6 and Sculptor version 2.0.     

StochPy: A Comprehensive,User-Friendly Tool for Simulating Stochastic Biological Processes

Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (Stochastic modeling in Python), which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool.     

Tangible interfaces for structural molecular biology

The evolving technology of computer autofabrication makes it possible to produce physical models for complex biological molecules and assemblies. Augmented reality has recently developed as a computer interface technology that enables the mixing of real-world objects and computer-generated graphics. We report an application that demonstrates the use of autofabricated tangible models and augmented reality for research and communication in molecular biology. We have extended our molecular modeling environment, PMV, to support the fabrication of a wide variety of physical molecular models, and have adapted an augmented reality system to allow virtual 3D representations to be overlaid onto the tangible molecular models. Users can easily change the overlaid information, switching between different representations of the molecule, displays of molecular properties, or dynamic information. The physical models provide a powerful, intuitive interface for manipulating the computer models, streamlining the interface between human intent, the physical model, and the computational activity.