Atomistic modelling of scattering data in the Collaborative Computational Project for Small Angle Scattering (CCP-SAS)

The capabilities of current computer simulations provide a unique opportunity to model small-angle scattering (SAS) data at the atomistic level, and to include other structural constraints ranging from molecular and atomistic energetics to crystallography, electron microscopy and NMR. This extends the capabilities of solution scattering and provides deeper insights into the physics and chemistry of the systems studied. Realizing this potential, however, requires integrating the experimental data with a new generation of modelling software. To achieve this, the CCP-SAS collaboration (http://www.ccpsas.org/) is developing open-source, high-throughput and user-friendly software for the atomistic and coarse-grained molecular modelling of scattering data. Robust state-of-the-art molecular simulation engines and molecular dynamics and Monte Carlo force fields provide constraints to the solution structure inferred from the small-angle scattering data, which incorporates the known physical chemistry of the system. The implementation of this software suite involves a tiered approach in which GenApp provides the deployment infrastructure for running applications on both standard and high-performance computing hardware, and SASSIE provides a workflow framework into which modules can be plugged to prepare structures, carry out simulations, calculate theoretical scattering data and compare results with experimental data. GenApp produces the accessible web-based front end termed SASSIE-web, and GenApp and SASSIE also make community SAS codes available. Applications are illustrated by case studies: (i) inter-domain flexibility in two- to six-domain proteins as exemplified by HIV-1 Gag, MASP and ubiquitin; (ii) the hinge conformation in human IgG2 and IgA1 antibodies; (iii) the complex formed between a hexameric protein Hfq and mRNA; and (iv) synthetic 'bottlebrush' polymers

The Open AUC Project

Progress in analytical ultracentrifugation (AUC) has been hindered by obstructions to hardware innovation and by software incompatibility. In this paper, we announce and outline the Open AUC Project. The goals of the Open AUC Project are to stimulate AUC innovation by improving instrumentation, detectors, acquisition and analysis software, and collaborative tools. These improvements are needed for the next generation of AUC-based research. The Open AUC Project combines on-going work from several different groups. A new base instrument is described, one that is designed from the ground up to be an analytical ultracentrifuge. This machine offers an open architecture, hardware standards, and application programming interfaces for detector developers. All software will use the GNU Public License to assure that intellectual property is available in open source format. The Open AUC strategy facilitates collaborations, encourages sharing, and eliminates the chronic impediments that have plagued AUC innovation for the last 20 years. This ultracentrifuge will be equipped with multiple and interchangeable optical tracks so that state-of-the-art electronics and improved detectors will be available for a variety of optical systems. The instrument will be complemented by a new rotor, enhanced data acquisition and analysis software, as well as collaboration software. Described here are the instrument, the modular software components, and a standardized database that will encourage and ease integration of data analysis and interpretation software

Parsimonious Regularization Using Genetic Algorithms Applied to the Analysis of Analytical Ultracentrifugation Experiments

Frequently in the physical sciences experimental data are analyzed to determine model parameters using techniques known as parameter estimation. Eliminating the effects of noise from experimental data often involves Tikhonov or Maximum-Entropy regularization. These methods introduce a bias which smoothes the solution. In the problems considered here, the exact answer is sharp, containing a sparse set of parameters. Therefore, it is desirable to find the simplest set of model parameters for the data with an equivalent goodness-of-fit. This paper explains how to bias the solution towards a parsimonious model with a careful application of Genetic Algorithms. A method of representation, initialization and mutation is introduced to efficiently find this model. The results are compared with results from two other methods on simulated data with known content. Our method is shown to be the only one to achieve the desired results. Analysis of Analytical Ultracentrifugation sedimentation velocity experimental data is the primary example application

GenApp: Extensible Tool for Rapid Generation of Science Gateways and Standalone GUI Applications

GenApp (Generalized Application Framework) is a universal and extensible tool for rapid deployment of scientific codes onto web platforms and generation of standalone GUI applications. Among the main unique features of GenApp are the minimal technical expertise requirement for the end user and an open-end design ensuring sustainability of generated applications. To produce fully functional applications GenApp weaves libraries of fragments and user defined modules as directed by simple definition files. Preserving such simplicity with GenApp's maturation is one of the main developmental strategies. Because of the conceptual ease of use GenApp is ideally suited to scientists who are not professional developers and whose immediate expertise lies in traditional fields of sciences such as Physics, Chemistry and Biology. Currently, GenApp is used to generate multiple web based science gateways primarily in the Small Angle Scattering (SAS) field, to expose suites of programs enabling molecular simulation of soft-matter systems to model X-ray and neutron scattering experimental data to scientific community. Over 30 publications have resulted from use of GenApp framework

A Parametrically Constrained Optimization Method for Fitting Sedimentation Velocity Experiments

AbstractA method for fitting sedimentation velocity experiments using whole boundary Lamm equation solutions is presented. The method, termed parametrically constrained spectrum analysis (PCSA), provides an optimized approach for simultaneously modeling heterogeneity in size and anisotropy of macromolecular mixtures. The solutions produced by PCSA are particularly useful for modeling polymerizing systems, where a single-valued relationship exists between the molar mass of the growing polymer chain and its corresponding anisotropy. The PCSA uses functional constraints to identify this relationship, and unlike other multidimensional grid methods, assures that only a single molar mass can be associated with a given anisotropy measurement. A description of the PCSA algorithm is presented, as well as several experimental and simulated examples that illustrate its utility and capabilities. The performance advantages of the PCSA method in comparison to other methods are documented. The method has been added to the UltraScan-III software suite, which is available for free download from http://www.ultrascan.uthscsa.edu

Characterization of Size, Anisotropy, and Density Heterogeneity of Nanoparticles by Sedimentation Velocity

A critical problem in materials science is the accurate characterization of the size dependent properties of colloidal inorganic nanocrystals. Due to the intrinsic polydispersity present during synthesis, dispersions of such materials exhibit simultaneous heterogeneity in density <i>ρ,</i> molar mass <i>M</i>, and particle diameter <i>d.</i> The density increments ∂ρ/∂<i>d</i> and ∂ρ/∂<i>M</i> of these nanoparticles, if known, can then provide important information about crystal growth and particle size distributions. For most classes of nanocrystals, a mixture of surfactants is added during synthesis to control their shape, size, and optical properties. However, it remains a challenge to accurately determine the amount of passivating ligand bound to the particle surface post synthesis. The presence of the ligand shell hampers an accurate determination of the nanocrystal diameter. Using CdSe and PbS semiconductor nanocrystals, and the ultrastable silver nanoparticle (M₄Ag₄₄(p-MBA)₃₀), as model systems, we describe a Custom Grid method implemented in UltraScan-III for the characterization of nanoparticles and macromolecules using sedimentation velocity analytical ultracentrifugation. We show that multiple parametrizations are possible, and that the Custom Grid method can be generalized to provide high resolution composition information for mixtures of solutes that are heterogeneous in two out of three parameters. For such cases, our method can simultaneously resolve arbitrary two-dimensional distributions of hydrodynamic parameters when a third property can be held constant. For example, this method extracts partial specific volume and molar mass from sedimentation velocity data for cases where the anisotropy can be held constant, or provides anisotropy and partial specific volume if the molar mass is known