Ultrascan solution modeler: integrated hydrodynamic parameter and small angle scattering computation and fitting tools

This is a preprint of a paper in the proceedings of the XSEDE12 conference, held July 16-19, 2012 in Chicago, IL. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.UltraScan Solution Modeler (US-SOMO) processes atomic and lower-resolution bead model representations of biological and other macromolecules to compute various hydrodynamic parameters, such as the sedimentation and diffusion coefficients, relaxation times and intrinsic viscosity, and small angle scattering curves, that contribute to our understanding of molecular structure in solution. Knowledge of biological macromolecules' structure aids researchers in understanding their function as a path to disease prevention and therapeutics for conditions such as cancer, thrombosis, Alzheimer's disease and others. US-SOMO provides a convergence of experimental, computational, and modeling techniques, in which detailed molecular structure and properties are determined from data obtained in a range of experimental techniques that, by themselves, give incomplete information. Our goal in this work is to develop the infrastructure and user interfaces that will enable a wide range of scientists to carry out complicated experimental data analysis techniques on XSEDE. Our user community predominantly consists of biophysics and structural biology researchers. A recent search on PubMed reports 9,205 papers in the decade referencing the techniques we support. We believe our software will provide these researchers a convenient and unique framework to refine structures, thus advancing their research. The computed hydrodynamic parameters and scattering curves are screened against experimental data, effectively pruning potential structures into equivalence classes. Experimental methods may include analytical ultracentrifugation, dynamic light scattering, small angle X-ray and neutron scattering, NMR, fluorescence spectroscopy, and others. One source of macromolecular models is X-ray crystallography. However, the conformation in solution may not match that observed in the crystal form. Using computational techniques, an initial fixed model can be expanded into a search space utilizing high temperature molecular dynamic approaches or stochastic methods such as Brownian dynamics. The number of structures produced can vary greatly, ranging from hundreds to tens of thousands or more. This introduces a number of cyberinfrastructure challenges. Computing hydrodynamic parameters and small angle scattering curves can be computationally intensive for each structure, and therefore cluster compute resources are essential for timely results. Input and output data sizes can vary greatly from less than 1 MB to 2 GB or more. Although the parallelization is trivial, along with data size variability there is a large range of compute sizes, ranging from one to potentially thousands of cores with compute time of minutes to hours. In addition to the distributed computing infrastructure challenges, an important concern was how to allow a user to conveniently submit, monitor and retrieve results from within the C++/Qt GUI application while maintaining a method for authentication, approval and registered publication usage throttling. Middleware supporting these design goals has been integrated into the application with assistance from the Open Gateway Computing Environments (OGCE) collaboration team. The approach was tested on various XSEDE clusters and local compute resources. This paper reviews current US-SOMO functionality and implementation with a focus on the newly deployed cluster integration.This work was supported by NIH grant K25GM090154 to EB, NSF grant OCI-1032742 to MP, NSF grant TG-MCB070040N to BD, and NIH grant RR-022200 to B

Shape2SAS -- a web application to simulate small-angle scattering data and pair distance distributions from user-defined shapes

Shape2SAS is a web application that allows researchers and students to build intuition and understanding of small-angle scattering. It is available at https://somo.chem.utk.edu/shape2sas. The user defines a model of arbitrary shape by combining geometrical subunits, and Shape2SAS then calculates and displays the scattering intensity, the pair distance distribution as well as a visualization of the user-defined shape. Simulated data with realistic noise are also generated. We demonstrate how Shape2SAS can calculate and display the different scattering patterns for various geometrical shapes, such as spheres and cylinders. We also demonstrate how the effect of structure factors can be visualized. Finally, we show how multi-contrast particles can readily be generated, and how the calculated scattering may be used to validate and visualize analytical models generated in analysis software for fitting small-angle scattering data

Two-dimensional grid optimization for sedimentation velocity analysis in the analytical ultracentrifuge

Accepted author manuscriptSedimentation velocity experiments performed in the analytical ultracentrifuge are modeled using finite-element solutions of the Lamm equation. During modeling, three fundamental parameters are optimized: the sedimentation coefficients, the diffusion coefficients, and the partial concentrations of all solutes present in a mixture. A general modeling approach consists of fitting the partial concentrations of solutes defined in a two-dimensional grid of sedimentation and diffusion coefficient combinations that cover the range of possible solutes for a given mixture. An increasing number of grid points increase the resolution of the model produced by the subsequent analysis, with denser grids giving rise to a very large system of equations. Here, we evaluate the efficiency and resolution of several regular grids and show that traditionally defined grids tend to provide inadequate coverage in one region of the grid, while at the same time being computationally wasteful in other sections of the grid. We describe a rapid and systematic approach for generating efficient two-dimensional analysis grids that balance optimal information content and model resolution for a given signal-to-noise ratio with improved calculation efficiency. These findings are general and apply to one- and two-dimensional grids, although they no longer represent regular grids. We provide a recipe for an improved grid-point spacing in both directions which eliminates unnecessary points, while at the same time providing a more uniform resolution that can be scaled based on the stochastic noise in the experimental data.Ye

Fibrinogen species as resolved by HPLC-SAXS data processing within the UltraScan Solution Modeler ( US-SOMO ) enhanced SAS module

Fibrinogen is a large heterogeneous aggregation/degradation-prone protein playing a central role in blood coagulation and associated pathologies, whose structure is not completely resolved. When a high-molecular-weight fraction was analyzed by size-exclusion high-performance liquid chromatography/small-angle X-ray scattering (HPLC-SAXS), several composite peaks were apparent and because of the stickiness of fibrinogen the analysis was complicated by severe capillary fouling. Novel SAS analysis tools developed as a part of the UltraScan Solution Modeler ( US-SOMO ; http://somo.uthscsa.edu/), an open-source suite of utilities with advanced graphical user interfaces whose initial goal was the hydrodynamic modeling of biomacromolecules, were implemented and applied to this problem. They include the correction of baseline drift due to the accumulation of material on the SAXS capillary walls, and the Gaussian decomposition of non-baseline-resolved HPLC-SAXS elution peaks. It was thus possible to resolve at least two species co-eluting under the fibrinogen main monomer peak, probably resulting from in-column degradation, and two others under an oligomers peak. The overall and cross-sectional radii of gyration, molecular mass and mass/length ratio of all species were determined using the manual or semi-automated procedures available within the US-SOMO SAS module. Differences between monomeric species and linear and sideways oligomers were thus identified and rationalized. This new US-SOMO version additionally contains several computational and graphical tools, implementing functionalities such as the mapping of residues contributing to particular regions of P ( r ), and an advanced module for the comparison of primary I ( q ) versus q data with model curves computed from atomic level structures or bead models. It should be of great help in multi-resolution studies involving hydrodynamics, solution scattering and crystallographic/NMR data

Defining the intrinsically disordered C-terminal domain of SSB reveals DNA-mediated compaction

The bacterial single-stranded DNA (ssDNA) binding protein SSB is a strictly conserved and essential protein involved in diverse functions of DNA metabolism, including replication and repair. SSB comprises a well-characterized tetrameric core of N-terminal oligonucleotide binding OB folds that bind ssDNA and four intrinsically disordered C-terminal domains of unknown structure that interact with partner proteins. The generally accepted, albeit speculative, mechanistic model in the field postulates that binding of ssDNA to the OB core induces the flexible, undefined C-terminal arms to expand outwards encouraging functional interactions with partner proteins. In this structural study, we show that the opposite is true. Combined small-angle scattering with X-rays and neutrons coupled to coarse-grained modeling reveal that the intrinsically disordered C-terminal arms are relatively collapsed around the tetrameric OB core and collapse further upon ssDNA binding. This implies a mechanism of action, in which the disordered C-terminal domain collapse traps the ssDNA and pulls functional partners onto the ssDNA

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

US-SOMO: Methods for Construction and Hydration of Macromolecular Hydrodynamic Models

No abstract available