scholarly journals Ab initio structure determination from experimental fluctuation X-ray scattering data

2018 ◽  
Vol 115 (46) ◽  
pp. 11772-11777 ◽  
Author(s):  
Kanupriya Pande ◽  
Jeffrey J. Donatelli ◽  
Erik Malmerberg ◽  
Lutz Foucar ◽  
Christoph Bostedt ◽  
...  
Keyword(s):  
Ab Initio ◽  
Particle System ◽  
Scattering Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ab Initio Structure ◽  
Filtering Technique ◽  
Processing Techniques ◽  
Practical Feasibility ◽  
Ray Scattering

Fluctuation X-ray scattering (FXS) is an emerging experimental technique in which X-ray solution scattering data are collected from particles in solution using ultrashort X-ray exposures generated by a free-electron laser (FEL). FXS experiments overcome the low data-to-parameter ratios associated with traditional solution scattering measurements by providing several orders of magnitude more information in the final processed data. Here we demonstrate the practical feasibility of FEL-based FXS on a biological multiple-particle system and describe data-processing techniques required to extract robust FXS data and significantly reduce the required number of snapshots needed by introducing an iterative noise-filtering technique. We showcase a successful ab initio electron density reconstruction from such an experiment, studying the Paramecium bursaria Chlorella virus (PBCV-1).

scholarly journals Obtaining Tertiary Protein Structures by the ab Initio Interpretation of Small Angle X-ray Scattering Data

2020 ◽  
Vol 16 (3) ◽  
pp. 1985-2001 ◽  
Author(s):  
Christopher Prior ◽  
Owen R. Davies ◽  
Daniel Bruce ◽  
Ehmke Pohl
Keyword(s):  
Ab Initio ◽  
Small Angle ◽  
Protein Structures ◽  
Scattering Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

scholarly journals Obtaining tertiary protein structures by the ab-initio interpretation of small angle X-ray scattering data

2019 ◽  
Author(s):  
Christopher Prior ◽  
Owen R Davies ◽  
Daniel Bruce ◽  
Ehmke Pohl
Keyword(s):  
Ab Initio ◽  
Small Angle ◽  
Protein Structures ◽  
Scattering Data ◽  
Ab Initio Method ◽  
Primary Sequence ◽  
Saxs Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

ABSTRACTSmall angle X-ray scattering (SAXS) has become an important tool to investigate the structure of proteins in solution. In this paper we present a novel ab-initio method to represent polypeptide chains as discrete curves that can be used to derive a meaningful three-dimensional model from only the primary sequence and experimental SAXS data. High resolution crystal structures were used to generate probability density functions for each of the common secondary structural elements found in proteins. These are used to place realistic restraints on the model curve’s geometry. To evaluate the quality of potential models and demonstrate the efficacy of this novel technique we developed a new statistic to compare the entangled geometry of two open curves, based on mathematical techniques from knot theory. The chain model is coupled with a novel explicit hydration shell model in order derive physically meaningful 3D models by optimizing configurations against experimental SAXS data using a monte-caro based algorithm. We show that the combination of our ab-initio method with spatial restraints based on contact predictions successfully derives a biologically plausible model of the coiled–coil component of the human synaptonemal complex central element protein.SIGNIFICANCESmall-angle X-ray scattering allows for structure determination of biological macromolecules and their complexes in aqueous solution. Using a discrete curve representation of the polypeptide chain and combining it with empirically determined constraints and a realistic solvent model we are now able to derive realistic ab-initio 3-dimensional models from BioSAXS data. The method only require a primary sequence and the scattering data form the user.

scholarly journals Global Small-Angle X-ray Scattering Data Analysis of Triacylglycerols in the Molten State (Part I)

2018 ◽  
Vol 122 (45) ◽  
pp. 10320-10329 ◽  
Author(s):  
Amin Sadeghpour ◽  
Marjorie Ladd Parada ◽  
Josélio Vieira ◽  
Megan Povey ◽  
Michael Rappolt
Keyword(s):  
Data Analysis ◽  
Small Angle ◽  
Scattering Data ◽  
Molten State ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Reconstruction of viruses from solution x-ray scattering data

1995 ◽  
Author(s):  
Yibin Zheng ◽  
Peter C. Doerschuk ◽  
John E. Johnson
Keyword(s):  
Scattering Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Combined Experimental and Ab Initio Multireference Configuration Interaction Study of the Resonant Inelastic X-ray Scattering Spectrum of CO2

2014 ◽  
Vol 118 (35) ◽  
pp. 20163-20175 ◽  
Author(s):  
Dimitrios Maganas ◽  
Paw Kristiansen ◽  
Laurent-Claudius Duda ◽  
Axel Knop-Gericke ◽  
Serena DeBeer ◽  
...  
Keyword(s):  
Ab Initio ◽  
Configuration Interaction ◽  
Interaction Study ◽  
X Ray ◽  
X Ray Scattering ◽  
Scattering Spectrum ◽  
Ray Scattering

scholarly journals REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures

2020 ◽  
Author(s):  
Steve P. Meisburger ◽  
Da Xu ◽  
Nozomi Ando
Keyword(s):  
A Priori ◽  
Scattering Data ◽  
Biological Macromolecules ◽  
Time Resolved ◽  
X Ray ◽  
X Ray Scattering ◽  
Open Source Software Package ◽  
Saxs Analysis ◽  
Structural Methods ◽  
Ray Scattering

AbstractMixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with time-resolved, high-throughput, or chromatography-coupled setups. Deconvolution and interpretation of the resulting datasets, however, are nontrivial when neither the scattering components nor the way in which they evolve are known a priori. To address this issue, we introduce the REGALS method (REGularized Alternating Least Squares), which incorporates simple expectations about the data as prior knowledge and utilizes parameterization and regularization to provide robust deconvolution solutions. The restraints used by REGALS are general properties such as smoothness of profiles and maximum dimensions of species, which makes it well-suited for exploring datasets with unknown species. Here we apply REGALS to analyze experimental data from four types of SAXS experiment: anion-exchange (AEX) coupled SAXS, ligand titration, time-resolved mixing, and time-resolved temperature jump. Based on its performance with these challenging datasets, we anticipate that REGALS will be a valuable addition to the SAXS analysis toolkit and enable new experiments. The software is implemented in both MATLAB and python and is available freely as an open-source software package.

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