Vesicle Viewer: Online Analysis of Small Angle Scattering from Lipid Vesicles

10.26434/chemrxiv.14454645.v1 ◽

2021 ◽

Author(s):

Aislyn Lewis-Laurent ◽

Milka Doktorova ◽

Frederick A. Heberle ◽

Drew Marquardt

Keyword(s):

Lipid Bilayers ◽

Small Angle ◽

Web Applications ◽

Structural Parameters ◽

Lipid Vesicles ◽

Small Angle Scattering ◽

Scattering Data ◽

Web Based ◽

Angle Scattering ◽

Area Per Lipid

In this project, we developed an internet-based application, called Vesicle Viewer, to visualize and analyze small angle scattering data generated in the study of lipid bilayers. Vesicle Viewer models SAS data using the EZ-SDP model. In this way, key bilayer structural parameters, such as area per lipid and bilayer thickness, can be easily determined. This application primarily uses Django, a python package specialized for the development of robust web applications. In addition, several other libraries are used to support the more technical aspects of the project – notable examples are MatPlotLib (for graphs) and NumPy (for calculations). Without the barrier of downloading and installing software, the development of this web-based application will allow scientists all over the world to take advantage of this solution, regardless of their preferred operating system.

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Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

Journal of Applied Crystallography ◽

10.1107/s1600576720000552 ◽

2020 ◽

Vol 53 (2) ◽

pp. 326-334

Author(s):

Richard K. Archibald ◽

Mathieu Doucet ◽

Travis Johnston ◽

Steven R. Young ◽

Erika Yang ◽

...

Keyword(s):

Machine Learning ◽

Small Angle ◽

Structural Parameters ◽

Nearest Neighbors ◽

Small Angle Scattering ◽

Machine Learning Techniques ◽

Stochastic Gradient Descent ◽

Scattering Data ◽

Gradient Descent Method ◽

Angle Scattering

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

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Lipid Bilayer Structure Refinement with SAXS/SANS Based Restrained Ensemble Molecular Dynamics

10.26434/chemrxiv.14182763.v2 ◽

2021 ◽

Author(s):

Yevhen Cherniavskyi ◽

Svetlana Baoukina ◽

Bryan W. Holland ◽

D. Peter Tieleman

Keyword(s):

Molecular Dynamics ◽

Lipid Bilayers ◽

Small Angle ◽

Structure Refinement ◽

Small Angle Scattering ◽

Scattering Data ◽

Bilayer Structure ◽

Lipid Molecule ◽

Scattering Intensity ◽

Angle Scattering

Small-angle scattering is a powerful technique that can probe the structure of lipid bilayers on the nanometer scale. Retrieving the real space structure of lipid bilayers from the scattering intensity can be a challenging task, as their fluid nature results in a liquid-like scattering pattern which is hard to interpret. The standard approach to this problem is to describe the bilayer structure as a sum of density distributions of separate components of the lipid molecule and then to fit the parameters of the distributions against experimental data. The accuracy of the density-based analysis is partially limited by the choice of the functions used to describe component distributions, especially in the case of multi-component bilayers. The number of parameters in the model is balanced by the need for an accurate description of the underlying bilayer structure and the risk of overfitting the data. Here, we present an alternative method for the interpretation of small-angle scattering intensity data for lipid bilayers. The method is based on restrained ensemble molecular dynamics simulations that allow direct incorporation of the scattering data into the simulations in the form of a restraining potential. This approach combines the information implicitly contained in the simulation force field with structural data from the scattering intensity and is free from prior assumptions regarding the bilayer structure.<br>

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Lipid Bilayer Structure Refinement with SAXS/SANS Based Restrained Ensemble Molecular Dynamics

10.26434/chemrxiv.14182763.v1 ◽

2021 ◽

Author(s):

Yevhen Cherniavskyi ◽

Svetlana Baoukina ◽

Bryan W. Holland ◽

D. Peter Tieleman

Keyword(s):

Molecular Dynamics ◽

Lipid Bilayers ◽

Small Angle ◽

Structure Refinement ◽

Small Angle Scattering ◽

Scattering Data ◽

Bilayer Structure ◽

Lipid Molecule ◽

Scattering Intensity ◽

Angle Scattering

Small-angle scattering is a powerful technique that can probe the structure of lipid bilayers on the nanometer scale. Retrieving the real space structure of lipid bilayers from the scattering intensity can be a challenging task, as their fluid nature results in a liquid-like scattering pattern which is hard to interpret. The standard approach to this problem is to describe the bilayer structure as a sum of density distributions of separate components of the lipid molecule and then to fit the parameters of the distributions against experimental data. The accuracy of the density-based analysis is partially limited by the choice of the functions used to describe component distributions, especially in the case of multi-component bilayers. The number of parameters in the model is balanced by the need for an accurate description of the underlying bilayer structure and the risk of overfitting the data. Here, we present an alternative method for the interpretation of small-angle scattering intensity data for lipid bilayers. The method is based on restrained ensemble molecular dynamics simulations that allow direct incorporation of the scattering data into the simulations in the form of a restraining potential. This approach combines the information implicitly contained in the simulation force field with structural data from the scattering intensity and is free from prior assumptions regarding the bilayer structure.<br>

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Lipid Bilayer Structure Refinement with SAXS/SANS Based Restrained Ensemble Molecular Dynamics

10.26434/chemrxiv.14182763 ◽

2021 ◽

Author(s):

Yevhen Cherniavskyi ◽

Svetlana Baoukina ◽

Bryan W. Holland ◽

D. Peter Tieleman

Keyword(s):

Molecular Dynamics ◽

Lipid Bilayers ◽

Small Angle ◽

Structure Refinement ◽

Small Angle Scattering ◽

Scattering Data ◽

Bilayer Structure ◽

Lipid Molecule ◽

Scattering Intensity ◽

Angle Scattering

Small-angle scattering is a powerful technique that can probe the structure of lipid bilayers on the nanometer scale. Retrieving the real space structure of lipid bilayers from the scattering intensity can be a challenging task, as their fluid nature results in a liquid-like scattering pattern which is hard to interpret. The standard approach to this problem is to describe the bilayer structure as a sum of density distributions of separate components of the lipid molecule and then to fit the parameters of the distributions against experimental data. The accuracy of the density-based analysis is partially limited by the choice of the functions used to describe component distributions, especially in the case of multi-component bilayers. The number of parameters in the model is balanced by the need for an accurate description of the underlying bilayer structure and the risk of overfitting the data. Here, we present an alternative method for the interpretation of small-angle scattering intensity data for lipid bilayers. The method is based on restrained ensemble molecular dynamics simulations that allow direct incorporation of the scattering data into the simulations in the form of a restraining potential. This approach combines the information implicitly contained in the simulation force field with structural data from the scattering intensity and is free from prior assumptions regarding the bilayer structure.<br>

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2017 publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update

Acta Crystallographica Section D Structural Biology ◽

10.1107/s2059798317011597 ◽

2017 ◽

Vol 73 (9) ◽

pp. 710-728 ◽

Cited By ~ 107

Author(s):

Jill Trewhella ◽

Anthony P. Duff ◽

Dominique Durand ◽

Frank Gabel ◽

J. Mitchell Guss ◽

...

Keyword(s):

Small Angle ◽

Task Force ◽

Data Bank ◽

Small Angle Scattering ◽

Scattering Data ◽

Quality Data ◽

Angle Scattering ◽

Publication Guidelines ◽

Guidelines And Recommendations

In 2012, preliminary guidelines were published addressing sample quality, data acquisition and reduction, presentation of scattering data and validation, and modelling for biomolecular small-angle scattering (SAS) experiments. Biomolecular SAS has since continued to grow and authors have increasingly adopted the preliminary guidelines. In parallel, integrative/hybrid determination of biomolecular structures is a rapidly growing field that is expanding the scope of structural biology. For SAS to contribute maximally to this field, it is essential to ensure open access to the information required for evaluation of the quality of SAS samples and data, as well as the validity of SAS-based structural models. To this end, the preliminary guidelines for data presentation in a publication are reviewed and updated, and the deposition of data and associated models in a public archive is recommended. These guidelines and recommendations have been prepared in consultation with the members of the International Union of Crystallography (IUCr) Small-Angle Scattering and Journals Commissions, the Worldwide Protein Data Bank (wwPDB) Small-Angle Scattering Validation Task Force and additional experts in the field.

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