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Atomistic modelling of scattering data in the Collaborative Computational Project for Small Angle Scattering
Stephen J Perkins1, David W Wright1, Hailiang Zhang2
1Department of Structural and Molecular Biology, University College London , Darwin Building, Gower Street, London WC1E 6BT, UK.
Journal of Applied Crystallography
|December 17, 2016
Summary
Computer simulations now model small-angle scattering (SAS) data atomistically. New CCP-SAS software integrates experimental data with molecular modeling for deeper structural insights into biological and synthetic systems.
Area of Science:
- Structural Biology
- Computational Chemistry
- Materials Science
Background:
- Current computer simulations offer advanced modeling of small-angle scattering (SAS) data at the atomistic level.
- Integrating diverse structural constraints like energetics, crystallography, electron microscopy, and NMR enhances solution scattering capabilities.
- Deeper insights into system physics and chemistry necessitate advanced modeling software integrated with experimental data.
Purpose of the Study:
- To develop open-source, user-friendly software for atomistic and coarse-grained molecular modeling of scattering data.
- To create a robust platform integrating experimental SAS data with molecular simulation engines and force fields.
- To provide a tiered software suite (GenApp and SASSIE) for preparing structures, running simulations, and comparing results with experimental data.
Main Methods:
- Utilizing GenApp for deployment infrastructure on various computing hardware.
- Employing SASSIE as a workflow framework for modular integration of simulation and analysis tools.
- Integrating molecular dynamics and Monte Carlo force fields to constrain solution structures inferred from SAS data.
Main Results:
- Demonstrated applications include modeling inter-domain flexibility in proteins (HIV-1 Gag, MASP, ubiquitin) and antibody hinge conformations (IgG2, IgA1).
- Successfully modeled the complex of hexameric Hfq protein with mRNA.
- Applied to the structural analysis of synthetic 'bottlebrush' polymers.
Conclusions:
- The developed software suite enables atomistic and coarse-grained modeling of SAS data, providing enhanced structural insights.
- Integration of experimental data with advanced simulation tools deepens understanding of complex biological and synthetic systems.
- The open-source, high-throughput software facilitates broader accessibility and application in structural studies.