@inbook{55b9137b915d45659b5dce4c0d3765dd,
title = "Combining small angle X-ray scattering (SAXS) with protein structure predictions to characterize conformations in solution",
abstract = "Accurate protein structure predictions, enabled by recent advances in machine learning algorithms, provide an entry point to probing structural mechanisms and to integrating and querying many types of biochemical and biophysical results. Limitations in such protein structure predictions can be reduced and addressed through comparison to experimental Small Angle X-ray Scattering (SAXS) data that provides protein structural information in solution. SAXS data can not only validate computational predictions, but can improve conformational and assembly prediction to produce atomic models that are consistent with solution data and biologically relevant states. Here, we describe how to obtain protein structure predictions, compare them to experimental SAXS data and improve models to reflect experimental information from SAXS data. Furthermore, we consider the potential for such experimentally-validated protein structure predictions to broadly improve functional annotation in proteins identified in metagenomics and to identify functional clustering on conserved sites despite low sequence homology.",
keywords = "BILBOMD, CASP-SAXS, FoXS, Hybrid method, Metagenomics, Protein flexibility, Protein structure prediction",
author = "Chinnam, {Naga Babu} and Aleem Syed and Greg Hura and Michal Hammel and Tainer, {John A.} and Tsutakawa, {Susan E.}",
note = "Funding Information: We thank long-time collaborators and colleagues in the SAXS field, including Dmitry Svergun, Robert P. Rambo, Andrej Sali, Dina Schneidman-Duhovny, Jesse Hopkins, and Daniel Rosenberg for their many insights and contributions, including useful programs for SAXS data collection and analysis. Work is supported by NCI P01 CA092584 (to S.E.T., G.H., M.H., J.A.T.) and 1R01GM137021 (To S.E.T. and G.H.). The SIBYLS beamline's efforts are supported by DOE-BER IDAT under contract DE-AC02-05CH11231. Publisher Copyright: {\textcopyright} 2022 Elsevier Inc.",
year = "2022",
doi = "10.1016/bs.mie.2022.09.023",
language = "English (US)",
series = "Methods in Enzymology",
publisher = "Academic Press Inc.",
booktitle = "Methods in Enzymology",
}