Combining Small Angle X-ray Scattering (Saxs) With Protein Structure Predictions to Characterize Conformations in Solution

Authors

Naga Babu Chinnam
Aleem Syed
Greg L Hura
Michal Hammel
John A Tainer
Susan E Tsutakawa

Publication Date

1-1-2023

Journal

Methods in Enzymology

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

Protein Conformation, X-Ray Diffraction, Scattering, Small Angle, X-Rays, Models, Molecular, Proteins

DOI

10.1016/bs.mie.2022.09.023

PMID

36641214

PMCID

PMC10132260

PubMedCentral® Posted Date

1-1-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes