AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank

Perspectives on solution-based small angle X-ray scattering for protein and biological macromolecule structural biology

Small-angle X-ray scattering (SAXS) is used to extract structural information from a wide variety of non-crystalline samples in different fields (e.g., materials science, physics, chemistry, and biology). This review provides an overview of SAXS as applied to structural biology, specifically for proteins and other biomacromolecules in solution with an emphasis on extracting key structural parameters and the interpretation of SAXS data using a diverse array of techniques. These techniques cover aspects of building and assessing models to describe data measured from monodispersed and ideal dilute samples through to more complicated structurally polydisperse systems. Ab initio modelling, rigid body modelling as well as normal-mode analysis, molecular dynamics, mixed component and structural ensemble modelling are discussed. Dealing with polydispersity both physically in terms of component separation as well as approaching the analysis and modelling of data of mixtures and evolving systems are described, including methods for data decomposition such as single value decomposition/principle component analysis and evolving factor analysis. This review aims to highlight that solution SAXS, with the cohort of developments in data analysis and modelling, is well positioned to build upon the traditional 'single particle view' foundation of structural biology to take the field into new areas for interpreting the structures of proteins and biomacromolecules as population-states and dynamic structural systems.

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.

Beyond the VSG layer: Exploring the role of intrinsic disorder in the invariant surface glycoproteins of African trypanosomes

In the bloodstream of mammalian hosts, African trypanosomes face the challenge of protecting their invariant surface receptors from immune detection. This crucial role is fulfilled by a dense, glycosylated protein layer composed of variant surface glycoproteins (VSGs), which undergo antigenic variation and provide a physical barrier that shields the underlying invariant surface glycoproteins (ISGs). The protective shield's limited permeability comes at the cost of restricted access to the extracellular host environment, raising questions regarding the specific function of the ISG repertoire. In this study, we employ an integrative structural biology approach to show that intrinsically disordered membrane-proximal regions are a common feature of members of the ISG super-family, conferring the ability to switch between compact and elongated conformers. While the folded, membrane-distal ectodomain is buried within the VSG layer for compact conformers, their elongated counterparts would enable the extension beyond it. This dynamic behavior enables ISGs to maintain a low immunogenic footprint while still allowing them to engage with the host environment when necessary. Our findings add further evidence to a dynamic molecular organization of trypanosome surface antigens wherein intrinsic disorder underpins the characteristics of a highly flexible ISG proteome to circumvent the constraints imposed by the VSG coat.

SAXS/WAXS data of conformationally flexible ribose binding protein

Modern artificial intelligence-based protein structure prediction methods, such as Alphafold2, can predict structures of folded proteins with reasonable accuracy. However, Alphafold2 provides a static view of a protein, which does not show the conformational variability of the protein, domain movement in a multi-domain protein, or ligand-induced conformational changes it might undergo in solution. Small-angle X-ay scattering (SAXS) and wide-angle X-ray scattering (WAXS) are solution techniques that can aid in integrative modeling of conformationally flexible proteins, or in validating their predicted ensemble structures. While SAXS is sensitive to global structural features, WAXS can expand the scope of structural modeling by including information about local structural changes. We present SAXS and WAXS datasets obtained from conformationally flexible d-ribose binding protein (RBP) from Escherichia coli in the ribose bound and unbound forms. SAXS/WAXS datasets of RBP provided here may aid in method development efforts for more accurate prediction of structural ensembles of conformationally flexible proteins, and their conformational changes.

AlphaFold, small-angle X-ray scattering and ensemble modelling: a winning combination for intrinsically disordered proteins

A recent article by Brookes, Rocco, Vachette & Trewhella [J. Appl. Cryst. (2023), 56, 910-926] on improving the accuracy of AlphaFold structural predictions for disordered proteins is discussed.