Predicting
the assembly of multiple proteins into specific complexes
is critical to understanding their biological function in an organism
and thus the design of drugs to address their malfunction. Proteins
are flexible molecules, which inherently pose a problem to any protein
docking computational method, where even a simple rearrangement of
the side chain and backbone atoms at the interface of binding partners
complicates the successful determination of the correct docked pose.
Herein, we present a means of representing protein surface, electrostatics,
and local dynamics within a single volumetric descriptor. We show
that our representations can be physically related to the surface-accessible
solvent area and mass of the protein. We then demonstrate that the
application of this representation into a protein–protein docking
scenario bypasses the need to compensate for, and predict, specific
side chain packing at the interface of binding partners. This representation
is leveraged in our de novo protein docking software, JabberDock,
which can accurately and robustly predict difficult target complexes
with an average success rate of >54%, which is comparable to or
greater
than the currently available methods.
Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.
Integral membrane proteins (IMPs) are biologically highly significant but challenging to study because they require maintaining a cellular lipid-like environment. Here, we explore the application of mass photometry (MP) to IMPs and membrane mimetic systems at the single particle level. We apply MP to amphipathic vehicles, such as detergents and amphipols, as well as to lipid and native nanodiscs, characterising the particle size, sample purity and heterogeneity. Using methods established for cryogenic electron microscopy, we eliminate detergent background, enabling high-resolution studies of membrane protein structure and interactions. We find evidence that, when extracted from native membranes using native styrene-maleic acid nanodiscs, the potassium channel KcsA is present as a dimer of tetramersin contrast to results obtained using detergent purification. Finally, using lipid nanodiscs, we show that MP can help distinguish between functional and non-functional nanodisc assemblies, as well as determine the critical factors for lipid nanodisc formation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.