Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide–protein interactions. Our simple implementation of AlphaFold2 generates peptide–protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide–protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

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Multi-angle light scattering coupled with size exclusion chromatography (SEC-MALS) is a standard and common approach for characterizing protein mass, overall shape, aggregation, oligomerization, interactions and purity. The limited resolution of analytical SEC restricts in some instances the accurate analysis that can be accomplished by MALS. These include mixtures of protein populations with identical or very similar molecular masses, oligomers with poor separation and short peptides. Here we show that combining MALS with the higher resolution separation technique ion exchange (IEX-MALS) can allow precise analyses of samples that cannot be resolved by SEC-MALS. We conclude that IEX-MALS is a valuable and complementary method for protein characterization, especially for protein systems that could not be fully analyzed by SEC-MALS.

Our understanding of protein evolution would greatly benefit from mapping of binding landscapes, i.e., changes in protein-protein binding affinity due to all single mutations. However, experimental generation of such landscapes is a tedious task due to a large number of possible mutations. Here, we use a simple computational protocol to map the binding landscape for two homologous high-affinity complexes, involving a snake toxin fasciculin and acetylcholinesterase from two different species. To verify our computational predictions, we experimentally measure binding between 25 Fas mutants and the 2 enzymes. Both computational and experimental results demonstrate that the Fas sequence is close to the optimum when interacting with its targets, yet a few mutations could further improve Kd, kon, and koff. Our computational predictions agree well with experimental results and generate distributions similar to those observed in other high-affinity PPIs, demonstrating the potential of simple computational protocols in capturing realistic binding landscapes.

The subfamily of bacterial dimeric avidins is being extended through the discovery of additional members originating from diverse sources. All of these newly discovered dimeric avidin forms exhibit high affinity towards biotin, despite their lack of critical Trp in the classical tetrameric forms. The common feature of forming cylinder‐like multimers (hexamers and octamers) seems to be more than a random occurrence, which generally characterizes their apo forms in the crystalline state and also in some cases in solution. Afifavidin from the Gram‐negative α‐proteobacterium Afifella pfennigii is the fourth member of the subfamily of dimers, which, in the intact apo form, also congregates into octamers both in the solution and in the crystalline state, whereby the C‐terminal extended segments stretch into the biotin‐binding sites of adjacent non‐canonical monomers. The intact apo afifavidin molecule self‐assembles into toroid‐shaped nanostructures that dissociate into the inherent dimers upon binding biotin. On removal of the C‐terminal regions, the short‐form of afifavidin forms dimers both in the solution and in the crystalline states. The high affinity of the dimeric forms of afifavidin towards biotin is maintained, due to the conserved disulfide bridge between L3,4 and L5,6 and the presence of Phe50 in L3,4 that compensate for the lack of the critical Trp in the tetrameric avidins. These cyclic multimeric‐avidin assemblies may be exploited in the future to further diversify biotin‐based nanotechnology or to serve as building blocks in the construction of bio‐inspired materials. Database Structural data are available in the PDB databases under the accession numbers: http://www.rcsb.org/pdb/search/structidSearch.do?structureId=6HDV, http://www.rcsb.org/pdb/search/structidSearch.do?structureId=6HDS, http://www.rcsb.org/pdb/search/structidSearch.do?structureId=6HDT.