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.

Cellobiose dehydrogenase (CDH) is an extracellular haemoflavoenzyme that is produced by a number of wood-degrading and phytopathogenic fungi and it has a proposed role in the early events of lignocellulose degradation and wood colonisation. In the presence of a suitable electron acceptor, e.g. 2,6-dichloro-indophenol, cytochrome c, or metal ions, CDH oxidises cellobiose to cellobionolactone. When screening 11 different Trametes spp. for the formation of CDH activity, all the strains investigated were found to secrete significant amounts of CDH when cultivated on a cellulose-containing medium. Amongst others, Trametes pubescens and Trametes villosa were identified as excellent, not-yet-described, producer strains of this enzyme activity that has various potential applications in biotechnology. CDH from both strains was purified to apparent homogeneity and subsequently characterised. Both monomeric enzymes have a molecular mass of approximately 90 kDa (gel filtration) and a pI value of 4.2-4.4. The best substrates are cellobiose and cellooligosaccharides; additionally, lactose, thiocellobiose, and xylobiose are efficiently oxidised. Glucose and maltose are poor substrates. The preferred substrate is cellobiose with a Km value of 0.21 mM and a kcat value of 22 s(-1) for CDH from T. pubescens; the corresponding values for the T. villosa enzyme are 0.21 mM and 24 s(-1), respectively. Both enzymes showed very high activity with one-electron acceptors such as ferricenium, ferricyanide, or the azino-bis-(3-ethyl-benzthiazolin-6-sulfonic acid) cation radical.

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins.

Detailed target-selectivity information and experiment-based efficacy prediction tools are primarily available for Streptococcus pyogenes Cas9 (SpCas9). One obstacle to develop such tools is the rarity of accurate data. Here, we report a method termed ‘Self-targeting sgRNA Library Screen’ (SLS) for assaying the activity of Cas9 nucleases in bacteria using random target/sgRNA libraries of self-targeting sgRNAs. Exploiting more than a million different sequences, we demonstrate the use of the method with the SpCas9-HF1 variant to analyse its activity and reveal motifs that influence its target-selectivity. We have also developed an algorithm for predicting the activity of SpCas9-HF1 with an accuracy matching those of existing tools. SLS is a facile alternative to the much more expensive and laborious approaches used currently and has the capability of delivering sufficient amount of data for most of the orthologs and variants of SpCas9.