Hydrophobins represent an important group of proteins from both a biological and nanotechnological standpoint. They are the means through which filamentous fungi affect their environment to promote growth, and their properties at interfaces have resulted in numerous applications. In our study we have combined protein docking, molecular dynamics simulation, and electron cryo-microscopy to gain atomistic level insight into the surface structure of films composed of two class II hydrophobins: HFBI and HFBII produced by Trichoderma reesei. Together our results suggest a unit cell composed of six proteins; however, our computational results suggest P6 symmetry, while our experimental results show P3 symmetry with a unit cell size of 56 Å. Our computational results indicate the possibility of an alternate ordering with a three protein unit cell with P3 symmetry and a smaller unit cell size, and we have used a Monte Carlo simulation of a spin model representing the hydrophobin film to show how this alternate metastable structure may play a role in increasing the rate of surface coverage by hydrophobin films, possibly indicating a mechanism of more general significance to both biology and nanotechnology.

No abstract

Using the D3R Grand Challenge 4 dataset containing Beta-secretase 1 (BACE) and Cathepsin S (CatS) inhibitors, we have evaluated the performance of our in-house docking workflow that involves in the first step the selection of the most suitable docking software for the system of interest based on structural and functional information available in public databases, followed by the docking of the dataset to predict the binding modes and ranking of ligands. The macrocyclic nature of the BACE ligands brought additional challenges, which were dealt with by a careful preparation of the three-dimensional input structures for ligands. This provided top-performing predictions for BACE, in contrast with CatS, where the predictions in the absence of guiding constraints provided poor results. These results highlight the importance of previous structural knowledge that is needed for correct predictions on some challenging targets. After the end of the challenge, we also carried out free energy calculations (i.e. in a non-blinded manner) for CatS using the pmx software and several force fields (AMBER, Charmm). Using knowledge-based starting pose construction allowed reaching remarkable accuracy for the CatS free energy estimates. Interestingly, we show that the use of a consensus result, by averaging the results from different force fields, increases the prediction accuracy.