Antigen uptake and processing by innate immune cells is crucial to initiate the immune response. Therein, the endocytic C-type lectin receptors serve as pattern recognition receptors, detecting pathogens by their glycan structures. Herein, we studied the carbohydrate recognition domain of Langerin, a C-type lectin receptor involved in the host defense against viruses such as HIV and influenza as well as bacteria and fungi. Using a combination of nuclear magnetic resonance and molecular dynamics simulations, we unraveled the molecular determinants underlying cargo capture and release encoded in the receptor architecture. Our findings revealed receptor dynamics over several time scales associated with binding and release of the essential cofactor Ca(2+) controlled by the coupled motions of two loops. Applying mutual information theory and site-directed mutagenesis, we identified an allosteric intradomain network that modulates the Ca(2+) affinity depending on the pH, thereby promoting fast ligand release.

Computational methods assisting drug discovery and development are routine in the pharmaceutical industry. Digital recording of ADMET assays has provided a rich source of data for development of predictive models. Despite the accumulation of data and the public availability of advanced modeling algorithms, the utility of prediction in ADMET research is not clear. Here, we present a critical evaluation of the relationships between data volume, modeling algorithm, chemical representation and grouping, and temporal aspect (time sequence of assays) using an inhouse ADMET database. We find no large difference in prediction algorithms nor any systemic and substantial gain from increasingly large datasets. Temporal-based data enlargement led to performance improvement in only in a limited number of assays, and with fractional improvement at best. Assays that are well-, intermediately-, or poorlysuited for ADMET predictions and reasons for such behavior are systematically identified, generating realistic expectations for areas in which computational models can be used to guide decision making in molecular design and development.

Herein, the chemical synthesis and binding analysis of functionalizable rigid and flexible core trivalent sialosides bearing oligoethylene glycol (OEG) spacers interacting with spike proteins of influenza A virus (IAV) X31 is described. Although the flexible Tris‐based trivalent sialosides achieved micromolar binding constants, a trivalent binder based on a rigid adamantane core dominated flexible tripodal compounds with micromolar binding and hemagglutination inhibition constants. Simulation studies indicated increased conformational penalties for long OEG spacers. Using a systematic approach with molecular modeling and simulations as well as biophysical analysis, these findings emphasize on the importance of the scaffold rigidity and the challenges associated with the spacer length optimization.

No abstract

Protein−protein interactions often rely on specialized recognition domains, such as WW domains, which bind to specific proline-rich sequences. The specificity of these protein−protein interactions can be increased by tandem repeats, i.e., two WW domains connected by a linker. With a flexible linker, the WW domains can move freely with respect to each other. Additionally, the tandem WW domains can bind in two different orientations to their target sequences. This makes the elucidation of complex structures of tandem WW domains extremely challenging. Here, we identify and characterize two complex structures of the tandem WW domain of human forminbinding protein 21 and a peptide sequence from its natural binding partner, the core-splicing protein SmB/B′. The two structures differ in the ligand orientation and, consequently, also in the relative orientation of the two WW domains. We analyze and probe the interactions in the complexes by molecular simulations and NMR experiments. The workflow to identify the complex structures uses molecular simulations, density-based clustering, and peptide docking. It is designed to systematically generate possible complex structures for repeats of recognition domains. These structures will help us to understand the synergistic and multivalency effects that generate the astonishing versatility and specificity of protein−protein interactions.