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Kapp-Joswig, J.-O.

Publications and source records attributed to Kapp-Joswig, J.-O..

2 recordsLinked to original sources

A cryptic pocket allosterically modulates oligosaccharide binding to DC-SIGN

DC-SIGN is a C-type lectin receptor expressed on antigen-presenting cells, crucial for pathogen recognition and immune modulation. Here, we identify and characterize a previously unrecognized cryptic allosteric pocket in DC-SIGN using molecular dynamics simulations, NMR spectroscopy, cryogenic electron microscopy and biochemical assays. Rotation of the gatekeeper residue M270 exposes the pocket whose occupancy modulates glycan binding. Mutations M270F and T314A mimic the occupied and unoccupied states of this pocket, respectively, shifting the conformational equilibrium of -helix 2 and altering oligosaccharide affinity via the extended carbohydrate binding site. While Ca{superscript 2} coordination at the canonical binding site remains unaffected, our data reveal a complex interplay between the Ca{superscript 2} binding sites and the canonical and extended glycan binding surfaces. These findings uncover a hierarchical allosteric mechanism that enables selective tuning of glycan affinity and suggest the cryptic pocket as a novel target for drug discovery in C-type lectins.

biochemistry↗

CommonNNClustering--A Python package for generic common-nearest-neighbour clustering

Density-based clustering procedures are widely used in a variety of data science applications. Their advantage lies in the capability to find arbitrarily shaped and sized clusters and robustness against outliers. In particular, they proved effective in the analysis of Molecular Dynamics simulations, where they serve to identify relevant, low energetic molecular conformations. As such, they can provide a convenient basis for the construction of kinetic (coreset) Markov-state models. Here we present the opensource Python project CommonNNClustering, which provides an easy-to-use and efficient re-implementation of the commonnearest-neighbour (CommonNN) method. The package provides functionalities for hierarchical clustering and an evaluation of the results. We put our emphasis on a generic API design to keep the implementation flexible and open for customisation.

bioinformatics↗