bioRxivBiophysicsPreprint
Decoding CBM2 binding modes: how similar folds achieve distinct substrate preferences
Susanne Hansen TroøyeniD, Andrew Philip RennisoniD, Tina Dahlgren, Peter FojaniD, Marie Sofie MølleriD, Gastón CourtadeiD
Abstract
Understanding the mechanisms underlying substrate recognition and selectivity is essential for developing new biotechnological applications of carbohydrate-binding modules (CBMs). Many members of CBM family 2 (CBM2) prefer binding to crystalline substrates and show diverse substrate specificities despite sharing similar folds. We show here that CBM2 specificity cannot be rationalized solely based on sequence and fold. Rather, specific binding of CBM2s to crystalline surfaces depends on a balance of topological complementarity, hydrophobic dehydration, and hydrogen-bonding interactions. We compare the binding modes of ScCBM2 and BaCBM2 on cellulose, chitin, and polyethylene terephthalate (PET) with respect to protein and substrate properties. Using MD simulations and NMR, we explore the CBMs' surface topology, hydration, solvent accessibility, and electrostatics. The results indicate that the two CBMs may bind to crystalline substrates in opposite orientations, and that the interplay between hydrophobic and {pi}-stacking interactions along the aromatic triad and hydrogen-bonding interactions on either side determines their orientation. We further show that higher water density at the aromatic triad correlates with higher substrate affinity. Lastly, we probe the interaction dynamics of BaCBM2 with PET nanoparticles and the monomer bis(2-hydroxyethyl) terephthalate (BHET) using NMR relaxation and solvent paramagnetic relaxation enhancement (sPRE) experiments, revealing a mixture of transient, low-population states involved in PET binding. These insights clarify the interplay among factors that drive CBM2 substrate recognition and highlight the importance of evaluating surface hydration and protein dynamics in future CBM2 studies.
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- approach: simulation
- Citations
- 0
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