Machine learning-based modulation of Ca²⁺-binding affinity in EF-hand proteins and comparative structural insights into site-specific cooperative binding

Abstract

Ca2+-binding proteins are present in almost all living organisms and different types display different levels of binding affinities for the cation. Here, we applied two new scoring schemes enabling the user to manipulate the binding affinities of such proteins. We specifically designed a unique EF-hand loop capable of binding calcium with high affinity by altering five residues of the loop based on the scoring scheme. We worked on the N-terminal domain of Entamoeba histolytica calcium-binding protein1 (NtEhCaBP1), and used site-directed mutagenesis to incorporate the designed loop sequence into the second EF hand motif of this protein. The binding isotherms calculated using ITC calorimetry showed a ~500-fold greater association constant (Ka) for the mutant. The crystal structure of the mutant was also determined, and displayed more compact Ca2+-coordination spheres in both of its EF loops than did the structure of the wildtype protein, consistent with the greater calcium-binding affinities of the mutant. The NtEhCaBP1 mutant was also shown to form a hexamer rather than just a trimer, and this hexamer formation was attributed to the position of the last helix of the mutant having been changed as a result of the strong calcium coordination. Further dynamic correlation analysis revealed that the mutation in the second EF loop changed the entire residue network of the monomer, resulting in a stronger coordination of Ca2+.