Computationally Driven De Novo Design and Engineering of a β-D-Glucose Binding Protein

Abstract

De novo protein design, the computational development of proteins from the ground up, bypasses the evolutionary complexity of biological systems, simplifying studies on cellular pathways and proposing various applications. This study presents the development of a novel protein that binds to β-D-Glucose, a cyclic sugar in plant and animal tissues integral to glycolysis and metabolic pathways. Expanding upon a structurally stable beta-barrel scaffold, an iterative protocol was utilized to construct a robust binding pocket. Sidechain residues were modified through the ChimeraX protein editing software, which were then fed into AlphaFold3/Chai Discovery, an application that assesses the folding and viability of the protein, and LigandMPNN, a deep learning application that generates optimized protein sequences given a protein-ligand input. VMD (Visual Molecular Dynamics), which models the protein-ligand complex in solution over time, was then utilized for validation and further iteration. The final protein exhibited modest protein-ligand confidence in Chai Discovery with an ipTM (interface predicted template modeling score) of 0.74. Regarding molecular dynamic simulations, the protein RMSD (root mean square deviation) converged to 3.75A, implying overall protein instability, and determined 13 h-bonds, with at least 4-6 stable bonds with an occupancy of at least 16.47%. These data indicate that the protein-ligand complex was relatively stable at the expense of a compromised backbone structure. Further validation encompasses fluorescence titration and SPR (Surface Plasmon Resonance), an optical method to quantify protein-ligand kinetics and affinities. Ultimately, optimization of this novel β-D-Glucose-binding protein proposes numerous applications in biotechnology and studies on glucose metabolism and protein-ligand behavior.