Computational Design of Ligand Binding Proteins with High Affinity and Selectivity

By C. Tinberg, Sagar D. Khare, Jiayi Dou, Lindsey Doyle, Jorgen W. Nelson, Alberto Schena, W. Jankowski, Charalampos G. Kalodimos, Kai Johnsson, Barry L. Stoddard and 1 more. 2013

425 citations recorded by the source corpus.

The ability to design proteins with high affinity and selectivity for any given small molecule is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition. Attempts to rationally design ligand-binding proteins have met with little success, however, and the computational design of protein–small-molecule interfaces remains an unsolved problem. Current approaches for designing ligand-binding proteins for medical and biotechnological uses rely on raising antibodies against a target antigen in immunized animals and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand, neither of which allows complete control over the interactions involved in binding. Here we describe a general computational method for designing pre-organized and shape complementary small-molecule-binding sites, and use it to generate protein binders to the steroid digoxigenin (DIG). Of seventeen experimentally characterized designs, two bind DIG; the model of the higher affinity binder has the most energetically favourable and pre-organized interface in the design set. A comprehensive binding-fitness landscape of this design, generated by library selections and deep sequencing, was used to optimize its binding affinity to a picomolar level, and X-ray co-crystal structures of two variants show atomic-level agreement with the corresponding computational models. The optimized binder is selective for DIG over the related steroids digitoxigenin, progesterone and β-oestradiol, and this steroid binding preference can be reprogrammed by manipulation of explicitly designed hydrogen-bonding interactions. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics and diagnostics.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom (2023, neutral): “Previous attempts to design digoxigenin-binding proteinsreliedonprotein scaffolds with experimentally determined structures and prespecified binding pockets and interacting motifs( 54 ).”
Protein sequence design with a learned potential (2022, background): “omputational protein design has emerged as a powerful tool for rational protein design, enabling signi ﬁ cant achievements in the engineering of therapeutics 1 – 3 , 4 – 6 , enzymes 7,8 , and more 9 – 11 .”
The coming of age of de novo protein design (2016, background): “Such efforts have yielded enzymes that have attained high catalytic efficiencies through directed evolution 67–73 , inhibitors of protein–protein interactions that can protect animals from viral infection 74 and small-molecule binding proteins that can be incorporated into in vivo biosensors 75–77 .”
Protein sequence design with a learned potential (2020, background): “These advances have enabled signiﬁcant achievements in the engineering of therapeutics [13, 14, 15], biosensors [16, 17, 18], enzymes [19, 20, 21, 22], and more [23, 24, 25, 26, 27].”
Designing Peptides on a Quantum Computer (2019, background): “…design new protein topologies ( 2 , 3 ) , large macromolecular assemblies ( 4 – 7 ) , proteins with the ability to bind to toxic small molecules ( 8 , 9 ) or to other proteins of therapeutic interest ( 10 , 11 ) , and enzymes able to catalyze reactions that no known natural enzyme can catalyze (…”
Computational design of environmental sensors for the potent opioid fentanyl (2017, methodology): “Unlike previous computationally designed ligand binders (Tinberg et al., 2013), the method that generated Fen49 did not involve any manual intervention, and hence could be rapidly applied to many ligands.”
Achievements and Challenges in Computational Protein Design. (2017, background): “In 2013 Baker and coworkers applied CPD for the de novo design of selective binders to the steroid digoxigenin (DIG), an example of a small molecule to which a protein binder can be designed [110].”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Highly stable helical bundles (58) 9 5 5 5 3 Ideal α-β proteins (30) 54 45 32 17 16 5 Ideal α-β proteins (61) 72 64 47 39 17 6 Proteins with curved β-sheets (62) 66 58 53 54 25 8 Proteins with the jelly roll topology (31) 19 16 2 2 2 1 Novel helical folds (36) 11 8 4 4 2 FoldIt player designed prote”
Measuring the activity of protein variants on a large scale using deep mutational scanning (2014, methodology): “For example, protein display has been used to conduct deep mutational scans of protein-peptide 4,8–11 , protein-protein 12–14 and protein-small molecule 15 interactions.”
Automating human intuition for protein design (2014, background): “4; design of a de novo galactosidase (GA and GF prefixes; Bjelic unpublished); design of a binder for the fluorophore 3,5-difluoro-4-hydroxybenzylidene imidazolinone (DFHBI; MB prefix; Bick unpublished); design of a beta-lactamase (BL prefix; Khersonsky unpublished); and design of de novo…”