title: Physics-based enzyme design: predicting binding affinity and catalytic activity.
abstract: Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications.
authors.affiliation: Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02140.
authors.forename: Sarah
authors.initials: S
authors.lastname: Sirin
authors.forename: David A
authors.initials: DA
authors.lastname: Pearlman
authors.forename: Woody
authors.initials: W
authors.lastname: Sherman
mesh_terms: D057205:Automation, Laboratory; D001426:Bacterial Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D001665:Binding Sites; D020134:Catalytic Domain; D030562:Databases, Protein; D019020:Directed Molecular Evolution / Q000379:methods*; D004735:Energy Transfer; D005903:Gliadin / Q000737:chemistry / Q000378:metabolism; D007555:Isoxazoles / Q000737:chemistry / Q000378:metabolism; D008958:Models, Molecular; D008968:Molecular Conformation; D056004:Molecular Dynamics Simulation; D050505:Mutant Proteins / Q000737:chemistry / Q000378:metabolism; D010825:Physics / Q000379:methods*; D015202:Protein Engineering / Q000379:methods*; D011550:Pseudomonas aeruginosa / Q000201:enzymology / Q000378:metabolism; D012697:Serine Endopeptidases / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D012738:Sex Hormone-Binding Globulin / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D012986:Software Validation; D012997:Solvents / Q000737:chemistry; D013379:Substrate Specificity; D013499:Surface Properties
chemical_list: D001426:Bacterial Proteins; D007555:Isoxazoles; D050505:Mutant Proteins; D012738:Sex Hormone-Binding Globulin; D012997:Solvents; D005903:Gliadin; D012697:Serine Endopeptidases
keywords: DIG-binder protein; Kemp eliminase (KE07); automated workflow; binding optimization; computational enzyme design; ranking; structure-based design; transition state optimization; α-gliadin peptidase