@article{Crawshaw2021,

title = {Engineering an efficient and enantioselective enzyme for the Morita-Baylis-Hillman reaction},

author = {Crawshaw, Rebecca

and Crossley, Amy E.

and Johannissen, Linus

and Burke, Ashleigh J.

and Hay, Sam

and Levy, Colin

and Baker, David

and Lovelock, Sarah L.

and Green, Anthony P.},

url = {https://www.nature.com/articles/s41557-021-00833-9

https://www.bakerlab.org/wp-content/uploads/2022/01/Crawshaw_etal_NatChem_Engineering_enantioselective_enzyme_Morita-Baylis-Hillman_reaction.pdf},

doi = {10.1038/s41557-021-00833-9},

year  = {2021},

date = {2021-12-16},

journal = {Nature Chemistry},

abstract = {The combination of computational design and directed evolution could offer a general strategy to create enzymes with new functions. So far, this approach has delivered enzymes for a handful of model reactions. Here we show that new catalytic mechanisms can be engineered into proteins to accelerate more challenging chemical transformations. Evolutionary optimization of a primitive design afforded an efficient and enantioselective enzyme (BH32.14) for the Morita–Baylis–Hillman (MBH) reaction. BH32.14 is suitable for preparative-scale transformations, accepts a broad range of aldehyde and enone coupling partners and is able to promote selective monofunctionalizations of dialdehydes. Crystallographic, biochemical and computational studies reveal that BH32.14 operates via a sophisticated catalytic mechanism comprising a His23 nucleophile paired with a judiciously positioned Arg124. This catalytic arginine shuttles between conformational states to stabilize multiple oxyanion intermediates and serves as a genetically encoded surrogate of privileged bidentate hydrogen-bonding catalysts (for example, thioureas). This study demonstrates that elaborate catalytic devices can be built from scratch to promote demanding multi-step processes not observed in nature.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Quijano-Rubio2021,

title = {De novo design of modular and tunable protein biosensors},

author = {Quijano-Rubio, Alfredo

and Yeh, Hsien-Wei

and Park, Jooyoung

and Lee, Hansol

and Langan, Robert A.

and Boyken, Scott E.

and Lajoie, Marc J.

and Cao, Longxing

and Chow, Cameron M.

and Miranda, Marcos C.

and Wi, Jimin

and Hong, Hyo Jeong

and Stewart, Lance

and Oh, Byung-Ha

and Baker, David},

url = {https://www.nature.com/articles/s41586-021-03258-z, Nature

https://www.bakerlab.org/wp-content/uploads/2021/02/Rubio_et_al_Nature_COVID_LOCKR_sensors.pdf, Download PDF},

doi = {10.1038/s41586-021-03258-z},

year  = {2021},

date = {2021-01-27},

urldate = {2021-01-27},

journal = {Nature},

abstract = {Naturally occurring protein switches have been repurposed for developing novel biosensors and reporters for cellular and clinical applications1, but the number of such switches is limited, and engineering them is often challenging as each is different. Here, we show that a very general class of protein-based biosensors can be created by inverting the flow of information through de novo designed protein switches in which binding of a peptide key triggers biological outputs of interest2. The designed sensors are modular molecular devices with a closed dark state and an open luminescent state; binding of the analyte of interest drives switching from the closed to the open state. Because the sensor is based purely on thermodynamic coupling of analyte binding to sensor activation, only one target binding domain is required, which simplifies sensor design and allows direct readout in solution. We demonstrate the modularity of this platform by creating biosensors that, with little optimization, sensitively detect the anti-apoptosis protein Bcl-2, the IgG1 Fc domain, the Her2 receptor, and Botulinum neurotoxin B, as well as biosensors for cardiac Troponin I and an anti-Hepatitis B virus (HBV) antibody that achieve the sub-nanomolar sensitivity necessary to detect clinically relevant concentrations of these molecules. Given the current need for diagnostic tools for tracking COVID-193, we used the approach to design sensors of antibodies against SARS-CoV-2 protein epitopes and of the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein. The latter, which incorporates a de novo designed RBD binder4, has a limit of detection of 15 pM and a signal over background of over 50-fold. The modularity and sensitivity of the platform should enable the rapid construction of sensors for a wide range of analytes and highlights the power of de novo protein design to create multi-state protein systems with new and useful functions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{499,

title = {Computational design of a pH-sensitive IgG binding protein},

author = { Eva-Maria Strauch and Sarel J Fleishman and David Baker},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Strauch-1313605111_PNAS_13W.pdf},

issn = {1091-6490},

year  = {2013},

date = {2013-12-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

abstract = {Computational design provides the opportunity to program protein-protein interactions for desired applications. We used de novo protein interface design to generate a pH-dependent Fc domain binding protein that buries immunoglobulin G (IgG) His-433. Using next-generation sequencing of na"ive and selected pools of a library of design variants, we generated a molecular footprint of the designed binding surface, confirming the binding mode and guiding further optimization of the balance between affinity and pH sensitivity. In biolayer interferometry experiments, the optimized design binds IgG with a Kd of ~4 nM at pH 8.2, and approximately 500-fold more weakly at pH 5.5. The protein is extremely stable, heat-resistant and highly expressed in bacteria, and allows pH-based control of binding for IgG affinity purification and diagnostic devices.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}