@article{Silva2018,

title = {Structures and disulfide cross‐linking of de novo designed therapeutic mini‐proteins},

author = {Silva, Daniel-Adriano and Stewart, Lance and Lam, Kwok-Ho and Jin, Rongsheng and Baker, David},

url = {https://febs.onlinelibrary.wiley.com/doi/abs/10.1111/febs.14394

},

doi = {10.1111/febs.14394},

year  = {2018},

date = {2018-02-01},

journal = {FEBS Journal},

volume = {285},

number = {10},

pages = {1783-1785},

abstract = {Recent advances in computational protein design now enable the massively parallel de novo design and experimental characterization of small hyperstable binding proteins with potential therapeutic activity. By providing experimental feedback on tens of thousands of designed proteins, the design-build-test-learn pipeline provides a unique opportunity to systematically improve our understanding of protein folding and binding. Here, we review the structures of mini-protein binders in complex with Influenza hemagglutinin and Bot toxin, and illustrate in the case of disulfide bond placement how analysis of the large datasets of computational models and experimental data can be used to identify determinants of folding and binding.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chevalier2017,

title = {Massively parallel de novo protein design for targeted therapeutics},

author = {Aaron Chevalier* and Daniel-Adriano Silva* and Gabriel J. Rocklin* and Derrick R. Hicks and Renan Vergara and Patience Murapa and Steffen M. Bernard and Lu Zhang and Kwok-Ho Lam and Guorui Yao and Christopher D. Bahl and Shin-Ichiro Miyashita and Inna Goreshnik and 	James T. Fuller and	Merika T. Koday and Cody M. Jenkins and Tom Colvin and Lauren Carter and Alan Bohn and Cassie M. Bryan and D. Alejandro Fernández-Velasco and Lance Stewart and Min Dong and Xuhui Huang and Rongsheng Jin and Ian A. Wilson and Deborah H. Fuller and David Baker	},

url = {https://www.nature.com/nature/journal/v550/n7674/full/nature23912.html

https://www.bakerlab.org/wp-content/uploads/2017/12/Nature_Chevalier_etal_2017.pdf},

doi = {10.1038/nature23912},

issn = {0028-0836},

year  = {2017},

date = {2017-10-05},

journal = {Nature},

volume = {550},

number = {7674},

pages = {74-79},

abstract = {De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37–43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{626,

title = {Immobilizing affinity proteins to nitrocellulose: a toolbox for paper-based assay developers.},

author = { Carly A Holstein and Aaron Chevalier and Steven Bennett and Caitlin E Anderson and Karen Keniston and Cathryn Olsen and Bing Li and Brian Bales and David R Moore and Elain Fu and David Baker and Paul Yager},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Holstien_Anal_Bioanal_Chem_2015.pdf},

doi = {10.1007/s00216-015-9052-0},

issn = {1618-2650},

year  = {2015},

date = {2015-10-01},

journal = {Analytical and bioanalytical chemistry},

abstract = {To enable enhanced paper-based diagnostics with improved detection capabilities, new methods are needed to immobilize affinity reagents to porous substrates, especially for capture molecules other than IgG. To this end, we have developed and characterized three novel methods for immobilizing protein-based affinity reagents to nitrocellulose membranes. We have demonstrated these methods using recombinant affinity proteins for the influenza surface protein hemagglutinin, leveraging the customizability of these recombinant "flu binders" for the design of features for immobilization. The three approaches shown are: (1) covalent attachment of thiolated affinity protein to an epoxide-functionalized nitrocellulose membrane, (2) attachment of biotinylated affinity protein through a nitrocellulose-binding streptavidin anchor protein, and (3) fusion of affinity protein to a novel nitrocellulose-binding anchor protein for direct coupling and immobilization. We also characterized the use of direct adsorption for the flu binders, as a point of comparison and motivation for these novel methods. Finally, we demonstrated that these novel methods can provide improved performance to an influenza hemagglutinin assay, compared to a traditional antibody-based capture system. Taken together, this work advances the toolkit available for the development of next-generation paper-based diagnostics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{505,

title = {Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.},

author = { Rocco Moretti and Sarel J Fleishman and Rudi Agius and Mieczyslaw Torchala and Paul A Bates and Panagiotis L Kastritis and Jo~ao P G L M Rodrigues and Mika"el Trellet and Alexandre M J J Bonvin and Meng Cui and Marianne Rooman and Dimitri Gillis and Yves Dehouck and Iain Moal and Miguel Romero-Durana and Laura Perez-Cano and Chiara Pallara and Brian Jimenez and Juan Fernandez-Recio and Samuel Flores and Michael Pacella and Krishna Praneeth Kilambi and Jeffrey J Gray and Petr Popov and Sergei Grudinin and Juan Esquivel-Rodr'iguez and Daisuke Kihara and Nan Zhao and Dmitry Korkin and Xiaolei Zhu and Omar N A Demerdash and Julie C Mitchell and Eiji Kanamori and Yuko Tsuchiya and Haruki Nakamura and Hasup Lee and Hahnbeom Park and Chaok Seok and Jamica Sarmiento and Shide Liang and Shusuke Teraguchi and Daron M Standley and Hiromitsu Shimoyama and Genki Terashi and Mayuko Takeda-Shitaka and Mitsuo Iwadate and Hideaki Umeyama and Dmitri Beglov and David R Hall and Dima Kozakov and Sandor Vajda and Brian G Pierce and Howook Hwang and Thom Vreven and Zhiping Weng and Yangyu Huang and Haotian Li and Xiufeng Yang and Xiaofeng Ji and Shiyong Liu and Yi Xiao and Martin Zacharias and Sanbo Qin and Huan-Xiang Zhou and Sheng-You Huang and Xiaoqin Zou and Sameer Velankar and Jo"el Janin and Shoshana J Wodak and David Baker},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Moretti_Proteins_2013.pdf},

doi = {10.1002/prot.24356},

issn = {1097-0134},

year  = {2013},

date = {2013-11-01},

journal = {Proteins},

volume = {81},

pages = {1980-7},

abstract = {Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{480,

title = {Computational design of ligand-binding proteins with high affinity and selectivity},

author = { Christine E Tinberg and Sagar D Khare and Jiayi Dou and Lindsey Doyle and Jorgen W Nelson and Alberto Schena and Wojciech Jankowski and Charalampos G Kalodimos and Kai Johnsson and Barry L Stoddard and David Baker},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Tinberg13K.pdf},

doi = {10.1038/nature12443},

issn = {1476-4687},

year  = {2013},

date = {2013-09-01},

journal = {Nature},

volume = {501},

pages = {212-6},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{474,

title = {Computational design of novel protein binders and experimental affinity maturation},

author = { Timothy A Whitehead and David Baker and Sarel J Fleishman},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Whitehead_MethEnzymology_13V.pdf},

doi = {10.1016/B978-0-12-394292-0.00001-1},

issn = {1557-7988},

year  = {2013},

date = {2013-00-01},

journal = {Methods in enzymology},

volume = {523},

pages = {1-19},

abstract = {Computational design of novel protein binders has recently emerged as a useful technique to study biomolecular recognition and generate molecules for use in biotechnology, research, and biomedicine. Current limitations in computational design methodology have led to the adoption of high-throughput screening and affinity maturation techniques to diagnose modeling inaccuracies and generate high activity binders. Here, we scrutinize this combination of computational and experimental aspects and propose areas for future methodological improvements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{592,

title = {Hotspot-centric de novo design of protein binders},

author = { Sarel J Fleishman and Jacob E Corn and Eva-Maria Strauch and Timothy A Whitehead and John Karanicolas and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2018/06/1-s2.0-S0022283611009909-main.pdf

https://www.sciencedirect.com/science/article/pii/S0022283611009909?via%3Dihub},

doi = {10.1016/j.jmb.2011.09.001},

issn = {1089-8638},

year  = {2011},

date = {2011-11-01},

journal = {Journal of molecular biology},

volume = {413},

pages = {1047-62},

abstract = {Protein-protein interactions play critical roles in biology, and computational design of interactions could be useful in a range of applications. We describe in detail a general approach to de novo design of protein interactions based on computed, energetically optimized interaction hotspots, which was recently used to produce high-affinity binders of influenza hemagglutinin. We present several alternative approaches to identify and build the key hotspot interactions within both core secondary structural elements and variable loop regions and evaluate the methodtextquoterights performance in natural-interface recapitulation. We show that the method generates binding surfaces that are more conformationally restricted than previous design methods, reducing opportunities for off-target interactions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}