Dataset For Article: "Massively Parallel De Novo Protein Design For Targeted Therapeutics",  Doi: 10.1038/Nature23912

"Massively parallel de novo protein design for targeted therapeutics" DOI: 10.1038/nature23912 Supplementary Information. Archive of designs, Rosetta metrics and experimental results. Authors: Aaron Chevalier*, Daniel-Adriano Silva*, Gabriel J. Rocklin*, Derrick R. Hicks, Renan Vergara, Patience Murapa, Steffen M. Bernard, Lu Zhang, Kwok-ho Lam, Guorui Yao, Christopher D. Bahl, Shin-ichiro Miyashita, Inna Goreshnik, James T. Fuller, Merika T. Koday, Cody Jenkins, Tom Colvin, Lauren Carter, Alan Bohn, Cassie M. Bryan, D. Alejandro Fernández-Velasco, Lance Stewart, Min Dong, Xuhui huang, Rongsheng Jin, Ian A. Wilson, Deborah H. Fuller & David Baker *These authors contributed equally to this work. Correspondence to: dabaker@uw.edu Dataset Compiled by D-A.S. Date: 13/Sep/2017

Related Organizations

University of Mary
United States

Keywords

De novo, High-throughput, Non-immunogenic, Therapeutics, Protein design

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	1
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average