Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Peter, C. St"'
A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing
Externí odkaz:
http://arxiv.org/abs/2212.09925
Autor:
John, Peter C. St., Phillips, Caleb, Kemper, Travis W., Wilson, A. Nolan, Crowley, Michael F., Nimlos, Mark R., Larsen, Ross E.
Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural netw
Externí odkaz:
http://arxiv.org/abs/1807.10363
Autor:
Chen Ling, George L. Peabody, Davinia Salvachúa, Young-Mo Kim, Colin M. Kneucker, Christopher H. Calvey, Michela A. Monninger, Nathalie Munoz Munoz, Brenton C. Poirier, Kelsey J. Ramirez, Peter C. St. John, Sean P. Woodworth, Jon K. Magnuson, Kristin E. Burnum-Johnson, Adam M. Guss, Christopher W. Johnson, Gregg T. Beckham
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-14 (2022)
Muconic acid is a platform chemical with wide industrial applicability. Here, the authors report efficient muconate production from glucose and xylose by engineered Pseudomonas putida strain using adaptive laboratory evolution, metabolic modeling, an
Externí odkaz:
https://doaj.org/article/b5842f1b194c4c5081ddd1bbb47e1529
Production of chemicals from engineered organisms in a batch culture involves an inherent trade-off between productivity, yield, and titer. Existing strategies for strain design typically focus on designing mutations that achieve the highest yield po
Externí odkaz:
http://arxiv.org/abs/1610.01114
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 19, Iss , Pp 214-225 (2021)
Microorganisms rely on protein interactions to transmit signals, react to stimuli, and grow. One of the best ways to understand these protein interactions is through structural characterization. However, in the past, structural knowledge was limited
Externí odkaz:
https://doaj.org/article/5d65b9f3d2894dba87869c1ff93bd4d2
In the mammalian suprachiasmatic nucleus (SCN), a population of noisy cell-autonomous oscillators synchronizes to generate robust circadian rhythms at the organism-level. Within these cells two isoforms of Cryptochrome, Cry1 and Cry2, participate in
Externí odkaz:
http://arxiv.org/abs/1411.4624
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
Bond dissociation enthalpies are key quantities in determining chemical reactivity, their computations with quantum mechanical methods being highly demanding. Here the authors develop a machine learning approach to calculate accurate dissociation ent
Externí odkaz:
https://doaj.org/article/964aa89eb79f42c7a32be1f33359d400
Publikováno v:
Frontiers in Bioengineering and Biotechnology, Vol 9 (2021)
Prior engineering of the ethanologen Zymomonas mobilis has enabled it to metabolize xylose and to produce 2,3-butanediol (2,3-BDO) as a dominant fermentation product. When co-fermenting with xylose, glucose is preferentially utilized, even though xyl
Externí odkaz:
https://doaj.org/article/9c2047d1d1574a8886668f072dd7f93b
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-3 (2020)
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Externí odkaz:
https://doaj.org/article/5a60048b206545b69205db7160c99178
Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks
Publikováno v:
JACS Au. 3:113-123
The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b093725f685bce5e7db7748c41008b97
https://doi.org/10.1021/jacsau.2c00540
https://doi.org/10.1021/jacsau.2c00540