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of 6
pro vyhledávání: '"Kadina E Johnston"'
DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering
Autor:
Jason Yang, Julie Ducharme, Kadina E. Johnston, Francesca-Zhoufan Li, Yisong Yue, Frances H. Arnold
With advances in machine learning (ML)-assisted protein engineering, models based on data, biophysics, and natural evolution are being used to propose informed libraries of protein variants to explore. Synthesizing these libraries for experimental sc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0aaeb784af60f4f78d28035141d22ab
https://doi.org/10.1101/2023.05.11.540424
https://doi.org/10.1101/2023.05.11.540424
Publikováno v:
ACS Synthetic Biology. 11:1313-1324
Widespread availability of protein sequence-fitness data would revolutionize both our biochemical understanding of proteins and our ability to engineer them. Unfortunately, even though thousands of protein variants are generated and evaluated for fit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c1c8397e413f90d6917d5a0c897003a
https://doi.org/10.1021/acssynbio.1c00592
https://doi.org/10.1021/acssynbio.1c00592
Autor:
Jody Mou, Bruce J. Wittmann, Samuel Goldman, Nicholas Bhattacharya, Kevin K. Yang, Ali Madani, Kadina E Johnston, Christian Dallago
Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Critical to its use in designing proteins with desired properties, machine learning models must capture the protein se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a06d70574542d276137a45c73aea873
https://doi.org/10.1101/2021.11.09.467890
https://doi.org/10.1101/2021.11.09.467890
Autor:
Kadina E Johnston, Yuyuan Wang, Nisakorn Yodsanit, Ruosen Xie, Mingzhou Ye, Shaoqin Gong, Yi Zhao
Publikováno v:
ACS Applied Materials & Interfaces. 11:42865-42872
A double-network nanogel, composed of a silane-cross-linked polyethylenimine (PEI) network (i.e., PEI-S) and a pH-responsive poly(2-(hexamethyleneimino) ethyl methacrylate) (PC7A) polymer, was developed for efficient DNA transfection. The chemical cr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14f3c7c0586c39e4d2b128a25e37fd85
https://doi.org/10.1021/acsami.9b12492
https://doi.org/10.1021/acsami.9b12492
Publikováno v:
Current opinion in structural biology. 69
Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::944110608a0b0d02848af3bf4e7f08bc
https://pubmed.ncbi.nlm.nih.gov/33647531
https://pubmed.ncbi.nlm.nih.gov/33647531
Autor:
Sophia M. Sdao, Zach J. Simmons, Matthew J. Merrins, Jack T. Postlewaite, Ethan T. Nethery, Kadina E Johnston, Kaitlyn Gabardi, Jeremy D. Rogers, Benjamin A. Ratliff, Angela M. Kita, John W. Rupel
Publikováno v:
The Biophysicist. 1
Advances in fluorescent biosensors allow researchers to spatiotemporally monitor a diversity of biochemical reactions and secondary messengers. However, commercial microscopes for the specific application of Förster Resonance Energy Transfer (FRET)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50eb61b78635347a0994964e71dccd9b
https://doi.org/10.35459/tbp.2019.000117
https://doi.org/10.35459/tbp.2019.000117