233 related articles for article (PubMed ID: 34593817)
1. ECNet is an evolutionary context-integrated deep learning framework for protein engineering.
Luo Y; Jiang G; Yu T; Liu Y; Vo L; Ding H; Su Y; Qian WW; Zhao H; Peng J
Nat Commun; 2021 Sep; 12(1):5743. PubMed ID: 34593817
[TBL] [Abstract][Full Text] [Related]
2. Low-N protein engineering with data-efficient deep learning.
Biswas S; Khimulya G; Alley EC; Esvelt KM; Church GM
Nat Methods; 2021 Apr; 18(4):389-396. PubMed ID: 33828272
[TBL] [Abstract][Full Text] [Related]
3. SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering.
Li M; Kang L; Xiong Y; Wang YG; Fan G; Tan P; Hong L
J Cheminform; 2023 Feb; 15(1):12. PubMed ID: 36737798
[TBL] [Abstract][Full Text] [Related]
4. Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
Guthrie VB; Allen J; Camps M; Karchin R
PLoS Comput Biol; 2011 Sep; 7(9):e1002184. PubMed ID: 21966264
[TBL] [Abstract][Full Text] [Related]
5. An experimentally informed evolutionary model improves phylogenetic fit to divergent lactamase homologs.
Bloom JD
Mol Biol Evol; 2014 Oct; 31(10):2753-69. PubMed ID: 25063439
[TBL] [Abstract][Full Text] [Related]
6. UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution.
Zurek PJ; Knyphausen P; Neufeld K; Pushpanath A; Hollfelder F
Nat Commun; 2020 Nov; 11(1):6023. PubMed ID: 33243970
[TBL] [Abstract][Full Text] [Related]
7. Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.
Qu Y; Niu Z; Ding Q; Zhao T; Kong T; Bai B; Ma J; Zhao Y; Zheng J
Int J Mol Sci; 2023 Nov; 24(22):. PubMed ID: 38003686
[TBL] [Abstract][Full Text] [Related]
8. Triplet nucleotide removal at random positions in a target gene: the tolerance of TEM-1 beta-lactamase to an amino acid deletion.
Jones DD
Nucleic Acids Res; 2005 May; 33(9):e80. PubMed ID: 15897323
[TBL] [Abstract][Full Text] [Related]
9. Learning Strategies in Protein Directed Evolution.
Cadet XF; Gelly JC; van Noord A; Cadet F; Acevedo-Rocha CG
Methods Mol Biol; 2022; 2461():225-275. PubMed ID: 35727454
[TBL] [Abstract][Full Text] [Related]
10. Selection and characterization of beta-lactam-beta-lactamase inactivator-resistant mutants following PCR mutagenesis of the TEM-1 beta-lactamase gene.
Vakulenko SB; Geryk B; Kotra LP; Mobashery S; Lerner SA
Antimicrob Agents Chemother; 1998 Jul; 42(7):1542-8. PubMed ID: 9660980
[TBL] [Abstract][Full Text] [Related]
11. Simultaneous enhancement of multiple functional properties using evolution-informed protein design.
Fram B; Su Y; Truebridge I; Riesselman AJ; Ingraham JB; Passera A; Napier E; Thadani NN; Lim S; Roberts K; Kaur G; Stiffler MA; Marks DS; Bahl CD; Khan AR; Sander C; Gauthier NP
Nat Commun; 2024 Jun; 15(1):5141. PubMed ID: 38902262
[TBL] [Abstract][Full Text] [Related]
12. Unified rational protein engineering with sequence-based deep representation learning.
Alley EC; Khimulya G; Biswas S; AlQuraishi M; Church GM
Nat Methods; 2019 Dec; 16(12):1315-1322. PubMed ID: 31636460
[TBL] [Abstract][Full Text] [Related]
13. Evolutionary engineering of a beta-Lactamase activity on a D-Ala D-Ala transpeptidase fold.
Peimbert M; Segovia L
Protein Eng; 2003 Jan; 16(1):27-35. PubMed ID: 12646690
[TBL] [Abstract][Full Text] [Related]
14. Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1.
Figliuzzi M; Jacquier H; Schug A; Tenaillon O; Weigt M
Mol Biol Evol; 2016 Jan; 33(1):268-80. PubMed ID: 26446903
[TBL] [Abstract][Full Text] [Related]
15. Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning.
Rotrattanadumrong R; Yokobayashi Y
Nat Commun; 2022 Aug; 13(1):4847. PubMed ID: 35977956
[TBL] [Abstract][Full Text] [Related]
16. Engineering proteinase K using machine learning and synthetic genes.
Liao J; Warmuth MK; Govindarajan S; Ness JE; Wang RP; Gustafsson C; Minshull J
BMC Biotechnol; 2007 Mar; 7():16. PubMed ID: 17386103
[TBL] [Abstract][Full Text] [Related]
17. Computational Protein Design with Deep Learning Neural Networks.
Wang J; Cao H; Zhang JZH; Qi Y
Sci Rep; 2018 Apr; 8(1):6349. PubMed ID: 29679026
[TBL] [Abstract][Full Text] [Related]
18. Neural networks to learn protein sequence-function relationships from deep mutational scanning data.
Gelman S; Fahlberg SA; Heinzelman P; Romero PA; Gitter A
Proc Natl Acad Sci U S A; 2021 Nov; 118(48):. PubMed ID: 34815338
[TBL] [Abstract][Full Text] [Related]
19. Capturing the mutational landscape of the beta-lactamase TEM-1.
Jacquier H; Birgy A; Le Nagard H; Mechulam Y; Schmitt E; Glodt J; Bercot B; Petit E; Poulain J; Barnaud G; Gros PA; Tenaillon O
Proc Natl Acad Sci U S A; 2013 Aug; 110(32):13067-72. PubMed ID: 23878237
[TBL] [Abstract][Full Text] [Related]
20. The Lactamase Engineering Database: a critical survey of TEM sequences in public databases.
Thai QK; Bös F; Pleiss J
BMC Genomics; 2009 Aug; 10():390. PubMed ID: 19698099
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]