scholarly journals Directed Evolution Reveals the Functional Sequence Space of an Adenylation Domain Specificity Code

2019 ◽  
Vol 14 (9) ◽  
pp. 2044-2054 ◽  
Author(s):  
Kurt Throckmorton ◽  
Vladimir Vinnik ◽  
Ratul Chowdhury ◽  
Taylor Cook ◽  
Marc G. Chevrette ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Sequence Space ◽  
Domain Specificity ◽  
Adenylation Domain ◽  
Functional Sequence

scholarly journals Directed Evolution of an Adenylation Domain Specificity Code

2018 ◽  
Vol 32 (S1) ◽  
Author(s):  
Vladimir Vinnik ◽  
Kurt Throckmorton ◽  
Taylor B. Cook ◽  
Brian F. Pfleger ◽  
Michael G. Thomas
Keyword(s):  
Directed Evolution ◽  
Domain Specificity ◽  
Adenylation Domain

scholarly journals Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

2015 ◽  
Vol 44 (5) ◽  
pp. 1172-1239 ◽  
Author(s):  
Andrew Currin ◽  
Neil Swainston ◽  
Philip J. Day ◽  
Douglas B. Kell
Keyword(s):  
Synthetic Biology ◽  
Directed Evolution ◽  
Sequence Space ◽  
Search Spaces

Improving enzymes by directed evolution requires the navigation of very large search spaces; we survey how to do this intelligently.

scholarly journals Machine-learning-guided library design cycle for directed evolution of enzymes: the effects of training data composition on sequence space exploration

2021 ◽  
Author(s):  
Yutaka Saito ◽  
Misaki Oikawa ◽  
Takumi Sato ◽  
Hikaru Nakazawa ◽  
Tomoyuki Ito ◽  
...  
Keyword(s):  
Machine Learning ◽  
Enzyme Activity ◽  
Directed Evolution ◽  
Sequence Space ◽  
Training Data ◽  
Sortase A ◽  
Library Design ◽  
Design Cycle ◽  
High Enzyme ◽  
Evolution Study

Machine learning (ML) is becoming an attractive tool in mutagenesis-based protein engineering because of its ability to design a variant library containing proteins with a desired function. However, it remains unclear how ML guides directed evolution in sequence space depending on the composition of training data. Here, we present a ML-guided directed evolution study of an enzyme to investigate the effects of a known "highly positive" variant (i.e., variant known to have high enzyme activity) in training data. We performed two separate series of ML-guided directed evolution of Sortase A with and without a known highly positive variant called 5M in training data. In each series, two rounds of ML were conducted: variants predicted by the first round were experimentally evaluated, and used as additional training data for the second-round prediction. The improvements in enzyme activity were comparable between the two series, both achieving enzyme activity 2.2-2.5 times higher than 5M. Intriguingly, the sequences of the improved variants were largely different between the two series, indicating that ML guided the directed evolution to the distinct regions of sequence space depending on the presence/absence of the highly positive variant in the training data. This suggests that the sequence diversity of improved variants can be expanded not only by conventional ML using the whole training data, but also by ML using a subset of the training data even when it lacks highly positive variants. In summary, this study demonstrates the importance of regulating the composition of training data in ML-guided directed evolution.

Introduction of a Non-Natural Amino Acid into a Nonribosomal Peptide Antibiotic by Modification of Adenylation Domain Specificity

2012 ◽  
Vol 51 (29) ◽  
pp. 7181-7184 ◽  
Author(s):  
Jenny Thirlway ◽  
Richard Lewis ◽  
Laura Nunns ◽  
Majid Al Nakeeb ◽  
Matthew Styles ◽  
...  
Keyword(s):  
Amino Acid ◽  
Domain Specificity ◽  
Peptide Antibiotic ◽  
Adenylation Domain ◽  
Natural Amino Acid ◽  
Nonribosomal Peptide

scholarly journals Site-Selective C-H Halogenation using Flavin-Dependent Halogenases Identified via Family-Wide Activity Profiling

2019 ◽  
Author(s):  
Brian F. Fisher ◽  
Harrison M. Snodgrass ◽  
Krysten A. Jones ◽  
Mary C. Andorfer ◽  
Jared C. Lewis
Keyword(s):  
Mass Spectrometry ◽  
Directed Evolution ◽  
High Throughput ◽  
Sequence Space ◽  
Late Stage ◽  
Narrow Range ◽  
Function Analysis ◽  
Complex Compounds ◽  
Time Savings ◽  
Site Selective

<p>Herein, we describe the use of a high-throughput mass spectrometry-based screen to evaluate a broad set of over one hundred putative FDH sequences drawn from throughout the FDH family. Halogenases with novel substrate scope and complementary regioselectivity on large, three-dimensionally complex compounds were identified. This effort involved far more extensive sequence-function analysis than has been accomplished using the relatively narrow range of FDHs characterized to date, providing a clearer picture of the regions in FDH sequence space that are most likely to contain enzymes suitable for halogenating small molecule substrates. The representative enzyme panel constructed in this study also provides a rapid means to identify FDHs for lead diversification via late-stage C-H functionalization. In many cases, these enzymes provide activities that required several rounds of directed evolution to accomplish in previous efforts, highlighting that this approach can achieve significant time savings for biocatalyst identification and provide advanced starting points for further evolution.</p>

scholarly journals A comprehensive search of functional sequence space using large mammalian display libraries created by gene editing

mAbs ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 884-898 ◽  
Author(s):  
Kothai Parthiban ◽  
Rajika L. Perera ◽  
Maheen Sattar ◽  
Yanchao Huang ◽  
Sophie Mayle ◽  
...  
Keyword(s):  
Sequence Space ◽  
Gene Editing ◽  
Functional Sequence ◽  
Comprehensive Search

Introduction of a Non-Natural Amino Acid into a Nonribosomal Peptide Antibiotic by Modification of Adenylation Domain Specificity

2012 ◽  
Vol 124 (29) ◽  
pp. 7293-7296 ◽  
Author(s):  
Jenny Thirlway ◽  
Richard Lewis ◽  
Laura Nunns ◽  
Majid Al Nakeeb ◽  
Matthew Styles ◽  
...  
Keyword(s):  
Amino Acid ◽  
Domain Specificity ◽  
Peptide Antibiotic ◽  
Adenylation Domain ◽  
Natural Amino Acid ◽  
Nonribosomal Peptide
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