scholarly journals Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

2018 ◽  
Vol 7 (9) ◽  
pp. 2014-2022 ◽  
Author(s):  
Yutaka Saito ◽  
Misaki Oikawa ◽  
Hikaru Nakazawa ◽  
Teppei Niide ◽  
Tomoshi Kameda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Directed Evolution ◽  
Fluorescent Proteins

Directed Evolution of a Selective and Sensitive Serotonin Biosensor Via Machine Learning

2019 ◽  
Author(s):  
Elizabeth Unger ◽  
Jacob Pearson Keller ◽  
Michael Altermatt ◽  
Ruqiang Liang ◽  
Zi Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Directed Evolution

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.

scholarly journals Bright split red fluorescent proteins for the visualization of endogenous proteins and synapses

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Siyu Feng ◽  
Aruna Varshney ◽  
Doris Coto Villa ◽  
Cyrus Modavi ◽  
John Kohler ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Gene Editing ◽  
Fluorescent Proteins ◽  
Protein Assembly ◽  
Scaffolding Protein ◽  
Fluorescence Signal ◽  
Cell Contacts ◽  
Neuronal Synapses ◽  
Red Fluorescent Proteins ◽  
Endogenous Proteins

Abstract Self-associating split fluorescent proteins (FPs) are split FPs whose two fragments spontaneously associate to form a functional FP. They have been widely used for labeling proteins, scaffolding protein assembly and detecting cell-cell contacts. Recently developments have expanded the palette of self-associating split FPs beyond the original split GFP1-10/11. However, these new ones have suffered from suboptimal fluorescence signal after complementation. Here, by investigating the complementation process, we have demonstrated two approaches to improve split FPs: assistance through SpyTag/SpyCatcher interaction and directed evolution. The latter has yielded two split sfCherry3 variants with substantially enhanced overall brightness, facilitating the tagging of endogenous proteins by gene editing. Based on sfCherry3, we have further developed a new red-colored trans-synaptic marker called Neuroligin-1 sfCherry3 Linker Across Synaptic Partners (NLG-1 CLASP) for multiplexed visualization of neuronal synapses in living C. elegans, demonstrating its broad applications.

scholarly journals Two-photon directed evolution of green fluorescent proteins

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Caleb R. Stoltzfus ◽  
Lauren M. Barnett ◽  
Mikhail Drobizhev ◽  
Geoffrey Wicks ◽  
Alexander Mikhaylov ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Fluorescent Proteins ◽  
Green Fluorescent Proteins ◽  
Two Photon ◽  
Green Fluorescent

scholarly journals Machine Learning-Assisted Directed Evolution Navigates a Combinatorial Epistatic Fitness Landscape with Minimal Screening Burden

2020 ◽  
Author(s):  
Bruce J. Wittmann ◽  
Yisong Yue ◽  
Frances H. Arnold
Keyword(s):  
Machine Learning ◽  
Directed Evolution ◽  
Path Dependence ◽  
Fitness Landscape ◽  
Combinatorial Libraries ◽  
Single Step ◽  
Saturation Mutagenesis ◽  
Global Maximum ◽  
Training Procedure ◽  
Greedy Optimization

AbstractDue to screening limitations, in directed evolution (DE) of proteins it is rarely feasible to fully evaluate combinatorial mutant libraries made by mutagenesis at multiple sites. Instead, DE often involves a single-step greedy optimization in which the mutation in the highest-fitness variant identified in each round of single-site mutagenesis is fixed. However, because the effects of a mutation can depend on the presence or absence of other mutations, the efficiency and effectiveness of a single-step greedy walk is influenced by both the starting variant and the order in which beneficial mutations are identified—the process is path-dependent. We recently demonstrated a path-independent machine learning-assisted approach to directed evolution (MLDE) that allows in silico screening of full combinatorial libraries made by simultaneous saturation mutagenesis, thus explicitly capturing the effects of cooperative mutations and bypassing the path-dependence that can limit greedy optimization. Here, we thoroughly investigate and optimize an MLDE workflow by testing a number of design considerations of the MLDE pipeline. Specifically, we (1) test the effects of different encoding strategies on MLDE efficiency, (2) integrate new models and a training procedure more amenable to protein engineering tasks, and (3) incorporate training set design strategies to avoid information-poor low-fitness protein variants (“holes”) in the training data. When applied to an epistatic, hole-filled, four-site combinatorial fitness landscape of protein G domain B1 (GB1), the resulting focused training MLDE (ftMLDE) protocol achieved the global fitness maximum up to 92% of the time at a total screening burden of 470 variants. In contrast, minimal-screening-burden single-step greedy optimization over the GB1 fitness landscape reached the global maximum just 1.2% of the time; ftMLDE matching this minimal screening burden (80 total variants) achieved the global optimum up to 9.6% of the time with a 49% higher expected maximum fitness achieved. To facilitate further development of MLDE, we present the MLDE software package (https://github.com/fhalab/MLDE), which is designed for use by protein engineers without computational or machine learning expertise.

scholarly journals Improved yellow-green split fluorescent proteins for protein labeling and signal amplification

2020 ◽  
Author(s):  
Shuqin Zhou ◽  
Siyu Feng ◽  
David Brown ◽  
Bo Huang
Keyword(s):  
Directed Evolution ◽  
Signal Amplification ◽  
Mammalian Cells ◽  
Rational Design ◽  
Fluorescent Proteins ◽  
Protein Labeling ◽  
Small Fragment ◽  
Wide Range ◽  
Split System ◽  
Low Expression

AbstractThe flexibility and versatility of self-complementing split fluorescent proteins (FPs) have enabled a wide range of applications. In particular, the FP1-10/11 split system contains a small fragment that facilitates efficient generation of endogenous-tagged cell lines and animals as well as signal amplification using tandem FP11 tags. To improve the FP1-10/11 toolbox we previously developed, here we used a combination of directed evolution and rational design approaches, resulting in two mNeonGreen (mNG)-based split FPs (mNG3A1-10/11 and mNG3K1-10/11) and one mClover-based split FP (CloGFP1-10/11). mNG3A1-10/11 and mNG3K1-10/11 not only enhanced the complementation efficiency at low expression levels, but also allowed us to demonstrate signal amplification using tandem mNG211 fragments in mammalian cells.

scholarly journals Can Machine Learning Guide Directed Evolution of Functional Proteins

2020 ◽  
Vol 118 (3) ◽  
pp. 339a
Author(s):  
Yutaka Saito ◽  
Misaki Oikawa ◽  
Hikaru Nakazawa ◽  
Takumi Sato ◽  
Tomoshi Kameda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Directed Evolution ◽  
Learning Guide
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