Engineering Genetically-Encoded Mineralization and Magnetism via Directed Evolution

Liu, Xueliang; Lopez, Paola A.; Giessen, Tobias W.; Giles, M. A.; Way, Jeffrey C.; Silver, Pamela A.

doi:10.1038/srep38019

Cited by 36 publications

(35 citation statements)

References 48 publications

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“…This results in the acquisition of mutated bacteria strains with significantly enhanced cellular magnetism, which, in turn, leads to improved physical attraction of ferritin‐expressing cells to magnets and augmented contrast for cellular MRI. [ 318 ] More recently, repeating segments of silaffin proteins derived from diatoms have been displayed on E. coli surfaces through genetic manipulation for regulating the synthesis of nanostructured TiO 2 anatase ( Figure ). The displayed surface proteins catalyze the hydrolysis of TiO 2 precursors through hydrogen bonding interaction on the cell surface.…”

Section: Applicationsmentioning

confidence: 99%

Microbe‐Mediated Extracellular and Intracellular Mineralization: Environmental, Industrial, and Biotechnological Applications

et al. 2020

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Bacteria play an important role in the calcification process of natural minerals and within organisms ( Table 1). It is Microbe-mediated mineralization is ubiquitous in nature, involving bacteria, fungi, viruses, and algae. These mineralization processes comprise calcification, silicification, and iron mineralization. The mechanisms for mineral formation include extracellular and intracellular biomineralization. The mineral precipitating capability of microbes is often harnessed for green synthesis of metal nanoparticles, which are relatively less toxic compared with those synthesized through physical or chemical methods. Microbe-mediated mineralization has important applications ranging from pollutant removal and nonreactive carriers, to other industrial and biomedical applications. Herein, the different types of microbe-mediated biomineralization that occur in nature, their mechanisms, as well as their applications are elucidated to create a backdrop for future research.

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Section: Applicationsmentioning

confidence: 99%

Microbe‐Mediated Extracellular and Intracellular Mineralization: Environmental, Industrial, and Biotechnological Applications

et al. 2020

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“…Three biological replicates in one experiment. Iron sensor functionality has been verified with controls and other protein sequences 8 , replicated more than three times in lab.) (c) Several candidates also gave rise to increased cellular magnetism (magnetic column retention) due to possible iron biomineralization compared to uninduced cells (3 biological replicates in one experiment.)…”

Section: Figure 4 Experimental Validation Of Predicted Iron Sequestermentioning

confidence: 88%

“…The fluorescence measured were normalized to culture density by dividing by OD600 measured by the same platereader. The increase in normalized fluorescence of the cells was plotted against the increase in bipyridine (or consequent decrease in free iron) and modeled to determine the conversion between fluorescence reading and free iron concentration 8 .…”

Section: Iron Level Characterization By Genetic Sensormentioning

confidence: 99%

Deep Recurrent Neural Network for Protein Function Prediction from Sequence

Liu

2017

Preprint

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As highthroughput biological sequencing becomes faster and cheaper, the need to extract useful information from sequencing becomes ever more paramount, often limited by lowthroughput experimental characterizations. For proteins, accurate prediction of their functions directly from their primary aminoacid sequences has been a long standing challenge. Here, machine learning using artificial recurrent neural networks (RNN) was applied towards classification of protein function directly from primary sequence without sequence alignment, heuristic scoring or feature engineering.The RNN models containing longshorttermmemory (LSTM) units trained on public, annotated datasets from UniProt achieved high performance for inclass prediction of four important protein functions tested, particularly compared to other machine learning algorithms using sequencederived protein features. RNN models were used also for outofclass predictions of phylogenetically distinct protein families with similar functions, including proteins of the CRISPRassociated nuclease, ferritinlike iron storage and cytochrome P450 families. Applying the trained RNN models on the partially unannotated UniRef100 database predicted not only candidates validated by existing annotations but also currently unannotated sequences. Some RNN predictions for the ferritinlike iron sequestering function were experimentally validated, even though their sequences differ significantly from known, characterized proteins and from each other and cannot be easily predicted using popular bioinformatics methods. As sequencing and experimental characterization data increases rapidly, the machinelearning approach based on RNN could be useful for discovery and prediction of homologues for a wide range of protein functions.peer-reviewed)

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“…As we learn more about the chemical composition and crystal structure of iron oxide nanoparticles generated in ferritin and how these interact with magnetic fields, it should be possible to engineer a wider range and better tools for cell regulation. A number of studies have already used directed evolution to generate mutant ferritins, producing nanoparticles that confer increased cellular magnetization (Matsumoto et al 2015;Liu et al 2016). Similarly, modifications to the multimodal channels binding ferritin and testing additional ion channels should extend the available tools for cell modulation.…”

Section: Discussionmentioning

confidence: 99%

Electromagnetic Regulation of Cell Activity

Stanley

Friedman

2018

Cold Spring Harb Perspect Med

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The ability to observe the effects of rapidly and reversibly regulating cell activity in targeted cell populations has provided numerous physiologic insights. Over the last decade, a wide range of technologies have emerged for regulating cellular activity using optical, chemical, and, more recently, electromagnetic modalities. Electromagnetic fields can freely penetrate cells and tissue and their energy can be absorbed by metal particles. When released, the absorbed energy can in turn gate endogenous or engineered receptors and ion channels to regulate cell activity. In this manner, electromagnetic fields acting on external nanoparticles have been used to exert mechanical forces on cell membranes and organelles to generate heat and interact with thermally activated proteins or to induce receptor aggregation and intracellular signaling. More recently, technologies using genetically encoded nanoparticles composed of the iron storage protein, ferritin, have been used for targeted, temporal control of cell activity in vitro and in vivo. These tools provide a means for noninvasively modulating gene expression, intracellular organelles, such as endosomes, and whole-cell activity both in vitro and in freely moving animals. The use of magnetic fields interacting with external or genetically encoded nanoparticles thus provides a rapid noninvasive means for regulating cell activity.

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Engineering Genetically-Encoded Mineralization and Magnetism via Directed Evolution

Cited by 36 publications

References 48 publications

Microbe‐Mediated Extracellular and Intracellular Mineralization: Environmental, Industrial, and Biotechnological Applications

Microbe‐Mediated Extracellular and Intracellular Mineralization: Environmental, Industrial, and Biotechnological Applications

Deep Recurrent Neural Network for Protein Function Prediction from Sequence

Electromagnetic Regulation of Cell Activity

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