Computational Design of a Photocontrolled Cytosine Deaminase

Blacklock, Kristin; Yachnin, Brahm J.; Woolley, G. Andrew; Khare, Sagar D.

doi:10.1021/jacs.7b08709

Cited by 26 publications

(25 citation statements)

References 25 publications

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“…Chemical incorporation of molecular photoswitches into a protein scaffold can also be efficiently supported with computational design, and has become a powerful tool in protein engineering . A photoresponsive yeast cytosine deaminase (yCD) with threefold activity photomodulation was recently designed by using algorithms from the Rosetta family (Figure ) . This methodology is likely to be extended to a variety of other enzymes.…”

Section: Photoswitches At the Interface Of Biology And Chemistrymentioning

confidence: 99%

Recent Implementations of Molecular Photoswitches into Smart Materials and Biological Systems

Pianowski

2019

Chemistry A European J

286

226

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Light is a nearly ideal stimulus for molecular systems. It delivers information encoded in the form of wavelengths and their intensities with high precision in space and time. Light is a mild trigger that does not permanently contaminate targeted samples. Its energy can be reversibly transformed into molecular motion, polarity, or flexibility changes. This leads to sophisticated functions at the supramolecular and macroscopic levels, from light‐triggered nanomaterials to photocontrol over biological systems. New methods and molecular adapters of light are reported almost daily. Recently reported applications of photoresponsive systems, particularly azobenzenes, spiropyrans, diarylethenes, and indigoids, for smart materials and photocontrol of biological setups are described herein with the aim to demonstrate that the 21st century has become the Age of Enlightenment—“Le siècle des Lumières”—in molecular sciences.

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Section: Photoswitches At the Interface Of Biology And Chemistrymentioning

confidence: 99%

Recent Implementations of Molecular Photoswitches into Smart Materials and Biological Systems

Pianowski

2019

Chemistry A European J

286

226

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“…This strategy has been used to create photoactivatable proteins for controlling oligomerization and catalysis in living cells [165][166][167] . Protein design software can also be used to inform the placement of photoactivatable chemical groups in proteins to control conformational switching 168,169 .…”

Section: Design Of Protein Switchesmentioning

confidence: 99%

Advances in protein structure prediction and design

Kuhlman

Bradley

2019

Nat Rev Mol Cell Biol

654

433

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The stunning diversity of molecular functions performed by naturally evolved proteins is made possible by their finely tuned three-dimensional structures, which are in turn determined by their genetically encoded amino acid sequences. A predictive understanding of the relationship between amino acid sequence and protein structure would therefore open up new avenues, both for the prediction of function from genome sequence data and also for the rational engineering of novel protein functions through the design of amino acid sequences with specific structures. The past decade has seen dramatic improvements in our ability to predict and design the three-dimensional structures of proteins, with potentially far-reaching implications for medicine and our understanding of biology. New machine-learning algorithms have been developed that analyse the patterns of correlated mutations in protein families, to predict structurally interacting residues from sequence information alone 1,2. Improved protein energy functions 3,4 have for the first time made it possible to start with an approximate structure prediction model and move it closer to the experimentally determined structure by an energy-guided refinement process 5,6. Advances in protein conformational sampling and sequence optimization have permitted the design of novel protein structures and complexes 7,8 , some of which show promise as therapeutics 9. These advances in protein structure prediction and design have been fuelled by technological breakthroughs as well as by a rapid growth in biological databases. Protein-modelling algorithms (Box 1) are computationally demanding both to develop and to apply. The rapid increase in computing power available to researchers (both CPU-based and, increasingly, GPU-based computing power) facilitates rapid benchmarking of new algorithms and enables their application to larger molecules and molecular assemblies. At the same time, next-generation sequencing has fuelled a dramatic increase in protein sequence databases as genomic and metagenomic sequencing efforts have expanded 10. Advances in software and automation have increased the pace of experimental structure determination, speeding the growth of the database of experimentally determined protein structures (the Protein Data Bank (PDB)) 11 , which now contains close to 150,000 macromolecular structures. Deep-learning algorithms 12 that have revolutionized image processing and speech recognition are now being adopted by protein modellers seeking to take advantage of these expanded sequence and structural databases. In this Review, we highlight a selection of recent breakthroughs that these technological advances have enabled. We describe current approaches to the prediction and design of protein structures, focusing primarily on template-free methods that do not require an

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“…scaffold was amplified from the helical design plasmids by PCR (Table S7, primers [21][22]. The sixteen designs were ordered as gBlocks (Integrated DNA Technologies Inc.) and inserted into this vector using Gibson assembly.…”

Section: Preparation Of Individual Pro-cpg2 Design Constructs the Vementioning

confidence: 99%

“…Nonetheless, challenges in limiting the off-target activity of the circulating enzyme have precluded its widespread clinical adoption, and speak to the utility of a tissue-activatable form of DEPT enzymes. While we previously explored such a strategy by designing a light-activatable enzyme, 22 a protocol that exploits the natural properties of tumors to express elevated levels of highly specific proteases would be more desirable from a clinical standpoint. Designed activatable forms of CPG2 which respond to tissue-specific proteases could therefore be broadly useful therapeutic reagents for both delivering and ablating toxicity in a tissue-specific manner.…”

Section: Introductionmentioning

confidence: 99%

Massively parallel, computationally-guided design of a pro-enzyme

Yachnin

Azouz

White

et al. 2021

Preprint

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The ability to confine the activity of a protein to a specific microenvironment by design would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as pro-enzymes that are activated by proteolysis, it has been a difficult challenge to effectively re-design any chosen enzyme to be similarly stimulus-responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for pro-enzyme design. As a model system, we employ CPG2, a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug (methotrexate) toxicity. Our designed pro-enzymes are inhibited up to fivefold, and their activity is restored following incubation with specific proteases expressed by various tumor cell types. Pro-enzymes exhibit significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization of pro-CPG2 enzymes provides insights into the mechanisms associated with pro-domain inhibition. The described methodology is general and should enable the design of a variety of pro-proteins for precise spatial regulation of their functions.

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Computational Design of a Photocontrolled Cytosine Deaminase

Cited by 26 publications

References 25 publications

Recent Implementations of Molecular Photoswitches into Smart Materials and Biological Systems

Recent Implementations of Molecular Photoswitches into Smart Materials and Biological Systems

Advances in protein structure prediction and design

Massively parallel, computationally-guided design of a pro-enzyme

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