De novo metalloprotein design

Chalkley, Matthew J.; Mann, Samuel I.; DeGrado, William F.

doi:10.1038/s41570-021-00339-5

Cited by 73 publications

(50 citation statements)

References 252 publications

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“…This approach has become possible with the advent of machine learning and deep learning [31]. AlphaFold has been a hallmark success, but the application of machine learning in protein studies was already underway before it.…”

Section: Structural Analysismentioning

confidence: 99%

Machine Learning Approaches for Metalloproteins

Wang

Teo

2022

Molecules

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Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

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Section: Structural Analysismentioning

confidence: 99%

Machine Learning Approaches for Metalloproteins

Wang

Teo

2022

Molecules

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“…In the present review, we focus on how rational design and directed evolution have provided insight into the roles of the SCS in tuning the functionality of ArMs, with particular emphasis toward systems employing natural protein scaffolds and exhibiting catalytic activity. A more comprehensive perspective on catalysis and electron transfer by de novo metalloenzymes can be found in the review of Pecoraro and co-workers in this issue, and several recent reviews provide detailed coverage of the design and breadth of applications of these scaffolds. − For extensive reviews of the design and catalytic capabilities of ArMs, we refer the reader to many outstanding reviews in this area. − Fe- and Cu-based ArMs constitute a major portion of designed metalloenzymes, and therefore, this review has been broadly organized into sections addressing the fields of heme Fe, nonheme Fe, and Cu followed by multinuclear ArMs, and finally systems containing alternative metal cofactors including V, Cr, Mn, Co, Ni, Zn, Ru, Rh, Pd, and Ir.…”

Section: Introductionmentioning

confidence: 99%

Designing Artificial Metalloenzymes by Tuning of the Environment beyond the Primary Coordination Sphere

et al. 2022

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Metalloenzymes catalyze a variety of reactions using a limited number of natural amino acids and metallocofactors. Therefore, the environment beyond the primary coordination sphere must play an important role in both conferring and tuning their phenomenal catalytic properties, enabling active sites with otherwise similar primary coordination environments to perform a diverse array of biological functions. However, since the interactions beyond the primary coordination sphere are numerous and weak, it has been difficult to pinpoint structural features responsible for the tuning of activities of native enzymes. Designing artificial metalloenzymes (ArMs) offers an excellent basis to elucidate the roles of these interactions and to further develop practical biological catalysts. In this review, we highlight how the secondary coordination spheres of ArMs influence metal binding and catalysis, with particular focus on the use of native protein scaffolds as templates for the design of ArMs by either rational design aided by computational modeling, directed evolution, or a combination of both approaches. In describing successes in designing heme, nonheme Fe, and Cu metalloenzymes, heteronuclear metalloenzymes containing heme, and those ArMs containing other metal centers (including those with non-native metal ions and metallocofactors), we have summarized insights gained on how careful controls of the interactions in the secondary coordination sphere, including hydrophobic and hydrogen bonding interactions, allow the generation and tuning of these respective systems to approach, rival, and, in a few cases, exceed those of native enzymes. We have also provided an outlook on the remaining challenges in the field and future directions that will allow for a deeper understanding of the secondary coordination sphere a deeper understanding of the secondary coordintion sphere to be gained, and in turn to guide the design of a broader and more efficient variety of ArMs.

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“…The advancement of synthetic methodologies and biochemical tools such as protein engineering has recently reinvigorated the study of artificial metalloproteins (ArMs). This hybrid approach at the interface of chemical biology and synthetic chemistry aims to prepare new systems that address problems in a wide range of applications, including biocatalysis, biotechnology, , protein structure and assembly, , and model chemistry …”

Section: Introductionmentioning

confidence: 99%

Artificial Metalloproteins: At the Interface between Biology and Chemistry

Kerns

Biswas

Minnetian

et al. 2022

JACS Au

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Artificial metalloproteins (ArMs) have recently gained significant interest due to their potential to address issues in a broad scope of applications, including biocatalysis, biotechnology, protein assembly, and model chemistry. ArMs are assembled by the incorporation of a non-native metallocofactor into a protein scaffold. This can be achieved by a number of methods that apply tools of chemical biology, computational de novo design, and synthetic chemistry. In this Perspective, we highlight select systems in the hope of demonstrating the breadth of ArM design strategies and applications and emphasize how these systems address problems that are otherwise difficult to do so with strictly biochemical or synthetic approaches.

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De novo metalloprotein design

Cited by 73 publications

References 252 publications

Machine Learning Approaches for Metalloproteins

Machine Learning Approaches for Metalloproteins

Designing Artificial Metalloenzymes by Tuning of the Environment beyond the Primary Coordination Sphere

Artificial Metalloproteins: At the Interface between Biology and Chemistry

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