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Nitrite reductase activity within an antiparallel de novo scaffold.

ABSTRACT: Copper nitrite reductase (CuNiR) is a copper enzyme that converts nitrite to nitric oxide and is an important part of the global nitrogen cycle in bacteria. The relatively simple CuHis₃ binding site of the CuNiR active site has made it an enticing target for small molecule modeling and de novo protein design studies. We have previously reported symmetric CuNiR models within parallel three stranded coiled coil systems, with activities that span a range of three orders of magnitude. In this report, we investigate the same CuHis₃ binding site within an antiparallel three helical bundle scaffold, which allows the design of asymmetric constructs. We determine that a simple CuHis₃ binding site can be designed within this scaffold with enhanced activity relative to the comparable construct in parallel coiled coils. Incorporating more complex designs or repositioning this binding site can decrease this activity as much as 15 times. Comparing these constructs, we reaffirm a previous result in which a blue shift in the 1s to 4p transition energy determined by Cu(I) X-ray absorption spectroscopy is correlated with an enhanced activity within imidazole-based constructs. With this step and recent successful electron transfer site designs within this scaffold, we are one step closer to a fully functional de novo designed nitrite reductase.

SUBMITTER: Koebke KJ

PROVIDER: S-EPMC11232943 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Nitrite reductase activity within an antiparallel de novo scaffold.

Koebke Karl J KJ Tebo Alison G AG Manickas Elizabeth C EC Deb Aniruddha A Penner-Hahn James E JE Pecoraro Vincent L VL

Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry 20210906 7

Copper nitrite reductase (CuNiR) is a copper enzyme that converts nitrite to nitric oxide and is an important part of the global nitrogen cycle in bacteria. The relatively simple CuHis<sub>3</sub> binding site of the CuNiR active site has made it an enticing target for small molecule modeling and de novo protein design studies. We have previously reported symmetric CuNiR models within parallel three stranded coiled coil systems, with activities that span a range of three orders of magnitude. In ...[more]

PMID: 34487215

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Project description:Enzymatic reactions involving redox processes are highly sensitive to the local electrostatic environment. Despite considerable effort, the complex interactions among different influential factors in native proteins impede progress toward complete understanding of the structure-function relationship. Of particular interest is the type 2 copper center Cu(His)3, which may act as an electron transfer center in peptidylglycine α-hydroxylating monooxygenase (PHM) or a catalytic center in copper nitrite reductase (CuNiR). A de novo design strategy is used to probe the effect of modifying charged amino acid residues around, but not directly bound to, a Cu(His)3 center embedded in three-stranded coiled coils (TRI-H)3 [TRI-H = Ac-G WKALEEK LKALEEK LKALEEK HKALEEK G-NH2]. Specifically, the peptide TRI-EH (=TRI-HK22E) alters an important lysine to glutamate just above the copper binding center. With a series of TRI-EH peptides mutated below the metal center, we use a variety of spectroscopies (EPR, UV-vis, XAS) to show a direct impact on the protonation equilibria, copper binding affinities, reduction potentials, and nitrite reductase activities of these copper-peptide complexes. The potentials at a specific pH vary by 100 mV, and the nitrite reductase activities range over a factor of 4 in rates. We also observe that the affinities, potentials, and catalytic activities are strongly influenced by the pH conditions (pH 5.8-7.4). In general, Cu(II) affinities for the peptides are diminished at low pH values. The interplay among these factors can lead to a 200 mV shift in reduction potential across these peptides, which is determined by the pH-dependent affinities of copper in both oxidation states. This study illustrates the strength of de novo protein design in elucidating the influence of ionizable residues on a particular redox system, an important step toward understanding the factors that govern the properties of this metalloenzyme with a goal of eventually improving the catalytic activity.

Project description:Molecular generative models trained with small sets of molecules represented as SMILES strings can generate large regions of the chemical space. Unfortunately, due to the sequential nature of SMILES strings, these models are not able to generate molecules given a scaffold (i.e., partially-built molecules with explicit attachment points). Herein we report a new SMILES-based molecular generative architecture that generates molecules from scaffolds and can be trained from any arbitrary molecular set. This approach is possible thanks to a new molecular set pre-processing algorithm that exhaustively slices all possible combinations of acyclic bonds of every molecule, combinatorically obtaining a large number of scaffolds with their respective decorations. Moreover, it serves as a data augmentation technique and can be readily coupled with randomized SMILES to obtain even better results with small sets. Two examples showcasing the potential of the architecture in medicinal and synthetic chemistry are described: First, models were trained with a training set obtained from a small set of Dopamine Receptor D2 (DRD2) active modulators and were able to meaningfully decorate a wide range of scaffolds and obtain molecular series predicted active on DRD2. Second, a larger set of drug-like molecules from ChEMBL was selectively sliced using synthetic chemistry constraints (RECAP rules). In this case, the resulting scaffolds with decorations were filtered only to allow those that included fragment-like decorations. This filtering process allowed models trained with this dataset to selectively decorate diverse scaffolds with fragments that were generally predicted to be synthesizable and attachable to the scaffold using known synthetic approaches. In both cases, the models were already able to decorate molecules using specific knowledge without the need to add it with other techniques, such as reinforcement learning. We envision that this architecture will become a useful addition to the already existent architectures for de novo molecular generation.

Project description:De novo protein design represents an attractive approach for testing and extending our understanding of metalloprotein structure and function. Here, we describe our work on the design of DF (Due Ferri or two-iron in Italian), a minimalist model for the active sites of much larger and more complex natural diiron and dimanganese proteins. In nature, diiron and dimanganese proteins protypically bind their ions in 4-Glu, 2-His environments, and they catalyze diverse reactions, ranging from hydrolysis, to O2-dependent chemistry, to decarbonylation of aldehydes. In the design of DF, the position of each atom-including the backbone, the first-shell ligands, the second-shell hydrogen-bonded groups, and the well-packed hydrophobic core-was bespoke using precise mathematical equations and chemical principles. The first member of the DF family was designed to be of minimal size and complexity and yet to display the quintessential elements required for binding the dimetal cofactor. After thoroughly characterizing its structural, dynamic, spectroscopic, and functional properties, we added additional complexity in a rational stepwise manner to achieve increasingly sophisticated catalytic functions, ultimately demonstrating substrate-gated four-electron reduction of O2 to water. We also briefly describe the extension of these studies to the design of proteins that bind nonbiological metal cofactors (a synthetic porphyrin and a tetranuclear cluster), and a Zn2+/proton antiporting membrane protein. Together these studies demonstrate a successful and generally applicable strategy for de novo metalloprotein design, which might indeed mimic the process by which primordial metalloproteins evolved. We began the design process with a highly symmetrical backbone and binding site, by using point-group symmetry to assemble the secondary structures that position the amino acid side chains required for binding. The resulting models provided a rough starting point and initial parameters for the subsequent precise design of the final protein using modern methods of computational protein design. Unless the desired site is itself symmetrical, this process requires reduction of the symmetry or lifting it altogether. Nevertheless, the initial symmetrical structure can be helpful to restrain the search space during assembly of the backbone. Finally, the methods described here should be generally applicable to the design of highly stable and robust catalysts and sensors. There is considerable potential in combining the efficiency and knowledge base associated with homogeneous metal catalysis with the programmability, biocompatibility, and versatility of proteins. While the work reported here focuses on testing and learning the principles of natural metalloproteins by designing and studying proteins one at a time, there is also considerable potential for using designed proteins that incorporate both biological and nonbiological metal ion cofactors for the evolution of novel catalysts.

Dataset Information

Nitrite reductase activity within an antiparallel de novo scaffold.

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Nitrite reductase activity within an antiparallel de novo scaffold.

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