De novo design of metal‐binding cleft in a Trp‐Trp stapled thermostable β‐hairpin peptide

2021 ◽  
Author(s):  
Muralikrishna Lella ◽  
Radhakrishnan Mahalakshmi
Keyword(s):  
Metal Binding ◽  
De Novo ◽  
De Novo Design ◽  
Binding Cleft

scholarly journals Computational approaches for de novo design and redesign of metal-binding sites on proteins

2017 ◽  
Vol 37 (2) ◽  
Author(s):  
Gunseli Bayram Akcapinar ◽  
Osman Ugur Sezerman
Keyword(s):  
Metal Ions ◽  
Metal Binding ◽  
Binding Sites ◽  
Metal Ion ◽  
De Novo ◽  
De Novo Design ◽  
Ion Binding ◽  
Chemical Transformations ◽  
Metal Ion Binding ◽  
Metal Binding Sites

Metal ions play pivotal roles in protein structure, function and stability. The functional and structural diversity of proteins in nature expanded with the incorporation of metal ions or clusters in proteins. Approximately one-third of these proteins in the databases contain metal ions. Many biological and chemical processes in nature involve metal ion-binding proteins, aka metalloproteins. Many cellular reactions that underpin life require metalloproteins. Most of the remarkable, complex chemical transformations are catalysed by metalloenzymes. Realization of the importance of metal-binding sites in a variety of cellular events led to the advancement of various computational methods for their prediction and characterization. Furthermore, as structural and functional knowledgebase about metalloproteins is expanding with advances in computational and experimental fields, the focus of the research is now shifting towards de novo design and redesign of metalloproteins to extend nature’s own diversity beyond its limits. In this review, we will focus on the computational toolbox for prediction of metal ion-binding sites, de novo metalloprotein design and redesign. We will also give examples of tailor-made artificial metalloproteins designed with the computational toolbox.

scholarly journals Allosteric cooperation in a de novo-designed two-domain protein

2020 ◽  
Vol 117 (52) ◽  
pp. 33246-33253
Author(s):  
Fabio Pirro ◽  
Nathan Schmidt ◽  
James Lincoff ◽  
Zachary X. Widel ◽  
Nicholas F. Polizzi ◽  
...  
Keyword(s):  
De Novo ◽  
Phenol Oxidase ◽  
De Novo Design ◽  
Computational Method ◽  
Tight Coupling ◽  
Structure And Dynamics ◽  
Tightly Coupled ◽  
Binding Cleft ◽  
Binding Sequence ◽  
Oxidase Reaction

We describe the de novo design of an allosterically regulated protein, which comprises two tightly coupled domains. One domain is based on the DF (Due Ferri in Italian or two-iron in English) family of de novo proteins, which have a diiron cofactor that catalyzes a phenol oxidase reaction, while the second domain is based on PS1 (Porphyrin-binding Sequence), which binds a synthetic Zn-porphyrin (ZnP). The binding of ZnP to the original PS1 protein induces changes in structure and dynamics, which we expected to influence the catalytic rate of a fused DF domain when appropriately coupled. Both DF and PS1 are four-helix bundles, but they have distinct bundle architectures. To achieve tight coupling between the domains, they were connected by four helical linkers using a computational method to discover the most designable connections capable of spanning the two architectures. The resulting protein, DFP1 (Due Ferri Porphyrin), bound the two cofactors in the expected manner. The crystal structure of fully reconstituted DFP1 was also in excellent agreement with the design, and it showed the ZnP cofactor bound over 12 Å from the dimetal center. Next, a substrate-binding cleft leading to the diiron center was introduced into DFP1. The resulting protein acts as an allosterically modulated phenol oxidase. Its Michaelis–Menten parameters were strongly affected by the binding of ZnP, resulting in a fourfold tighter Km and a 7-fold decrease in kcat. These studies establish the feasibility of designing allosterically regulated catalytic proteins, entirely from scratch.

Towards the de novo design of DNA based catalytic sites using a combined NMR and in silico approach

2014 ◽  
Author(s):  
Dieter Buyst ◽  
V. Gheerardijn ◽  
J. Van Den Begin ◽  
A. Madder ◽  
J. C. Martins
Keyword(s):  
In Silico ◽  
De Novo ◽  
De Novo Design ◽  
Catalytic Sites

scholarly journals De Novo Design of Selective Quadruplex‐Duplex Junction Ligands and Structural Characterisation of their Binding Mode: Targeting the G4 Hot‐Spot

2020 ◽  
Author(s):  
Laura Díaz-Casado ◽  
Israel Serrano-Chacón ◽  
Laura Montalvillo-Jiménez ◽  
Francisco Corzana ◽  
Agatha Bastida ◽  
...  
Keyword(s):  
De Novo ◽  
Hot Spot ◽  
Binding Mode ◽  
De Novo Design ◽  
Structural Characterisation

scholarly journals De novo design of modular and tunable protein biosensors

Nature ◽  
2021 ◽  
Author(s):  
Alfredo Quijano-Rubio ◽  
Hsien-Wei Yeh ◽  
Jooyoung Park ◽  
Hansol Lee ◽  
Robert A. Langan ◽  
...  
Keyword(s):  
De Novo ◽  
De Novo Design

scholarly journals Front Cover: De Novo Design of Selective Quadruplex–Duplex Junction Ligands and Structural Characterisation of Their Binding Mode: Targeting the G4 Hot‐Spot (Chem. Eur. J. 20/2021)

2021 ◽  
Vol 27 (20) ◽  
pp. 6101-6101
Author(s):  
Laura Díaz‐Casado ◽  
Israel Serrano‐Chacón ◽  
Laura Montalvillo‐Jiménez ◽  
Francisco Corzana ◽  
Agatha Bastida ◽  
...  
Keyword(s):  
De Novo ◽  
Hot Spot ◽  
Binding Mode ◽  
De Novo Design ◽  
Front Cover ◽  
Structural Characterisation

scholarly journals Machine learning differentiates enzymatic and non-enzymatic metals in proteins

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ryan Feehan ◽  
Meghan W. Franklin ◽  
Joanna S. G. Slusky
Keyword(s):  
Machine Learning ◽  
Metal Binding ◽  
Binding Sites ◽  
Active Sites ◽  
De Novo ◽  
Enzyme Design ◽  
Metal Binding Sites ◽  
Ensemble Machine Learning ◽  
Machine Learning Model ◽  
Physicochemical Features

AbstractMetalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic  metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model’s ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.

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