Search for: All records

Nature utilizes self-assembled protein-based structures as subcellular compartments in prokaryotes to sequester catalysts for specialized biochemical reactions. These protein cage structures provide unique isolated environments for the encapsulated enzymes. Understanding these systems is useful in the bioinspired design of synthetic catalytic organelle-like nanomaterials. The DNA binding protein from starved cells (Dps), isolated from Sulfolobus solfataricus , is a 9 nm dodecameric protein cage making it the smallest known naturally occurring protein cage. It is naturally over-expressed in response to oxidative stress. The small size, natural biodistribution to the kidney, and ability to cross the glomerular filtration barrier in in vivo experiments highlight its potential as a synthetic antioxidant. Cytochrome C (CytC) is a small heme protein with peroxidase-like activity involved in the electron transport chain and also plays a critical role in cellular apoptosis. Here we report the encapsulation of CytC inside the 5 nm interior cavity of Dps and demonstrate the catalytic activity of the resultant Dps nanocage with enhanced antioxidant behavior. The small cavity can accommodate a single CytC and this was achieved through self-assembly of chimeric cages comprising Dps subunits and a Dps subunit to which the CytC was fused. For selective isolation of CytC containing Dps cages, we utilized engineered polyhistidine tag present only on the enzyme fused Dps subunits (6His-Dps-CytC). The catalytic activity of encapsulated CytC was studied using guaiacol and 3,3′,5,5′-tetramethylbenzidine (TMB) as two different peroxidase substrates and compared to the free (unencapsulated) CytC activity. The encapsulated CytC showed better pH dependent catalytic activity compared to free enzyme and provides a proof-of-concept model to engineer these small protein cages for their potential as catalytic nanoreactors. 

Abstract Motivation Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models. Results We developed a convolutional deep neural network-based approach named DOcking decoy selection with Voxel-based deep neural nEtwork (DOVE) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein–protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions. Availability and implementation Codes available at http://github.com/kiharalab/DOVE, http://kiharalab.org/dove/. Supplementary information Supplementary data are available at Bioinformatics online.