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Continuum of prion protein structures enciphers a multitude of prion isolate-specified phenotypes.

ABSTRACT: On passaging synthetic prions, two isolates emerged with incubation times differing by nearly 100 days. Using conformational-stability assays, we determined the guanidine hydrochloride (Gdn.HCl) concentration required to denature 50% of disease-causing prion protein (PrP(Sc)) molecules, denoted as the [Gdn.HCl](1/2) value. For the two prion isolates enciphering shorter and longer incubation times, [Gdn.HCl](1/2) values of 2.9 and 3.7 M, respectively, were found. Intrigued by this result, we measured the conformational stabilities of 30 prion isolates from synthetic and naturally occurring sources that had been passaged in mice. When the incubation times were plotted as a function of the [Gdn.HCl](1/2) values, a linear relationship was found with a correlation coefficient of 0.93. These findings demonstrate that (i) less stable prions replicate more rapidly than do stable prions, and (ii) a continuum of PrP(Sc) structural states enciphers a multitude of incubation-time phenotypes. Our data argue that cellular machinery must exist for propagating a large number of different PrP(Sc) conformers, each of which enciphers a distinct biological phenotype as reflected by a specific incubation time. The biophysical explanation for the unprecedented plasticity of PrP(Sc) remains to be determined.

SUBMITTER: Legname G

PROVIDER: S-EPMC1748184 | biostudies-other | 2006 Dec

REPOSITORIES: biostudies-other

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Continuum of prion protein structures enciphers a multitude of prion isolate-specified phenotypes.

Legname Giuseppe G Nguyen Hoang-Oanh B HO Peretz David D Cohen Fred E FE DeArmond Stephen J SJ Prusiner Stanley B SB

Proceedings of the National Academy of Sciences of the United States of America 20061201 50

On passaging synthetic prions, two isolates emerged with incubation times differing by nearly 100 days. Using conformational-stability assays, we determined the guanidine hydrochloride (Gdn.HCl) concentration required to denature 50% of disease-causing prion protein (PrP(Sc)) molecules, denoted as the [Gdn.HCl](1/2) value. For the two prion isolates enciphering shorter and longer incubation times, [Gdn.HCl](1/2) values of 2.9 and 3.7 M, respectively, were found. Intrigued by this result, we meas ...[more]

PMID: 17142317

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Project description:BackgroundThe relationship between the sequence of a protein, its structure, and the resulting connection between its structure and function, is a foundational principle in biological science. Only recently has the computational prediction of protein structure based only on protein sequence been addressed effectively by AlphaFold, a neural network approach that can predict the majority of protein structures with X-ray crystallographic accuracy. A question that is now of acute relevance is the "inverse protein folding problem": predicting the sequence of a protein that folds into a specified structure. This will be of immense value in protein engineering and biotechnology, and will allow the design and expression of recombinant proteins that can, for instance, fold into specified structures as a scaffold for the attachment of recombinant antigens, or enzymes with modified or novel catalytic activities. Here we describe the development of SeqPredNN, a feed-forward neural network trained with X-ray crystallographic structures from the RCSB Protein Data Bank to predict the identity of amino acids in a protein structure using only the relative positions, orientations, and backbone dihedral angles of nearby residues.ResultsWe predict the sequence of a protein expected to fold into a specified structure and assess the accuracy of the prediction using both AlphaFold and RoseTTAFold to computationally generate the fold of the derived sequence. We show that the sequences predicted by SeqPredNN fold into a structure with a median TM-score of 0.638 when compared to the crystal structure according to AlphaFold predictions, yet these sequences are unique and only 28.4% identical to the sequence of the crystallized protein.ConclusionsWe propose that SeqPredNN will be a valuable tool to generate proteins of defined structure for the design of novel biomaterials, pharmaceuticals, catalysts, and reporter systems. The low sequence identity of its predictions compared to the native sequence could prove useful for developing proteins with modified physical properties, such as water solubility and thermal stability. The speed and ease of use of SeqPredNN offers a significant advantage over physics-based protein design methods.

Dataset Information

Continuum of prion protein structures enciphers a multitude of prion isolate-specified phenotypes.

Publications

Continuum of prion protein structures enciphers a multitude of prion isolate-specified phenotypes.

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