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Classification of self-assembling protein nanoparticle architectures for applications in vaccine design.

ABSTRACT: We introduce here a mathematical procedure for the structural classification of a specific class of self-assembling protein nanoparticles (SAPNs) that are used as a platform for repetitive antigen display systems. These SAPNs have distinctive geometries as a consequence of the fact that their peptide building blocks are formed from two linked coiled coils that are designed to assemble into trimeric and pentameric clusters. This allows a mathematical description of particle architectures in terms of bipartite (3,5)-regular graphs. Exploiting the relation with fullerene graphs, we provide a complete atlas of SAPN morphologies. The classification enables a detailed understanding of the spectrum of possible particle geometries that can arise in the self-assembly process. Moreover, it provides a toolkit for a systematic exploitation of SAPNs in bioengineering in the context of vaccine design, predicting the density of B-cell epitopes on the SAPN surface, which is critical for a strong humoral immune response.

SUBMITTER: Indelicato G

PROVIDER: S-EPMC5414263 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Classification of self-assembling protein nanoparticle architectures for applications in vaccine design.

Indelicato G G Burkhard P P Twarock R R

Royal Society open science 20170426 4

We introduce here a mathematical procedure for the structural classification of a specific class of self-assembling protein nanoparticles (SAPNs) that are used as a platform for repetitive antigen display systems. These SAPNs have distinctive geometries as a consequence of the fact that their peptide building blocks are formed from two linked coiled coils that are designed to assemble into trimeric and pentameric clusters. This allows a mathematical description of particle architectures in terms ...[more]

PMID: 28484626

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Project description:BackgroundsIn order to move towards the elimination and eradication of malaria in the world, the development of vaccines is inevitable. Many modern vaccines are based on recombinant technology; however, they may not provide a fully protective, long-lasting immune response. One of the strategies to improve recombinant vaccines is designing the nanovaccines such as self-assembling protein nanoparticles (SAPNs). Hence, the presentation of epitopes in a repeat array and correct conformation should be considered. P. falciparum generative cell-specific 1 (PfGCS1) is a main transmission-blocking vaccine candidate with two highly conserved fragments, HAP2-GCS1 and cd loop, inducing partial malaria transmission inhibitory antibodies. Therefore, to design an effective malaria vaccine, we used cd loop and HAP2-GCS1 fragments at the amino and carboxy terminuses of the SAPN-forming amino acid sequence, respectively.Methodology/principal findingsThe SAPN monomer (PfGCS1-SAPN) sequence was designed, and the three-dimensional (3D) structure was predicted. The result of this prediction ensured the presence of antigens on the SAPN surface. Then the accuracy of the predicted 3D structure and its stability were confirmed by 100 ns molecular dynamics (MD) simulation. The designed SAPN substructure sequence was synthesized, cloned, and expressed in Escherichia coli. With a gradual decrease in urea concentration in dialysis solutions, the purified proteins progressed to the final desired structure of the SAPN, which then was confirmed by Dynamic Light Scattering (DLS) and Field Emission Scanning Electron Microscopy (FESEM) tests. According to the Enzyme-Linked Immunosorbent Assay (ELISA), antigenic determinants were presented on the SAPN surface and interacted with antibodies in the serum of malaria patients.Conclusions/significanceOur results show that the SAPN formed by PfGCS1-SAPN has produced the correct shape and size, and the antigenic determinants are presented on the surface of the SAPN, which indicates that the designed SAPN has great potential to be used in the future as a malaria vaccine.

Dataset Information

Classification of self-assembling protein nanoparticle architectures for applications in vaccine design.

Publications

Classification of self-assembling protein nanoparticle architectures for applications in vaccine design.

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