Computational structure-based approach to study chimeric antigens using a new protein scaffold displaying foreign epitopes

The identification and recombinant production of functional antigens and/or epitopes of pathogens represent a crucial step for the development of an effective protein-based vaccine. Many vaccine targets are outer membrane proteins anchored into the lipidic bilayer through an extended hydrophobic portion making their recombinant production challenging. Moreover, only the extracellular loops, and not the hydrophobic regions, are naturally exposed to the immune system. In this work, the Domain 3 (D3) from Group B Streptococcus (GBS) pilus 2a backbone protein has been identified and engineered to be used as a scaffold for the display of extracellular loops of two Neisseria gonorrhoeae membrane proteins (PorB.1b and OpaB). A computational structure-based approach has been applied to the design of both the scaffold and the model antigens. Once identified the best D3 engineerable site, several different chimeric D3 displaying PorB.1b and OpaB extracellular loops were produced as soluble proteins. Each molecule has been characterized in terms of solubility, stability, and ability to correctly display the foreign epitope. This antigen dissection strategy allowed the identification of most immunogenic extracellular loops of both PorB.1b and OpaB gonococcal antigens. The crystal structure of chimeric D3 displaying PorB.1b immunodominant loop has been obtained confirming that the engineerization did not alter the predicted native structure of this epitope. Taken together, the reported data suggest that D3 is a novel protein scaffold for epitope insertion and display, and a valid alternative to the production of whole membrane protein antigens. Finally, this work describes a generalized computational structure-based approach for the identification, design, and dissection of epitopes in target antigens through chimeric proteins.

CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

Motivation

Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies, are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme.

Results

We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software.

Availability and implementation

www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe.

Supplementary information

Supplementary data are available at Bioinformatics online.

Palmitoylated acyl protein thioesterase APT2 deforms membranes to extract substrate acyl chains

Many biochemical reactions require controlled recruitment of proteins to membranes. This is largely regulated by posttranslational modifications. A frequent one is S-acylation, which consists of the addition of acyl chains and can be reversed by poorly understood acyl protein thioesterases (APTs). Using a panel of computational and experimental approaches, we dissect the mode of action of the major cellular thioesterase APT2 (LYPLA2). We show that soluble APT2 is vulnerable to proteasomal degradation, from which membrane binding protects it. Interaction with membranes requires three consecutive steps: electrostatic attraction, insertion of a hydrophobic loop and S-acylation by the palmitoyltransferases ZDHHC3 or ZDHHC7. Once bound, APT2 is predicted to deform the lipid bilayer to extract the acyl chain bound to its substrate and capture it in a hydrophobic pocket to allow hydrolysis. This molecular understanding of APT2 paves the way to understand the dynamics of APT2-mediated deacylation of substrates throughout the endomembrane system.

Recruitment of the amyloid precursor protein by γ-secretase at the synaptic plasma membrane

Γ-secretase is a membrane-embedded protease that cleaves single transmembrane helical domains of various integral membrane proteins. The amyloid precursor protein (APP) is an important substrate due to its pathological relevance to Alzheimer's disease. The mechanism of the cleavage of APP by γ-secretase that leads to accumulation of Alzheimer's disease causing amyloid-β (Aβ) is still unknown. Coarse-grained molecular dynamics simulations in this study reveal initial lipids raft formation near the catalytic site of γ-secretase as well as changes in dynamic behavior of γ-secretase once interacting with APP. The results suggest a precursor of the APP binding mode and hint at conformational changes of γ-secretase in the nicastrin (NCT) domain upon APP binding.

Protein post-translational modifications: In silico prediction tools and molecular modeling

Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins.

Effect of the Synaptic Plasma Membrane on the Stability of the Amyloid Precursor Protein Homodimer

The proteolytic cleavage of the transmembrane (TM) domain of the amyloid precursor protein (APP) releases amyloid-β (Aβ) peptides, which accumulation in the brain tissue is an early indicator of Alzheimer's disease. We used multiscale molecular dynamics simulations to investigate the stability of APP-TM dimer in realistic models of the synaptic plasma membrane (SPM). Between the two possible dimerization motifs proposed by NMR and EPR, namely G709XXXA713 and G700XXXG704XXXG708, our study revealed that the dimer promoted by the G709XXXA713 motif is not stable in the SPM due to the competition with highly unsaturated lipids that constitute the SPM. Under the same conditions, the dimer promoted by the G700XXXG704XXXG708 motif is instead the most stable species and likely the most biologically relevant. Independently of the dimerization state, both these motifs can be involved in the recruitment of cholesterol molecules.