Peptides derived from gp43, the most antigenic protein from Paracoccidioides brasiliensis, form amyloid fibrils in vitro: implications for vaccine development

Aggregation-prone peptides from within a non-self-protein homoaggregate are preferred for MHC association: Historical overview

New technologies assist re-evaluation of hypotheses on generation of immune cell repertoires and distinctions of self from non-self. Findings include positive correlations between peptide propensities to aggregate and their binding to major histocompatibility complex (MHC) proteins. This recalls the hypothesis that foreign proteins may homoaggregate in host cytosols prior to releasing their peptides (p) to form pMHC complexes. Clues to this included aggregation-related phenomena associated with infections (rouleaux formation, pyrexia, certain brain diseases). By virtue of 'promiscuous' gene expression by thymic presenting cells - perhaps adapted from earlier evolving gonadal mechanisms - developing T cells monitor surface pMHC clusterings. This evaluates intracellular concentrations of the corresponding proteins, and hence, following Burnet's two signal principle, degrees of self-reactivity. After positive selection in the thymic cortex for reactivity with 'near-self', high-level pMHC clustering suffices in the medulla for negatively selection. Following Burnet's principle, in the periphery low-level clustering suffices for T cell stimulation and high-level clustering again provokes negative selection (immunological tolerance). For evolving intracellular pathogens, fine-tuned polymorphisms of their host species have limited to 'near-self' some mimicking adaptations. It is proposed that while entire pathogen proteins may have evolved to minimize their aggregability, the greater aggregability of their peptides remains partially hidden within. Two-step proofreading mechanisms in prospective hosts select proteins containing aggregable peptide for the generation of pMHC clusters at the surface of presenting cells. Through mutations, some proteins of pathogens and cancer cells tend to converge towards the host 'near-self' that its T cells have auditioned to address.

Connection between MHC class II binding and aggregation propensity: The antigenic peptide 10 of Paracoccidioides brasiliensis as a benchmark study

The aggregation of epitopes that are also able to bind major histocompatibility complex (MHC) alleles raises questions around the potential connection between the formation of epitope aggregates and their affinities to MHC receptors. We first performed a general bioinformatic assessment over a public dataset of MHC class II epitopes, finding that higher experimental binding correlates with higher aggregation-propensity predictors. We then focused on the case of P10, an epitope used as a vaccine candidate against Paracoccidioides brasiliensis that aggregates into amyloid fibrils. We used a computational protocol to design variants of the P10 epitope to study the connection between the binding stabilities towards human MHC class II alleles and their aggregation propensities. The binding of the designed variants was tested experimentally, as well as their aggregation capacity. High-affinity MHC class II binders in vitro were more disposed to aggregate forming amyloid fibrils capable of binding Thioflavin T and congo red, while low affinity MHC class II binders remained soluble or formed rare amorphous aggregates. This study shows a possible connection between the aggregation propensity of an epitope and its affinity for the MHC class II cleft.

LambdaPP: Fast and accessible protein-specific phenotype predictions

The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.