Prediction of Antigenic B and T Cell Epitopes via Energy Decomposition Analysis: Description of the Web-Based Prediction Tool BEPPE

Peptide Antibodies: Current Status

Peptide antibodies have become one of the most important classes of reagents in molecular biology and clinical diagnostics. For this reason, methods for their production and characterization continue to be developed, including basic peptide synthesis protocols, peptide-conjugate production and characterization, conformationally restricted peptides, immunization procedures, etc. Detailed mapping of peptide antibody epitopes has yielded important information on antibody-antigen interaction in general and specifically in relation to antibody cross-reactivity and theories of molecular mimicry. This information is essential for detailed understanding of paratope-epitope dynamics, design of antibodies for research, design of peptide-based vaccines, development of therapeutic peptide antibodies, and de novo design of antibodies with predetermined specificity.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Immunoinformatic epitope prediction to select monoclonal antibodies for Phl p 1 quantification

BACKGROUND

Allergen quantification has become a relevant parameter for allergen extract characterization and to guarantee the consistency of the manufacturing process at allergen immunotherapy. The aim of this study was to develop and validate a method to quantify the major allergen Phl p 1 based on a prediction of the antigenic regions by immunoinformatic strategies.

METHODS

Phl p 1 was purified from a Phleum pratense native extract by chromatographic methods. Immunoinformatic tools were used to predict B-cell epitopes. In silico predictions were verified by mapping linear epitopes with a peptide library and used to select the appropriate regions for producing the mAbs to develop an ELISA method, which was validated. Phl p 1 was quantified in 24 batches of P. pratense extracts.

RESULTS

Phl p 1 was purified with 95 % purity and completely functional. Eight B-cell epitopes in each of the two Phl p 1 isoforms were predicted. Two of the predicted B-cell epitopes overlapped with the experimentally determined peptides recognized by two mAbs selected for development of the kit. The quantification method demonstrated to be specific to Phl p 1, linear, accurate and precise in the range from 7.7 to 123.3 μg/mg. Mean Phl p 1 content was 28.95 μg of allergen/mg of lyophilized native extract and 44.23 μg of allergen/mg of lyophilized depigmented extract.

CONCLUSIONS

An ELISA method for measuring Phl p 1 in P. pratense extracts was developed and validated by producing the appropriate mAbs against epitopes selected by immunoinformatic tools.

Structure and Computation in Immunoreagent Design: From Diagnostics to Vaccines

Novel immunological tools for efficient diagnosis and treatment of emerging infections are urgently required. Advances in the diagnostic and vaccine development fields are continuously progressing, with reverse vaccinology and structural vaccinology (SV) methods for antigen identification and structure-based antigen (re)design playing increasingly relevant roles. SV, in particular, is predicted to be the front-runner in the future development of diagnostics and vaccines targeting challenging diseases such as AIDS and cancer. We review state-of-the-art methodologies for structure-based epitope identification and antigen design, with specific applicative examples. We highlight the implications of such methods for the engineering of biomolecules with improved immunological properties, potential diagnostic and/or therapeutic uses, and discuss the perspectives of structure-based rational design for the production of advanced immunoreagents.

Evolving serodiagnostics by rationally designed peptide arrays: the Burkholderia paradigm in Cystic Fibrosis

Efficient diagnosis of emerging and novel bacterial infections is fundamental to guide decisions on therapeutic treatments. Here, we engineered a novel rational strategy to design peptide microarray platforms, which combines structural and genomic analyses to predict the binding interfaces between diverse protein antigens and antibodies against Burkholderia cepacia complex infections present in the sera of Cystic Fibrosis (CF) patients. The predicted binding interfaces on the antigens are synthesized in the form of isolated peptides and chemically optimized for controlled orientation on the surface. Our platform displays multiple Burkholderia-related epitopes and is shown to diagnose infected individuals even in presence of superinfections caused by other prevalent CF pathogens, with limited cost and time requirements. Moreover, our data point out that the specific patterns determined by combined probe responses might provide a characterization of Burkholderia infections even at the subtype level (genomovars). The method is general and immediately applicable to other bacteria.

Surface energetics and protein-protein interactions: analysis and mechanistic implications

Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions.