In silico immunogenicity assessment for sequences containing unnatural amino acids: A method using existing in silico algorithm infrastructure and a vision for future enhancements

Abstract

The in silico prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an in silico method for modeling the impact of a given UAA on a peptide’s likelihood of binding to HLA and, by extension, its immunogenic potential. In silico assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory in vitro, ex vivo, and in vivo assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating in silico immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and “correct” in silico estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.

Identification of potential immunotherapeutic targets and prognostic biomarkers in Graves' disease using weighted gene co-expression network analysis

“…These tools enable the analysis of large-scale gene expression data to identify key biological pathways and processes involved in the immunology of GA [ 46 ]. Diverging from traditional approaches, we integrate novel methodologies such as IPS, MCP, and X-cell algorithms to comprehensively decode the molecular intricacies governing autoantibody production in GD [ [17] , [18] , [19] , [20] , [22] , [23] , [24] , [25] ]. Through the amalgamation of expression data from stromal and immune cells within GD-affected tissues, we unveil a disrupted immune network, offering profound insights into the intricate interactions steering GD pathogenesis.…”

“…This computational approach entails a thorough assessment of the expression profiles linked to various immune cell types. IPS offers a quantitative measure, enabling the determination of the global immune cell landscape in the context of GD [ 17 , 18 ]. Additionally, MCP was employed to scrutinize the specifics of immune cell populations present in the GD datasets.…”

“…The initial triggers for autoantibody production and immune activation are still unknown, and the precise interactions between immune cells and autoantibodies in the thyroid gland require further investigation [ 12 , 16 ]. However, recent advances in computational tools, such as the immunogenicity-potential scoring (IPS) [ 17 , 18 ], microenvironment cell populations-counter (MCP) [ [19] , [20] , [21] ], X-cells algorithms [ 22 , 23 ], and weighted gene co-expression network analysis (WGCNA) [ 24 , 25 ], have enabled researchers to gain a deeper understanding of the molecular mechanisms underlying autoantibody production and the complex interactions between stromal and immune cells in the thyroid gland [ 26 , 27 ]. These cutting-edge tools allow for large-scale gene expression analysis to identify key biological pathways and processes involved in the path immunology of autoimmune disorders, providing valuable insights into the underlying mechanisms of these diseases [ 28 ].…”