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Abstract

AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries. AntiJen provides the most complete and up-to-date dataset of its kind. While AntiJen v2.0 retains a focus on both T cell and B cell epitopes, its greatest novelty is the archiving of continuous quantitative data on a variety of immunological molecular interactions. This includes thermodynamic and kinetic measures of peptide binding to TAP and the Major Histocompatibility Complex (MHC), peptide-MHC complexes binding to T cell receptors, antibodies binding to protein antigens and general immunological protein-protein interactions. The database also contains quantitative specificity data from position-specific peptide libraries and biophysical data, in the form of diffusion co-efficients and cell surface copy numbers, on MHCs and other immunological molecules. The uses of AntiJen include the design of vaccines and diagnostics, such as tetramers, and other laboratory reagents, as well as helping parameterize the bioinformatic or mathematical in silico modeling of the immune system. The database is accessible from the URL: .

T-Cell Epitope Prediction Methods: An Overview

“…Nonameric peptides derived from AntiJen [ 47 ] and SYFPEITHI databases were used to generate models for proteasomal cleavage, TAP binding, and MHC binding, trained them and tested them using "leave one out cross-validation," LOOCV using Receiver Operating Characteristic (ROC) curves.…”

Attractors in Sequence Space: Agent‐Based Exploration of MHC I Binding Peptides

“…Training data were compiled from public databases (AntiJen, [18] EPIMHC, [19] IEDB, [20] MHCBN [21] ), literature [14,22] and own unpublished results (cf. Supplementary Material).…”

Side-chain conformational space analysis (SCSA): A multi conformation-based QSAR approach for modeling and prediction of protein–peptide binding affinities

“…Here, peptide-major histocompatibility complex (MHC) interactions are ideally selected for SCSA analysis, ascribed to the following: (i) Many crystal structures of MHC-peptide complexes have been already solved [51]; (ii) Abundant experimental data of binding affinities ensures the statistical reliability of QSAR modeling (e.g., Peters et al [52] recently publish a benchmarking database comprising 48,828 quantitative peptide-binding affinity measurements of different MHC I binding peptides; Moreover, Toseland et al [53] have constructed the AntiJen database which also records numerous quantitative affinity entries of diverse peptides binding with immune receptors); (iii) Independent binding of side-chains (IBS) hypothesis proposed by Parker et al [54,55] pointed out that in MHC-peptide complex, each residue side chain binds independently of the rest of the peptide, that complies with that each peptide residue is independently calculated in SCSA; (iv) Immune system is a very important but extremely complex human tissue, and recently arisen computer-aided vaccine design (CAVD) [56] powerfully improves the developments of antigen-presenting mechanism, vaccine design and immunotherapy. Thus that performing CAVD studies by QSAR strategies becomes important, driving considerable interest [57].…”

“…The two anchor residues fall into the hydrophobic pocket of the peptidebinding cleft of HLA-A*0201, and they are normally referred as positions P 2 and P 9 , so the HLA-A*0201 binding nonapeptides are presented as P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 . 473 HLA-A*0201-restricted CTL epitopes (nonapeptides) were collected from the AntiJen database [53], and the binding affinity IC 50 An excellent QSAR model should be robust, stable and predictable, and usually cross-validated correlative coefficient q 2 is used to evaluate modeling quality. However, Tropsha et al [61][62][63] asserted that a more reliable result should be tested by external validation.…”