A Highly Effective System for Predicting MHC-II Epitopes With Immunogenicity.

In the past decade, the substantial achievements of therapeutic cancer vaccines have shed a new light on cancer immunotherapy. The major challenge for designing potent therapeutic cancer vaccines is to identify neoantigens capable of inducing sufficient immune responses, especially involving major histocompatibility complex (MHC)-II epitopes. However, most previous studies on T-cell epitopes were focused on either ligand binding or antigen presentation by MHC rather than the immunogenicity of T-cell epitopes. In order to better facilitate a therapeutic vaccine design, in this study, we propose a revolutionary new tool: a convolutional neural network model named FIONA (Flexible Immunogenicity Optimization Neural-network Architecture) trained on IEDB datasets. FIONA could accurately predict the epitopes presented by the given specific MHC-II subtypes, as well as their immunogenicity. By leveraging the human leukocyte antigen allele hierarchical encoding model together with peptide dense embedding fusion encoding, FIONA (with AUC = 0.94) outperforms several other tools in predicting epitopes presented by MHC-II subtypes in head-to-head comparison; moreover, FIONA has unprecedentedly incorporated the capacity to predict the immunogenicity of epitopes with MHC-II subtype specificity. Therefore, we developed a reliable pipeline to effectively predict CD4+ T-cell immune responses against cancer and infectious diseases.

Design and construction of small perturbation mutagenesis libraries for antibody affinity maturation using massive microchip-synthesized oligonucleotides.

We report a rational strategy to design and construct multiple small perturbation mutagenesis (SPM) libraries using massively parallel synthesis of oligonucleotides on a microchip for affinity maturation of an engineered anti-ErbB2 antibody chA21. On the basis of a comprehensive analysis of the sequence and structural relationships of six complementary determination regions (CDRs) in the Kabatman database, a computational algorithm was developed to introduce single-site and double-site mutations into variable CDR positions using ambiguous nucleotides. The six SPM libraries were composed of 419 degenerate oligonucleotides that can be expanded into 161,832 unique CDR sequences with a high coverage ratio of 95% natural amino acid diversity. We used Illumina next-generation sequencing to demonstrate that the synthetic CDR library sequences, as well as relative quantities per sequence, can be controlled precisely by adjusting reaction chamber assignment and input nucleoside composition. The microchip-synthesized oligonucleotides were used for construction of single-chain antibody fragment (scFv) phage libraries through one-step mutagenic PCR of double-stranded plasmids with >10(6)E. coli transformants. A variant with combinatorial mutations from four individual CDRs achieved more than 19-fold affinity increase. The strategy described herein should be broadly applicable to affinity and selectivity studies of antibodies and other proteins.