RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex‑I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning

Major histocompatibility complex (MHC) plays a vital
role in presenting
epitopes (short peptides from pathogenic proteins) to T-cell receptors
(TCRs) to trigger the subsequent immune responses. Vaccine design
targeting MHC generally aims to find epitopes with a high binding
affinity for MHC presentation. Nevertheless, to find novel epitopes
usually requires high-throughput screening of bulk peptide database,
which is time-consuming, labor-intensive, more unaffordable, and very
expensive. Excitingly, the past several years have witnessed the great
success of artificial intelligence (AI) in various fields, such as
natural language processing (NLP, e.g., GPT-4), protein structure
prediction and engineering (e.g., AlphaFold2), and so on. Therefore,
herein, we propose a deep reinforcement-learning (RL)-based generative
algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I
systems. Specifically, RLpMIEC combines the energetic spectrum (namely,
the molecular interaction energy component, MIEC) based on the peptide–MHC
interaction and the sequence information to generate peptides with
strong binding affinity and precise MIEC spectra to accelerate the
discovery of candidate peptide vaccines. RLpMIEC performs well in
all the generative capability evaluations and can generate peptides
with strong binding affinities and precise MIECs and, moreover, with
high interpretability, demonstrating its powerful capability in participation
for accelerating peptide-based vaccine development.