Integrating MHC Class I visibility targets into the ProteinMPNN protein design process

ProteinMPNN is crucial in many protein design pipelines, identifying amino acid (AA) sequences that fold into given 3D protein backbone structures. We explore ProteinMPNN in the context of designing therapeutic proteins that need to avoid triggering unwanted immune reactions. More specifically, we focus on intra-cellular proteins that face the challenge of evading detection by Cytotoxic T-lymphocytes (CTLs) that detect their presence via the MHC Class I (MHC-I) pathway. To reduce visibility of the designed proteins to this immune-system component, we develop a framework that uses the large language model (LLM) tuning method, Direct Preference Optimization (DPO), to guide ProteinMPNN in minimizing the number of predicted MHC-I epitopes in its designs. Our goal is to design proteins with low MHC-I immune-visibility while preserving the original structure and function. For our assessment, we first use AlphaFold to predict the 3D structures of designed protein sequences. We then use TM-score, that measures the structural alignment between the predicted design and original protein, to evaluate fidelity to the original protein structure. We find our LLM-based tuning method for constraining MHC-I visibility is able to effectively reduce visibility without compromising structural similarity to the original protein. ### Competing Interest Statement The authors have declared no competing interest. * AA : amino acid Ab : antibody AR : auto-regressive CTL : Cytotoxic T-lymphocyte DPO : Direct Preference Optimization GAN : Generative Adversarial Network LLM : large language model MD : Molecular Dynamics MHC-II : MHC Class II MHC-I : MHC Class I ML : machine learning MPNN : message passing neural network NLP : Natural Language Processing PPO : Proximal Policy Optimization PWM : position weight matrix RBF : radial basis function RL : reinforcement learning RLHF : reinforcement learning from human feedback RNA : ribonucleic acid SOTA : state of the art VAE : Variational Autoencoder