ProDualNet: Dual-Target Protein Sequence Design Method Based on Protein Language Model and Structure Model

Abstract

Proteins typically interact with multiple partners to regulate biological processes, and peptide drugs targeting multiple receptors have shown strong therapeutic potential, emphasizing the need for multi-target strategies in protein design. However, most current protein sequence design methods focus on interactions with a single receptor, often neglecting the complexity of designing proteins that can bind to two distinct receptors. We introduced ProDualNet, a novel approach for designing dual-target protein sequences by integrating sequence-structure information from two distinct receptors. ProDualNet used a heterogeneous graph network for pretraining and combines noise-augmented single-target data with real dual-target data for fine-tuning. This approach addressed the challenge of limited dual-target protein experimental structures. The efficacy of ProDualNet has been validated across multiple test sets, demonstrating better recovery and success rates compared to other multi-state design methods. In silico evaluation of cases like dual-target allosteric binding and non-overlapping interface binding highlights its potential for designing dual-target binding proteins. Furthermore, we validated ProDualNets ability to model the relationships between sequences, structures, and functions by zero-shot prediction tasks, including dual-target protein functional effects and mutant functional effects.