DeepUSPS: Deep Learning-Empowered Unconstrained-Structural Protein Sequence Design.

Abstract

Currently, the unconstrained-structural protein sequence design models suffer from low optimization efficiency, and their generated proteins exhibit significant similarities to natural proteins and low thermal stability. To address these challenges, we propose the Deep Learning-Empowered Unconstrained-Structural Protein Sequence Design (DeepUSPS) model. To effectively address the inadequate thermal stability problem, we employ the innovative Inverted Dense Residual Network (IDRNet). To mitigate the designed proteins similarity issue, the Sequence-Pairwise Features Extraction Synthetic Network (SPFESN) is constructed. Furthermore, we introduce the Warm Restart AngularGrad (WRA) optimizer to optimize the 3D Position-Specific Scoring Matrix (3Dpssm) for unconstrained-structural protein sequence, only involving 2100 iterations (140.36 min) updates to generate idealization (IDE) protein sequences. We obtained a total of 1000 IDE protein sequences. Then we utilized in silico experiments to evaluate them, including similarity, clarity and iterations, thermal stability, spatial distribution of similarity, and predicted local-distance difference test (pLDDT) confidence assessment. Notably, the mean lg(E-value) for IDE protein sequences reached -0.051, the mean TM-score for IDE protein structures reached 0.594, the iterations only need 2100, and the mean Tm (melting point) for thermal stability reached 74.78°C. The average pLDDT value for 3D structures reached 76. Additionally, the IDE proteins' 3D structures exhibit diverse types. These in silico results conclusively demonstrate the superior performance of DeepUSPS compared with Hallucinate.