<i>De Novo</i> Design of High-Performance Sec-type Signal Peptide via a Hybrid Deep Learning Architecture.

ABSTRACT: The rational design of signal peptides represents a fundamental bottleneck in biotechnology, where sequence optimization directly governs protein secretion efficiency and industrial scalability. Current approaches rely predominantly on natural variants or empirical mutations, constraining the accessible sequence space and limiting performance gains. Here, we develop the SPgo computational framework, which overcomes these limitations by combining rule-based domain assembly with a Transformer-enabled deep generative model to support the design of Sec-type signal peptides. Our hybrid architecture constructs optimal N- and C-terminal regions through biophysical constraints while deploying a BERT-LSTM pipeline to explore vast sequence landscapes within the critical hydrophobic core. Rigorous va

SUBMITTER: Dai XP 

PROVIDER: S-EPMC12569692 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature