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

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 validation of a variety of protein targets, from fluorescent proteins to industrial enzymes and bioactive peptides, showed that SPgo-designed sequences consistently outperformed natural sequences. Most notably, SPgo was able to achieve secretory production of snake venom peptides at an unprecedented yield of 154 mg/L, a 150-fold increase in target protein yield per unit culture volume compared to traditional intracellular expression, transforming previously intractable targets into viable biotechnology platforms. This work establishes a new paradigm for computational protein design, offering immediate applications in biomanufacturing while revealing the untapped potential of artificial sequence space to surpass natural evolutionary solutions. The SPgo framework data can be found on github (https://github.com/lzlinn801/SPgo).