〔Abstract〕 Objective To predict and screen potential biomarkers of systemic lupus eythematosus(SLE) based on machine learning algorithms and structural biology, and to reveal their mechanisms of action and to provide new targets for disease diagnosis and treatment. Methods Four machine learning algorithms, random forest(RF), eXtreme gradient boosting(XGBoost), support vector machine(SVM), least absolute shrinkage and selection operator(LASSO), were used to analyze the gene expression data of SLE patients in GEO(datasets: GSE121239 and GSE11907) to analyze the gene expression data of SLE patients and screen key markers. Peripheral blood single nucleated cells(PBMCs) from SLE patients were collected and RT-qPCR was used to detect differential gene expression levels. Subsequently, GSEA enrichment analysis was used to identify biomarker-related pathways. CIBERSORT immune infiltration analysis and protein interactions network were applied to calculate the sample immune cell infiltration abundance. Single-cell data were analyzed for gene expression specificity in immune cells. Interaction relationships in combination with AlphaFold3(AF3) were predicted. Results Multiple algorithms were screened together to identify the unique marker gene HERC5, and expression analysis of multiple datasets showed that HERC5 was highly expressed in SLE compared to the normal group(P