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( A ) Overview of the approach adopted for the generation of the EpiPred model, including the input datasets (blue) and the functional testing of the EpiPred predictions using three biochemical assays (stability, abundance, solubility) and <t>STX1</t> interaction in an overexpression model system (yellow). ( B ) Comparison of area under the receiver operator curve (AUROC) for EpiPred and a subset of high-performing globally-trained VEPs across the full truth set. ( C ) EpiPred Probability of PLP (EpiPred-P PLP ) score distributions in the full truth set separated by class as well as the full set of VUS. EpiPred-P PLP scores of 1 suggest pathogenicity, while scores of 0 suggest neutrality ( D ) EpiPred-P PLP output for known PLP and BLB variants used in the truth set mapped onto hSTXBP1 predicted structure (AlphaFold structure: AF-P61764-F1-model_v4), EpiPred-P PLP scores close to 1 are shown in purple, while scores close to zero appear in green.
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( A ) Overview of the approach adopted for the generation of the EpiPred model, including the input datasets (blue) and the functional testing of the EpiPred predictions using three biochemical assays (stability, abundance, solubility) and <t>STX1</t> interaction in an overexpression model system (yellow). ( B ) Comparison of area under the receiver operator curve (AUROC) for EpiPred and a subset of high-performing globally-trained VEPs across the full truth set. ( C ) EpiPred Probability of PLP (EpiPred-P PLP ) score distributions in the full truth set separated by class as well as the full set of VUS. EpiPred-P PLP scores of 1 suggest pathogenicity, while scores of 0 suggest neutrality ( D ) EpiPred-P PLP output for known PLP and BLB variants used in the truth set mapped onto hSTXBP1 predicted structure (AlphaFold structure: AF-P61764-F1-model_v4), EpiPred-P PLP scores close to 1 are shown in purple, while scores close to zero appear in green.
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( A ) Overview of the approach adopted for the generation of the EpiPred model, including the input datasets (blue) and the functional testing of the EpiPred predictions using three biochemical assays (stability, abundance, solubility) and STX1 interaction in an overexpression model system (yellow). ( B ) Comparison of area under the receiver operator curve (AUROC) for EpiPred and a subset of high-performing globally-trained VEPs across the full truth set. ( C ) EpiPred Probability of PLP (EpiPred-P PLP ) score distributions in the full truth set separated by class as well as the full set of VUS. EpiPred-P PLP scores of 1 suggest pathogenicity, while scores of 0 suggest neutrality ( D ) EpiPred-P PLP output for known PLP and BLB variants used in the truth set mapped onto hSTXBP1 predicted structure (AlphaFold structure: AF-P61764-F1-model_v4), EpiPred-P PLP scores close to 1 are shown in purple, while scores close to zero appear in green.

Journal: bioRxiv

Article Title: EpiPred: A gene-specific machine learning model for classifying missense variants in the epilepsy-related gene STXBP1

doi: 10.1101/2025.08.09.669488

Figure Lengend Snippet: ( A ) Overview of the approach adopted for the generation of the EpiPred model, including the input datasets (blue) and the functional testing of the EpiPred predictions using three biochemical assays (stability, abundance, solubility) and STX1 interaction in an overexpression model system (yellow). ( B ) Comparison of area under the receiver operator curve (AUROC) for EpiPred and a subset of high-performing globally-trained VEPs across the full truth set. ( C ) EpiPred Probability of PLP (EpiPred-P PLP ) score distributions in the full truth set separated by class as well as the full set of VUS. EpiPred-P PLP scores of 1 suggest pathogenicity, while scores of 0 suggest neutrality ( D ) EpiPred-P PLP output for known PLP and BLB variants used in the truth set mapped onto hSTXBP1 predicted structure (AlphaFold structure: AF-P61764-F1-model_v4), EpiPred-P PLP scores close to 1 are shown in purple, while scores close to zero appear in green.

Article Snippet: Following SDS-PAGE and transfer to PVDF membrane, samples were probed with anti-STX1 antibody (1:1000, 18572, Cell Signaling Technology).

Techniques: Functional Assay, Solubility, Over Expression, Comparison

Data from representative western blots (left) and quantification (right) are plotted for the WT STXBP1 and twenty missense variants. Each assay is plotted separately: (A) Abundance, ( B ) Stability, ( C ) Solubility, and ( D ) STX1 PPI. Scores are normalized to the WT reference allele score. The quantified data is sorted, left to right, by EpiPred-P PLP score from lowest (likely benign) to highest (likely pathogenic) and colored by class (wildtype, PLP, BLB, VUS predicted PLP/BLB). n=3, * p-value < 0.05, all raw data and p-values in Table S5,6 .

Journal: bioRxiv

Article Title: EpiPred: A gene-specific machine learning model for classifying missense variants in the epilepsy-related gene STXBP1

doi: 10.1101/2025.08.09.669488

Figure Lengend Snippet: Data from representative western blots (left) and quantification (right) are plotted for the WT STXBP1 and twenty missense variants. Each assay is plotted separately: (A) Abundance, ( B ) Stability, ( C ) Solubility, and ( D ) STX1 PPI. Scores are normalized to the WT reference allele score. The quantified data is sorted, left to right, by EpiPred-P PLP score from lowest (likely benign) to highest (likely pathogenic) and colored by class (wildtype, PLP, BLB, VUS predicted PLP/BLB). n=3, * p-value < 0.05, all raw data and p-values in Table S5,6 .

Article Snippet: Following SDS-PAGE and transfer to PVDF membrane, samples were probed with anti-STX1 antibody (1:1000, 18572, Cell Signaling Technology).

Techniques: Western Blot, Solubility