To address potential information loss due to mean-aggregation of the original and trimmed embeddings, we explored whether dimensionality-reduced versions, specifically AlphaFold2 CAE and ESM2 CAE, could affect the model performance. Our analysis, however, indicated no significant performance change between the dimensionally-reduced embeddings and their original or trimmed counterparts across various data splitting strategies, as detailed in Supplementary Tables S2-S7. Notably, in some cases the number of dimensions significantly impacted PFI, with AlphaFold2 CAE demonstrating a lower PFI compared to AlphaFold2 original and trimmed embeddings, and ESM2 CAE showing a higher PFI relative to ESM2 original and trimmed versions. However, no consistent dependency was observed across different scenarios (Supplementary Tables and). Moreover, there were no significant differences in PFI between ESM2 CAE and its original or trimmed versions across kinase and family splits, suggesting that the impact of protein features on model performance diminishes when data leakage is minimized. This reinforces the earlier observation that reducing data leakage improves model generalizability and mitigates the influence of excessive embedding dimensions on model output.