ProtLoc-Mex1: Interpretable Analysis of Amino Acids Sequence Chemical Feature and GO Annotations for Predicting Protein Subcellular Localization

Machine learning algorithms have revolutionized the study of protein subcellular localization; however, their black-box nature limits their interpretability. To address this, we built upon ProtLoc-Mex1, an automated pipeline incorporating interpretation techniques, by integrating chemical and GO annotation features to create a transparent random forest predictor. When applied to membrane protein type distinction and subcellular localization prediction, ProtLoc-Mex1 identified important features, explored the feature interaction effect and explored the functional feature semantic representation in language models, deepening our understanding of protein targeting mechanisms. We also created modules to facilitate their use for various prediction understanding in machine learning systems and provide a valuable resource for the scientific community. The code is available at ( https://github.com/yujuan-zhang/ProtLoc-mexl ).