Package: protloc-mex1
ProtLoc-Mex1Introduction ProtLoc-Mex1This project offers a comprehensive pipeline for the rapid development of subcellular localization prediction and model interpretation. It encompasses 42 amino acid feature characterization algorithms and Gene Ontology (GO) feature extraction based on the Doc2Vec approach. Additionally, two random forest models for protein localization prediction are provided.with support by SHAP package warped into ProtLoc-mex1 module，everyone can easily use two random forest models above to get the global explanation and local explanation of the feature (physiochemical characteristics of amino acid sequence and GO annotation semantics)  and model in  protein localization prediction.Note:If you are refer to our work titled "Interpretable Feature Extraction and Dimensionality Reduction in ESM2 for Protein Localization Prediction" , please only focus on  the sections ofInstallation,Dependencies, andAA_count. If you wish to explore more details related to the interpretation methods related to this work, please proceed to theGitHubrepository directly. The content following theGO_countsection is independent of the mentioned work.InstallationThis project's core code has been uploaded to the PyPI repository. To get it using a conda virtual environment, follow the steps below:First, create a new conda environment. For Windows systems, it is recommended to use Conda Prompt for this task. On Linux systems, you can use the Terminal. (You can also modify the environment name as needed, here, we use "myenv" as an example):conda create -n myenv python=3.9Then, activate the environment you just created:conda activate myenvFinally, use pip to install 'protloc_mex1' within this environment:pip install protloc_mex1DependenciesProtLoc-Mex1 requires Python  == 3.9Below are the Python packages required by ProtLoc-Mex1, which are automatically installed with it:dependencies = [
        "biopython==1.79",
        "numpy==1.20.3",
        "pandas==1.4.1",
        "seaborn==0.11.2",
        "matplotlib==3.5.1",
        "shap==0.41.0",
        "gensim==4.2.0"
]and other not automatically installed but also required Python packages：dependencies = [
       "scikit-learn==1.2.2",
       "captum == 0.6.0"
       "torch == 1.12.1"
]It is advised to obtain these dependent packages from their respective official sources, while carefully considering the implications of version compatibility.How to use ProtLoc-Mex1ProtLoc-Mex1 includes 6 modules: AA_count, GO_count, classifier_evalute, SHAP_conduct,  SHAP_plus.AA_countIn this module, we can perform protein sequence analysis. AA_count include three functions,dna_sequence_conduct(),rna_sequence_conduct(), andprotein_sequence_conduct(), they are designed to process DNA, RNA, and protein sequences, respectively, in a given DataFramedf.Thedna_sequence_conduct()andrna_sequence_conduct()functions:Filter out illegal bases in the DNA/RNA sequences in the DataFrame.Calculate and add columns for the frequency of carbon, hydrogen, nitrogen, and oxygen elements in the DNA/RNA sequences.Calculate and add columns for the frequency of each base (A, T, C, G for DNA and A, U, C, G for RNA) in the DNA/RNA sequences.Theprotein_sequence_conduct()function:Adds new columns for 20 standard amino acids, elemental properties, and various protein properties including molecular weight, aromaticity, instability index, flexibility, isoelectric point, secondary structure fraction, molar extinction coefficient for reduced cysteines and disulfide bridges, and gravy.Calculates and adds the frequency of each amino acid in the protein sequences.For sequences containing only 20 standard amino acids, it calculates and adds values for molecular weight, aromaticity, instability index, flexibility, isoelectric point, secondary structure fraction, molar extinction coefficient for reduced cysteines and disulfide bridges, and gravy.Calculates and adds the count of each element (oxygen, sulfur, carbon, hydrogen, nitrogen) and acidic, basic, polar, and non-polar properties in the protein sequences.for using AA_count example:>>>importpandasaspd>>>fromprotloc_mex1.AA_countimportdna_sequence_conduct,rna_sequence_conduct,protein_sequence_conduct# Example DataFrame with DNA sequences>>>df_dna=pd.DataFrame({...'gene_seq':['ATCGTGCA','TGCTAGCT','CTAGCTAG']...})# Call the dna_sequence_conduct function>>>df_dna_processed=dna_sequence_conduct(df_dna,'gene_seq')>>>print(df_dna_processed)# Assume you have a DataFrame containing RNA sequences>>>df_rna=pd.DataFrame({...'gene_seq':['AUCGUGCA','UGCUAGCU','CUAGCUAG']...})# Call the rna_sequence_conduct function>>>df_rna_processed=rna_sequence_conduct(df_rna,'gene_seq')>>>print(df_rna_processed)# Assume you have a DataFrame containing protein sequences>>>df_protein=pd.DataFrame({...'Sequence':['ACDEFGHIKLMNPQRSTVWY','ACDEFGHIKLMNPQRSTVWY']...})# Call the protein_sequence_conduct function>>>df_protein_processed=protein_sequence_conduct(df_protein,'Sequence')>>>print(df_protein_processed)GO_countGO_count are capability in using Doc2vec to get GO representation，for initialize model can see below：>>>importgensim>>>fromgensim.models.doc2vecimportDoc2Vec>>>fromprotloc_mex1.GO_countimportGO_pre_Process,model_training,vec_create_GO# Create dummy data>>>data=pd.DataFrame({...'Entry':['Gene1','Gene2','Gene3'],...'GO_BP':['some document GO:0008150','some document GO:0008150;GO:0009987','some document GO:0009987'],...'GO_MF':['some document GO:0003674','some document GO:0003674;GO:0003824','some document GO:0003824']...})# Preprocess data>>>data=GO_pre_Process(data,'\[GO:\d+\]','GO_BP')>>>data=GO_pre_Process(data,'\[GO:\d+\]','GO_MF')# Initialize models>>>BP_model=gensim.models.doc2vec.Doc2Vec(vector_size=50,min_count=2,epochs=40,window=5,workers=1,dm=0,seed=0)>>>MF_model=gensim.models.doc2vec.Doc2Vec(vector_size=50,min_count=2,epochs=40,window=5,workers=1,dm=0,seed=0)# Train models>>>BP_model_trained=model_training(data,BP_model,'GO_BP')>>>MF_model_trained=model_training(data,MF_model,'GO_MF')# Generate word vectors>>>BP_data=vec_create_GO(data,BP_model_trained,'GO_BP')>>>MF_data=vec_create_GO(data,MF_model_trained,'GO_MF')# Print word vectors>>>print(BP_data)>>>print(MF_data)for using pre-training BP and MF model in this article, can see below：## this example code and file will realse soonclassifier_evalute## this example code and file will realse soonSHAP_conduct## this example code and file will realse soonSHAP_plus## this example code and file will realse soonIG_calculator## this example code and file will realse soonSupplementary materialsAll supplementary materials associated with the article can be found in the Supplementary_material folder. Furthermore, the code and detailed explanations related to the two experimental research cases mentioned in the article, Case 1 and Case 2, are available in their respective directories.The subcellular localization classification models trained for Case 1 and Case 2 are stored separately in <Case1/Classification and feature filtering module/csae1_localization_model.pkl> and <Case2/Classification and feature filtering module/csae1_localization_model.pkl>. These models accept protein feature inputs identical to those in the demo file and generate corresponding predictions.CitationIf this library has been helpful in your work, please cite it using the following format:@misc{protloc_mex1,
    title={{protloc_mex1}: a comprehensive pipeline for the rapid development of subcellular localization prediction and model interpretation},
    author={Zeyu Luo, Rui Wang},
    howpublished = {\url{https://pypi.org/project/protloc-mex1/}},
    year={2023}
}If you intend to use the SHAP method, please refer to Lundberg's work:
Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In NIPS (2017). arXiv preprint arXiv:1705.07874.2017Acknowledgmentswe are acknowledge the contributions of the open-source community and the developers of the Python libraries used in this study.