Comprehensive characterization of multi-omic landscapes between gut-microbiota metabolites and the G-protein-coupled receptors. Yunguang Qiu, et al.
Instructions:
ML_code directory for ML models generation and prediction:
1. The features were calculated by using deltaVinaXGB (https://github.com/jenniening/deltaVinaXGB).
2. The mechine learning model were created and evaluated by using open-source Pycaret 2.2 [full version].
3. The process of creating ML models can be found in ml.ipynb;
Reference: 1.Lu J, et al., Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions. Journal of Chemical Information and Modeling 59, 4540-4549 (2019). 2. pycaret.org. PyCaret, April 2020. URL https://pycaret.org/about. PyCaret version 1.0.0.
Pocket_classfication_code directory for scripts of identifing and matching pocket position in GPCR receptor:
1. match generic numbers to sequence numbers for each model file: (e.g., all ClassA GPCR generic numbers)
model_residue_match.py
2. for each pocket file, judge whether pocket belong to orthersteric pocket: (mathch the intersection of sequence number of pocekt with pocket sequence number; e.g., classA: resi >=5)
pocket_generic_numer_match_resi5.py
3. for each docking result, match docking pocket to above analyzed type of pocket; (for each docking results file)
match_gpcruniq_file.py