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