BAL

Bayesian Active Learning for Optimization and Uncertainty Quantification with Applications in Protein Docking

Dependencies:

• C++ 4.8.5 or higher
• cNMA: Download and install the cNMA from "https://github.com/Shen-Lab/cNMA".
• Energy model: Please download the random forest energy model from "https://drive.google.com/file/d/17ByuNoYy0t1R8EjuTK_cMyul5K004MHa/view?usp=sharing"
• CHARMM: Download the executable 'CHARMM36a1.exe', GBSW: 'radius_gbsw.str', CHARMM27 topology and parameter files and put them into 'dependencies/'.
• ICE Library: https://drive.google.com/file/d/1pCow34TmhDgIaihVJXDZpVrg6MQO_8aO/view?usp=sharing
• Linux Environment

Change directory path

In src/configuration.h, please change the macros as follow:

• src_dir: change to your current "src" path.
• cnma_path: change to your cNMA path.
• scoring_path: change to your 'random_forest.sav' path.
• output_path: change to the directory where you want to output.

Add unbound proteins:

In src/configuration.h, please change the macro 'protein_name' to your 4-letter/digit protein code. Also please change the macro 'protein_path" to the path where the unbound protein is.

If your unbound protein is not in Protein Docking Benchmark 4.0 and you want to get the UQ results, please append its Kd value into 'src/kd_zero' and append the protein name into 'src/kd_list'. Otherwise, you will only get the refined structures and the area under the posterior.

Compile and Run

• Go to 'BAL/src/'.
• Type './complie' to compile.
• Type './for_train' to run BAL.

Output file:

• a.'end'(dir): containing the refined structures: receptor.pdb ligand.pdb

• b.'scores': The final energy (unit in Kcal/mol) of refined structures.

• c.'PMI_log': The log of area under the posterior.

• d.'cond_prob.dat': The conditional probability of refined structure: P(RMSD(x^,x*)<4 | x* \in Mi)

• e. 'Rmsd_dis': The RMSD(x^,x*) distribution of the posterior

• f. 'UQ': The [lb,ub] values

Citation:

@article{Cao537035,
        author = {Cao, Yue and Shen, Yang},
        title = {Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking},
        elocation-id = {537035},
        year = {2019},
        doi = {10.1101/537035},
        publisher = {Cold Spring Harbor Laboratory},
        URL = {https://www.biorxiv.org/content/early/2019/01/31/537035},
        eprint = {https://www.biorxiv.org/content/early/2019/01/31/537035.full.pdf},
        journal = {bioRxiv}
	}

Contact:

Yang Shen: yshen@tamu.edu