protein-fep-benchmark

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

protein-fep-benchmark

★ 9 Open GitHub ↗

Data and scripts for the Protein Binding FEP+ benchmark manuscript.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Protein Binding FEP+ Benchmark

Data and analysis scripts for Sampson, J. M. et al. "Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations."  J. Mol. Biol. 436, 168640 (2024). [https://doi.org/10.1016/j.jmb.2024.168640]()

The following analysis was performed on the systems in the paper.

1. Prepare and process the experimental data, downloading the SKEMPI2 data file if necessary.

```bash
    ( cd experimental_data && ./prepare_data.sh )
    ```

2. Place original 100 ns `out.fmp` files into `results/fmp`.  Due to size limitations, these files are not included in this repository but may be downloaded [here](https://learn.schrodinger.com/public/misc/fmp_files.zip) (340 MB).

3. Edit the `results/systems.csv` file to include the relevant metadata for each system.

4. Compile the FEP results dataset, including experimental data and derived data columns.

```bash
    ( cd results && ./compile_dataset.sh -v )
    ```

5. Flag probable outliers from the .fmp files.

```bash
    export SCHRODINGER=/path/to/Schrodinger_Suite_2024-2
    (
        cd results

# Calculate the .fmp features (this may take a while!)
        time ./calculate_features.sh -v

# Merge the features and results into a single dataset for stats
        Rscript merge_features_and_results.R

# Do the same for testing the case studies dataset
        Rscript merge_features_and_results.R --case-studies

# Flag probable outliers in the benchmark dataset
        # (writes `charged_flag_correction_term.txt`)
        Rscript flag_outliers.R \
            benchmark_merged_features_results.csv \
            -o benchmark_flagged_cases.csv

# Flag probable outliers in the case study dataset using
        # `charged_flag_correction_term.txt` from the benchmark dataset
        Rscript flag_outliers.R \
            case_studies_merged_features_results.csv \
            -o case_studies_flagged_cases.csv \
            --charged-flag-corr $(cat charged_flag_correction_term.txt)
    )
    ```

protein-fep-benchmark

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README