peptide_MDI

Authors

• Tianfei YU
• Tianshuo HU
• Yan LIU
• Qian LIN

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Introduction

peptide_MDI is a workflow designed for the prediction and screening of bioactive peptides from proteins. This streamlined process integrates computational tools to analyze protein sequences and identify potential peptides with biological activity.

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Workflow

Overview

Below is an overview of the workflow:

Step 1: Configure the Environment

Before running the workflow, ensure you have Anaconda installed on your system.

1. Create a new Conda environment:

   conda create -n peptide_MDI
   

2. Activate the environment：

   conda activate peptide_MDI
   

3. Install Nextflow：

   conda install -c bioconda nextflow
   

4. Install autogrid4

   sudo apt-get install autogrid
   

Step 2: Navigate to the Working Directory

Switch to the directory containing the workflow scripts and resources:

   cd /path/to/peptide_MDI

Step 3: Upload the Required Files into the in_put Folder

Ensure the following files are placed in the in_put folder before running the workflow:

• Protein sequence file:
  Protein.fasta
  Contains the protein sequences to be analyzed.

• Receptor structure file:
  Receptor.pdbqt
  The 3D structure of the receptor used for docking studies.

• Docking configuration file:
  Docking_config.gpf
  Provides information about the active pocket of the receptor.

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Step 4: Run the Workflow

Execute the workflow using Nextflow by running the following command:

nextflow run main.nf -with-conda

Configuration

If you need to modify the type of simulated enzyme or the peptide release settings, please edit the config.yaml file:

• protease: Set the type of protease used for simulation.
• min_len and max_len: Adjust the minimum and maximum length of the released peptide fragments.

Example configuration in config.yaml:

protease: trypsin
min_len: 2
max_len: 10

Contact

For any questions or issues, please contact:

• Email: tianfeiyu0721@163.com

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Citation

If you use this workflow in your research, please cite the following paper:

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References

The following references were utilized in the development and implementation of this workflow:

1. Adasme, M. F., Linnemann, K. L., Bolz, S. N., Kaiser, F., Salentin, S., Haupt, V. J., & Schroeder, M. (2021).
   PLIP 2021: Expanding the scope of the protein–ligand interaction profiler to DNA and RNA.
   Nucleic Acids Research, 49(W1), W530-W534.
   https://doi.org/10.1093/nar/gkab294

2. Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017).
   Nextflow enables reproducible computational workflows.
   Nature Biotechnology, 35(4), 316-319.
   https://doi.org/10.1038/nbt.3820

3. Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas F Tillack, Michel F Sanner, Andreas Koch, and Stefano Forli. (2021).
   Accelerating AutoDock4 with GPUs and Gradient-Based Local Search
   Journal of Chemical Theory and Computation, 17(2), 1060-1073.
   https://doi.org/10.1021/acs.jcim.1c00203

4. Guntuboina, C., Das, A., Mollaei, P., Kim, S., & Barati Farimani, A. (2023).
   PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction.
   The Journal of Physical Chemistry Letters, 14(46), 10427-10434.
   https://doi.org/10.1021/acs.jctc.0c01006

5. Tien, M. Z., Sydykova, D. K., Meyer, A. G., & Wilke, C. O. (2013).
   PeptideBuilder: A simple Python library to generate model peptides.
   PeerJ, 1, e80.
   https://doi.org/10.7717/peerj.80

6. Yang, J., Gao, Z., Ren, X., Sheng, J., Xu, P., Chang, C., & Fu, Y. (2021).
   DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning.
   Analytical Chemistry, 93(15), 6094-6103.
   https://doi.org/10.1021/acs.analchem.0c04704