ZEPPI (Z-score Evaluation of Protein-Protein Interfaces) is a method designed for proteome-scale sequence-based evaluation of protein-protein interfaces as defined by 3D atomic-level models of protein-protein interaction (PPI) direct binary complexes.
Structural models for the PPI complexes may be derived from a number of sources, including:
- Experimentally determined structure in The Protein Data Bank (PDB)
- Template-based modeling as in PrePPI
- Protein docking studies
- Deep learning algorithms such as those based on AlphaFold Multimer.
ZEPPI addresses the evaluation challenge by capitalizing on sequence co-evolution and conservation of the residues that come into interfacial contact. The hallmark of ZEPPI lies in its computation of the interface Z-score, the ZEPPI score, achieved through the comparison of interface-derived contact metrics against metrics derived from randomly selected surface residues. Thus, ZEPPI negates the need for factoring in indirect interactions, as the structural model delineates the interacting residues.
This repository contains code for ZEPPI and the needed input data for the tutorial examples. ZEPPI can be run either on a PC for a small number of protein-protein structural models or, for larger-scale explorations, as batched jobs on SGE- or SLURM-equipped clusters.
ZEPPI is implemented in Python 3 and requires the following libraries: biopython, numpy, numba, scipy, pandas, and the below published packages:
- Surfv to calculate solvent accessible surface area for protein structures
- HMMER to make multiple sequence alignments for sequence homologs of a query sequence
- HH-suite to search for protein sequences similar to a query sequence in protein sequence databases
The install time varies but typically should not exceed 30 minutes. This program has been tested on MacOS 12.6.7 and 13.4.1, Springdale Linux 7.9 (Verona), and Rocky Linux 8.5 (Green Obsidian) with bash-4.4.20, python-3.9.7, numpy-1.23.4, biopython-1.79, numba-0.56.2, scipy-1.9.1, and pandas-1.5.0.
Download the zipped GitHub codebase and unzip it. To run the tutorial examples, make sure the above-mentioned Python modules are installed on your machine.
Go to the unzipped folder, and edit the Run_ZEPPI.sh file with the ZEPPI_base variable being your directory path. Run it with bash to see the usage and available options for running ZEPPI.
bash Run_ZEPPI.shUsage: bash Run_ZEPPI.sh PPI_list.csv Output.csv -optionThe parameters are:
PPI_listPoints to the csv file containing the PPI name, chain names, and UniProt identifiers of your query PPIs.OutputContains the computed ZEPPI metrics for the input PPI list.-mTo calculate ZEPPI based on mutual information and conservation (recommended for heterodimers).-mdTo calculate ZEPPI based on mutual information, conservation, and direct coupling analysis (recommended for homodimers; memory-consuming).- Output files will be named after the
PPI_listfile with different endings.
In the Demo folder, we provided the input and output files of 10 bacterial dimers as an example. Try to run the below command in the Scratch folder, and compare your result with the provided output from the Demo folder. To run ZEPPI for your own structure models, the input files required and their formats are described in the Run_ZEPPI.sh file provided with the codebase. The expected run time for the demo PPIs is about 2 min for the -m option or 8 min for the -md option on a standard desktop computer. The expected output files are stored in the Demo folder with details in the Demo/Metrics folder.
bash Run_ZEPPI.sh ./Demo/bacteria_PDBdimer_demo.csv ./Scratch/bacteria_PDBdimer_demo_ZEPPI_m.csv -mThe final output file bacteria_PDBdimer_demo_ZEPPI.csv contains the ZEPPI score (last column) and other columns as described in the below:
| Column | Meaning |
|---|---|
| PPI | the name of PPI |
| N_MSA | the MSA depth |
| N_IFR | the number of interface residues |
| Zmean_MI | Z-score using the mean Mutual Information of all the interface contacts |
| Zmean_Con | Z-score using the mean Conservation score of all the interface contacts |
| Zmean_DCA | Z-score using the mean DCA score of all the interface contacts |
| Ztop_MI | Z-score using the top Mutual Information of all the interface contacts |
| Ztop_Con | Z-score using the top Conservation score of all the interface contacts |
| Ztop_DCA | Z-score using the mean DCA score of all the interface contacts |
For more details, please read our paper.
Haiqing Zhao (hz2592@cumc.columbia.edu) or Barry Honig (bh6@cumc.columbia.edu)