senseppi 0.5.7

SENSE-PPI: Sequence-based EvolutioNary ScalE Protein-Protein Interaction prediction

SENSE-PPI

SENSE-PPI is a Deep Learning model for predicting physical protein-protein interactions based on amino acid sequences. It is based on embeddings generated by ESM2 and uses Siamese RNN architecture to perform a binary classification.

Installation

SENSE-PPI requires Python 3.9 or higher. To install the package, run:

pip install senseppi

N.B.: if you intend to use the create_dataset command to generate new datasets from STRING, do not forget to additionally install the MMseqs2 software (instructions can be found at: https://github.com/soedinglab/MMseqs2). The mmseqs command should be available in your PATH.

Usage

There are 5 commands available in the package:

• train: trains SENSE-PPI on a given dataset
• test: computes test metrics (AUROC, AUPRC, F1, MCC, Presicion, Recall, Accuracy) on a given dataset
• predict: predicts interactions for a given dataset
• predict_string: predicts interactions for a given dataset using STRING database: the interactions are taken from the STRING database (based on seed proteins). Predictions are compared with the STRING database. Optionally, the graphs can be constructed.
• create_dataset: creates a dataset from the STRING database based on the taxonomic ID of the organism.

The original SENSE-PPI repository contains two models (checkpoints with weights) pretrained on human PPIs: senseppi.ckpt and dscript.ckpt pretrained on SENSE-PPI and DSCRIPT human datasets respectively.

• senseppi.ckpt: Download from here
• dscript.ckpt : Download from here

The package already comes with preinstalled model senseppi.ckpt that is used by default if model path is not specified.

N.B.: Both pretrained models were made to work with proteins in range 50-800 amino acids.

In order to cite the original SENSE-PPI paper, please use the following link: https://doi.org/10.1101/2023.09.19.558413