sethzhangjs / muclipred

MucLiPred is a deep learning model for predicting binding residues in proteins.

MucLiPred

Welcome to the MucLiPred repository.
The DOI of our work: https://doi.org/10.1021/acs.jcim.3c01471

Additional Information

In our study, we frequently refer to RBP and DBP. For clarity:

RBP (RNA Binding Protein): This refers to cases where we focus on the interactions or binding residues between proteins and RNA molecules.

DBP (DNA Binding Protein): This term refers to cases where we focus on the interactions or binding residues between proteins and DNA molecules.

Directory Structure

The repository is organized into several directories, each serving a distinct purpose in the model development and deployment process.

/MucLiPred/train

• protBert_main.py: Contains the code for training the model. This script is the entry point for initiating the training process.
• /other: This directory includes various experimental code snippets that have been used during the development and testing of the model.

/MucLiPred/model

• prot_bert.py: Houses the code defining the architecture of the model. This script details the layers and structure of the neural network used for predictions.

/MucLiPred/data

• This directory contains datasets utilized for training and testing the model. Ensure to follow any licensing or usage restrictions associated with the data.

/MucLiPred/configuration

• config.py: Stores various configuration parameters used during training. Parameters such as learning rate, number of epochs, and other hyperparameters can be adjusted here.

/MucLiPred/prot_bert_bfd

• This directory contains the pretrained models.

Getting Started

To get started with MucLiPred, clone the repository and install the required dependencies:

git clone https://github.com/sethzhangjs/MucLiPred