Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology

This is the full repository containing scripts for feature construction, feature selection, training, and testing of the interpretable protein-ligand binding affinity prediction algorithm Predicting Affinity Through Homology (PATH).

The inference code for PATH can be found in the OSPREY3 package. My implementation of TNet-BP from TopologyNet (Cang and Wei, 2017) can be found here.

Prerequisites

1. Python environment

1. Install conda or mamba (you only need one of the two).
2. Run conda env create -f tnet2017.yml or mamba env create -f tnet2017.yml to create the tnet2017 environment. Activate this environment by conda activate tnet2017.
3. pip install -r requirements.txt to install additional dependencies for this project that are not available through conda.

2. Redis server

I execute my computational tasks (jobs) in a compute cluster managed by SLURM. To keep track of the statuses of jobs and their results, I use a Redis database and a custom MapReduce-style system.

I first explain my custom MapReduce-style system. This system consists of two scripts, job_wrapper.py and dispatch_jobs.py, a SLURM scheduler, and a Redis database. If you are running these scripts in a SLURM cluster, you will need to modify the headers of the temporary shell scripts (see below) to fit the configuration of your cluster. If you are executing these scripts on a compute cluster with a different job scheduler, more changes will need to be made according how compute jobs are submitted on your cluster.

1. Each task is associated with a set of sequentially numbered key starting from a prefix, which is reflected in the KEY_PREFIX variable in dispatch_jobs.py.
2. dispatch_jobs.py will create an entry in the database for each key containing information about the job and the fields {started, finished, error} all set to False. It then submits by creating temporary shell scripts that execute python job_wrapper.py --key {k} and submit these shell scripts to the SLURM scheduler.
3. job_wrapper.py contains the instructions for execution when the work is allocated to a scheduler.

As mentioned, a Redis database is used for managing jobs submitted to the SLURM batch cluster. To set up this database,

1. Build and install Redis via [https://redis.io/docs/getting-started/installation/install-redis-from-source/].
2. Optionally, add the src folder of Redis to path.
3. Create a redis.conf file somewhere and set a default password by putting e.g. requirepass topology in that file.
4. Start the redis server on a host with your redis.conf and adjust the DB constant in dispatch_jobs.py accordingly.

3. Clone this repository

1. Install git lfs.
2. Clone this repository.

Persistence image construction

The persistence folder contains scripts that construct persistence images using the opposition distance. Start with persistence/README.md

Feature selection, model training, and validation

The gbr folder contains of scripts that perform feature selection, model training, and validation of the gradient boosting regressor on persistence images to eventually lead to the formulation of persistence fingerprint and PATH. Start with gbr/README.md.

Citation requirements

See CITING_OSPREY.txt.