Optimal-pH
The Optimal-pH challenge from Copenhagen Bioinformatics Hackathon: 2021 Protein Edition.
Benchmarking scoreboard: biolib.com/biohackathon/optimal-ph-scoreboard/
Challenge Aim
The focus of this challenge is to apply machine learning to predict the optimal pH of enzyme activity given the primary amino acid sequences as input. A training set of about 105,000 protein sequences and the growth pH values of their hosts organisms are provided.
Getting Started
To get started ensure to follow the guide here.
Training
This repository contains a baseline model and a training script that trains the baseline
model and saves it to a pickle file as src/model.pickle. You can run the training script
with python3 src/train.py
Dataset
The dataset for training can be found here. It contains 105,000 primary protein
sequences (sequence), and the growth pH values of their hosts organisms (mean_growth_PH).
Predictions
The goal is to predict mean_growth_PH. At prediction time you are given a csv file with the single column sequence
like:
sequence
MKKRAHIISFILILALLFTGCSGNKENTSKEPVKETTEKGTGNIKTGTTETNAKPIDDNYGT...
MGKGKRKKRIALYFKRAAVAMLVMVMLLQPIPGTAGSSVKSVEAAVTTGDYIDLQNTATELW...
MLFIDVLILTFALISSLWILLQSLYYTETPLKCEGHTSSNRKASIIVAIKDEPPKVIEELIE...
MIKPSIFLMILILIGFVVGIITYNQSPLLFSLLIQVNYFVLRGDYLSLITSILVTNSFTDFI...
Your src/predict.py code should output a file called predictions.csv in the following format:
prediction
6.75
7.0
5.0
3.5
You can run the prediction script with python3 src/predict.py --input_csv submission/input.csv