# Prediction of bacterial protein–compound interactions with only positive samples

## TL;DR

This paper introduces a new method to predict interactions between bacterial proteins and compounds using only positive examples, which is important for drug discovery and biotechnology.

## Contribution

A novel Positive-Unlabeled learning framework called BIN-PU is proposed for bacterial CPI prediction without negative samples.

## Key findings

- BIN-PU outperforms existing PU models in predicting bacterial CPIs using only positive samples.
- BIN-PU's performance was validated on bacterial CYP data and confirmed with biological experiments.
- The method is reproducible and effective on uncurated and human CYP datasets.

## Abstract

Prediction of Compound–Protein Interactions (CPI) in bacteria is crucial to advance various pharmaceutical and chemical engineering fields, including biocatalysis, drug discovery, and industrial processing. However, current CPI models cannot be applied for bacterial CPI prediction due to the lack of curated negative interaction samples.

We propose a novel Positive-Unlabeled (PU) learning framework, named BIN-PU, to address this limitation. BIN-PU generates pseudo positive and negative labels from known positive interaction data, enabling effective training of deep learning models for CPI prediction. We also propose a weighted positive loss function that weights to truly positive samples. We have validated BIN-PU coupled with multiple CPI backbone models, comparing the performance with the existing PU models using bacterial cytochrome P450 (CYP) data. Extensive experiments demonstrate the superiority of BIN-PU over the benchmark models in predicting CPIs with only truly positive samples. Furthermore, we have validated BIN-PU on additional bacterial proteins obtained from literature review, human CYP datasets, and uncurated data for its reproducibility. We have also validated the CPI prediction for the uncurated CYP data with biological and biophysical experiments. BIN-PU represents a significant advancement in CPI prediction for bacterial proteins, opening new possibilities for improving predictive models in related biological interaction tasks.

The source code and data are available at https://github.com/datax-lab/CYP.