A High Efficient Biological Language Model for Predicting Protein–Protein Interactions

TLDR

Protein–protein interactions (PPIs) underlie essential biological functions, yet efficient sequence‑based prediction methods remain elusive despite ongoing computational advances. The study aims to develop a purely biological language model that predicts PPIs solely from protein sequences. The model employs a Bio2Vec representation that segments sequences into bio‑words and learns distributed embeddings, which are then fed into a convolutional neural network for PPI prediction. The approach achieves significant performance gains over existing methods and inaugurates a new bio‑language processing paradigm that could transform PPI prediction and related tasks.

Abstract

Many life activities and key functions in organisms are maintained by different types of protein⁻protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein⁻protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a "bio-word" segmentation system and a word representation model used for learning the distributed representation for each "bio-word". The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of "bio language processing technology," which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.