PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences

TL;DR

This paper introduces PWM2Vec, a novel embedding method based on position-weight matrices, for classifying coronavirus hosts from spike protein sequences, achieving competitive results and identifying key amino acids involved in host specificity.

Contribution

The paper presents the first use of PWM-based embeddings for host classification from viral sequences, providing a new approach for analyzing coronavirus host specificity.

Findings

PWM2Vec performs comparably to baseline models.

Identifies important amino acids for host prediction.

Effective in classifying diverse coronavirus hosts.

Abstract

COVID-19 pandemic, is still unknown and is an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona-) viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is an important part of determining host specificity since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among avians, bats, camels, swines, humans and weasels, to name a few. We propose a feature embedding based on the well-known position-weight matrix (PWM), which we call PWM2Vec, and use to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications such as determining protein function, or identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs in the context of host classification from viral sequences to generate a fixed-length feature vector representation. The results on the real world data show that in using PWM2Vec, we are able to perform comparably well as compared to baseline models. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus.