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

Simple Summary

The family of coronaviruses comprises a diverse set of strains and variants which cause diseases from the common cold to COVID-19. Moreover, they infect a wide array of hosts from bats, camels, birds, to humans. Studying coronaviruses through the lens of host specificity provides a unique perspective to understanding the evolution, diversity and dynamics of this family. In particular, this can reveal groups of different hosts infected by similar strains, giving clues on strains which were more likely to have evolved to jump from one host to another. In this work, we frame host specificity as a classification task, in designing a very compact numerical representation of the spike sequences of different coronaviruses. Based on this numerical representation, classification methods are able to detect the target host with high accuracy. Such an approach can used to efficiently scale to large volumes of sequences, in order to unveil trends in the host specificity of different coronavirus strains.

Abstract

The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—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 important in 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 birds, bats, camels, swine, 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 we use it 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 and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. 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. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models.

Finding your way through Pneumocystis sequences in the NCBI gene database

Pneumocystis sequences can be downloaded from GenBank for purposes as primer/probe design or phylogenetic studies. Due to changes in nomenclature and assignment, available sequences are presented with a variety of inhomogeneous information, which renders practical utilization difficult. The aim of this study was the descriptive evaluation of different parameters of 532 Pneumocystis sequences of mitochondrial and ribosomal origin downloaded from GenBank with regard to completeness and information content. Pneumocystis sequences were characterized by up to four different names. Official changes in nomenclature have only been partly implemented and the usage of the "forma specialis", a special feature of Pneumocystis, has only been established fragmentary in the database. Hints for a mitochondrial or ribosomal genomic origin could be found, but can easily be overlooked, which renders the download of wrong reference material possible. The specification of the host was either not available or variable regarding the used language and the localization of this information in the title or several subtitles, which limits their applicability in phylogenetic studies. Declaration of products and geographic origin was incomplete. The print version of this manuscript is completed by an online database which contains detailed information to every accession number included in the meta-analysis.