Deciphering the code of viral-host adaptation through maximum entropy models

Understanding how the genome of a virus evolves depending on the host it infects is an important question that challenges our knowledge about several mechanisms of host-pathogen interactions, including mutational signatures, innate immunity, and codon optimization. A key facet of this general topic is the study of viral genome evolution after a host-jumping event, a topic which has experienced a surge in interest due to the fight against emerging pathogens such as SARS-CoV-2. In this work, we tackle this question by introducing a new method to learn Maximum Entropy Nucleotide Bias models (MENB) reflecting single, di- and tri- nucleotide usage, which can be trained from viral sequences that infect a given host. We show that both the viral family and the host leave a fingerprint in nucleotide usages which MENB models decode. When the task is to classify both the host and the viral family for a sequence of unknown viral origin MENB models outperform state of the art methods based on deep neural networks. We further demonstrate the generative properties of the proposed framework, presenting an example where we change the nucleotide composition of the 1918 H1N1 Influenza A sequence without changing its protein sequence, while manipulating the nucleotide usage, by diminishing its CpG content. Finally we consider two well-known cases of zoonotic jumps, for the H1N1 Influenza A and for the SARS-CoV-2 viruses, and show that our method can be used to track the adaptation to the new host and to shed light on the more relevant selective pressures which have acted on motif usage during this process. Our work has wide-ranging applications, including integration into metagenomic studies to identify hosts for diverse viruses, surveillance of emerging pathogens, prediction of synonymous mutations that effect immunogenicity during viral evolution in a new host, and the estimation of putative evolutionary ages for viral sequences in similar scenarios. Additionally, the computational framework introduced here can be used to assist vaccine design by tuning motif usage with fine-grained control. ### Competing Interest Statement The authors have declared no competing interest.