Proteins are the major carriers of biological processes and extant proteome contains tremendous diversity. However, the theoretical diversity of proteins greatly outnumbered the currently known, largely due to evolutionary constraints. Here, we propose that untouched protein space, either extant yet with unknown function, or unnatural proteins could have many proteins of desired functions, and outlined a roadmap for exploring such protein space with artificial intelligence. Particularly with the methods developed in natural language processing (NLP), we can first identify a large number of functional proteins and peptides encrypted in biological big data, for instance microbiome and virome data. Secondly, larger scale mutations and directed evolution can be carried out and facilitated by NLP, to achieve improved function based on known proteins. Lastly, sampling random sequences and applying NLP might reveal the more complete landscape of protein functions and enable de novo protein design.

Innovations in sequencing technology have generated voluminous microbial and host genomic data, making it possible to detect these genetic variations and analyze the function influenced by them. Recently, many studies have linked such genetic variations to phenotypes through association or comparative analysis, which have further advanced our understanding of multiple microbial functions. In this review, we summarized the application of association analysis in microbes like Mycobacterium tuberculosis, focusing on screening of microbial genetic variants potentially associated with phenotypes such as drug resistance, pathogenesis and novel drug targets etc.; reviewed the application of additional comparative genomic or transcriptomic methods to identify genetic factors associated with functions in microbes; expanded the scope of our study to focus on host genetic factors associated with certain microbes or microbiome and summarized the recent host genetic variations associated with microbial phenotypes, including susceptibility and load after infection of HIV, presence/absence of different taxa, and quantitative traits of microbiome, and lastly, discussed the challenges that may be encountered and the apparent or potential viable solutions. Gene-function analysis of microbe and microbiome is still in its infancy, and in order to unleash its full potential, it is necessary to understand its history, current status, and the challenges hindering its development.