ReviewhLife2023
A roadmap for exploring the untouched protein space for biology and medicine.
Review in hLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.