1. Mining folded proteomes in the era of accurate structure prediction.
- Author
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Bayly-Jones, Charles and Whisstock, James C.
- Subjects
- *
PROTEIN structure , *ARTIFICIAL intelligence , *BIOLOGICAL systems , *MINES & mineral resources , *INTERNET servers - Abstract
Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques and a wealth of experimentally determined structures, previously curated sequence databases have become a rich source of biological information. Here, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins. Author summary: Virtually every cellular process in all organisms on Earth is driven by molecular nano-machines known as proteins. The diverse functions of proteins are the result of the unique three-dimensional shape adopted by a given protein molecule. It is therefore important to determine the shape of a given protein, which unlike DNA and our genes, cannot be known from its sequence alone. Since two proteins with similar shapes typically have a similar function, knowing a protein shape provides crucial clues about its function. By virtue of decades of experimental work and advances in artificial intelligence, this complex shape can now be computationally predicted for any protein whose composition is known. Scientists have used these and other methods to produce enormous libraries of protein shapes consisting of nearly a million unique entries. However, these libraries are too large and too complex for researchers to 'read'. We use shape-comparison algorithms to carefully check these shape-libraries to gain insight into the potential function and biological role of previously unknown proteins. Furthermore, we identified new members of protein families using this technique. We show that shape-matching algorithms and computationally generated shape-libraries can be used effectively together to yield new insights and expedite scientific endeavours. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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