What is it about?

AlphaFold2 is an AI folding program trained to predict the 3D structures of proteins from their amino acid sequence. Proteins rarely attack themselves, a process called autocatalytic posttranslation modification, and it is very difficult to predict which proteins will react with themselves and which ones won’t. We had a challenging question for AlphaFold – had its structural training set taught it some chemistry? Could it tell whether amino acids would react with one another – a rare yet important occurrence? The result surprised us AlphaFold2 had learned some chemistry. It had figured out which amino acids in fluorescent proteins do the chemistry that makes them glow. We suspect that the protein databank training set and multiple sequence alignments enable AlphaFold2 to “think” like chemists and look for the amino acids required to react with one another to make the protein fluorescent.

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Why is it important?

A folding program learning some chemistry from its training set also has wider implications. By asking the right questions, what else can be gained from other deep learning algorithms? Could facial recognition algorithms find hidden markers for diseases? Could algorithms designed to predict spending patterns among consumers also find a propensity for minor theft or deception?


Most of my career (~30 years) I have used computer simulations to model the behavior of fluorescent proteins. This was a quick project to test the capabilities of AlphaFold2. I recruited 4 great Connecticut College undergraduate students and got Prof Zhuang to help with the statistics. We had low expectations going into the project. AlphaFold took less the a semester to blow our socks off.

Marc Zimmer
Connecticut College

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This page is a summary of: AlphaFold2 and RoseTTAFold predict posttranslational modifications. Chromophore formation in GFP-like proteins, PLoS ONE, June 2022, PLOS, DOI: 10.1371/journal.pone.0267560.
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