Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins
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Abstract
Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein “unknownme”. This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for 450 enzymes of unknown function from the model bacteria Escherichia coli using the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.
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Computational models could help propagate the experimentally validated functional annotations to the correct portion of the protein space
I've wondered whether there might be interesting signatures that could differentiate between 1) inappropriate transfer of functional annotations to seemingly similar proteins vs 2) incomplete annotations, i.e. where the other protein(s) may indeed have the originally hypothesized function AND a second or additional functions on top of this that confuses interpretation. Do you know of any work or models that is attempting to address this?
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very few of the proteins in UniprotKB54, the most widely used protein function database55, have been linked to experimental data
Curious if you might have a ballpark number in terms of % of entries for which there is direct experimental data? I've been trying to get a sense of this and agree that it's low, but haven't been able to track down a number.
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