Experimental investigation of enzyme functional annotations reveals extensive annotation error

Abstract

Only a small fraction of genes deposited to databases has been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy of existing annotations are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation.

The substrate scopes of enzymes: a general prediction model based on machine and deep learning

“…Deep learning models have been used to predict enzyme functions by either predicting their assignment to EC classes 19,20,21 , or by predicting functional domains within the protein sequence 22 . These approaches are complementary to the prediction of enzyme-substrate pairs, as the substrate scopes of different enzymes within a given EC class or with a specific domain architecture can be highly diverse 23 .…”

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

“…Most transporters are discovered via their effects on the uptake or efflux of a particular substrate of interest, and they are often codified accordingly. Our experience is that—just as with enzyme annotation more generally [ 250 ]—this leads to misannotation in that “any” activity discovered first is typically seen the main or even only activity. A classic example is SLC22A4, previously known as OCTN1.…”