Search

The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted manuscripts as well as published papers, we have noticed several recurring issues, which make some reported findings hard to understand and replicate. We suspect this may be due to biologists being unfamiliar with machine learning methodology, or conversely, machine learning experts may miss some of the knowledge needed to correctly apply their methods to proteins. Here, we aim to bridge this gap for developers of such methods. The most striking issues are linked to a lack of clarity: how were annotations of interest obtained; which benchmark metrics were used; how are positives and negatives defined. Others relate to a lack of rigor: If you sneak in structural information, your method is not sequence-based; if you compare your own model to "state-of-the-art," take the best methods; if you want to conclude that some method is better than another, obtain a significance estimate to support this claim. These, and other issues, we will cover in detail. These points may have seemed obvious to the authors during writing; however, they are not always clear-cut to the readers. We also expect many of these tips to hold for other machine learning-based applications in biology. Therefore, many computational biologists who develop methods in this particular subject will benefit from a concise overview of what to avoid and what to do instead. [ABSTRACT FROM AUTHOR]

Lipoxygenase (LOX) activity provides oxidative lipid metabolites, which are involved in inflammatory disorders and tumorigenesis. Activity‐based probes to detect the activity of LOX enzymes in their cellular context provide opportunities to explore LOX biology and LOX inhibition. Here, we developed Labelox B as a potent covalent LOX inhibitor for one‐step activity‐based labeling of proteins with LOX activity. Labelox B was used to establish an ELISA‐based assay for affinity capture and antibody‐based detection of specific LOX isoenzymes. Moreover, Labelox B enabled efficient activity‐based labeling of endogenous LOXs in living cells. LOX proved to localize in the nucleus, which was rationalized by identification of a functional bromodomain‐like consensus motif in 15‐LOX‐1. This indicates that 15‐LOX‐1 is not only involved in oxidative lipid metabolism, but also in chromatin binding, which suggests a potential role in chromatin modifications. [ABSTRACT FROM AUTHOR]