  Protein-protein interactions (PPIs) are critical to normal cellular function
and are related to many disease pathways. However, only 4% of PPIs are
annotated with PTMs in biological knowledge databases such as IntAct, mainly
performed through manual curation, which is neither time nor cost-effective. We
use the IntAct PPI database to create a distant supervised dataset annotated
with interacting protein pairs, their corresponding PTM type, and associated
abstracts from the PubMed database. We train an ensemble of BioBERT models -
dubbed PPI-BioBERT-x10 to improve confidence calibration. We extend the use of
ensemble average confidence approach with confidence variation to counteract
the effects of class imbalance to extract high confidence predictions. The
PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro
41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low
variation to identify high quality predictions, tuning the predictions for
precision, we retained 19% of the test predictions with 100% precision. We
evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6
million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter ~ 5700
(4584 unique) high confidence predictions. Of the 5700, human evaluation on a
small randomly sampled subset shows that the precision drops to 33.7% despite
confidence calibration and highlights the challenges of generalisability beyond
the test set even with confidence calibration. We circumvent the problem by
only including predictions associated with multiple papers, improving the
precision to 58.8%. In this work, we highlight the benefits and challenges of
deep learning-based text mining in practice, and the need for increased
emphasis on confidence calibration to facilitate human curation efforts.
