Motivation
Protein-protein interactions drive many relevant biological events, such as infection, replication, and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling, like protein-protein docking, can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling is that it generates a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers.
Results
Using weak supervision, we developed a data augmentation method that we named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 MCC on the test set, surpassing the state-of-the-art scoring functions.
Availability
Docking models from Benchmark 5 are available at https://doi.org/10.5281/zenodo.4012018. Processed tabular data available at https://repository.kaust.edu.sa/handle/10754/666961. Google colab available at https://colab.research.google.com/drive/1vbVrJcQSf6_C3jOAmZzgQbTpuJ5zC1RP?usp=sharing
Supplementary information
Supplementary data are available online.
Protein-protein interactions drive many important biological events, such as infection, replication, and recognition. We need to access the molecular details of the interaction provided by experimental 3D structures to control or engineer such events. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling like protein-protein docking can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling produces a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers. Using weak supervision, we developed a data augmentation method named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 MCC on the test set, surpassing the state-of-the-art scoring functions.
We describe a case of liver failure in twin infants after frequent therapeutic doses of acetaminophen over 4 days at home to manage pyrexia due to a viral illness. The children were treated successfully with intravenous Nacetylcysteine (NAC) following the hospital's protocol. This case highlights the importance of parental awareness of acetaminophen hepatotoxicity and physicians' advice to parents in preventing hepatotoxicity induced by acetaminophen overdose during viral illness. Although Twin 1 met the King's College Hospital criteria for liver transplantation, he was successfully treated with intravenous N-acetylcysteine for 10 days.
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