A deep-learning framework for amidation site prediction
Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. Existing computational methods for predicting amidation sites are based on feature extraction, which may result in incomplete or biased features. To address these issues, we proposed a novel deep-learning predictor for amidation sites. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of amidation patterns from the raw protein sequences, and hence it provides a powerful tool for improvement of amidation site prediction.