Deep learning for mining protein data

Abstract

The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.

Predicting Proteolysis in Complex Proteomes Using Deep Learning

“…However, to our knowledge, the predictions of these algorithms have not been experimentally validated against native ECM proteins, nor has their comparative performance been evaluated in this context. Moreover, although recurrent neural networks have shown promising results in protein sequence and function prediction [33], this architecture, again to our knowledge, has not previously been employed for proteolytic cleavage site prediction [34,35].…”

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

“…Consequently, extensive external validation is needed for fair comparison over bias in training. For chemogenomic models, there are many deep learning protein models, entailing inherent protein characteristics, that are not used in previous models [109,110]. Featurization by using those models will improve performance.…”

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

“…Deep learning is a popular artificial intelligence (AI) way that has been successfully applied in medical diagnoses [ 1 ], cellular image analysis [ 2 ], chemical syntheses [ 3 ], classification of drugs [ 4 ] and so on [ 5 ]. Deep learning is a promising technology for the development and discovery of innovative drugs.…”