A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions...

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Bibliographic Details
Published in:Computers in biology and medicine 2023-03, Vol.154, p.106446-106446, Article 106446
Main Authors: Yan, Tian-Ci, Yue, Zi-Xuan, Xu, Hong-Quan, Liu, Yu-Hong, Hong, Yan-Feng, Chen, Gong-Xing, Tao, Lin, Xie, Tian
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Amino acid sequence
Amino acids
Annotations
Classification
Computer applications
Datasets
Drug targets discovery
Genomes
Information sources
Learning algorithms
Localization
Machine Learning
Multi-algorithm integration
Multi-information fusion
Neural networks
Ontology
Performance evaluation
Predictions
Protein function prediction
Protein interaction
Protein Interaction Maps
Protein structure
Proteins
Proteins - chemistry
Semantic web
Sequences
State-of-the-art reviews
Structure-function relationships
Therapeutic targets
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Summary:New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies. •The methods for protein function prediction in the last five years are summarized.•Discussing methods apply multi-algorithm combinations to predict protein function.•Methods based on multi-information source prediction are discussed.•Difficulties are pointed out and breakthrough developments are emphasized.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2022.106446