ArticleInternational journal of molecular sciences2019
Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Autoregressive enzyme function prediction with multi-scale multi-modality fusion.Briefings in bioinformatics · 2025Article
- Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost.Frontiers in artificial intelligence · 2024Article
- LEGO-CSM: a tool for functional characterization of proteins.Bioinformatics (Oxford, England) · 2023Article
- Antimicrobial Peptides Prediction method based on sequence multidimensional feature embedding.Frontiers in genetics · 2022Article
- An Interpretable Double-Scale Attention Model for Enzyme Protein Class Prediction Based on Transformer Encoders and Multi-Scale Convolutions.Frontiers in genetics · 2022Article
- Article
- Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network.Frontiers in genetics · 2021Article
- Hypothetical Proteins as Predecessors of Long Non-coding RNAs.Current genomics · 2020Review
- Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning.The plant genome · 2020Article
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Authors and funding
7 authors.
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Abstract
During the past decade, due to the number of proteins in PDB database being increased gradually, traditional methods cannot better understand the function of newly discovered enzymes in chemical reactions. Computational models and protein feature representation for predicting enzymatic function are more important. Most of existing methods for predicting enzymatic function have used protein geometric structure or protein sequence alone. In this paper, the functions of enzymes are predicted from many-sided biological information including sequence information and structure information. Firstly, we extract the mutation information from amino acids sequence by the position scoring matrix and express structure information with amino acids distance and angle. Then, we use histogram to show the extracted sequence and structural features respectively. Meanwhile, we establish a network model of three parallel Deep Convolutional Neural Networks (DCNN) to learn three features of enzyme for function prediction simultaneously, and the outputs are fused through two different architectures. Finally, The proposed model was investigated on a large dataset of 43,843 enzymes from the PDB and achieved 92.34% correct classification when sequence information is considered, demonstrating an improvement compared with the previous result.
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