Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171862
Title: Enzyme catalytic residue prediction using deep learning methods
Authors: Guan, Jia Sheng
Keywords: Science::Biological sciences
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Guan, J. S. (2023). Enzyme catalytic residue prediction using deep learning methods. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171862
Abstract: Identification of catalytic residues in enzymes have important applications ranging from drug discovery to protein engineering. However, locating catalytic residues in laboratory is time consuming and costly. Through high throughput computational methods, potential catalytic residues could be elucidated. While many models trained to predict catalytic residues were published, there are still unexplored combinations of model features and data preparation methods. In this project, graph neural network (GNN) and multi-layer perceptron (MLP) models were constructed to predict catalytic residues. The choice of edge weight equation was discovered to have huge impact on GNN model performance. Embeddings from a large protein language model, Evolutionary Scale Modeling 2 (ESM-2), were experimented and found suitable as features for MLP and GNN models, rivaling many published models in performance. Atchley factors as features were investigated but results hinted that the information might have already been included in the ESM-2 embeddings. To address knowledge gap, structural information of entire protein complex was considered as GNN model feature but found no benefits as compared to using only monomer structures as in published models. To resolve class imbalance issue, down-sampling of non-catalytic to catalytic residues to a 10:1 ratio was tested but it did not improve models’ performances.
URI: https://hdl.handle.net/10356/171862
Schools: School of Biological Sciences 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SBS Student Reports (FYP/IA/PA/PI)

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