Glycation, a type of posttranslational modification,
preferentially
occurs on lysine and arginine residues, impairing protein functionality
and altering characteristics. This process is linked to diseases such
as Alzheimer’s, diabetes, and atherosclerosis. Traditional
wet lab experiments are time-consuming, whereas machine learning has
significantly streamlined the prediction of protein glycation sites.
Despite promising results, challenges remain, including data imbalance,
feature redundancy, and suboptimal classifier performance. This research
introduces Glypred, a lysine glycation site prediction model combining
ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional
long short-term memory network (BiLSTM) methodologies, with an additional
multihead attention mechanism integrated into the BiLSTM. To achieve
this, the study undertakes several key steps: selecting diverse feature
types to capture comprehensive protein information, employing a cluster-based
undersampling strategy to balance the data set, using LightGBM for
feature selection to enhance model performance, and implementing a
bidirectional LSTM network for accurate classification. Together,
these approaches ensure that Glypred effectively identifies glycation
sites with high accuracy and robustness. For feature encoding, five
distinct feature typesAAC, KMER, DR, PWAA, and EBGWwere
selected to capture a broad spectrum of protein sequence and biological
information. These encoded features were integrated and validated
to ensure comprehensive protein information acquisition. To address
the issue of highly imbalanced positive and negative samples, various
undersampling algorithms, including random undersampling, NearMiss,
edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately
chosen to remove redundant nonglycated training data, establishing
a balanced data set that enhances the model’s accuracy and
robustness. For feature selection, the LightGBM ensemble learning
algorithm was employed to reduce feature dimensionality by identifying
the most significant features. This approach accelerates model training,
enhances generalization capabilities, and ensures good transferability
of the model. Finally, a bidirectional long short-term memory network
was used as the classifier, with a network structure designed to capture
glycation modification site features from both forward and backward
directions. To prevent overfitting, appropriate regularization parameters
and dropout rates were introduced, achieving efficient classification.
Experimental results show that Glypred achieved optimal performance.
This model provides new insights for bioinformatics and encourages
the application of similar strategies in other fields. A lysine glycation
site prediction software tool was also developed using the PyQt5 library,
offering researchers an auxiliary screening tool to reduce workload
and improve efficiency. The software and data sets are available on
GitHub: .
