Prediction of protein acetylation sites with machine learning
Creators: name: Gattam, Sai Swaroop Reddy
Publisher: name: No Publisher Supplied
Year: 2024

description: Proteins are the workhorses of the cell, carrying out a wide range of functions essential for life. However, Post-translational modifications (PTMs), chemical modifications proteins undergo after they are translated from Deoxyribonucleic acid, can have a profound impact on protein function, localization, and stability. Among PTMs, Acetylation, the addition of an acetyl group to a lysine residue, is of particular interest due to its vital role as a critical regulator in numerous cellular events, including gene expression, cellular metabolism, signal transduction, protein-protein interaction, and cellular response to stress. Thus, being able to accurately predict acetylation in proteins can be considered as an important step to provide better understanding about the cell functioning mechanism. This study primarily examines the capacity to anticipate lysine site acetylation in proteins. Primarily, 20% of proteins are separated from the dataset for independent analysis with respect to metrics of prediction. On rest of the proteins, acknowledging the imbalance in the dataset, AllKNN under-sampling method is used to balance the positive and negative datapoints. Also, principal component analysis (PCA) is performed to reduce the dimensions of the data, eliminating columns with less than one percent variance in values. Various machine learning methods including Support Vector Machine, Stochastic Gradient Descent Classifier, alongside Ensemble learning methods such as Random Forest (RF), Extreme Gradient boosting (XGBoost), and Adaptive Boosting are explored in this study. Fostering the hyperparameter tuning technique, a large space of parameter combinations, i.e., about 157 of SVM, 46,876 of SGDC, 1,162 of RF, 2,634 of XGBoost and 16,179 of AdaBoost are investigated. Notably, application of PCA proved to be critical, as performance of all the models across all metrics of consideration improved post application. Among these methods, XGBoost yielded superior performance across several metrics such as Matthew’s Correlation Coefficient (MCC), Accuracy, Balanced Accuracy, Area under the Receiver Operating Characteristic Curve, Specificity, Precision, and F1-score, while RF proved best for Sensitivity. Remarkably, XGBoost outperformed the state-of-the-art model achieving 0.68 in terms of MCC. Subsequently, basic Convolutional Neural Networks (CNN) are used to predict the active sites, followed by employing transfer-learning technique to harness the prediction potential of state-of-the-art image classification models such as AlexNet, ResNet-18, VGG16, and ViT-L/16 (ViT). Techniques such as Batch Normalization and Dropout are utilized to prevent overfitting of data to the models. Activation functions such as Rectified linear unit and SoftMax are adopted for hidden and output layers of models respectively, and appropriately. ViT demonstrated better performance compared to the other CNN and Fully Connected neural networks classifiers. However, the performance of deep learning-based models fell short of the XGBoost model. To summarize, we have successfully developed a model that performs better in identifying lysine acetylation sites than previously proposed methods found in the literature. This advancement is significant as it allows for faster and more precise identification of acetylation sites.