# Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides
## 6 Results and Discussion
### Selecting the Input Feature Groups to Build Our Model
PepBind-SVM, its source code, and our newly generated protein binding peptides are publicly available at [[https://github.com/MLBC-lab/pepbind-SVM](https://github.com/MLBC-lab/pepbind-SVM)]([https://github.com/MLBC-lab/pepbind-SVM](https://github.com/MLBC-lab/pepbind-SVM)) (accessed on 1 September 2024). Achieving the best results using SVM with linear kernel as the classifier and composition as feature group, which are relatively simple combinations, can be directly related to the employed dataset. We prepared the dataset with several control cases to make sure that it is a very clear representation of protein-binding and non-binding peptides. Achieving promising results using this combination demonstrates that if we use proper data with high resolution and clarity, finding the pattern in the data is much more straightforward. It is important to highlight that despite promising results, we still have a long way to go to solve this problem. Hence, for our future study, we aim at using structural and evolutionary based features to tackle this problem. We also aim to use a complex deep-learning model to solve this problem. One of the limitations of this study is regarding data cleaning. We cleaned up our data using a binding affinity of less than 0.2 for non-binding peptides and over 0.8 for binding peptides. This threshold enables us to make sure that our positive samples and negative samples are correct. In the future, we aim to collect more data and experimentally validate the proper threshold or try other thresholds with more extensive data. Another limitation of this study is that our model is designed to identify protein-binding and non-binding peptides. However, it is not able to identify peptides with \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Acc (\%)** & **Sn (\%)** & **Sp (\%)** & **MCC** & **AUC** & **Fi-Score** \\ \hline SVM-RBF & 88.4 & 84.5 & 92.4 & 0.760 & 0.925 & 0.871 \\ \hline SVM-Linear & 89.1 & 85.4 & 92.9 & 0.784 & 0.931 & 0.887 \\ \hline RF-50 & 87.4 & 85.0 & 89.8 & 0.750 & 0.932 & 0.871 \\ \hline RF-100 & 88.2 & 86.1 & 90.4 & 0.765 & 0.937 & 0.880 \\ \hline RF-200 & 87.6 & 85.7 & 89.6 & 0.754 & 0.938 & 0.874 \\ \hline XGBoost & 87.8 & 86.1 & 89.6 & 0.758 & 0.937 & 0.876 \\ \hline \end{tabular}
\end{table} 
Table 7: Performance of our employed classifiers on the composition feature group using five-fold cross-validation. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Acc (\%)** & **Sn (\%)** & **Sp (\%)** & **MCC** & **AUC** & **Fi-Score** \\ \hline SVM-RBF & 77.7 & 68.8 & 86.6 & 0.564 & 0.859 & 0.756 \\ \hline SVM-Linear & 88.4 & 85.3 & 91.5 & 0.769 & 0.930 & 0.880 \\ \hline RF-50 & 83.2 & 80.6 & 85.7 & 0.665 & 0.903 & 0.827 \\ \hline RF-100 & 83.8 & 81.1 & 86.5 & 0.678 & 0.910 & 0.833 \\ \hline RF-200 & 84.0 & 81.6 & 86.5 & 0.682 & 0.910 & 0.836 \\ \hline XGBoost & 87.4 & 86.3 & 88.6 & 0.749 & 0.934 & 0.873 \\ \hline \end{tabular}
\end{table} 
Table 8: Performance of our employed classifiers using a combination of the composition and physicochemical feature groups using five-fold cross-validation. binding affinity between 0.2 and 0.8 (uncertain proteins). Our future direction is to identify a proper threshold, which would be validated experimentally with the help of experts, for uncertain peptides and identify them. Finally, since the employed dataset is new and was generated by us, there is no previous work with which to compare our model. However, we share this dataset along with this article for future studies to evaluate their model. Our future direction is also to use complex yet explainable models to tackle this problem. Next, we investigate the explainability of the proposed model using SHAP.