DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins
TL;DR
A machine learning model is trained, DBP-CanPred, to identify driver mutations in DBPs using the sequence-derived evolutionary features, as well as structure-based features such as mutation-perturbed structural descriptors, which contributes to understanding mutation patterns in DNA-binding proteins and supports variant interpretation in cancer research.
Abstract
Introduction The fundamental cellular processes, including transcriptional regulation, chromatin organization, and genome maintenance, are regulated by DNA-binding proteins (DBPs). Mutations in DBPs can alter protein-DNA interactions, leading to tumor development. However, identifying such driver mutations remains a major challenge due to limitations of experimental approaches. Methods We have trained a machine learning model, DBP-CanPred, to identify driver mutations in DBPs. We used the sequence-derived evolutionary features, as well as structure-based features such as mutation-perturbed structural descriptors. Results We evaluated DBP-CanPred using a curated test set, achieving an AU-ROC of 0.86 and a balanced accuracy of 0.79. Further analysis based on substitution-type showed consistent performance across different categories, especially higher performance on charged residues. In addition, we applied the model on an independent dataset and identified potential driver mutations with high confidence scores. Discussion The study contributes to understanding mutation patterns in DNA-binding proteins and supports variant interpretation in cancer research.