Aim and Objective: Missense mutation (MM) may lead to various human diseases by
disabling proteins. Accurate prediction of MM is important and challenging for both protein
function annotation and drug design. Although several computational methods yielded acceptable
success rates, there is still room for further enhancing the prediction performance of MM.
Materials and Methods: In the present study, we designed a new feature extracting method, which
considers the impact degree of residues in the microenvironment range to the mutation site.
Stringent cross-validation and independent test on benchmark datasets were performed to evaluate
the efficacy of the proposed feature extracting method. Furthermore, three heterogeneous
prediction models were trained and then ensembled for the final prediction. By combining the
feature representation method and classifier ensemble technique, we reported a novel MM
predictor called TargetMM for identifying the pathogenic mutations from the neutral ones.
Results: Comparison outcomes based on statistical evaluation demonstrate that TargetMM
outperforms the prior advanced methods on the independent test data. The source codes and
benchmark datasets of TargetMM are freely available at https://github.com/sera616/TargetMM.git
for academic use.