Incorporating Support Vector Machine With Sequential Minimal Optimization to Identify Anticancer Peptides

Abstract BackgroundCancer is a major cause of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has received increasing attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction.ResultsThis study chose amino acid composition (AAC), N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzed them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) distinguishing ACPs from non-ACPs. Since a growing number of studies have shown that some antimicrobial peptides (AMPs) exhibit anticancer function, a model (model 1) to distinguish ACPs from AMPs is also been developed. Comparing to previous models, models developed in this research show better performance (accuracy: 82.5% for model 1 and 93.5% for model 2).ConclusionsThis work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-models show better performance than unoptimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as feature, performs better than all other existing tools.