Prediction the Substrate Specificities of Membrane Transport Proteins Based on Support Vector Machine and Hybrid Features.

Membrane transport proteins and their substrate specificities play crucial roles in a variety of cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to the protein-target interaction prediction, drug design, membrane recruitment, and dysregulation analysis. However, experimental methods to this aim are time consuming, labor intensive, and costly. Therefore, we proposed a novel method basing on support vector machine (SVM) to predict substrate specificities of membrane transport proteins by integrating features from position-specific score matrix (PSSM), PROFEAT, and Gene Ontology (GO). Finally, jackknife cross-validation tests were adopted on a benchmark and independent datasets to measure the performance of the proposed method. The overall accuracy of 96.16 and 80.45 percent were obtained for two datasets, which are higher (from 2.12 to 20.44 percent) than that by the state-of-the-art tool. Comparison results indicate that the proposed model is more reliable and efficient for accurate prediction the substrate specificities of membrane transport proteins.