Prediction of “Aggregation-Prone” Peptides with Hybrid Classification Approach

Abstract

Protein aggregation is a biological phenomenon caused by misfolding proteins aggregation and is associated with a wide variety of diseases, such as Alzheimer’s, Parkinson’s, and prion diseases. Many studies indicate that protein aggregation is mediated by short “aggregation-prone” peptide segments. Thus, the prediction of aggregation-prone sites plays a crucial role in the research of drug targets. Compared with the labor-intensive and time-consuming experiment approaches, the computational prediction of aggregation-prone sites is much desirable due to their convenience and high efficiency. In this study, we introduce two computational approaches Aggre_Easy and Aggre_Balance for predicting aggregation residues from the sequence information; here, the protein samples are represented by the composition ofk-spaced amino acid pairs(CKSAAP). And we use the hybrid classification approach to predict aggregation-prone residues, which integrates the naïve Bayes classification to reduce the number of features, and two undersampling approaches EasyEnsemble and BalanceCascade to deal with samples imbalance problem. The Aggre_Easy achieves a promising performance with a sensitivity of 79.47%, a specificity of 80.70% and a MCC of 0.42; the sensitivity, specificity, and MCC of Aggre_Balance reach 70.32%, 80.70% and 0.42. Experimental results show that the performance of Aggre_Easy and Aggre_Balance predictor is better than several other state-of-the-art predictors. A user-friendly web server is built for prediction of aggregation-prone which is freely accessible to public at the website.

Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides

“…Furthermore, it is believed that short segments of proteins, like hexapeptides consisting of 6-residue fragments, can be responsible for amyloidogenic properties [ 16 ]. Since it is not possible to experimentally test all such sequences, several computational tools for predicting amyloid chains have emerged, inter alia, based on physicochemical properties [ 17 ] or using machine learning approach [ 18 – 21 ].…”