ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition

Abstract

Background Amyloids are insoluble fibrillar aggregates that are highly associated with complex human diseases, such as Alzheimer’s disease, Parkinson’s disease, and type II diabetes. Recently, many studies reported that some specific regions of amino acid sequences may be responsible for the amyloidosis of proteins. It has become very important for elucidating the mechanism of amyloids that identifying the amyloidogenic regions. Accordingly, several computational methods have been put forward to discover amyloidogenic regions. The majority of these methods predicted amyloidogenic regions based on the physicochemical properties of amino acids. In fact, position, order, and correlation of amino acids may also influence the amyloidosis of proteins, which should be also considered in detecting amyloidogenic regions. Results To address this problem, we proposed a novel machine-learning approach for predicting amyloidogenic regions, called ReRF-Pred. Firstly, the pseudo amino acid composition (PseAAC) was exploited to characterize physicochemical properties and correlation of amino acids. Secondly, tripeptides composition (TPC) was employed to represent the order and position of amino acids. To improve the distinguishability of TPC, all possible tripeptides were analyzed by the binomial distribution method, and only those which have significantly different distribution between positive and negative samples remained. Finally, all samples were characterized by PseAAC and TPC of their amino acid sequence, and a random forest-based amyloidogenic regions predictor was trained on these samples. It was proved by validation experiments that the feature set consisted of PseAAC and TPC is the most distinguishable one for detecting amyloidosis. Meanwhile, random forest is superior to other concerned classifiers on almost all metrics. To validate the effectiveness of our model, ReRF-Pred is compared with a series of gold-standard methods on two datasets: Pep-251 and Reg33. The results suggested our method has the best overall performance and makes significant improvements in discovering amyloidogenic regions. Conclusions The advantages of our method are mainly attributed to that PseAAC and TPC can describe the differences between amyloids and other proteins successfully. The ReRF-Pred server can be accessed at http://106.12.83.135:8080/ReRF-Pred/.

ENTAIL: Yet Another Amyloid Fibrils Classifier

“…Using the PseAAC and TPC, the authors demonstrated and developed an accessible ReRF-Pred, a revolutionary machine-learning for predicting amyloidogenic regions. The authors specifically examined all possible tripeptides with binomial distribution, focusing on those with significantly different distributions between positive and negative samples [13]. PredAmyl-MLP and RFAmyloid train on a dataset consisting of 165 amyloid proteins and 382 non-amyloid sequences.…”

“…Thanks to the fine-tuning step, we were able to identify the following classifiers configurations capable of overcoming inaccuracy in the performance reported in [13]:…”

“…Teng et al [13] presented and created an accessible ReRF-Pred, a novel machinelearning based on a multi-feature encoding strategy for predicting amyloidogenic regions, using the pseudo amino acid composition (PseAAC) [14] and tripeptides composition (TPC) [15]. They obtained an accuracy, specificity, sensitivity and MCC of 80.10%, 83.1%, 73.4% and 0.552, respectively.…”

IBPred: A sequence-based predictor for identifying ion binding protein in phage

“…Owing to the convenience and high efficiency, computational methods are a good choice for identifying IBPs. Many machine learning algorithms, such as support vector machine (SVM) [10] , [11] , [12] , deep learning (DL) [13] , [14] , [15] , [16] , [17] , [18] , [19] , extreme boosting algorithm (XGBoost) [20] , [21] , [22] , [23] , [24] , and stacking ensemble models [25] , [26] , [27] , [28] , [29] , [30] , etc., have been developed for protein function, structure, subcellular localization, and even other biological processes. Different feature descriptors such as amino acid composition (AAC) [31] , [32] , [33] , reduced amino acid composition [34] , [35] , [36] , g -gap dipeptide composition [37] , [38] , and secondary structure features [39] , etc., were adopted to represent protein sequences.…”

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier

“…Teng et al [ 16 ] presented and created an accessible ReRF-Pred, a novel machine-learning based on a multi-feature encoding strategy for predicting amyloidogenic regions, using the pseudo amino acid composition (PseAAC) [ 17 ] and tripeptides composition (TPC) [ 18 ]. They obtained an accuracy, specificity, sensitivity and MCC of 80.10%, 83.1%, 73.4% and 0.552, respectively.…”