Exploiting heterogeneous features to improve in silico prediction of peptide status – amyloidogenic or non-amyloidogenic

Abstract

BackgroundPrediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions.ResultsAccurate in silico prediction techniques of amyloidogenic peptide regions rely on the cooperation between informative features and classifier design. In this research article, we propose one such efficient fibril prediction implementation exploiting heterogeneous features based on bio-physio-chemical (BPC) properties, auto-correlation function of carefully selected amino acid indices and atomic composition within a protein fragment of amino acids in a window. In an attempt to get an optimal number of BPC features, an evolutionary Support Vector Machine (SVM) integrating a novel implementation of hybrid Genetic Algorithm termed Memetic Algorithm and SVM is utilized. Five prediction modules designed using Artificial Neural Network (ANN) models are trained with independent and integrated features in order to validate the fibril forming motifs. The results provide evidence that incorporating new feature namely auto-correlation function besides BPC, attempt to strengthen the sequence interaction effect in forming the feature vector thereby obtaining better prediction quality in terms of sensitivity, specificity, Mathews Correlation Coefficient and Area under the Receiver Operating Characteristics curve.ConclusionA significant improvement in performance is observed by introducing features like auto-correlation function that maintains sequence order effect, in addition to the conventional BPC properties selected through a novel optimization strategy to predict the peptide status – amyloidogenic or non-amyloidogenic. The proposed approach achieves acceptable results, comparable to most online predictors. Besides, it compensates the lacuna in existing amyloid fibril prediction tools by maintaining equilibrium between sensitivity and specificity.

A Consensus Method for the Prediction of ‘Aggregation-Prone’ Peptides in Globular Proteins

“…Recently, Nair et al . [38] published a paper in which they described the combination of SVMs with ANNs for the identification of amyloidogenic peptides.…”

Synthesis of novel 4-methylthiocoumarin and comparison with conventional coumarin derivative as a multi-target-directed ligand in Alzheimer’s disease

“…The interaction of Aβ was analyzed with anti-aggregatory molecules using computational methods. The amyloidogenic region was determined by FoldAmyloid software based on the average value of the parameter being greater to the threshold (Nair et al 2011). Docking study was performed by making a grid around the above mentioned amyloidogenic region.…”

On the amyloid datasets used for training PAFIG ­ how (not) to extend the experimental dataset of hexapeptides

“…New computational algorithms are trained or validated on the scarce experimental dataset. Two papers published in BMC Bioinformatics, presenting machine learning methods - Pafig [ 27 ] and another approach based on Pafig [ 28 ], used their own method for extending the training and testing datasets. The authors assumed that all hexapeptides that belong to an amyloid protein can be regarded as amylo-positive, while those from proteins never reported as amyloid are always amylo-negative.…”