Prediction of disorder with new computational tool: BVDEA

Abstract

Motivation:Recognizing that many intrinsically disordered regions in proteins play key roles in vital functions and also in some diseases, identification of the disordered regions has became a demanding process for structure prediction and functional characterization of proteins. Therefore, many studies have been motivated on accurate prediction of disorder. Mostly, machine learning techniques have been used for dealing with the prediction problem of disorder due to the capability of extracting the complex relationships and correlations hidden in large data sets. Results:In this study, a novel method, named Border Vector Detection and Extended Adaptation (BVDEA) was developed for predicting disorder as an alternative accurate classifier. The classifier performs the predictions by using three types of structural features belonging to proteins. For attesting the performance of the method, three computational learning techniques and eleven specific tools were used for comparison. Training was executed based on the data by 5-fold cross validation. When compared with the three learning methods of GRNN, LVQ and BVDA, the proposed method gives the best accuracy on classification. The BVDEA also provides faster and more robust learning as compared to the others. The new method provides a significant contribution to predicting disorder and order regions of proteins.

The Prediction of Intrinsically Disordered Proteins Based on Feature Selection

“…They play a crucial role in a variety of important biological functions [4]. It is confirmed that some of these IDPs are related to many important regulatory functions in the cell [5], which have great impact on diseases such as Parkinson's disease, Alzheimer's disease and certain types of cancers [6]. Thus, accurately identifying IDPs is vital to obtaining more effective drug designs, better protein expressions and functional annotations.…”

“…Thus, accurately identifying IDPs is vital to obtaining more effective drug designs, better protein expressions and functional annotations. However, it is often difficult to purify and crystallize the disordered protein regions [7], which makes the identification of IDPs usually both expensive and time-consuming with experimental approaches [6]. Thus, it is essential to predict IDPs through the computational approaches.…”

“…In this category, the prediction of IDPs is considered as a binary classification problem based on many machine learning algorithms, such as artificial neural network, support vector machine and so on. The examples of this category include PONDRs [13], RONN [14], DISOPRED3 [15], BVDEA [6], DisPSSMP [16], SPINE-D [17], ESpritz [18], and so on. In this paper, we choose ESpritz as the other scheme of comparison, which is based on a bidirectional recursive neural network and also performs well in predicting.…”

Homology modeling in the time of collective and artificial intelligence

“…Other approaches include single-molecule fluorescence resonance energy transfer [174] , and atomic-force microscopy [175] . Traditional methods are expensive and time-consuming, especially in the aspects of purifying and crystallizing IDPRs; therefor researchers were holding their hopes on integrating state-of-the-art tools with advanced computational methods [176] , [177] . The latter can be divided to three approaches: The first one depends on physicochemical properties and propensity scales ( e.g.…”

Prediction of Intrinsically Disordered Proteins Using Machine Learning Algorithms Based on Fuzzy Entropy Feature

“…The dataset DIS1616 is comprised of 1616 protein sequences with 2503 disordered regions and 2629 ordered regions, which include 186,069 disordered and 715,619 ordered residues. As a comparison, we run our schemes together with some existing schemes, such as DISOPRED2, RONN [24], DISPSSMP [25], Espritz and IsUnstructure [9] on the datasets R80 which are comprised of 78 protein sequences which include 2439 disordered and 19,412 ordered residues. The simulation results suggest that our scheme is at least as accurate as DiISPSSMP and requires computing only five features for each residue of a protein sequence, while the other need to compute 120 features for each residue, respectively.…”