Computational Identification of MoRFs in Protein Sequences Using Hierarchical Application of Bayes Rule

Abstract

MotivationIntrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is often the binding to globular protein domains via sequence elements known as molecular recognition features (MoRFs). Development of computational tools for the identification of candidate MoRF locations in amino acid sequences is an important task and an area of growing interest. Given the relative sparseness of MoRFs in protein sequences, the accuracy of the available MoRF predictors is often inadequate for practical usage, which leaves a significant need and room for improvement. In this work, we introduce MoRFCHiBi_Web, which predicts MoRF locations in protein sequences with higher accuracy compared to current MoRF predictors.MethodsThree distinct and largely independent property scores are computed with component predictors and then combined to generate the final MoRF propensity scores. The first score reflects the likelihood of sequence windows to harbour MoRFs and is based on amino acid composition and sequence similarity information. It is generated by MoRFCHiBi using small windows of up to 40 residues in size. The second score identifies long stretches of protein disorder and is generated by ESpritz with the DisProt option. Lastly, the third score reflects residue conservation and is assembled from PSSM files generated by PSI-BLAST. These propensity scores are processed and then hierarchically combined using Bayes rule to generate the final MoRFCHiBi_Web predictions.ResultsMoRFCHiBi_Web was tested on three datasets. Results show that MoRFCHiBi_Web outperforms previously developed predictors by generating less than half the false positive rate for the same true positive rate at practical threshold values. This level of accuracy paired with its relatively high processing speed makes MoRFCHiBi_Web a practical tool for MoRF prediction.Availability http://morf.chibi.ubc.ca:8080/morf/.

Understanding the penetrance of intrinsic protein disorder in rotavirus proteome

“…Disopred provide disorder region based upon sequence property training sets trained through neural networks [54]. MoRFchibi_web is the most advanced and accurate predictor available; it gives the combined output of MoRFchibi and MoRFDC by following the Bayes rules [55]. Therefore, we have used only MoRFchibi_web-based predictions to map the regions on available crystal structures of rotavirus proteome.…”

Presence and structure‐activity relationship of intrinsically disordered regions across mucins

“…The presence of MoRFs was determined via ANCHOR, as a component of D 2 P 2 that determines sequence motifs within an IDR that have a decrease in free energy upon binding with another protein . Interestingly, mucins contain a large number of predicted MoRFs within their IDRs (Table ).…”

OPAL+: Length‐Specific MoRF Prediction in Intrinsically Disordered Protein Sequences

“…The training and test sets used to develop OPAL+ were obtained from Disfani et al . and Malhis et al . These sets are called TRAIN, TEST, TEST464, and EXP53, and are described in Table S1, Supporting Information.…”

“…The training and test sets used to develop OPAL+ were obtained from Disfani et al [10] and Malhis et al [15] These sets are called TRAIN, TEST, TEST464, and EXP53, and are described in Table S1, Supporting Information. To assemble TRAIN, TEST, and TEST464 sets, sequences were collected and filtered from Protein Data Bank (PDB) depositions made before April 2008.…”

“…[5] Several computational methods are available to predict IDRs and their associated features, [6,7] including MoRFs. [8][9][10][11][12][13][14] Some of the available MoRF predictors include ANCHOR, [9] MoRFpred, [10] MoRFchibi, [11] MoRFpred-plus, [13] MoRFchibi-light, [12] MoRFchibi-web, [12,15] and OPAL. [14] ANCHOR uses the properties of binding regions located in disordered protein sequences to identify segments of protein regions that do not form sufficient interactions to fold on their own, but are likely to gain energy by interacting with globular proteins.…”