Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions

Meng, Fanchi; Uversky, Vladimir N.; Kurgan, Lukasz

doi:10.1007/s00018-017-2555-4

Cited by 157 publications

(146 citation statements)

References 141 publications

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“…This is similar to the 37% rate of DMRs among the IDRs included in DisProt, which was reported in Ref. . We focus our analysis on long (≥ 30 consecutive residues) putative DMRs and IDRs since they are recognized as a distinct class of biologically functional domains …”

Section: Resultssupporting

confidence: 74%

“…Proteins with IDRs carry out numerous functions that rely on protein–protein and protein–nucleic acids interactions (eg, translation, transcription, and chromosome condensation), are involved in a variety of signaling functions, and facilitate regulation of protein functions via posttranslational modifications . Substantial efforts have been made to predict and computationally characterize IDRs . There are over 40 methods that predict IDRs and some of them were empirically shown to provide very accurate predictions .…”

Section: Introductionmentioning

confidence: 99%

“…There are over 40 methods that predict IDRs and some of them were empirically shown to provide very accurate predictions . Moreover, progress has been made in recent years to predict functions of IDRs . Example predictors include Anchor, MoRFpred, fMoRFpred, and MoRFCHiBi that predict disordered protein–protein binding regions, DFLpred that outputs putative disordered linkers, and DisoRDPbind that predicts disordered protein–protein, RNA‐ and DNA‐binding regions.…”

Section: Introductionmentioning

confidence: 99%

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High‐throughput prediction of disordered moonlighting regions in protein sequences

Meng

Kurgan

2018

Proteins

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Intrinsically disordered regions lack stable structure in their native conformation but are nevertheless functional and highly abundant, particularly in Eukaryotes. Disordered moonlighting regions (DMRs) are intrinsically disordered regions that carry out multiple functions. DMRs are different from moonlighting proteins that could be structured and that are annotated at the whole-protein level. DMRs cannot be identified by current predictors of functions of disorder that focus on specific functions rather than multifunctional regions. We conceptualized, designed and empirically assessed first-of-its-kind sequence-based predictor of DMRs, DMRpred. This computational tool outputs propensity for being in a DMR for each residue in an input protein sequence. We developed novel amino acid indices that quantify propensities for functions relevant to DMRs and used evolutionary conservation, putative solvent accessibility and intrinsic disorder derived from the input sequence to build a rich profile that is suitable to accurately predict DMRs. We processed this profile to derive innovative features that we input into a Random Forest model to generate the predictions. Empirical assessment shows that DMRpred generates accurate predictions with area under receiver operating characteristic curve = 0.86 and accuracy = 82%. These results are significantly better than the closest alternative approaches that rely on sequence alignment, evolutionary conservation and putative disorder and disorder functions. Analysis of abundance of putative DMRs in the human proteome reveals that as many as 25% of proteins may have long >30 residues) DMRs. A webserver implementation of DMRpred is available at http://biomine.cs.vcu.edu/servers/DMRpred/.

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Section: Resultssupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High‐throughput prediction of disordered moonlighting regions in protein sequences

Meng

Kurgan

2018

Proteins

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“…A common module for molecular recognition within IDPs/IDRs is often known as molecular recognition features (MoRFs) or short linear motifs . The sequence features of MoRFs are distinct from the rest portion of IDPs/IDRs, enabling development of predictors to identify MoRFs . For example, ANCHOR predicts disordered binding regions based on the pairwise energy estimation from IUPred .…”

Section: Molecular Recognition Featuresmentioning

confidence: 99%

Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding

et al. 2019

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The sequence–structure–function paradigm of proteins has been revolutionized by the discovery of intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs). In contrast to traditional ordered proteins, IDPs/IDRs are unstructured under physiological conditions. The absence of well‐defined three‐dimensional structures in the free state of IDPs/IDRs is fundamental to their function. Folding upon binding is an important mode of molecular recognition for IDPs/IDRs. While great efforts have been devoted to investigating the complex structures and binding kinetics and affinities, our knowledge on the binding mechanisms of IDPs/IDRs remains very limited. Here, we review recent advances on the binding mechanisms of IDPs/IDRs. The structures and kinetic parameters of IDPs/IDRs can vary greatly, and the binding mechanisms can be highly dependent on the structural properties of IDPs/IDRs. IDPs/IDRs can employ various combinations of conformational selection and induced fit in a binding process, which can be templated by the target and/or encoded by the IDP/IDR. Further studies should provide deeper insights into the molecular recognition of IDPs/IDRs and enable the rational design of IDP/IDR binding mechanisms in the future.

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“…Positive scores mean that a given amino acid is more likely than expected to appear at the specific position. Besides being used for alignment, PSSM is also used to calculate evolutionary conservation of the query sequence (Valdar, ) and to predict protein secondary structure (Jones, ; Meng & Kurgan, ; Wang, Peng, Ma, & Xu, ), solvent accessibility (Heffernan et al., ; Magnan & Baldi, ), functional sites (Yan, Friedrich, & Kurgan, ; Zhang & Kurgan, ; Zhang, Ma, & Kurgan, ), and intrinsic disorder (Disfani et al., ; Meng, Uversky, & Kurgan, ; Mizianty et al., ).…”

Section: Psi‐blastmentioning

confidence: 99%

Sequence Similarity Searching

Kurgan

2018

CP Protein Science

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Sequence similarity searching has become an important part of the daily routine of molecular biologists, bioinformaticians and biophysicists. With the rapidly growing sequence databanks, this computational approach is commonly applied to determine functions and structures of unannotated sequences, to investigate relationships between sequences, and to construct phylogenetic trees. We introduce arguably the most popular BLAST-based family of the sequence similarity search tools. We explain basic concepts related to the sequence alignment and demonstrate how to search the current databanks using Web site versions of BLASTP, PSI-BLAST and BLASTN. We also describe the standalone BLAST+ tool. Moreover, this unit discusses the inputs, parameter settings and outputs of these tools. Lastly, we cover recent advances in the sequence similarity searching, focusing on the fast MMseqs2 method. © 2018 by John Wiley & Sons, Inc.

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Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions

Cited by 157 publications

References 141 publications

High‐throughput prediction of disordered moonlighting regions in protein sequences

High‐throughput prediction of disordered moonlighting regions in protein sequences

Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding

Sequence Similarity Searching

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