1 - 50 of 67 results

DisProt / Database of Protein Disorder

Annotates protein sequences for intrinsically disorder regions from the literature. DisProt classifies intrinsic disorder based on experimental methods and three ontologies for molecular function, transition and binding partner. It holds information on more than 800 entries of intrinsically disordered proteins (IDPs) or regions (IDRs) that exist and function without a well-defined three-dimensional structure. The assessment of disorder is based on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.

MFDp / Multilayered Fusion-based Disorder predictor

Combines per-residue disorder probabilities predicted by MFDp with per-sequence disorder content predicted by DisCon, and applies novel post-processing filters to provide disorder predictions with improved predictive quality. It outputs optimized per-residue disorder probability profiles, per-sequence disorder content, list (with analysis) of disordered segments, and several profiles that help in the interpretation of the results. The results are available online in graphical format and can be also downloaded in text-based (parsable) format.

FuzDB / Fuzzy complexes DataBase

A database of fuzzy protein complexes. FuzDB compiles experimentally observed fuzzy protein complexes, where intrinsic disorder (ID) is maintained upon interacting with a partner (protein, nucleic acid or small molecule) and directly impacts biological function. Entries in the database have both (i) structural evidence demonstrating the structural multiplicity or dynamic disorder of the ID region(s) in the partner bound form of the protein and (ii) in vitro or in vivo biological evidence that indicates the significance of the fuzzy region(s) in the formation, function or regulation of the assembly.


A web service that allows the user to plot the tendency within the query protein for order/globularity and disorder. GlobPlot successfully identifies inter-domain segments containing linear motifs, and also apparently ordered regions that do not contain any recognized domain. GlobPlot may be useful in domain hunting efforts. The plots indicate that instances of known domains may often contain additional N- or C- terminal segments that appear ordered. Thus GlobPlot may be of use in the design of constructs corresponding to globular proteins, as needed for many biochemical studies, particularly structural biology.

VB-DCMM / Variational Bayes-Double Chain Markov Model

Analyzes single molecule time trajectories that display dynamic disorder. VB-DCMM allows to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformation al transition within each internal state. VB-DCMM allows to decompose individual H-DNA time traces with dynamic disorder into multiple components, each of which should satisfies the property of homogeneous Markov chain.


Uses a single consensus-based prediction to be optimized for highly specific (i.e. few false positive) predictions of long intrinsic disorder (ID) of protein sequences. MobiDB-lite uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB-lite can be useful in large-scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases.

SPOT-disorder / Sequence-based Prediction Online Tools for disorder

Develops to be highly effective in predicting both short and long disordered regions without separated training, despite disordered regions of different sizes having different compositions of amino acids. SPOT-Disorder is a method steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests and applications to the targets from Critical Assessment of Structure Prediction (CASP9 and 10) have confirmed that SPOT-Disorder is comparable to or more accurate than all the methods compared, regardless of which datasets were employed for comparison.


Uses for protein order/disorder prediction. DeepCNF captures long-range sequence information by a deep hierarchical architecture and exploits interdependency between adjacent order/disorder labels, but also assigns different weights for each label during training and prediction to solve the label imbalance issue that was known as a long-standing problem in order/disorder prediction. It combines the advantages of both conditional neural fields (CNF) and deep convolutional neural networks.


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Predicts the RNA-, DNA-, and protein-binding residues located in the intrinsically disordered regions. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder, and sequence alignment. Its outputs complement predictions of representative methods that were built using structured DNA- and RNA-binding residues. Predictions of disordered protein-binding residues generated by DisoRDPbind are characterized by strong correlations, better predictive performance and higher runtime when compared with the closest ANCHOR method.


Predicts the likelihood of a residue to undergo disorder-to-order transition upon binding to a partner protein. Proteus uses random-forest-based protean predictor. The prediction is based on features that can be calculated using the amino acid sequence alone. This tool compares favourably with existing methods predicting twice as many true positives as the second best method (55% vs. 27%) at a much higher precision on an independent data set. Proteus also shades some light on a possible 'disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDP, and on a real-life structural modelling of an IDPR (intrinsically disordered proteins containing regions of disorder).


A deep learning method for protein disorder prediction. AUCpreD distinguishes itself from the others in that it applies a deep probabilistic graphical model DeepCNF to model complex sequence–structure relationship and directly optimizes the area under the ROC curve (AUC) measure to deal with the imbalanced distribution of disordered and ordered residues. DeepCNF allows us to model complex sequence–disorder relationship by a deep hierarchical architecture, and exploit interdependency between adjacent order/disorder states. Experimental results show that AUCpreD performs much better than the state-of-the-art methods of the same category in terms of AUC, AUCpr and Mcc. On long disordered regions and terminal/internal regions, AUCpreD also performs the best. Even without using sequence profile, AUCpreD still compares favorably to or outperforms the methods that use sequence profile or even protein templates.


A sequence-based predictor of disordered flexible linkers (DFLs). DFLpred outputs propensity to form DFLs for each residue in the input sequence. DFLpred uses a small set of empirically selected features that quantify propensities to form certain secondary structures, disordered regions and structured regions, which are processed by a fast linear model. Our high-throughput predictor can be used on the whole-proteome scale; it needs <1 h to predict entire proteome on a single CPU. When assessed on an independent test dataset with low sequence-identity proteins, it secures area under the receiver operating characteristic curve equal 0.715 and outperforms existing alternatives that include methods for the prediction of flexible linkers, flexible residues, intrinsically disordered residues and various combinations of these methods.


Provides an ultimate resource for functional site classifications in intrinsically disordered proteins (IDPs). DisBind is a database dedicated to residue-level classification of functional binding sites in disordered and structured regions of intrinsically disordered proteins. This resource compiles information from the structural database (protein databank), the database of experimentally validated disordered proteins (DisProt), and the comprehensive protein sequence and functional database (UniProt).


An efficient procedure for the simulation of structure flexibility of folded globular proteins. Three consecutive steps follow the CABS simulation procedure: (i) structural clustering, (ii) models reconstruction to an all-atom representation and (iii) models superimposition, analysis and visualization. All these tasks are performed in the CABS-flex pipeline by well-established and extensively tested methods (typical for multi-scale protein modeling procedures) or general purpose scientific software.


Serves as an openly accessible database for the deposition of structural ensembles of intrinsically disordered proteins (IDPs) and of denatured proteins based on nuclear magnetic resonance spectroscopy, small-angle X-ray scattering and other data measured in solution. PE-DB is open for submissions from the community, and is intended as a forum for disseminating the structural ensembles and the methodologies used to generate them. While the need to represent the IDP structures is clear, methods for determining and evaluating the structural ensembles are still evolving. The availability of the pE-DB database is expected to promote the development of new modeling methods and leads to a better understanding of how function arises from disordered states.

DIBS / Disordered Binding Site

Provides an extensive collection of interactions formed by a disordered protein region and one or more ordered protein partners. DIBS marks proteins as disordered if a closely homologous protein was described to lack intrinsic structure. This application incorporates annotations about functional motifs in disordered partners, further connecting the two complementary models of such interactions. It also serves as the basis for a more complete understanding of Intrinsically Disordered Proteins (IDPs) interactions.

MFIB / Mutual Folding Induced by Binding

Aims to serve as a starting point for functional and structural analysis of interactions between intrinsically Disordered Proteins (IDPs). MFIB is based on the integration of structural and sequence annotation coupled with the results of an extensive manual literature survey. The data contained in MFIB provide a wide coverage of possible IDP-IDP interactions in many ways. Its entries cover the majority of possible oligomeric compositions from dimers to hexamers, including both hetero- and homo-oligomers.

Disorder Atlas

Facilitates the interpretation of intrinsic disorder predictions using proteome-based descriptive statistics. Disorder Atlas is a web-based software that enables a standardized interpretation of intrinsic disorder predictions, and provides researchers with a tool to assess disorder on multiple scales. This service is also equipped to facilitate large-scale systematic exploratory searches for proteins encompassing disorder features of interest, and further allows users to browse the prevalence of multiple disorder features at the proteome level.


A computational approach for fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines the outcomes of two SVM models that take advantage of two different kernels with high noise tolerance. The first, SVMS, is designed to extract maximal information from the general contrast in amino acid compositions between MoRFs, their surrounding regions (Flanks), and the remainders of the sequences. The second, SVMT, is used to identify similarities between regions in a query sequence and MoRFs of the training set.


A computational tool to measure the similarity of different disorder curves by using dynamic programming. This tool is able to identify similar patterns among disorder curves, as well as to present the distribution of intrinsic disorder in query proteins. The disorder-based information generated by IDalign is significantly different from the information retrieved from classical sequence alignments. This tool can also be used to infer functions of disordered regions and disordered proteins.

SLiMFinder / Short, Linear Motif Finder

A de novo motif discovery tool that identifies statistically over-represented motifs in a set of protein sequences, accounting for the evolutionary relationships between them. Motifs are returned with an intuitive P-value that greatly reduces the problem of false positives and is accessible to biologists of all disciplines. Input can be uploaded by the user or extracted directly from UniProt. Numerous masking options give the user great control over the contextual information to be included in the analyses.

DisCons / Disorder Conservation

A pipelined tool that combines the quantification of sequence- and disorder conservation to classify disordered residue positions. According to this scheme, the most interesting categories (for functional purposes) are constrained disordered residues and flexible disordered residues. The former residues show conservation of both the sequence and the property of disorder and are associated mainly with specific binding functionalities (e.g., short, linear motifs, SLiMs), whereas the latter class correspond to segments where disorder as a feature is important for function as opposed to the identity of the underlying sequence (e.g., entropic chains and linkers). DisCons therefore helps with elucidating the function(s) arising from the disordered state by analyzing individual proteins as well as large-scale proteomics datasets.


Many disordered proteins function via binding to a structured partner and undergo a disorder-to-order transition. In order to predict disordered binding regions, ANCHOR seeks to identify segments that reside in disordered regions, cannot form enough favorable intrachain interactions to fold on their own and are likely to gain stabilizing energy by interacting with a globular protein partner. The approach relies on the pairwise energy estimation approach that is the basis for IUPred, a general disorder prediction method.

MoRFpred / Molecular Recognition Feature predictor

The server is designed for protein Molecular Recognition Feature (MoRF) prediction. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors.