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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.
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.
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.
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.
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.
Offers a platform for determining protein structural features and tertiary structures. SCRATCH is a web application including ten modules for determining three and eight class: (1) secondary structure, (2) relative solvent accessibility, (3) domain boundaries, (4) disordered regions, (5) disulfide bridges, (6) the effect of single amino acid mutation on stability, (7) residue-residue contact maps, and (8) tertiary structures as well as contacts with other residues compared to average.
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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.
A web app to predict protein disorder. Metadisorder is a meta method which means that it tries to calculate "consensus" from results returned by other methods, consisting in 4 parts: matedisorder, metadirsorder3d (to fold recognition methods), metadisordermf (optimize components integration using a combination of the first two tools) and metadisordermd2 (metadisordermd with different scoring function). Metadisorder is one of the best predictors of protein disorder, evaluated during independent tests (CASP8 and CASP9).
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.
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 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.
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.
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.
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 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.
ncSPC / Neighbor Corrected Structural Propensity Calculator
Calculates the propensity for structural order and disorder in proteins. ncSPC is a web application that uses nuclear magnetic resonance (NMR) chemical shift data as the sole input to determine the molecular conformation of proteins, in both the disordered and ordered state. The software is able to detect and classify areas of disorder. It can perform the analyses on any query protein for which assigned chemical shift data are available.
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.
Predicts random coil chemical shifts (RCCSs) from protein sequence. POTENCI takes pH and temperature into account and includes sequence-dependent nearest and next-nearest neighbor corrections. The software is suitable for detecting small, but relevant RCCS deviations in intrinsically disordered proteins (IDPs) that may be correlated with functional outcomes. It can be applied to predict the multidimensional spectra of IDPs when used in conjunction with spectrum simulation programs.
DisMeta / Disorder Prediction Meta-Server
Allows design and optimization of protein constructs expressed for both nuclear magnetic resonance (NMR) and crystallization studies. DisMeta is a meta-server that provides a consensus analysis of several disorder predictors, as well as predictions of secondary structure, signal peptides, trans-membrane helical regions, and low complexity regions of the protein sequence using publicly available servers. It can be useful both to small laboratories focused on specific biological problems and to large-scale protein sample production efforts, including antigen sample production projects.
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.
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 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.
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