What is iupred2a?
IUPred2A is a combined web interface that allows to identify disordered protein regions using IUPred2 and disordered binding regions using ANCHOR2. IUPred2A is also capable of identifying protein regions that do or do not adopt a stable structure depending on the redox state of their environment.
What are the different types of iupred2 predictions?
For IUPred2 predictions, three types of predictions can be chosen depending on the type of structural regions the user wants to analyse: short stretches of disorder (such as flexible loops or linkers), long disordered regions (such as disordered domains), or structured domains.
Can iupred2 and anchor2 recognize disordered protein regions correctly?
However, owing to their biophysics-based models, IUPred2 and ANCHOR2 are expected to be able to correctly recognize protein regions that share limited to no resemblance to currently known disordered regions or binding sites.
How does IUPred work?
THE IUPred SERVER. The web server takes a single amino acid sequence as an input and calculates the pairwise energy profile along the sequence. The energy values are then transformed into a probabilistic score ranging from 0 (complete order) to 1 (complete disorder).
What is Pondr?
PONDR® VSL2 is a combination of neural network predictors for both short and long disordered regions. A length limit of 30 residues divides short and long disordered regions. Each individual predictor is trained by the dataset containing sequences of that specific length.
When were intrinsically disordered proteins discovered?
Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable tertiary structure yet carry out a diverse array of biological functions [1,2,3,4]. Probably the first development of this concept was made in 1940 by Pauling [5].
What is IUPred2A server?
The current paper presents the new IUPred2A server that serves as a unified platform for both generic and context-dependent prediction of protein disorder. IUPred2A combines and supersedes our general disorder prediction method IUPred and disordered binding region prediction method ANCHOR. While IUPred2 features only slight improvements over its predecessor, ANCHOR2 was completely re-trained and re-tested built on a new architecture, bringing a significant improvement over the original version. In addition, IUPred2A also incorporates a new experimental feature that targets the identification of protein regions capable of redox-state dependent transition between disordered and ordered states. These methods are available through a completely rewritten server at a new location. The IUPred2A server retains all options for data input from previous versions, but also significantly expands its functionality by introducing RESTful services, and automated data integration from a range of databases with information about protein structure. Furthermore, completely rewritten codes for IUPred2 and ANCHOR2 are available for download to aid local large-scale analyses.
What is the role of E1A in apoptosis?
E1A is a largely disordered protein ( 51 ), which is essential for forcing the host cell into S phase via modulation of the Rb1/E2F1 pathway ( 52) and the inhibition of apoptosis via modulation of p53 degradation ( 53 ). These host-pathogen interactions are mediated by several binding events.
Can a decoy set contain disordered binding regions?
While the decoy set can in theory contain any number of disordered binding regions, due to the random assignment we expect their numbers to be fairly low. In contrast to the energy gain term, the I term of the score primarily describes the separation between disordered binding regions and ordered proteins.
Quick Start
The functions iupred (), iupredAnchor (), and iupredRedox () are all designed to fetch predictions of intrinsic disorder from the IUPred2A REST API. To fetch results, a UniProt ID is needed.
Background
The primary structure of a protein, also known as the amino acid sequence, can be an accurate predictor of protein folding. Because of this, many different tools have been developed to make probabilistic predictions of intrinsic disorder based on various known properties of Intrinsically Disordered Proteins (IDPs) (Li et al., 2015).
Installation
The idpr package can be installed from Bioconductor with the following line of code. It requires the BiocManager package to be installed.
iupred function and iupredType arguments
iupred () has is the core prediction of intrinsic disorder. The argument iupredType = is important to specify depending on the goal of the analysis.
iupredAnchor
IDPs and IDRs serve many important roles in a cell, one prominent role is the ability to act as a hub for protein-protein interactions (Uversky, 2013). Additionally, many disordered regions undergo what is known as “induced folding”.
iupredRedox
Another factor influencing the environmental chemistry is the redox potential. As mentioned before, under native conditions IDPs are unstructured, however when entering a different environment higher-order structures may form and allow IDPs to execute their function (Kovacs et al., 2013).
Additional Example
While the aesthetics of the plots above are meant to represent a middleground of the graphics available on and the other plots generated by idpr, a user may wish to use the data frames for data analysis or unique graphics. Another way to represent the data is using the sequenceMap () function.
What is IUPred2A? What are its functions?
IUPred2A is a combined prediction tool designed to discover intrinsically disordered or conditionally disordered proteins and protein regions. Intrinsically disordered regions exist without a well-defined three-dimensional structure in isolation but carry out important biological functions. Over the years, various prediction methods have been developed to characterize disordered regions. The existence of disordered segments can also be dependent on different factors such as binding partners or environmental traits like pH or redox potential, and recognizing such regions represents additional computational challenges. In this article, we present detailed instructions on how to use IUPred2A, one of the most widely used tools for the prediction of disordered regions/proteins or conditionally disordered segments, and provide examples of how the predictions can be interpreted in different contexts. © 2020 The Authors. Basic Protocol 1: Analyzing disorder propensity with IUPred2A online Basic Protocol 2: Analyzing disordered binding regions using ANCHOR2 Support Protocol 1: Interpretation of the results Basic Protocol 3: Analyzing redox-sensitive disordered regions Support Protocol 2: Download options Support Protocol 3: REST API for programmatic purposes Basic Protocol 4: Using IUPred2A locally.
What is RLIP76/RalBP1?
RLIP76/RalBP1 is an ATP-dependent transporter of glutathione conjugates, which is overexpressed in various human cancers, but its diverse functions in normal cells, which include endocytosis, stress response and mitochondrial dynamics, are still not fully understood. The protein can be divided into three distinct regions, each with its own structural properties. At the centre of the protein are two well-defined domains, a GTPase activating protein domain targeting Rho family small G proteins and a small coiled-coil that binds to the Ras family small GTPases RalA and RalB. In engaging with Rho and Ral proteins, RLIP76 bridges these two distinct G protein families. The N-terminal region is predicted to be disordered and is rich in basic amino acids, which may mediate membrane association, consistent with its role in transport. RLIP76 is an ATP-dependent transporter with ATP-binding sites within the N-terminus and the Ral binding domain. Furthermore, RLIP76 is subject to extensive phosphorylation, particularly in the N-terminal region. In contrast, the C-terminal region is thought to form an extensive coiled-coil that could mediate dimerization. Here, we review the structural features of RLIP76, including experimental data and computational predictions, and discuss the implications of its various post-translational modifications.
Introduction
Methods
- IUPred2
The resulting method, IUPred (19) is able to recognize regions of proteins that are not compatible with ordered regions based on their inability to form enough favorable intrachain interactions. As the method relies on a low-resolution biophysical model of protein folding, its parameters are ea… - ANCHOR2
Similarly to IUPred, ANCHOR also utilizes the energy estimation approach, for the identification of disordered binding sites. Besides the general disorder tendency, two additional terms were also incorporated into the method that estimate the energy associated with interaction with a globula…
Server Description
- Input
To ease the transition of users of the original IUPred server, the user interface of IUPred2A inherits a lot from its predecessor, enabling fast and straightforward usage. The main page features entry boxes, which accept a FASTA formatted or plain protein sequence, or any valid Un… - Output
The latest version of Bokeh (0.12.14) is responsible for the visualization of the results that is directly integrated into the Django framework. The graphical output presents the requested predictions. By default, it contains disorder predictions from IUPred2 and binding site prediction…
Examples
- ANCHOR2 can correctly recognize many disordered binding regions that machine learning methods are likely to overlook due to their very conservative estimates of the occurrence of these functional modules. This is demonstrated through the example of the oncogenic Human adenovirus C early E1A protein (Figure 2). E1A is a largely disordered protein (51), which is esse…
Conclusion
- The current paper presents the new IUPred2A server that serves as a unified platform for both generic and context-dependent prediction of protein disorder. IUPred2A combines and supersedes our general disorder prediction method IUPred and disordered binding region prediction method ANCHOR. While IUPred2 features only slight improvements over its predeces…
Acknowledgements
- The authors are grateful to Domenico Cozzetto and David T. Jones for kindly providing the flexible linker dataset used for benchmarking DISOPRED3. The constructive remarks of László Dobson concerning IUPred2A functionality are gratefully acknowledged.
Funding
- Hungarian Academy of Sciences [LP2014-18 ‘Lendület’]; Országos Tudományos Kutatási Alapprogramok [K108798]. Funding for open access charge: OTKA K108798. Conflict of interest statement. None declared.