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

One of the groundbreaking discoveries of recent years in the field of molecular biology was the observation that a significant portion of various genomes encodes intrinsically disordered proteins and protein regions (IDPs/IDRs). These protein segments exist without well-defined three-dimensional structures, and are best characterized by an ensemble of highly flexible conformations in isolation, but still retain the ability to have important biological functions, defying the traditional structure-function paradigm (van der Lee et al., 2014; Wright & Dyson, 1999). Some of the functionalities of IDPs are a direct consequence of their disordered nature: they can serve as flexible linkers or rely on their entropic properties to act as springs, spacers, or bristles. IDPs can also mediate interactions with other proteins, macromolecules such as DNA and RNA, or the membrane. A special class of functional sites located within disordered regions corresponds to short linear motifs that mediate interactions with specific globular domains through a few key residues. Many such cases are represented in the ELM database (see Current Protocols article: Gouw et al., 2017). From a structural point of view, disordered regions can undergo a disorder-to-order transition and adopt a more defined three-dimensional structure upon recognizing their specific partners. However, some IDRs retain a significant amount of flexibility even in the bound form, forming fuzzy complexes (Borgia et al., 2018; Miskei, Antal, & Fuxreiter, 2017). It is increasingly recognized that in addition to binding, disorder-to-order or order-to-disorder transitions can also be induced by post-translational modifications (PTMs) and various environmental conditions such as pH or change in redox potential (Jakob, Kriwacki, & Uversky, 2014). These disordered binding sites and other conditionally disordered regions are commonly found throughout signaling pathways or as a part of regulatory elements.

Due to the critical biological roles of IDPs, there is an ever-growing need for the development of prediction methods to recognize disordered regions in proteins and to identify their potential functions. While there are dedicated databases to collect experimentally verified disordered proteins, for example, the DisProt database (Hatos et al., 2020), in most cases computational tools are needed to start the characterization of disordered regions. IUPred is one of the most widely used prediction methods for protein disorder. The basic assumption behind this method is that disordered regions are composed of amino acids that cannot form favorable interactions with each other. This property of protein sequences is captured by an energy-estimation approach utilizing statistical potentials (Dosztányi, Csizmók, Tompa, & Simon, 2005). The ANCHOR method is based on a similar biophysical approach and recognizes specific regions that undergo disorder-to-order transition upon binding (Mészáros, Simon, & Dosztányi, 2009). ANCHOR estimates the amount of additional favorable interactions a residue might be able to form by interacting with a more structured environment. Both the original IUPred and ANCHOR packages are popular predictor methods in their categories due to their high speed and accuracy.

In a recently launched new installment, we combined IUPred with ANCHOR (hence the name IUPred2A; Mészáros, Erdos, & Dosztányi, 2018). In this new implementation, we reparametrized ANCHOR on newly available datasets, such as the comprehensive database of disordered proteins DisProt (Piovesan et al., 2017) and complexes of ordered and disordered proteins (Schad et al., 2018). Furthermore, we enhanced the capabilities of our web server by adding multiple new features. Importantly, we also expanded the conditional disorder prediction capabilities of IUPred2A by incorporating a new tool to predict regions that can undergo a disorder-to-order (or order-to-disorder) transition upon change in the redox potential of the environment. While this feature is in a highly experimental stage, our high-throughput analysis of whole proteomes indicates that redox potential−dependent switches in cells are more frequent and important than previously thought (Erdős, Mészáros, Reichmann, & Dosztányi, 2019).

In this article, we give an overview of how IUPred2A can be used in different contexts and how the results can be interpreted.

IUPred2A offers a single entry point to access a family of prediction tools for characterizing disordered regions in proteins. First of all, it predicts ordered and disordered regions for a single amino acid sequence. The user can choose between three different options: short, long, and structured domains. These options were developed using slightly different parameter settings. The long option is the default and is recommended to be used in most cases. The short option was developed to predict missing residues collected from PDB structures. These regions are typically short, only 3 to 10 residues long, but have traditionally represented important test cases for disorder-prediction methods (Jin & Dunbrack, 2005). The structured-domain option aims to help the identification of larger structural units even when the prediction profile is noisy. In addition to general disorder prediction, IUPred2A also offers predictions of certain types of conditionally disordered regions such as disordered binding regions or redox-regulated disordered regions. The main advantage of using the web server is that it provides a graphical representation of the prediction output, which presents additional features that can help in the interpretation of the results.

Necessary Resources

Hardware

While IUPred2A works best on desktop or laptop computers, it is also usable on tablets and smartphones. The device must have an active internet connection.

Software

Any modern browser with HTML5 and WebP support (for example Chrome 30+, Firefox 65+, Opera 19+).

Input data

The online application of IUPred2A accepts UniProt (UniProt Consortium, 2019) identifiers (both accessions and identifier codes are allowed) or a protein sequence in plain text or FASTA format.

Submission page

1.Visit the web page of IUPred2A (https://iupred2a.elte.hu, Fig. 1). To analyze a protein, either use a UniProt identifier (both accession and identifier codes are allowed) or a protein sequence in the respective entry box.

0. If the UniProt identifier or accession code is given, the web server automatically collects the corresponding amino acid sequence and runs the prediction on it.

1. The server accepts protein sequence in FASTA format or in plain text as well.

2. The webserver of IUPred2A is able to analyze multiple proteins or even whole proteomes of smaller size. The user can upload proteins in a multi-FASTA-formatted file to the server, and provide an e-mail address. The results will be sent as an e-mail attachment to the given address in text format (for more information on text format, see Support Protocol2). Please note that currently the maximum file size that can be uploaded is 1 Mb.

2.Select the type of analysis.

0. As default, “IUPred2 long disorder” and the “ANCHOR2” option from the “Context-dependent predictions” submenu are selected. To carry out a disorder prediction only, uncheck the “Context-dependent predictions” option (for context-dependent disorder methods, see Basic Protocols2and3).

1. Option: Use “IUPred2 short disorder” for the prediction of short disordered regions

2.         Option: Use “IUPred2 structured domains” for the prediction of structured domains in the query protein sequence. If this option is selected, a textbox will appear below the graphical interface. Inside the textbox, the number of predicted globular domains and their locations will be shown alongside a string representation of the protein sequence, where capital letters represent structured domains.

        The short option is recommended when the main focus of the analysis is missing residues in a PDB structure, while the structured domain option can be used to identify larger structured segments that could be suitable for structure determination. In most scenarios, the long option is recommended.

3.Click on “Submit” to start the analysis.

Results page

4.After a couple of seconds, a new page loads showing the results. The main panel on the page shows a graphical representation of the analysis. Depending on the input, we can find information about the protein from UniProt above the graphical results. Clicking this header will lead us to the corresponding UniProt page. To the right of the title, a link to download the results can be found.

5.The main graphical panel displays the prediction profile corresponding to the submitted protein sequence. Predictions are provided for each residue in the sequence. Residues with scores above 0.5 are predicted as disordered, while scores below 0.5 indicate order.

6.There are four small icons on the left-hand side of the graph that enable the manipulation of the plot. By default, the graph can be panned along the x axis by dragging the plot, or zoomed in and out using the mouse wheel. These options can be turned on and off using their respective icons. Below these icons we also have an option to reset the plot to its original state. The figure can also be saved in standard “png” format.

7.In the case where the submitted input was a UniProt accession or identifier code, the graphical interface displays information collected from additional sources, including annotations about conserved sequence families, short linear motifs, experimental evidence for disordered status, and known PDB structures corresponding to a given entry. When a plain protein sequence is submitted, IUPred2A tries to map that sequence to a protein in UniProt. If the sequence only matches one single protein in the database, its identifier will be associated with the sequence, and it will be used for further analysis. If the UniProt entry corresponding to the submitted sequence cannot be unambiguously identified, only Pfam information is displayed.

8.Sequence family information is collected from the Pfam database (El-Gebali et al., 2019) and labeled as Pfam. Different types of sequence annotations such as Domains, Families, Motifs, and Repeats are displayed using associated colors.

9.The line ELM collects experimentally verified short linear motifs from the Eukaryotic Linear Motif database (see Current Protocols article: Gouw et al., 2018).

10.The PTM lines contain information about post-translational modification sites (PTMs) collected from the PhosphoSitePlus database (Hornbeck et al., 2012). The upper line shows a display of different phosphorylation sites with Ser, Thr, and Tyr sites indicated by different colors, while the lower one shows acetylation and methylation sites colored by the type of the modification.

11.The EXP DIS line displays experimentally verified disordered regions from the DisProt (Hatos et al., 2020), DIBS (Schad et al., 2018), and MFIB (Fichó, Reményi, Simon, & Mészáros, 2017) databases.

12.The subplot entitled PDB shows coverage of the residues for which a structure is available in the Protein Data Bank (Berman et al., 2000). When structure hits are found for the submitted protein, a “Show all structures” checkbox is present below the PDB subplot. Marking this checkbox will display the coverage of each individual PDB entry with its respective link to the database.

13.Hovering over the complementary plot boxes displays additional information about the selected annotation or structure, such as the exact positions, identifiers, and types.

IUPred2A now incorporates ANCHOR2, which predicts regions that undergo a disorder-to-order transition upon binding to another protein. ANCHOR2 is one of the special cases of context-dependent disorder analysis.

Necessary Resources

• See Basic Protocol 1

1.Fill in the query protein as described in Basic Protocol 1, and either leave the “Context-dependent predictions” selection box on default or select ANCHOR2.

2.Click on “Submit.” After a few seconds, the results will appear.

3.The plot shows the disordered sequence−prediction profile (long option on default) indicated in red, as well as the predicted disordered binding sites shown in blue. Predicted disordered binding sites are positions with score above 0.5.

4.The user can turn on/off both the IUPred or ANCHOR2 profile by clicking on the corresponding label at the top right corner in the plot.

The interpretation of order and disorder information can be quite complex. Many proteins have modular architecture, and in reality there is a continuum of structural state which cannot be fully represented in a simple prediction output. Here we present a use case to demonstrate how complementary information can be utilized to interpret the prediction profiles.

Materials

• See Basic Protocol 1
• An example output is shown in Figure 4 using UniProt accession code P35222 corresponding to the human β-catenin protein

1.IUPred2A predicts most of the N-terminal region and the C-terminal region as disordered. There is also a small peak of predicted disorder for residues 547-558.

2.ANCHOR2 predicts disordered binding regions for the N-terminal and the C-terminal regions.

3.There are four Pfam annotations indicated, which correspond to armadillo repeat regions in the middle of the sequence. It is worth keeping in mind that repeat regions are often incompletely annotated in Pfam, so there could be additional repeat units.

4.Known motifs can be found at multiple locations, according to the ELM annotations. At the N-terminus there are multiple overlapping motif sites that correspond to phosphorylation sites, and a phosphodegron motif which regulates the degradation of the protein (Wu et al., 2003). There are three kinase docking sites annotated along the sequence in addition to the C-terminal PDZ binding motif. Motif sites are generally located within disordered regions, but can also occur within loop regions of ordered domains.

5.There are multiple PTM sites annotated for this protein. They are particularly enriched in the N-terminal phosphodegron site. Phosphorylation sites are also located within the docking sites, but at additional positions along the central region as well.

6.This protein contains two experimentally verified disordered regions based on annotation from the DIBS database.

7.There are multiple PDB structures corresponding to this protein, which cover different parts of the protein. We can see this after selecting “Show structures” at the bottom of the graphical interface.

8.While the presence of structure is often interpreted as an indication of order, this protein indicates that it can be more complicated. There are multiple structures corresponding to the N-terminal region. However, this region is predicted as disordered, which is corroborated by known linear motifs, multiple PTM sites, and, primarily, by the existence of experimental evidence for disorder. Taking a closer look at the PDB structures of this region, they all contain the region of β-catenin in complex.

9.The middle section also has various solved structures (e.g., 1g3j, 3tx7). These cover all armadillo Pfam regions. Based on the well-formed structures, the region between 130 and 670 can be considered as mostly ordered.

10.However, the peak of predicted disorder is confirmed by the lack of electron density in the known structures. See https://www.rcsb.org/pdb/protein/P35222?addPDB=3SLA for more details.

11.The C-terminal region has PDB coverage from a single PDB structure (PDB code 2Z6H), and, on closer inspection, this region is actually missing from the electron density. Therefore, the structural annotation does not contradict the disorder status for the last 120 residues.

The new implementation of IUPred2A contains an additional feature to predict regions that undergo a disorder-to-order transition upon changes of the redox potential in the environment. The key residues of redox regulation are cysteines, which can provide large stabilizing effects through disulfide bond formation under oxidative conditions or through zinc ion coordination under reducing conditions. The lack of these stabilizing contributions can be modeled by mutating all cysteine residues to serines in the sequence. In most cases, the two scenarios are not significantly different. However, in the case of redox-sensitive disordered regions, redox_plus is expected to be mostly ordered, while redox_minus is expected to be mostly disordered. The actual region is calculated based on a heuristic segmentation approach (Mészáros et al., 2018).

Necessary Resources

• See Basic Protocol 1

1.Fill in the query protein as described in Basic Protocol 1, and use the “Context-dependent predictions” selection box to select “Redox state.”

2.Click on “Submit.” After a few seconds, the results will appear. The plot shows two lines corresponding to redox_plus and redox_minus scenarios. An identified redox-sensitive disordered region will be indicated by shading the region between the two lines, and will also be highlighted by a box below the graph in a complementary plot entitled REDOX.

In addition to the graphical output, IUPred2A offers multiple formats in which to download the results. On each analysis, a button called “Download results” appears to the right of the header. Clicking on the button offers two options: Text or JSON. Choosing the “Text” button shows a plain text−formatted version of the analysis results. Each line in the header starts with a “#” symbol, to separate them from the relevant data. The results represent each residue on a separate line. Each line is split into columns. The first column contains the position of the given residue in the sequence; the second shows the one-letter IUPAC representation of the residue. The remaining columns are based on the type of analysis. They will show the disordered prediction score (IUPred2A), the prediction scores for IUPred and ANCHOR (ANCHOR2), or the Redox plus and Redox minus predictions (Redox). In the case of redox state calculations, an additional column is present with a binary classification. A 1 shows that the residue is within a redox potential−sensitive region, while a 0 represents the contrary.

Choosing the “JSON” option leads to a JSON representation of the results. This representation always contains a “sequence” key, with the protein sequence as value, as well as a “type” key with the corresponding analysis type as value. The rest of the keys are dependent on the type of the analysis. For IUPred2 calculations, the key “iupred2” will contain a list representation of IUPred2 scores; for context-dependent calculations, the “anchor2” key contains the ANCHOR2 scores; “iupred2_redox_plus” and “iupred2_redox_min” contain the redox state result scores; and the key “redox_sensitive_regions” shows the region boundaries in a list. The value of the “meta” key contains information about the publication of the method.

IUPred2A is also accessible via REST API to enable automated/large-scale use. Requests should follow the syntax: https://iupred2a.elte.hu/iupred2a/::accession:: or https://iupred2a.elte.hu/iupred2a/::analysis_type::/::accession:: where ::accession:: must be a valid UniProt accession and ::analysis_type:: is either “long,” “short,” “glob,” “anchor,” or “redox,” depending on the type of analysis. In cases where “:: analysis_type::” is not given, the default “long” will be used. If the requested URL ends with “.json” the output will be JSON type; in any other case it will be simple text.

For example:

https://iupred2a.elte.hu/iupred2a/q32p44

https://iupred2a.elte.hu/iupred2a/q32p44.json

https://iupred2a.elte.hu/iupred2a/short/q32p44

Here you can find a simple python script that shows the programmatic use of the IUPred2A REST API.

Requirements

Python3.6, python3-requests library version 2.22.0, python3-json library version 2.0.9

Sample code

• import requests
• import json
• URL = "https://iupred2a.elte.hu/iupred2a/anchor/\q32p44.json"
• response = json.loads(requests.get(URL).text)
• print("\n".join(["{}\t{}".format(response["sequence"][n], x) for n, x in enumerate(response["anchor2"])]))

Results

This example loads the results of an ANCHOR2 analysis for the human EML3 protein (Q32P44), and loads the JSON-formatted results as standard python dictionary into the variable “response.”

The IUPred2A website offers a downloadable package (in tar.gz format) for local use, which is free for academic users. Along with the IUPred2A software, the experimental “IUPred2A redox” can be downloaded as well. Both packages contain an executable file together with a python library for programmatic use.

Necessary Resources

Hardware

• There are no definite hardware requirements for IUPred2A. It uses minimal amount of memory, and has no GPU requirements.

Software

• Python 3.x runtime environment
• Libraries: os, math, textwrap. These libraries are a part of the default Python 3 installation.

Input data

• A protein sequence in either FASTA or plain text format

The downloaded package requires no installation process. To be able to use it, simply unpack the compressed directory. Depending on your choice, the executable file will either be called “iupred2a.py” for the standard version or “iupred2_redox.py” for the redox potential region prediction. To start a prediction, simply call the executable file of your choice in a terminal and pass the location of the protein sequence as an argument along with the type of prediction you want to carry out (either “long,” “short,” or “glob” for the prediction of long or short disorder or globular regions, respectively). The main package also contains the example sequence for the human p53 protein in FASTA format in a file called “P53_HUMAN.seq.”

Example 1

• $ python3 iupred2a.py P53_HUMAN.seq long

Expected output:

• IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding

• Balint Meszaros, Gabor Erdos, Zsuzsanna Dosztanyi

• Nucleic Acids Research 2018;46(W1):W329-W337.

• Prediction type: long

• Prediction output

The output will be displayed on a table-like structure inside your chosen terminal in text format (for more information on display formats see Support Protocol 2).

To carry out an ANCHOR2 analysis, use the -a switch.

Example 2

• $ python3 iupred2a.py -a P53_HUMAN.seq long

Expected output:

• IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding

• Balint Meszaros, Gabor Erdos, Zsuzsanna Dosztanyi

• Nucleic Acids Research 2018;46(W1):W329-W337.

• Prediction type: long

• Prediction output

Option : IUPred2A requires multiple data files, which are a part of the downloadable package, and can be found inside the “data” folder. By default, this folder is at the same directory level as the executable; however, if you want to modify the structure, you can specify the location of the data files by using the -d switch.

Example:

• $ python3 iupred2a.py -d /path/to/iupred2a/data/directory -a P53_HUMAN.seq long

For programmatic usage, the downloaded package also comes with a python library package called “iupred2a_lib.py.” There are multiple approaches to using this library. One is to include its absolute path in the PYTHONPATH environment variable on LINUX systems, or their equivalent in the python interpreter on Windows- or Mac-based machines. Once the path has been added to the environment variable, we can simply import the library and use the functions inside. The library contains four major functions and some minor helper functions. The first function is called “iupred.” It requires as an argument a protein sequence in a format of a standard python string. Note that this string must only contain pure uppercase one-letter amino acid code, and not a FASTA-formatted sequence. The function also takes an optional argument called “mode,” where we can specify the type of analysis. The accepted options are “long,” “short,” or “glob” for the prediction of long or short disordered regions or structured domains, respectively. The default option is “long.” The function returns the predicted values in the form of a list of floating-point numbers. Note that if the “glob” optional argument is used, the function will return a tuple, where the first element will be the prediction scores while the second will be the string representation of the structured domains that we can see on the website, as described in Basic Protocol 1.

Example 3

• import iupred2a_lib

• my_sequence =

  • "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD"

• print(iupred2a_lib.iupred(my_sequence))

The function called “anchor2” works in the same fashion as “iupred.” It takes a sequence as input, and returns the predicted ANCHOR2 values in a list of floating-point numbers.

Example 4

• import iupred2a_lib

• my_sequence =

  • "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD"

• print(iupred2a_lib.anchor2(my_sequence))

This library also contains a function to predict the experimental redox-sensitive disordered regions. The main function is called “get_redox_regions,” which requires two arguments. The first must be a list of floating-point numbers from an iupred prediction where the cysteine residues have been switched to serines (the result of the helper function “iupred2_redox”), and the second must be the result of a standard iupred prediction (the function “iupred2”). The results will be represented in a standard python dictionary, where each key is the starting position of the region and each value is the end of the corresponding starting point.

Example 5

• import iupred2a_lib

• my_sequence =

  • "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD"

• iupred_result = iupred2a_lib.iupred(my_sequence)

• iupred_redox = iupred2a_lib.iupred_redox(my_sequence)

• print(iupred2a_lib.get_redox_regions(iupred_redox, iupred_result))

Example 6: Genome-wide analysis

Using the downloaded package, we can carry out a genome-wide analysis using IUPred2A. The following example will download the entire human proteome from the UniProt database and calculate the frequency of disordered residues and frequency of proteins that have at least one disordered segment longer than 30 residues in the proteome. Please note that this example may take more than 5 min, depending on the computer.

Hardware

Internet connection

Software

Python 3.x

Libraries: iupred2a_lib, gzip, ftplib, itertools

• import ftplib
• import gzip
• import iupred2a_lib
• from itertools import groupby
• ftp = ftplib.FTP("ftp.uniprot.org")
• ftp.login()
• ftp_file = "pub/databases/uniprot/current_release/\knowledgebase/reference_proteomes/Eukaryota/\UP000005640_9606.fasta.gz"
• ftp.retrbinary("RETR" + ftp_file, open("human_proteome.fasta", "wb").write)
• proteome = {}
• header ="
• with gzip.open("human_proteome.fasta", mode="rt") as file_handler:
• for line in file_handler:
• if line.startswith(""):
• header = line
• proteome[header] = ""
• elif header:
• proteome[header] += line.strip()
• disordered_residues = 0
• disordered_proteins = 0
• all_residues = 0
• for header, seq in proteome.items():
• iupred_scores = iupred2a_lib.iupred(seq, "long")
• if max([sum([1 for _ in y]) if × == 1 else 0 for x, y in groupby([1 if x>=0.5 else 0 for × in iupred_scores])]) > 30:
• disordered_proteins += 1
• disordered_residues += sum([1 for × in iupred_scores if x>=0.5])
• all_residues += len(seq)
• print("Fraction of disordered residues: {:.2f}".format(disordered_residues/all_residues))
• print("Fraction of disordered proteins: {:.2f}".format(disordered_proteins/len(proteome)))

Acknowledgments

This work was completed in the ELTE Thematic Excellence Programme (ED-18-1-2019-0030) supported by the Hungarian Ministry for Innovation and Technology. This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 778247. E. G. also acknowledges funding from the Joseph Cours Scholarship.

• Bardwell, A. J., & Bardwell, L. (2015). Two hydrophobic residues can determine the specificity of mitogen-activated protein kinase docking interactions. The Journal of Biological Chemistry , 290(44), 26661–26674. doi: 10.1074/jbc.M115.691436.
• Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., … Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research , 28(1), 235–242. doi: 10.1093/nar/28.1.235.
• Borgia, A., Borgia, M. B., Bugge, K., Kissling, V. M., Heidarsson, P. O., Fernandes, C. B., … Schuler, B. (2018). Extreme disorder in an ultrahigh-affinity protein complex. Nature , 555(7694), 61–66. doi: 10.1038/nature25762.
• Delaforge, E., Kragelj, J., Tengo, L., Palencia, A., Milles, S., Bouvignies, G., … Jensen, M. R. (2018). Deciphering the dynamic interaction profile of an intrinsically disordered protein by NMR exchange spectroscopy. Journal of the American Chemical Society , 140(3), 1148–1158. doi: 10.1021/jacs.7b12407.
• Dosztányi, Z., Csizmók, V., Tompa, P., & Simon, I. (2005). The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. Journal of Molecular Biology , 347(4), 827–839. doi: 10.1016/j.jmb.2005.01.071.
• El-Gebali, S., Mistry, J., Bateman, A., Eddy, S. R., Luciani, A., Potter, S. C., … Finn, R. D. (2019). The Pfam protein families database in 2019. Nucleic Acids Research , 47(D1), D427–D432. doi: 10.1093/nar/gky995.
• Erdős, G., Mészáros, B., Reichmann, D., & Dosztányi, Z. (2019). Large-scale analysis of redox-sensitive conditionally disordered protein regions reveals their widespread nature and key roles in high-level eukaryotic processes. Proteomics , 19(6), e1800070. doi: 10.1002/pmic.201800070.
• Fichó, E., Reményi, I., Simon, I., & Mészáros, B. (2017). MFIB: A repository of protein complexes with mutual folding induced by binding. Bioinformatics , 33(22), 3682–3684. doi: 10.1093/bioinformatics/btx486.
• Garai, Á., Zeke, A., Gógl, G., Törő, I., Fördős, F., Blankenburg, H., … Reményi, A. (2012). Specificity of linear motifs that bind to a common mitogen-activated protein kinase docking groove. Science Signaling , 5(245), ra74. doi: 10.1126/scisignal.2003004.
• Gouw, M., Michael, S., Sámano-Sánchez, H., Kumar, M., Zeke, A., Lang, B., … Gibson, T. J. (2018). The eukaryotic linear motif resource—2018 update. Nucleic Acids Research , 46(D1), D428–D434. doi: 10.1093/nar/gkx1077.
• Gouw, M., Sámano-Sánchez, H., Van Roey, K., Diella, F., Gibson, T. J., & Dinkel, H. (2017). Exploring short linear motifs using the ELM database and tools. Current Protocols in Bioinformatics , 58, 8.22.1–8.22.35.
• Hatos, A., Hajdu-Soltész, B., Monzon, A. M., Palopoli, N., Álvarez, L., Aykac-Fas, B., & … Piovesan, D. (2020). DisProt: Intrinsic protein disorder annotation in 2020. Nucleic Acids Research , 48(D1), D269–D276.
• Ho, D. T., Bardwell, A. J., Abdollahi, M., & Bardwell, L. (2003). A docking site in MKK4 mediates high affinity binding to JNK MAPKs and competes with similar docking sites in JNK substrates. The Journal of Biological Chemistry , 278(35), 32662–32672. doi: 10.1074/jbc.M304229200.
• Hornbeck, P. V., Kornhauser, J. M., Tkachev, S., Zhang, B., Skrzypek, E., Murray, B., … Sullivan, M. (2012). PhosphoSitePlus: A comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Research , 40(Database issue), D261–D270. doi: 10.1093/nar/gkr1122.
• Jacks, A., Babon, J., Kelly, G., Manolaridis, I., Cary, P. D., Curry, S., & Conte, M. R. (2003). Structure of the C-terminal domain of human La protein reveals a novel RNA recognition motif coupled to a helical nuclear retention element. Structure , 11(7), 833–843. doi: 10.1016/S0969-2126(03)00121-7.
• Jakob, U., Kriwacki, R., & Uversky, V. N. (2014). Conditionally and transiently disordered proteins: Awakening cryptic disorder to regulate protein function. Chemical Reviews , 114(13), 6779–6805. doi: 10.1021/cr400459c.
• Jin, Y., & Dunbrack, R. L., Jr. (2005). Assessment of disorder predictions in CASP6. Proteins , 61(Suppl 7), 167–175. doi: 10.1002/prot.20734.
• Mészáros, B., Erdos, G., & Dosztányi, Z. (2018). IUPred2A: Context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Research , 46(W1), W329–W337. doi: 10.1093/nar/gky384.
• Mészáros, B., Simon, I., & Dosztányi, Z. (2009). Prediction of protein binding regions in disordered proteins. PLoS Computational Biology , 5(5), e1000376. doi: 10.1371/journal.pcbi.1000376.
• Miskei, M., Antal, C., & Fuxreiter, M. (2017). FuzDB: Database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies. Nucleic Acids Research , 45(D1), D228–D235. doi: 10.1093/nar/gkw1019.
• Piovesan, D., Tabaro, F., Mičetić, I., Necci, M., Quaglia, F., Oldfield, C. J., … Tosatto, S. C. E. (2017). DisProt 7.0: A major update of the database of disordered proteins. Nucleic Acids Research , 45(D1), D219–D227. doi: 10.1093/nar/gkw1056.
• Reichmann, D., Xu, Y., Cremers, C. M., Ilbert, M., Mittelman, R., Fitzgerald, M. C., & Jakob, U. (2012). Order out of disorder: Working cycle of an intrinsically unfolded chaperone. Cell , 148(5), 947–957. doi: 10.1016/j.cell.2012.01.045.
• Schad, E., Fichó, E., Pancsa, R., Simon, I., Dosztányi, Z., & Mészáros, B. (2018). DIBS: A repository of disordered binding sites mediating interactions with ordered proteins. Bioinformatics , 34(3), 535–537. doi: 10.1093/bioinformatics/btx640.
• UniProt Consortium. (2019). UniProt: A worldwide hub of protein knowledge. Nucleic Acids Research , 47(D1), D506–D515. doi: 10.1093/nar/gky1049.
• van der Lee, R., Buljan, M., Lang, B., Weatheritt, R. J., Daughdrill, G. W., Dunker, A. K., … Babu, M. M. (2014). Classification of intrinsically disordered regions and proteins. Chemical Reviews , 114(13), 6589–6631. doi: 10.1021/cr400525m.
• Wright, P. E., & Dyson, H. J. (1999). Intrinsically unstructured proteins: Re-assessing the protein structure-function paradigm. Journal of Molecular Biology , 293(2), 321–331. doi: 10.1006/jmbi.1999.3110.
• Wu, G., Xu, G., Schulman, B. A., Jeffrey, P. D., Harper, J. W., & Pavletich, N. P. (2003). Structure of a beta-TrCP1-Skp1-beta-catenin complex: Destruction motif binding and lysine specificity of the SCF(beta-TrCP1) ubiquitin ligase. Molecular Cell , 11(6), 1445–1456. doi: 10.1016/S1097-2765(03)00234-X.

Number of times cited according to CrossRef: 192

• Colleen E Hannon, Michael B Eisen, Intrinsic protein disorder is insufficient to drive subnuclear clustering in embryonic transcription factors, eLife, 10.7554/eLife.88221.2, 12 , (2024).
• Colleen E Hannon, Michael B Eisen, Intrinsic protein disorder is insufficient to drive subnuclear clustering in embryonic transcription factors, eLife, 10.7554/eLife.88221, 12 , (2024).
• Carlyle Ribeiro Lima, Deborah Antunes, Ernesto Caffarena, Nicolas Carels, Structural Characterization of Heat Shock Protein 90β and Molecular Interactions with Geldanamycin and Ritonavir: A Computational Study, International Journal of Molecular Sciences, 10.3390/ijms25168782, 25 , 16, (8782), (2024).
• Gelsomina Mansueto, Giovanna Fusco, Giovanni Colonna, A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms, Biomolecules, 10.3390/biom14050541, 14 , 5, (541), (2024).
• Evan John, Callum Verdonk, Karam B. Singh, Richard P. Oliver, Leon Lenzo, Shota Morikawa, Jessica L. Soyer, Jordi Muria-Gonzalez, Daniel Soo, Carl Mousley, Silke Jacques, Kar-Chun Tan, Regulatory insight for a Zn2Cys6 transcription factor controlling effector-mediated virulence in a fungal pathogen of wheat, PLOS Pathogens, 10.1371/journal.ppat.1012536, 20 , 9, (e1012536), (2024).
• Diana Martínez-Valencia, Cecilia Bañuelos, Guillermina García-Rivera, Daniel Talamás-Lara, Esther Orozco, The Entamoeba histolytica Vps26 (EhVps26) retromeric protein is involved in phagocytosis: Bioinformatic and experimental approaches, PLOS ONE, 10.1371/journal.pone.0304842, 19 , 8, (e0304842), (2024).
• Richard A. Stewart, Zhihao Ding, Ung Seop Jeon, Lauren B. Goodman, Jeannine J. Tran, John P. Zientko, Malavika Sabu, Ken M. Cadigan, Wnt target gene activation requires β-catenin separation into biomolecular condensates, PLOS Biology, 10.1371/journal.pbio.3002368, 22 , 9, (e3002368), (2024).
• Ali Vural, Stephen M. Lanier, Properties of biomolecular condensates defined by Activator of G-protein Signaling 3, Journal of Cell Science, 10.1242/jcs.261326, 137 , 4, (2024).
• Ting Li, Yongqin Wang, Annelore Natran, Yi Zhang, Hao Wang, Kangxi Du, Peng Qin, Hua Yuan, Weilan Chen, Bin Tu, Dirk Inzé, Marieke Dubois, C‐TERMINAL DOMAIN PHOSPHATASE‐LIKE 3 contributes to GA‐mediated growth and flowering by interaction with DELLA proteins, New Phytologist, 10.1111/nph.19742, 242 , 6, (2555-2569), (2024).
• Miranda Evans, Richard Hagan, Oliver J. Boyd, Manon Bondetti, Oliver E. Craig, Matthew J. Collins, Jessica Hendy, The impact of cooking and burial on proteins: a characterisation of experimental foodcrusts and ceramics, Royal Society Open Science, 10.1098/rsos.240610, 11 , 9, (2024).
• Maggie Witham, Sarah R Hengel, The role of RAD51 regulators and variants in primary ovarian insufficiency, endometriosis, and polycystic ovary syndrome, NAR Molecular Medicine, 10.1093/narmme/ugae010, 1 , 4, (2024).
• Victor Reys, Jean-Luc Pons, Gilles Labesse, SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks, Nucleic Acids Research, 10.1093/nar/gkae398, 52 , W1, (W313-W317), (2024).
• Paulina Kettel, Laura Marosits, Elena Spinetti, Michael Rechberger, Caterina Giannini, Philipp Radler, Isabell Niedermoser, Irmgard Fischer, Gijs A Versteeg, Martin Loose, Roberto Covino, G Elif Karagöz, Disordered regions in the IRE1α ER lumenal domain mediate its stress-induced clustering, The EMBO Journal, 10.1038/s44318-024-00207-0, (2024).
• Gregory Foran, Ryan Douglas Hallam, Marvel Megaly, Anel Turgambayeva, Daniel Antfolk, Yifeng Li, Vincent C. Luca, Aleksandar Necakov, Notch1 Phase Separation Coupled Percolation facilitates target gene expression and enhancer looping, Scientific Reports, 10.1038/s41598-024-71634-6, 14 , 1, (2024).
• Hamlet Khachatryan, Mher Matevosyan, Vardan Harutyunyan, Smbat Gevorgyan, Anastasiya Shavina, Irina Tirosyan, Yeva Gabrielyan, Marusya Ayvazyan, Marine Bozdaganyan, Zeynab Fakhar, Sajjad Gharaghani, Hovakim Zakaryan, Computational evaluation and benchmark study of 342 crystallographic holo-structures of SARS-CoV-2 Mpro enzyme, Scientific Reports, 10.1038/s41598-024-65228-5, 14 , 1, (2024).
• Kinga K. Nagy, Kristóf Takács, Imre Németh, Bálint Varga, Vince Grolmusz, Mónika Molnár, Beáta G. Vértessy, Novel enzymes for biodegradation of polycyclic aromatic hydrocarbons identified by metagenomics and functional analysis in short-term soil microcosm experiments, Scientific Reports, 10.1038/s41598-024-61566-6, 14 , 1, (2024).
• Thomas M. Bartlett, Tyler A. Sisley, Aaron Mychack, Suzanne Walker, Richard W. Baker, David Z. Rudner, Thomas G. Bernhardt, FacZ is a GpsB-interacting protein that prevents aberrant division-site placement in Staphylococcus aureus, Nature Microbiology, 10.1038/s41564-024-01607-y, 9 , 3, (801-813), (2024).
• Laure Maneix, Polina Iakova, Charles G. Lee, Shannon E. Moree, Xuan Lu, Gandhar K. Datar, Cedric T. Hill, Eric Spooner, Jordon C. K. King, David B. Sykes, Borja Saez, Bruno Di Stefano, Xi Chen, Daniela S. Krause, Ergun Sahin, Francis T. F. Tsai, Margaret A. Goodell, Bradford C. Berk, David T. Scadden, André Catic, Cyclophilin A supports translation of intrinsically disordered proteins and affects haematopoietic stem cell ageing, Nature Cell Biology, 10.1038/s41556-024-01387-x, 26 , 4, (593-603), (2024).
• Chengyun Wu, Xingsong Wang, Yan Li, Weibo Zhen, Chunfei Wang, Xiaoqing Wang, Zhouli Xie, Xiumei Xu, Siyi Guo, José Ramón Botella, Binglian Zheng, Wei Wang, Chun-Peng Song, Zhubing Hu, Sequestration of DBR1 to stress granules promotes lariat intronic RNAs accumulation for heat-stress tolerance, Nature Communications, 10.1038/s41467-024-52034-w, 15 , 1, (2024).
• Samuel Naudi-Fabra, Carlos A. Elena-Real, Ida Marie Vedel, Maud Tengo, Kathrin Motzny, Pin-Lian Jiang, Peter Schmieder, Fan Liu, Sigrid Milles, An extended interaction site determines binding between AP180 and AP2 in clathrin mediated endocytosis, Nature Communications, 10.1038/s41467-024-50212-4, 15 , 1, (2024).
• Nishit Goradia, Stefan Werner, Edukondalu Mullapudi, Sarah Greimeier, Lina Bergmann, Andras Lang, Haydyn Mertens, Aleksandra Węglarz, Simon Sander, Grzegorz Chojnowski, Harriet Wikman, Oliver Ohlenschläger, Gunhild von Amsberg, Klaus Pantel, Matthias Wilmanns, Master corepressor inactivation through multivalent SLiM-induced polymerization mediated by the oncogene suppressor RAI2, Nature Communications, 10.1038/s41467-024-49488-3, 15 , 1, (2024).
• Mónika Bokor, Ágnes Tantos, General Characterization of Properties of Ordered and Disordered Proteins by Wide-Line 1 H NMR , ACS Omega, 10.1021/acsomega.4c00517, 9 , 22, (23468-23475), (2024).
• Muthu Raj Salaikumaran, Pallavi P. Gopal, Rational Design of TDP-43 Derived α-Helical Peptide Inhibitors: An In Silico Strategy to Prevent TDP-43 Aggregation in Neurodegenerative Disorders , ACS Chemical Neuroscience, 10.1021/acschemneuro.3c00659, 15 , 6, (1096-1109), (2024).
• Chinmaya Kumar Patel, Tushar Kanti Mukherjee, Biomolecular Condensation of Trypsin Prevents Autolysis and Promotes Ca 2+ -Mediated Activation of Esterase Activity , Biomacromolecules, 10.1021/acs.biomac.4c00736, 25 , 9, (6082-6092), (2024).
• Mallari Praveen, Characterizing the West Nile Virus's polyprotein from nucleotide sequence to protein structure – Computational tools, Journal of Taibah University Medical Sciences, 10.1016/j.jtumed.2024.01.001, 19 , 2, (338-350), (2024).
• Olga Zimmermannová, Diego Velázquez, Klára Papoušková, Vojtěch Průša, Viktorie Radová, Pierre Falson, Hana Sychrová, The Hydrophilic C-terminus of Yeast Plasma-membrane Na+/H+ Antiporters Impacts Their Ability to Transport K+, Journal of Molecular Biology, 10.1016/j.jmb.2024.168443, 436 , 4, (168443), (2024).
• Greta Bianchi, Marco Mangiagalli, Diletta Ami, Junaid Ahmed, Silvia Lombardi, Sonia Longhi, Antonino Natalello, Peter Tompa, Stefania Brocca, Condensation of the N-terminal domain of human topoisomerase 1 is driven by electrostatic interactions and tuned by its charge distribution, International Journal of Biological Macromolecules, 10.1016/j.ijbiomac.2023.127754, 254 , (127754), (2024).
• Yu-Xi Xiao, Seon Yong Lee, Magali Aguilera-Uribe, Reuben Samson, Aaron Au, Yukti Khanna, Zetao Liu, Ran Cheng, Kamaldeep Aulakh, Jiarun Wei, Adrian Granda Farias, Taylor Reilly, Saba Birkadze, Andrea Habsid, Kevin R. Brown, Katherine Chan, Patricia Mero, Jie Qi Huang, Maximilian Billmann, Mahfuzur Rahman, Chad Myers, Brenda J. Andrews, Ji-Young Youn, Christopher M. Yip, Daniela Rotin, W. Brent Derry, Julie D. Forman-Kay, Alan M. Moses, Iva Pritišanac, Anne-Claude Gingras, Jason Moffat, The TSC22D, WNK, and NRBP gene families exhibit functional buffering and evolved with Metazoa for cell volume regulation, Cell Reports, 10.1016/j.celrep.2024.114417, 43 , 7, (114417), (2024).
• Nagaraja Chappidi, Thomas Quail, Simon Doll, Laura T. Vogel, Radoslav Aleksandrov, Suren Felekyan, Ralf Kühnemuth, Stoyno Stoynov, Claus A.M. Seidel, Jan Brugués, Marcus Jahnel, Titus M. Franzmann, Simon Alberti, PARP1-DNA co-condensation drives DNA repair site assembly to prevent disjunction of broken DNA ends, Cell, 10.1016/j.cell.2024.01.015, 187 , 4, (945-961.e18), (2024).
• Monoj Sutradhar, Brijesh Kumar Singh, Subhasis Samanta, Md Nasim Ali, Nirmal Mandal, The overexpression of OsMed 37_6, a mediator complex subunit enhances salt stress tolerance in rice, Biocatalysis and Agricultural Biotechnology, 10.1016/j.bcab.2024.103212, 58 , (103212), (2024).
• Nikolaos A. Papanikolaou, Maria Kakavoulia, Christos Ladias, Athanasios G. Papavassiliou, The ras-related protein RAB22A interacts with hypoxia-inducible factor 1-alpha (HIF-1α) in MDA-MB-231 breast cancer cells in hypoxia, Molecular Biology Reports, 10.1007/s11033-024-09516-3, 51 , 1, (2024).
• Joseph Yayen, Ching Chan, Ching-Mei Sun, Su-Fen Chiang, Tzyy-Jen Chiou, Conservation of land plant-specific receptor-like cytoplasmic kinase subfamily XI possessing a unique kinase insert domain, Frontiers in Plant Science, 10.3389/fpls.2023.1117059, 14 , (2023).
• Morgan W. Mann, Yao Fu, Robert L. Gearhart, Xiaofang Xu, David S. Roberts, Yi Li, Jia Zhou, Ying Ge, Allan R. Brasier, Bromodomain-containing Protein 4 regulates innate inflammation via modulation of alternative splicing, Frontiers in Immunology, 10.3389/fimmu.2023.1212770, 14 , (2023).
• Isabel C. Vallecillo-Viejo, Gjendine Voss, Caroline B. Albertin, Noa Liscovitch-Brauer, Eli Eisenberg, Joshua J. C. Rosenthal, Squid express conserved ADAR orthologs that possess novel features, Frontiers in Genome Editing, 10.3389/fgeed.2023.1181713, 5 , (2023).
• Mary-Louise Wilde, Ushma Ruparel, Theresa Klemm, V Vern Lee, Dale J Calleja, David Komander, Christopher J Tonkin, Characterisation of the OTU domain deubiquitinase complement of Toxoplasma gondii , Life Science Alliance, 10.26508/lsa.202201710, 6 , 6, (e202201710), (2023).
• Mikel Martinez-Goikoetxea, Andrei N. Lupas, A conserved motif suggests a common origin for a group of proteins involved in the cell division of Gram-positive bacteria, PLOS ONE, 10.1371/journal.pone.0273136, 18 , 1, (e0273136), (2023).
• Daniel W. Biner, Jason S. Grosch, Peter J. Ortoleva, B-cell epitope discovery: The first protein flexibility-based algorithm–Zika virus conserved epitope demonstration, PLOS ONE, 10.1371/journal.pone.0262321, 18 , 3, (e0262321), (2023).
• Jérôme Tubiana, Lucia Adriana-Lifshits, Michael Nissan, Matan Gabay, Inbal Sher, Marina Sova, Haim J. Wolfson, Maayan Gal, Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions, PLOS Computational Biology, 10.1371/journal.pcbi.1010874, 19 , 2, (e1010874), (2023).
• Rachel E. Cherney, Quinn E. Eberhard, Gilbert Giri, Christine A. Mills, Alessandro Porrello, Zhiyue Zhang, David White, Jackson B. Trotman, Laura E. Herring, Daniel Dominguez, J. Mauro Calabrese, SAFB associates with nascent RNAs and can promote gene expression in mouse embryonic stem cells, RNA, 10.1261/rna.079569.122, 29 , 10, (1535-1556), (2023).
• Siwei Chu, Xinyi Xie, Carla Payan, Ursula Stochaj, Valosin containing protein (VCP): initiator, modifier, and potential drug target for neurodegenerative diseases, Molecular Neurodegeneration, 10.1186/s13024-023-00639-y, 18 , 1, (2023).
• Michael Antonietti, David J. Taylor Gonzalez, Mak Djulbegovic, Guy W. Dayhoff, Vladimir N. Uversky, Carol L. Shields, Carol L. Karp, Intrinsic disorder in PRAME and its role in uveal melanoma, Cell Communication and Signaling, 10.1186/s12964-023-01197-y, 21 , 1, (2023).
• Katarzyna Sołtys, Andrzej Ożyhar, Phase separation propensity of the intrinsically disordered AB region of human RXRβ, Cell Communication and Signaling, 10.1186/s12964-023-01113-4, 21 , 1, (2023).
• Marc A. J. Morgan, Saeid Mohammad Parast, Marta Iwanaszko, Yuki Aoi, DongAhn Yoo, Zachary J. Dumar, Benjamin C. Howard, Kathryn A. Helmin, Qianli Liu, William R. Thakur, Jacob M. Zeidner, Benjamin D. Singer, Evan E. Eichler, Ali Shilatifard, ELOA3 : A primate-specific RNA polymerase II elongation factor encoded by a tandem repeat gene cluster , Science Advances, 10.1126/sciadv.adj1261, 9 , 47, (2023).
• William B. Reinar, Anne Greulich, Ida M. Stø, Jonfinn B. Knutsen, Trond Reitan, Ole K. Tørresen, Sissel Jentoft, Melinka A. Butenko, Kjetill S. Jakobsen, Adaptive protein evolution through length variation of short tandem repeats in Arabidopsis , Science Advances, 10.1126/sciadv.add6960, 9 , 12, (2023).
• Jiayu Li, Na Li, Dawn M. Roellig, Wentao Zhao, Yaqiong Guo, Yaoyu Feng, Lihua Xiao, High subtelomeric GC content in the genome of a zoonotic Cryptosporidium species, Microbial Genomics, 10.1099/mgen.0.001052, 9 , 7, (2023).
• Aarti Kolluri, Dan Li, Nan Li, Zhijian Duan, Lewis R. Roberts, Mitchell Ho, Human VH-based chimeric antigen receptor T cells targeting glypican 3 eliminate tumors in preclinical models of HCC, Hepatology Communications, 10.1097/HC9.0000000000000022, 7 , 2, (e0022-e0022), (2023).
• Istvan Redl, Carlo Fisicaro, Oliver Dutton, Falk Hoffmann, Louie Henderson, Benjamin M J Owens, Matthew Heberling, Emanuele Paci, Kamil Tamiola, ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers, NAR Genomics and Bioinformatics, 10.1093/nargab/lqad041, 5 , 2, (2023).
• Joseph V W Meeussen, Wim Pomp, Ineke Brouwer, Wim J de Jonge, Heta P Patel, Tineke L Lenstra, Transcription factor clusters enable target search but do not contribute to target gene activation, Nucleic Acids Research, 10.1093/nar/gkad227, 51 , 11, (5449-5468), (2023).
• Ryan J Palumbo, Yuan Yang, Juli Feigon, Steven D Hanes, Catalytic activity of the Bin3/MePCE methyltransferase domain is dispensable for 7SK snRNP function in Drosophila melanogaster , GENETICS, 10.1093/genetics/iyad203, 226 , 1, (2023).
• Isabella M. Gaeta, Matthew J. Tyska, BioID2 screening identifies KIAA1671 as an EPS8 proximal factor that marks sites of microvillus growth, Molecular Biology of the Cell, 10.1091/mbc.E22-11-0498, 34 , 4, (2023).
• John T. Williams, Robert B. Abramovitch, Molecular Mechanisms of MmpL3 Function and Inhibition, Microbial Drug Resistance, 10.1089/mdr.2021.0424, 29 , 5, (190-212), (2023).
• Nidhi Malhotra, Shantanu Khatri, Ajit Kumar, Akanksha Arun, Purba Daripa, Saman Fatihi, Sureshkumar Venkadesan, Niyati Jain, Lipi Thukral, AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway, Autophagy, 10.1080/15548627.2023.2238578, 19 , 12, (3201-3220), (2023).
• Anamika Ghosh, Debabani Ganguly, Structural impairment of p53 C-terminal due to the effect of phosphorylation and acetylation: a study on the interdependence of PTM, Journal of Biomolecular Structure and Dynamics, 10.1080/07391102.2023.2279270, (1-10), (2023).
• Hakan Alici, Vladimir N. Uversky, David E. Kang, Junga Alexa Woo, Orkid Coskuner-Weber, The impacts of the mitochondrial myopathy-associated G58R mutation on the dynamic structural properties of CHCHD10, Journal of Biomolecular Structure and Dynamics, 10.1080/07391102.2023.2227713, 42 , 11, (5607-5616), (2023).
• Ann-Christin Borchers, Maren Janz, Jan-Hannes Schäfer, Arne Moeller, Daniel Kümmel, Achim Paululat, Christian Ungermann, Lars Langemeyer, Regulatory sites in the Mon1–Ccz1 complex control Rab5 to Rab7 transition and endosome maturation, Proceedings of the National Academy of Sciences, 10.1073/pnas.2303750120, 120 , 30, (2023).
• Lorna O'Donoghue, Shane P. Comer, Dishon W. Hiebner, Ingmar Schoen, Alex von Kriegsheim, Albert Smolenski, RhoGAP6 interacts with COPI to regulate protein transport, Biochemical Journal, 10.1042/BCJ20230013, 480 , 14, (1109-1127), (2023).
• Yuanzhong Wu, Liwen Zhou, Yezi Zou, Yijun Zhang, Meifang Zhang, Liping Xu, Lisi Zheng, Wenting He, Kuai Yu, Ting Li, Xia Zhang, Zhenxuan Chen, Ruhua Zhang, Penghui Zhou, Nu Zhang, Limin Zheng, Tiebang Kang, Disrupting the phase separation of KAT8–IRF1 diminishes PD-L1 expression and promotes antitumor immunity, Nature Cancer, 10.1038/s43018-023-00522-1, 4 , 3, (382-400), (2023).
• Martyna Kosno, Simon L. Currie, Ashwani Kumar, Chao Xing, Michael K. Rosen, Molecular features driving condensate formation and gene expression by the BRD4-NUT fusion oncoprotein are overlapping but distinct, Scientific Reports, 10.1038/s41598-023-39102-9, 13 , 1, (2023).
• Kristin Graham, Aravind Chandrasekaran, Liping Wang, Aly Ladak, Eileen M. Lafer, Padmini Rangamani, Jeanne C. Stachowiak, Liquid-like VASP condensates drive actin polymerization and dynamic bundling, Nature Physics, 10.1038/s41567-022-01924-1, 19 , 4, (574-585), (2023).
• Shuyao Hu, Yufeng Zhang, Qianqian Yi, Cuiwei Yang, Yanfen Liu, Yun Bai, Time-resolved proteomic profiling reveals compositional and functional transitions across the stress granule life cycle, Nature Communications, 10.1038/s41467-023-43470-1, 14 , 1, (2023).
• Chen Qiu, Zihan Zhang, Robert N. Wine, Zachary T. Campbell, Jun Zhang, Traci M. Tanaka Hall, Intra- and inter-molecular regulation by intrinsically-disordered regions governs PUF protein RNA binding, Nature Communications, 10.1038/s41467-023-43098-1, 14 , 1, (2023).
• Naohiro Kuwayama, Tomoya Kujirai, Yusuke Kishi, Rina Hirano, Kenta Echigoya, Lingyan Fang, Sugiko Watanabe, Mitsuyoshi Nakao, Yutaka Suzuki, Kei-ichiro Ishiguro, Hitoshi Kurumizaka, Yukiko Gotoh, HMGA2 directly mediates chromatin condensation in association with neuronal fate regulation, Nature Communications, 10.1038/s41467-023-42094-9, 14 , 1, (2023).
• Juan Manuel Valverde, Geronimo Dubra, Michael Phillips, Austin Haider, Carlos Elena-Real, Aurélie Fournet, Emile Alghoul, Dhanvantri Chahar, Nuria Andrés-Sanchez, Matteo Paloni, Pau Bernadó, Guido van Mierlo, Michiel Vermeulen, Henk van den Toorn, Albert J. R. Heck, Angelos Constantinou, Alessandro Barducci, Kingshuk Ghosh, Nathalie Sibille, Puck Knipscheer, Liliana Krasinska, Daniel Fisher, Maarten Altelaar, A cyclin-dependent kinase-mediated phosphorylation switch of disordered protein condensation, Nature Communications, 10.1038/s41467-023-42049-0, 14 , 1, (2023).
• Beatrice Ramm, Dominik Schumacher, Andrea Harms, Tamara Heermann, Philipp Klos, Franziska Müller, Petra Schwille, Lotte Søgaard-Andersen, Biomolecular condensate drives polymerization and bundling of the bacterial tubulin FtsZ to regulate cell division, Nature Communications, 10.1038/s41467-023-39513-2, 14 , 1, (2023).
• Federica Luppino, Ivan A. Adzhubei, Christopher A. Cassa, Agnes Toth-Petroczy, DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features, Nature Communications, 10.1038/s41467-023-37661-z, 14 , 1, (2023).
• Bhawna Saini, Tushar Kanti Mukherjee, Biomolecular Condensates Regulate Enzymatic Activity under a Crowded Milieu: Synchronization of Liquid–Liquid Phase Separation and Enzymatic Transformation, The Journal of Physical Chemistry B, 10.1021/acs.jpcb.2c07684, 127 , 1, (180-193), (2023).
• Chinmaya Kumar Patel, Chanchal Rani, Rajesh Kumar, Tushar Kanti Mukherjee, Macromolecular Crowding Promotes Re-entrant Liquid–Liquid Phase Separation of Human Serum Transferrin and Prevents Surface-Induced Fibrillation, Biomacromolecules, 10.1021/acs.biomac.3c00550, 24 , 8, (3917-3928), (2023).
• Oliver D. Caspari, Clotilde Garrido, Chris O. Law, Yves Choquet, Francis-André Wollman, Ingrid Lafontaine, Converting antimicrobial into targeting peptides reveals key features governing protein import into mitochondria and chloroplasts, Plant Communications, 10.1016/j.xplc.2023.100555, 4 , 4, (100555), (2023).
• David N. Bernard, Chitra Narayanan, Tim Hempel, Khushboo Bafna, Purva Prashant Bhojane, Myriam Létourneau, Elizabeth E. Howell, Pratul K. Agarwal, Nicolas Doucet, Conformational exchange divergence along the evolutionary pathway of eosinophil-associated ribonucleases, Structure, 10.1016/j.str.2022.12.011, 31 , 3, (329-342.e4), (2023).
• Yaara Makaros, Anat Raiff, Richard T. Timms, Ajay R. Wagh, Mor Israel Gueta, Aizat Bekturova, Julia Guez-Haddad, Sagie Brodsky, Yarden Opatowsky, Michael H. Glickman, Stephen J. Elledge, Itay Koren, Ubiquitin-independent proteasomal degradation driven by C-degron pathways, Molecular Cell, 10.1016/j.molcel.2023.04.023, 83 , 11, (1921-1935.e7), (2023).
• Mikel Martinez-Goikoetxea, Andrei N. Lupas, New protein families with hendecad coiled coils in the proteome of life, Journal of Structural Biology, 10.1016/j.jsb.2023.108007, 215 , 3, (108007), (2023).
• Andrew C. Marshall, Jerry Cummins, Simon Kobelke, Tianyi Zhu, Jocelyn Widagdo, Victor Anggono, Anthony Hyman, Archa H. Fox, Charles S. Bond, Mihwa Lee, Different Low-complexity Regions of SFPQ Play Distinct Roles in the Formation of Biomolecular Condensates, Journal of Molecular Biology, 10.1016/j.jmb.2023.168364, 435 , 24, (168364), (2023).
• Anukool A. Bhopatkar, Rakez Kayed, Flanking regions, amyloid cores, and polymorphism: the potential interplay underlying structural diversity, Journal of Biological Chemistry, 10.1016/j.jbc.2023.105122, 299 , 9, (105122), (2023).
• Irina Schaefer, Clement Verkest, Lucas Vespermann, Thomas Mair, Hannah Voß, Nadja Zeitzschel, Stefan G. Lechner, PKA mediates modality-specific modulation of the mechanically gated ion channel PIEZO2, Journal of Biological Chemistry, 10.1016/j.jbc.2023.104782, 299 , 6, (104782), (2023).
• Chi L.L. Pham, Gustavo A. Titaux-Delgado, Nikhil R. Varghese, Paula Polonio, Karyn L. Wilde, Margaret Sunde, Miguel Mompeán, NMR characterization of an assembling RHIM (RIP homotypic interaction motif) amyloid reveals a cryptic region for self-recognition, Journal of Biological Chemistry, 10.1016/j.jbc.2023.104568, 299 , 4, (104568), (2023).
• Morgan A. Gingerich, Jie Zhu, Biaoxin Chai, Michael P. Vincent, Nuli Xie, Vaibhav Sidarala, Nicholas A. Kotov, Debashish Sahu, Daniel J. Klionsky, Santiago Schnell, Scott A. Soleimanpour, Reciprocal regulatory balance within the CLEC16A–RNF41 mitophagy complex depends on an intrinsically disordered protein region, Journal of Biological Chemistry, 10.1016/j.jbc.2023.103057, 299 , 4, (103057), (2023).
• Ayyam Y. Ibrahim, Nathan P. Khaodeuanepheng, Dhanush L. Amarasekara, John J. Correia, Karen A. Lewis, Nicholas C. Fitzkee, Loren E. Hough, Steven T. Whitten, Intrinsically disordered regions that drive phase separation form a robustly distinct protein class, Journal of Biological Chemistry, 10.1016/j.jbc.2022.102801, 299 , 1, (102801), (2023).
• Mak Djulbegovic, David J. Taylor Gonzalez, Michael Antonietti, Vladimir N. Uversky, Carol L. Shields, Carol L. Karp, Intrinsic disorder may drive the interaction of PROS1 and MERTK in uveal melanoma, International Journal of Biological Macromolecules, 10.1016/j.ijbiomac.2023.126027, 250 , (126027), (2023).
• Laura Machado Lara Carvalho, Elisa Varella Branco, Raquel Delgado Sarafian, Gerson Shigeru Kobayashi, Fabiano Tófoli de Araújo, Lucas Santos Souza, Danielle de Paula Moreira, Gabriella Shih Ping Hsia, Eny Maria Goloni Bertollo, Cecília Barbosa Buck, Silvia Souza da Costa, Davi Mendes Fialho, Felipe Tadeu Galante Rocha de Vasconcelos, Luciano Abreu Brito, Luciana Elena de Souza Fraga Machado, Igor Cabreira Ramos, Lygia da Veiga Pereira, Celia Priszkulnik Koiffmann, Maria Rita dos Santos e Passos-Bueno, Tiago Antonio de Oliveira Mendes, Ana Cristina Victorino Krepischi, Carla Rosenberg, Establishment of iPSC lines and zebrafish with loss-of-function AHDC1 variants: Models for Xia-Gibbs syndrome, Gene, 10.1016/j.gene.2023.147424, 871 , (147424), (2023).
• Jorge Morales, Georg Ehret, Gereon Poschmann, Tobias Reinicke, Anay K. Maurya, Lena Kröninger, Davide Zanini, Rebecca Wolters, Dhevi Kalyanaraman, Michael Krakovka, Miriam Bäumers, Kai Stühler, Eva C.M. Nowack, Host-symbiont interactions in Angomonas deanei include the evolution of a host-derived dynamin ring around the endosymbiont division site, Current Biology, 10.1016/j.cub.2022.11.020, 33 , 1, (28-40.e7), (2023).
• Xiao Han, Zixin Hu, Wahyu Surya, Qianqian Ma, Feng Zhou, Lars Nordenskiöld, Jaume Torres, Lanyuan Lu, Yansong Miao, The intrinsically disordered region of coronins fine-tunes oligomerization and actin polymerization, Cell Reports, 10.1016/j.celrep.2023.112594, 42 , 6, (112594), (2023).
• Jacqueline F. Pelham, Alexander E. Mosier, Samuel C. Altshuler, Morgan L. Rhodes, Christopher L. Kirchhoff, William B. Fall, Catherine Mann, Lisa S. Baik, Joanna C. Chiu, Jennifer M. Hurley, Conformational changes in the negative arm of the circadian clock correlate with dynamic interactomes involved in post-transcriptional regulation, Cell Reports, 10.1016/j.celrep.2023.112376, 42 , 4, (112376), (2023).
• Swastik G. Pattanashetty, Ashish Joshi, Anuja Walimbe, Samrat Mukhopadhyay, Guidelines for experimental characterization of liquid–liquid phase separation in vitro, Droplets of Life, 10.1016/B978-0-12-823967-4.00012-9, (233-249), (2023).
• Xin Liu, Jian-Kang Zhu, Chunzhao Zhao, Liquid-liquid phase separation as a major mechanism of plant abiotic stress sensing and responses, Stress Biology, 10.1007/s44154-023-00141-x, 3 , 1, (2023).
• Joshua Jeong, Joyce H Lee, Claudia C Carcamo, Matthew W Parker, James M Berger, DNA-Stimulated Liquid-Liquid phase separation by eukaryotic topoisomerase ii modulates catalytic function, eLife, 10.7554/eLife.81786, 11 , (2022).
• Karl F Lechtreck, Yi Liu, Jin Dai, Rama A Alkhofash, Jack Butler, Lea Alford, Pinfen Yang, Chlamydomonas ARMC2/PF27 is an obligate cargo adapter for intraflagellar transport of radial spokes, eLife, 10.7554/eLife.74993, 11 , (2022).
• saeed pirmoradi, Design of New Inhibitory Ligands of Monoamine Oxidase A-Enzyme for Use in the Control of Depression and Mental Disorders Using Bioinformatics Tools, The Neuroscience Journal of Shefaye Khatam, 10.61186/shefa.10.2.33, 10 , 2, (33-45), (2022).
• Meng Tang, Ying Xia, Taoran Xiao, Ruiyu Cao, Yu Cao, Bo Ouyang, Structural Exploration on Palmitoyltransferase DHHC3 from Homo sapiens, Polymers, 10.3390/polym14153013, 14 , 15, (3013), (2022).
• Margot Paco-Chipana, Camilo Febres-Molina, Jorge Alberto Aguilar-Pineda, Badhin Gómez, Novel In Silico Insights into Rv1417 and Rv2617c as Potential Protein Targets: The Importance of the Medium on the Structural Interactions with Exported Repetitive Protein (Erp) of Mycobacterium tuberculosis, Polymers, 10.3390/polym14132577, 14 , 13, (2577), (2022).
• Ralph H. Loring, Speculation on How RIC-3 and Other Chaperones Facilitate α7 Nicotinic Receptor Folding and Assembly, Molecules, 10.3390/molecules27144527, 27 , 14, (4527), (2022).
• Gerda Wachtl, Éva Schád, Krisztina Huszár, Antonio Palazzo, Zoltán Ivics, Ágnes Tantos, Tamás I. Orbán, Functional Characterization of the N-Terminal Disordered Region of the piggyBac Transposase, International Journal of Molecular Sciences, 10.3390/ijms231810317, 23 , 18, (10317), (2022).
• Siarhei A. Dabravolski, Stanislav V. Isayenkov, Evolution of the Membrane Transport Protein Domain, International Journal of Molecular Sciences, 10.3390/ijms23158094, 23 , 15, (8094), (2022).
• Ekaterina A. Litus, Alexey S. Kazakov, Evgenia I. Deryusheva, Ekaterina L. Nemashkalova, Marina P. Shevelyova, Andrey V. Machulin, Aliya A. Nazipova, Maria E. Permyakova, Vladimir N. Uversky, Sergei E. Permyakov, Ibuprofen Favors Binding of Amyloid-β Peptide to Its Depot, Serum Albumin, International Journal of Molecular Sciences, 10.3390/ijms23116168, 23 , 11, (6168), (2022).
• Xingyu Zhai, Kaijuan Wu, Rui Ji, Yiming Zhao, Jianhong Lu, Zheng Yu, Xuewei Xu, Jing Huang, Structure and Function Insight of the α-Glucosidase QsGH13 From Qipengyuania seohaensis sp. SW-135, Frontiers in Microbiology, 10.3389/fmicb.2022.849585, 13 , (2022).
• Guanting Lu, Liya Ma, Pei Xu, Binqiang Xian, Lianying Wu, Jianying Ding, Xiaoyan He, Huiyun Xia, Wuwu Ding, Zhirong Yang, Qiongling Peng, A de Novo ZMIZ1 Pathogenic Variant for Neurodevelopmental Disorder With Dysmorphic Facies and Distal Skeletal Anomalies, Frontiers in Genetics, 10.3389/fgene.2022.840577, 13 , (2022).
• Daniel Ocampo Daza, Christina A. Bergqvist, Dan Larhammar, The Evolution of Oxytocin and Vasotocin Receptor Genes in Jawed Vertebrates: A Clear Case for Gene Duplications Through Ancestral Whole-Genome Duplications, Frontiers in Endocrinology, 10.3389/fendo.2021.792644, 12 , (2022).
• Bruno Betschart, Marco Bisoffi, Ferial Alaeddine, Identification and characterization of epicuticular proteins of nematodes sharing motifs with cuticular proteins of arthropods, PLOS ONE, 10.1371/journal.pone.0274751, 17 , 10, (e0274751), (2022).
• William M. McFadden, Judith L. Yanowitz, idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R, PLOS ONE, 10.1371/journal.pone.0266929, 17 , 4, (e0266929), (2022).
• Emily L. Starke, Keelan Zius, Scott A. Barbee, FXS causing missense mutations disrupt FMRP granule formation, dynamics, and function, PLOS Genetics, 10.1371/journal.pgen.1010084, 18 , 2, (e1010084), (2022).
• Karl Lechtreck, Cargo adapters expand the transport range of intraflagellar transport, Journal of Cell Science, 10.1242/jcs.260408, 135 , 24, (2022).
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