https://github.com/matthiasblum/idrpred
A consensus-based predictor of intrinsically disordered regions in proteins
https://github.com/matthiasblum/idrpred
bioinformatics sequence-analysis
Last synced: about 2 months ago
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A consensus-based predictor of intrinsically disordered regions in proteins
- Host: GitHub
- URL: https://github.com/matthiasblum/idrpred
- Owner: matthiasblum
- License: gpl-2.0
- Created: 2023-11-02T23:21:14.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-07-29T10:59:06.000Z (8 months ago)
- Last Synced: 2025-07-29T12:56:24.844Z (8 months ago)
- Topics: bioinformatics, sequence-analysis
- Language: Python
- Homepage:
- Size: 292 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-idr-prediction - IDRPred，a modern implementation of MobiDB-lite
README
# IDRPred
[](https://zenodo.org/doi/10.5281/zenodo.13356721)
[](https://hub.docker.com/r/matblum/idrpred)
IDRPred is a modern implementation of [MobiDB-lite](https://github.com/BioComputingUP/MobiDB-lite)[1],
a method for identifying intrinsically disordered regions (IDRs) in proteins.
MobiDB-lite uses multiple predictors to derive a consensus, which is filtered
for spurious short predictions in a second step.
The main advantage of IDRPred is that it only requires Python 3 while MobiDB-lite requires both Python 2 and 3.
## Installation
```sh
pip install git+https://github.com/matthiasblum/idrpred
```
### Docker
A Docker image of `idrpred` is available from [Docker Hub](https://hub.docker.com/r/matblum/idrpred).
## Usage
```sh
idrpred [options] [infile] [outfile]
```
Positional arguments:
* `infile`: The FASTA file of sequences to process. If `-` or not specified, read from standard input.
* `outfile`: The TSV file of predicted intrinsically disordered regions. If `-` or not specified, write to standard output.
### Available options
| Options | Description |
|-------------------|-------------------------------------------------------------------------------------------------|
| `--force` | Derive a consensus as long as one predictor did not fail |
| `--skip-features` | Do not indentify sequence features, such as domains of low complexity |
| `--round` | Round scores reported by individual predictors, like MobiDB-lite does |
| `--tempdir PATH` | Create temporary files in PATH, instead of the default temporary directory (most likely `/tmp`) |
| `--threads N` | Process up to `N` sequences concurrently, default: `1` |
## Predictors
Only predictors whose licence authorises distribution have been included in IDRPred.
| Method | Reference | Available |
|------------------|----------:|:-----------:|
| ANCHOR | [2] | ❌ |
| DisEMBL-465 | [3] | ✔ |
| DisEMBL-HotLoops | [3] | ✔ |
| DynaMine | [4] | ❌ |
| ESpritz-DisProt | [5] | ✔ |
| ESpritz-NMR | [5] | ✔ |
| ESpritz-Xray | [5] | ✔ |
| FeSS | [6] | ❌ |
| GlobPlot | [7] | ✔ |
| IUPred-Long | [8] | ✔ |
| IUPred-Short | [8] | ✔ |
| JRONN | [9] | ❌ |
| Pfilt | [10] | ❌ |
| SEG | [11] | ✔ |
| VSL2b | [12] | ❌ |
## Comparison
### Annotations
| Reference proteome | Sequences | Default options | IDRPred: `--round` option |
|--------------------|----------:|------------------------------------------------|------------------------------------------------------|
| *A. thaliana* | 39,320 |  |  |
| *D. melanogaster* | 26,706 |  |  |
| *E. Coli* | 4,403 |  |  |
| *H. Sapiens* | 82,492 |  |  |
| *S. cerevisiae* | 6,060 |  |  |
### Performances
#### Single-threaded
Wall clock time to annotate common proteomes using one thread:
#### Multithreaded
Wall clock time to annotate common proteomes using eight threads:
Wall clock time to annotate one million sequences
randomly selected from [UniParc](https://www.uniprot.org/uniparc/)
using sixteen threads:
## References
1. Necci M, Piovesan D, Clementel D, Dosztányi Z, Tosatto SCE. MobiDB-lite 3.0: fast consensus annotation of intrinsic disorder flavors in proteins. Bioinformatics. 2021 Apr 1;36(22-23):5533-5534. DOI: [10.1093/bioinformatics/btaa1045](https://doi.org/10.1093/bioinformatics/btaa1045). PMID: [33325498](https://europepmc.org/article/MED/33325498).
2. Dosztányi Z, Mészáros B, Simon I. ANCHOR: web server for predicting protein binding regions in disordered proteins. Bioinformatics. 2009 Oct 15;25(20):2745-6. DOI: [10.1093/bioinformatics/btp518](https://doi.org/10.1093/bioinformatics/btp518). Epub 2009 Aug 28. PMID: [19717576](https://europepmc.org/article/MED/19717576); PMCID: PMC2759549.
3. Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB. Protein disorder prediction: implications for structural proteomics. Structure. 2003 Nov;11(11):1453-9. DOI: [10.1016/j.str.2003.10.002](https://doi.org/10.1016/j.str.2003.10.002). PMID: [14604535](https://europepmc.org/article/MED/14604535).
4. Cilia E, Pancsa R, Tompa P, Lenaerts T, Vranken WF. From protein sequence to dynamics and disorder with DynaMine. Nat Commun. 2013;4:2741. DOI: [10.1038/ncomms3741](https://doi.org/10.1038/ncomms3741). PMID: [24225580](https://europepmc.org/article/MED/24225580).
5. Walsh I, Martin AJ, Di Domenico T, Tosatto SC. ESpritz: accurate and fast prediction of protein disorder. Bioinformatics. 2012 Feb 15;28(4):503-9. DOI: [10.1093/bioinformatics/btr682](https://doi.org/10.1093/bioinformatics/btr682). Epub 2011 Dec 20. PMID: [22190692](https://europepmc.org/article/MED/22190692).
6. Piovesan D, Walsh I, Minervini G, Tosatto SCE. FELLS: fast estimator of latent local structure. Bioinformatics. 2017 Jun 15;33(12):1889-1891. DOI: [10.1093/bioinformatics/btx085](https://doi.org/10.1093/bioinformatics/btx085). PMID: [28186245](https://europepmc.org/article/MED/28186245).
7. Linding R, Russell RB, Neduva V, Gibson TJ. GlobPlot: Exploring protein sequences for globularity and disorder. Nucleic Acids Res. 2003 Jul 1;31(13):3701-8. DOI: [10.1093/nar/gkg519](https://doi.org/10.1093/nar/gkg519). PMID: [12824398](https://europepmc.org/article/MED/12824398); PMCID: PMC169197.
8. Mészáros B, Erdos G, Dosztányi Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res. 2018 Jul 2;46(W1):W329-W337. DOI: [10.1093/nar/gky384](https://doi.org/10.1093/nar/gky384). PMID: [29860432](https://europepmc.org/article/MED/29860432); PMCID: PMC6030935.
9. Yang ZR, Thomson R, McNeil P, Esnouf RM. RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics. 2005 Aug 15;21(16):3369-76. DOI: [10.1093/bioinformatics/bti534](https://doi.org/10.1093/bioinformatics/bti534). Epub 2005 Jun 9. PMID: [15947016](https://europepmc.org/article/MED/15947016).
10. Jones DT, Swindells MB. Getting the most from PSI-BLAST. Trends Biochem Sci. 2002 Mar;27(3):161-4. DOI: [10.1016/s0968-0004(01)02039-4](https://doi.org/10.1016/s0968-0004(01)02039-4). PMID: [11893514](https://europepmc.org/article/MED/11893514).
11. Wootton JC. Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem. 1994 Sep;18(3):269-85. DOI: [10.1016/0097-8485(94)85023-2](https://doi.org/10.1016/0097-8485(94)85023-2). PMID: [7952898](https://europepmc.org/article/MED/7952898).
12. Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z. Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics. 2006 Apr 17;7:208. DOI: [10.1186/1471-2105-7-208](https://doi.org/10.1186/1471-2105-7-208). PMID: [16618368](https://europepmc.org/article/MED/16618368); PMCID: PMC1479845.