Biology:List of disorder prediction software

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Computational methods exploit the sequence signatures of disorder to predict whether a protein is disordered, given its amino acid sequence. The table below, which was originally adapted from[1] and has been recently updated, shows the main features of software for disorder prediction. Note that different software use different definitions of disorder.

Predictor Year Published What is predicted Based on Generates and uses multiple sequence alignment? Free for commercial use
PFVM[2] 2023 Predict the protein intrinsic disorder regions, degree of disorder as well as folding patterns. Based on five amino acids, the folding variations along sequence are presented by Protein Folding Shape Code (PFSC) in Protein Folding Variation Matrix (PFVM). No Yes, Login=public; Password=public; select “Prediction”
SPOT-Disorder2[3] 2020 Per-residue probability of a sequence residue being disordered. Ensemble of Bidirectional Long Short-Term Memory and Inception-Residual Squeeze-and-Excitation Convolutional Neural Networks Yes No
Disprot[4] 2019
NetSurfP-2.0[5] 2019 Secondary structure and disorder prediction method Long Short-Term Memory and Convolutional Neural Networks Yes No
SPOT-Disorder-Single[6] 2018 Per-residue disorder predictor for a single-sequence input (i.e. no MSA profile). An ensemble of Long Short-Term Memory Bidirectional Recurrent Neural Networks and residual convolutional networks. No No
IUPred 2005-2018 Regions that lack a well-defined 3D-structure under native conditions Energy resulting from inter-residue interactions, estimated from local amino acid composition No No
MobiDB-lite[7] 2017 Consensus-based prediction of residue disorder Eight separate disorder predictors from various groups No No
SPOT-Disorder[8] 2017 Outputs the probability of each residue in a protein sequence of being disordered or ordered. A deep recurrent neural network architecture using Long Short-Term Memory (LSTM) cells. Yes No
Disopred2[9] 2004-2015 Regions devoid of ordered regular secondary structure Cascaded support vector machine classifiers trained on PSI-BLAST profiles Yes No
s2D 2015 Predict secondary structure and intrinsic disorder in one unified statistical framework based on the analysis of NMR chemical shifts[10] Neural networks trained on NMR solution-based data. Yes No
DisPredict_v1.0 [11] 2015 Assigns binary order/disorder class and corresponding confidence score for each protein residues using optimized SVM with Radial basis kernel from protein sequence AA composition, Physical Properties, Helix, strand and coil probability, Accessible surface area, torsion angle fluctuation, monogram, bigram. No ?
SLIDER[12] 2014 A binary prediction of whether a protein has a long disordered region (>30 residues) Physicochemical properties of amino acids, sequence complexity, and amino acid composition No ?
MFDp2 [13] 2013 Helix, strand and coil probability, relative entropy and per residue disorder prediction. A combination of MFDp and DisCon predictors with unique post processing. Improved prediction over MFDp. Yes No
ESpritz 2012 Disorder definitions include: missing x-ray atoms (short), Disprot style disorder (long), and NMR flexibility. A probability of disorder is supplied with two decision thresholds which depend on a user preferred false positive rate. Bi-directional neural networks with diverse and high quality data derived from the Protein Data Bank and DisProt. Compares extremely well with other CASP 9 servers. The method was designed to be very fast. No No
GeneSilico Metadisorder[14] 2012 Regions that lack a well-defined 3D structure under native conditions (REMARK-465) Meta method, which uses other disorder predictors (like RONN, IUPred, POODLE, and many more). Based on them the consensus is calculated according method accuracy (optimized using ANN, filtering and other techniques). Currently the best available method (first 2 places in last CASP experiment (blind test)) Yes No
SPINE-D[15] 2012 Output long/short disorder and semi-disorder (0.4-0.7) and full disorder (0.7-1.0). Semi-disorder is semi-collapsed with some secondary structure. A neural network based three-state predictor based on both local and global features. Ranked in Top 5 based on AUC in CASP 9. Yes No
CSpritz 2011 Disorder definitions include: missing x-ray atoms (short) and DisProt style disorder (long). A probability of disorder is supplied with two decision thresholds which depend on the false positive rate. Linear motifs within a disorder segment are determined by simple pattern matching from ELM. Support Vector Machine and Bi-directional neural networks with high quality and diverse data derived from the Protein Data Bank and Disprot. Structural information is also supplied in the form of homologous templates. Compares extremely well with other CASP 9 servers. Yes No
PONDR 1999-2010 All regions that are not rigid including random coils, partially unstructured regions, and molten globules Local aa composition, flexibility, hydropathy, etc. No No
MFDp [16] 2010 Different types of disorder including random coils, unstructured regions, molten globules, and REMARK-465-based regions. An ensemble of 3 SVMs specialized for the prediction of short, long and generic disordered regions, which combines three complementary disorder predictors, sequence, sequence profiles, predicted secondary structure, solvent accessibility, backbone dihedral torsion angles, residue flexibility and B-factors. MFDp (unofficially) secured 3rd place in last CASP experiment) Yes No
FoldIndex[17] 2005 Regions that have a low hydrophobicity and high net charge (either loops or unstructured regions) Charge/hydrophaty analyzed locally using a sliding window No ?
RONN 2005 Regions that lack a well-defined 3D structure under native conditions Bio-basis function neural network trained on disordered proteins No No
GlobPlot 2003 Regions with high propensity for globularity on the Russell/Linding scale (propensities for secondary structures and random coils) Russell/Linding scale of disorder No Yes
DisEMBL 2003 LOOPS (regions devoid of regular secondary structure); HOT LOOPS (highly mobile loops); REMARK465 (regions lacking electron density in crystal structure) Neural networks trained on X-ray structure data No Yes
SEG 1994 Low-complexity segments that is, “simple sequences” or “compositionally biased regions”. Locally optimized low-complexity segments are produced at defined levels of stringency and then refined according to the equations of Wootton and Federhen No ?

Methods not available anymore:

Predictor What is predicted Based on Generates and uses multiple sequence alignment?
OnD-CRF[18] The transition between structurally ordered and mobile or disordered amino acids intervals under native conditions. OnD-CRF applies Conditional Random Fields, CRFs, which rely on features generated from the amino acid sequence and from secondary structure prediction. No
NORSp Regions with No Ordered Regular Secondary Structure (NORS). Most, but not all, are highly flexible. Secondary structure and solvent accessibility Yes
HCA (Hydrophobic Cluster Analysis) Hydrophobic clusters, which tend to form secondary structure elements Helical visualization of amino acid sequence No
PreLink Regions that are expected to be unstructured in all conditions, regardless of the presence of a binding partner Compositional bias and low hydrophobic cluster content. No
MD (Meta-Disorder predictor)[19] Regions of different "types"; for example, unstructured loops and regions containing few stable intra-chain contacts A neural-network based meta-predictor that uses different sources of information predominantly obtained from orthogonal approaches Yes
IUPforest-L Long disordered regions in a set of proteins Moreau-Broto auto-correlation function of amino acid indices (AAIs) No
MeDor (Metaserver of Disorder)[20] Regions of different "types". MeDor provides a unified view of multiple disorder predictors. Meta method, which uses other disorder predictors (like FoldIndex, DisEMBL REMARK465, IUPred, RONN ...) and provides additional features (like HCA plot, Secondary Structure prediction, Transmembrane domains ... ) that all together help the user in defining regions involved in disorder. No

References

  1. ↑ "A practical overview of protein disorder prediction methods". Proteins 65 (1): 1–14. October 2006. doi:10.1002/prot.21075. PMID 16856179. 
  2. ↑ Yang, J; Cheng, WX; Zhao, XF; Wu, G; Sheng, ST; Hu, Q; Ge, H; Qin, Q et al. (Nov 2020). "Comprehensive folding variations for protein folding" (in en). PROTEINS: Structure, Function, and Bioinformatics 90 (11): 1851–1872. doi:10.1002/prot.26381. PMID 35514069. 
  3. ↑ Hanson, Jack; Paliwal, Kuldip K.; Litfin, Thomas; Zhou, Yaoqi (2020-03-13). "SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning" (in en). Genomics, Proteomics & Bioinformatics 17 (6): 645–656. doi:10.1016/j.gpb.2019.01.004. ISSN 1672-0229. PMID 32173600. 
  4. ↑ Hatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; Álvarez, Lucía; Aykac-Fas, Burcu; Bassot, Claudio; Benítez, Guillermo I. et al. (8 January 2020). "DisProt: intrinsic protein disorder annotation in 2020". Nucleic Acids Research 48 (D1): D269–D276. doi:10.1093/nar/gkz975. ISSN 1362-4962. PMID 31713636. 
  5. ↑ "NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning". Proteins: Structure, Function, and Bioinformatics 87 (6): 520–527. 2019. doi:10.1002/prot.25674. PMID 30785653. https://semanticscholar.org/paper/a6dfca71fdda41542538d66bfa1f2675ebe1a8e5. 
  6. ↑ "Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures". Journal of Chemical Information and Modeling 58 (11): 2369–2376. 2018. doi:10.1021/acs.jcim.8b00636. PMID 30395465. 
  7. ↑ Necci, Marco; Piovesan, Damiano; Dosztányi, Zsuzsanna; Tosatto, Silvio C.E. (2017-01-18). "MobiDB-lite: Fast and highly specific consensus prediction of intrinsic disorder in proteins" (in en). Bioinformatics 33 (9): 1402–1404. doi:10.1093/bioinformatics/btx015. ISSN 1367-4803. PMID 28453683. 
  8. ↑ "Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks". Bioinformatics 33 (5): 685–692. 2016. doi:10.1093/bioinformatics/btw678. PMID 28011771. 
  9. ↑ "Prediction and functional analysis of native disorder in proteins from the three kingdoms of life". J. Mol. Biol. 337 (3): 635–45. March 2004. doi:10.1016/j.jmb.2004.02.002. PMID 15019783. 
  10. ↑ "The s2D Method: Simultaneous Sequence- Based Prediction of the Statistical Populations of Ordered and Disordered Regions in Proteins". J. Mol. Biol. 427 (4): 982–996. February 2015. doi:10.1016/j.jmb.2014.12.007. PMID 25534081. 
  11. ↑ Sumaiya Iqbal; Md Tamjidul Hoque (October 2015). "DisPredict: A Predictor of Disordered Protein using Optimized RBF Kernel, content and profiles". PLOS ONE 10 (10): e0141551. doi:10.1371/journal.pone.0141551. PMID 26517719. 
  12. ↑ "Genome-scale prediction of proteins with long intrinsically disordered regions.". Proteins 82 (1): 145–58. Jan 2014. doi:10.1002/prot.24348. PMID 23798504. 
  13. ↑ Marcin J. Miziantya, Zhenling Penga & Lukasz Kurgan (April 2013). "Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles". Intrinsically Disordered Proteins 1 (1): e24428. doi:10.4161/idp.24428. PMID 28516009. 
  14. ↑ Kozlowski, L. P.; Bujnicki, J. M. (2012). "MetaDisorder: A meta-server for the prediction of intrinsic disorder in proteins". BMC Bioinformatics 13: 111. doi:10.1186/1471-2105-13-111. PMID 22624656. 
  15. ↑ "SPINE-D: Accurate prediction of short and long disordered regions by a single neural-network based method". Journal of Biomolecular Structure and Dynamics 29 (4): 799–813. February 2012. doi:10.1080/073911012010525022. PMID 22208280. PMC 3297974. http://www.jbsdonline.com/mc_images/category/4317/32-keith_dunker_jbsd_29_4.pdf. 
  16. ↑ "Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources". Bioinformatics 26 (18): i489–96. September 2010. doi:10.1093/bioinformatics/btq373. PMID 20823312. 
  17. ↑ "FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded". Bioinformatics 21 (16): 3435–8. August 2005. doi:10.1093/bioinformatics/bti537. PMID 15955783. 
  18. ↑ "OnD-CRF: predicting order and disorder in proteins using conditional random fields". Bioinformatics 24 (11): 1401–2. June 2008. doi:10.1093/bioinformatics/btn132. PMID 18430742. 
  19. ↑ Orgel, Joseph P. R. O., ed (2009). "Improved disorder prediction by combination of orthogonal approaches". PLOS ONE 4 (2): e4433. doi:10.1371/journal.pone.0004433. PMID 19209228. Bibcode: 2009PLoSO...4.4433S. 
  20. ↑ "MeDor: a metaserver for predicting protein disorder". BMC Genomics 16 (Suppl 2): S25. September 2008. doi:10.1186/1471-2164-9-S2-S25. PMID 18831791. 

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