Skip to main content

PreDisorder: ab initio sequence-based prediction of protein disordered regions

Abstract

Background

Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation.

Results

This paper presents our protein disorder prediction server, PreDisorder. It is based on our ab initio prediction method (MULTICOM-CMFR) which, along with our meta (or consensus) prediction method (MULTICOM), was recently ranked among the top disorder predictors in the eighth edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP8). We systematically benchmarked PreDisorder along with 26 other protein disorder predictors on the CASP8 data set and assessed its accuracy using a number of measures. The results show that it compared favourably with other ab initio methods and its performance is comparable to that of the best meta and clustering methods.

Conclusion

PreDisorder is a fast and reliable server which can be used to predict protein disordered regions on genomic scale. It is available at http://casp.rnet.missouri.edu/predisorder.html.

Background

While most regions of a protein adopt localized, stable structures, there are some segments of the protein chain which do not. These are regions whose coordinates are hard to determine by experimental techniques or that simply do not fold into stable structures [1, 2]. Such regions are known as disordered regions. Proteins with disordered regions are capable of binding to multiple partners and participating in various reactions and pathways [35]. Disordered regions can also give rise to the poor expression of a protein, making it difficult to produce for crystallization or other purposes [6]. Consequently, the prediction of disordered regions in proteins has implications for protein production, structure prediction and determination, function annotation and cellular process recognition.

Measuring native disorder experimentally is time consuming and expensive and thus computational approaches for the prediction of protein disordered regions have received considerable attention in recent years [7]. As a result, a number of disorder prediction software and web services and their underlying methods are quickly becoming a valuable tool for protein structure prediction, determination, and function annotation [818]. To stimulate further development of disorder prediction, CASP has dedicated a category to blindly benchmark the current state of the art. Here we benchmark our ab initio and consensus (or meta) disorder predictors along with dozens of other predictors that participated in the CASP8 experiment. The good performance of our PreDisorder server makes it a valuable and accurate tool for protein structure prediction, protein determination and protein engineering.

Implementation

Ab initio neural network method

Our server, PreDisorder, is based on our ab initio method that participated in CASP8 under the group name MULTICOM_CMFR. This is a machine learning approach using 1-D Recursive Neural Networks. With this approach, a target protein sequence is first aligned against several template profiles using PSI-BLAST. This creates an input profile of the sequence. This profile along with the predicted secondary structure and solvent accessibility is fed into a 1D Recursive Neural Network (1D-RNN) that makes the disorder predictions [6]. More specifically for each protein sequence, the input is a 1-dimentional array I whose length is the total number of the residues in the sequence. Each element I i of the array is a vector with 25 values which represent the residue i. Of these 25 values, 20 represent the frequencies of each amino acid at the corresponding position from PSI-BLAST profile [19]. The other five are binary values used to encode the predicted secondary structure (Helix, Strand or Coil) and solvent accessibility of the residue [2022]. Based on the input I, the 1D-RNN produces an array of real numbers O, where the ithelement O i is the probability that the ithresidue will be disordered. A large curated dataset was randomly divided into ten subsets of approximately equal size in the preparation for the following ten-fold cross-validated training and testing. And then, this 1D-RNN was trained and cross-validated using the ten subsets [23]. Finally, the predicted disorder probabilities of the residues were re-scaled so that the ratio of residues with disorder probability greater than or equal to 0.5 is close to the ratio of the disorder residues in the training dataset [23]. Specifically, the scaling method first identified a probability threshold t (e.g. 0.1) for selecting predicted disorder residues such that the ratio (the number of predicted disordered residues/the number of total residues in the test dataset) is equal to the ratio of disorder residues in the training dataset (e.g. 5%). And then the predicted disorder probabilities (x) was re-scaled as x/t * 0.5 (if x <= t) or 0.5 + 0.5 * (x - t)/(1 - t) (if x >t).

Meta method

A meta method is a consensus approach that makes predictions based on the output of other predictors. Similar ideas have been applied to solve many prediction problems such as protein fold recognition and achieved much better performance than individual predictors. One such example of this approach is 3D-Jury. 3D-Jury is an automated protein structure meta prediction system available through Meta Server, and it generates meta-predictions from a variety of models gained by variable methods [24][25][26]. Our new meta predictor MULTICOM makes predictions based on a consensus formed from other CASP8 disorder predictors. It removes a few very inaccurate disorder predictors and then averages the output of the remaining disorder predictors. Our simple averaging approach is different from other meta methods based on consensus voting.

Results and discussion

We evaluated 27 disorder predictors that participated in CASP8. Among these predictors were our ab initio method predictor (MULTICOM-CMFR) and meta predictor (MULTICOM). They were evaluated on 117 protein targets whose structures were available when our evaluation was conducted. These targets contain 25431 residues and all the disorder predictions for them were downloaded from the CASP8 web site [27]. When evaluating the disorder predictions against the protein targets, target residues that did not have corresponding coordinates in its PDB file were considered to be disordered. The disorder annotations for the targets were curated by Dr.McGuffin [28]. Each residue in the target sequence is tagged with a binary label of "O" (order) or "D" (disorder). We evaluated the methods on all 117 targets and two subsets (97 X-ray structures and 20 NMR structures), respectively. It is worth pointing out that our evaluation serves as a complementary, comparative benchmark of our methods. Readers should refer to the CASP8 assessment paper for the official assessment of disorder predictions [29].

In evaluating the disorder predictors, we considered a number of different, commonly used measurements of performance for binary classifiers. One such measurement was the ROC score. This value represents the area under the Receiver Operating Characteristic (AUC) curve and measures the performance of a classifier system and its dependence upon its discrimination threshold. Ranking the predictors using ROC curves is a widely used method in bioinformatics and CASP competitions [7, 30, 31].

Another set of commonly used measurements for classifier systems are sensitivity and specificity. For each disorder predictor, we calculated the Positive Sensitivity (), Positive Specificity (),

Negative Sensitivity () and Negative Specificity () [31]. Here, TP is the number of true positives (residues correctly identified as disordered) and FP is the number of false positives (residues predicted as disordered, but experimentally ordered). TN is the number of true negatives (residues correctly identified as ordered) and FN the number of false negatives (residues predicted as ordered, but experimentally disordered).

While in principle it is possible for a system to achieve both high values for positive and negative sensitivity, in practice it does not happen often. Usually, a sharp increased in one, results in a decrease in the other. An extreme example would be a predictor which identifies all residues as disordered. Such a system would have a positive sensitivity of 100% and a negative sensitivity of 0%. In an attempt to join several of these measurements into one, we considered the product of positive sensitivity and negative sensitivity and the harmonic mean, or F-measure, of the positive sensitivity and positive specificity [32].

We also calculated a weighted score for each predictor. This is a measure which was introduced in CASP6 and is defined as Score () where Wdisorder is set to 92.63 and Worder to 7.37 [31]. As defined, this measure greatly rewards disordered residues correctly identified as classified as disordered while heavily penalizing any disordered residue that is misclassified.

Table 1 reports the ROC scores, weighted score, positive sensitivity, negative specificity, negative sensitivity, negative specificity, product of positive sensitivity and negative sensitivity, F-measure respectively of all the disorder predictors. Moreover, Table 1 also shows the total number of residues predicted by each predictor respectively. For comparison, we also repeated the evaluation for the "only x-ray" and the "only NMR" sets, and the results are shown in Table 2 and Table 3. Figure 1 shows the ROC curves for the predictors. The predictors are ordered by ROC scores since the ROC measure is probably the most balanced measurement.

Table 1 Results for protein disorder predictors that participated in CASP8 on 117 targets.
Table 2 Results for protein disorder predictors that participated in CASP8 on the 20 NMR targets (T0437, T0460, T0462, T0464, T0466, T0467, T0468, T0469, T0471, T0472, T0473, T0474, T0475, T0476, T0480, T0482, T0484, T0492, T0498, T0499).
Table 3 Results for protein disorder predictors that participated in CASP8 on the 97 x-ray targets.
Figure 1
figure1

ROC curves of CASP8 predictors (ordered by ROC score) on the CASP8 dataset which consisted of 117 protein targets.

The CASP8 disorder prediction methods can be classified into four main categories [33]: (1) Meta method. Predictors like MULTICOM, GS-MetaServer, Metaprdos, GeneSilico, GSMetaDisorder and Distill use this method to fulfill disorder prediction. (2) Clustering method. For instance, it is used by predictor DISOclust. DISOclust first gains multiple 3D models from the nFOLD3 server and then makes disorder predictions by combining the results obtained from running the DISOclust method and DISOPRED3 method. (3) Ab initio method. A large number of predictors in CASP8 adopt this method and examples include 3Dpro, Mariner, Spritz, biomine, CBRC_poodle, disopred, OnD-CRF and our predictor MULTICOM_CMFR. (4) Hybrid method. Fais-server is a hybrid method that combines both ab initio predictions and homology-based template information. Both ab initio and hybrid methods usually exist as standalone packages, while meta methods rely on other predictors.

In examining the results, no one method appears to perform decisively better than the rest according to all the measures. Predictors from each of the three types of methods (ab initio, meta and clustering) are represented in the top seven when comparing the predictors only on the basis of ROC score, weighted score, specificity or sensitivity. The meta method MULITCOM, the clustering method DISOclust, the hybrid method Fais-server and ab initio method MULTICOM-CMFR and 3Dpro are among top 5 in terms of ROC scores. Other ab initio predictors such as mariner1 and Distill-Punch also performed well. Interestingly, our ab initio predictor MULTICOM-CMFR also ranks first in weighted score and product of positive and negative sensitivity. Being an ab initio method, it also has the benefit of being able to make predictions solely on an input sequence. The other types of methods need additional information such as output from other predictors (e.g. meta methods), tertiary structure models (clustering methods), or homologous structure templates (hybrid methods). Consequently, our PreDisorder server based on MULTICOM-CMFR is generally an accurate predictor that can be applied to the genome-scale annotation of protein disordered regions. Especially regarding the limits of predictability of intrinsically disordered residues from crystallographic experiments, both of our methods performed well on the X-ray targets shown in Table 3[34]. Several methods (e.g., MULTICOM, DISOclust, fais-server, MULTICOM-CMFR, 3Dpro, mariner and Distill-Punch) yield similarly good AUC scores (>= 0.846), suggesting that the accuracy of disorder predictions might be close to the limit [34].

All of the predictors do quite well with respect to negative specificity and negative sensitivity. This is not too surprising as the most of the residues in a protein are ordered and hence the number of true negatives (TN) is very close to the true negatives plus false positives (TN+FP) and to the true negatives plus the false negatives (TN+FN).

Conclusion

This paper presents our disorder prediction web server, PreDisorder, and evaluates its performance against several other disorder predictors. We benchmarked MULTICOM-CMFR, the method employed by Predisorder and our meta method MULTICOM, along with several other protein disorder predictors on the 117 targets used in CASP8. The results show that our method is among the best and provides reliable protein disordered region predictions. Therefore, our server (PreDisorder) is a useful tool for structural and functional genomics.

Availability and Requirements

Project name: PreDisorder

Project Home Page: http://casp.rnet.missouri.edu/predisorder.html

Operating system(s): Platform independent (web server)

Programming languages: Perl, C, C++

Other requirements: None

License: Web application is freely accessible for all users.

Any restrictions to use by non-academics: None

The use of PreDisorder is straight forward and takes place through a simple input form. The input form requires only three inputs: email address, target name and protein sequence. PreDisorder can make predictions in a very short time and sends the results back to users via email. Disorder prediction results include the user-defined target name, the author, any predictor remarks and the disorder predictions. These predictions are in CASP format and occupy several lines. Each line contains the residue code, an order or disorder assignment code and the number specifying the associated probability of disorder. We also return the results in graphical form, as seen in Figure 2. In this graph, users can visualize changes in the likelihood of disorder from residue to residue over the submitted sequence. The red curve shows our predicted probability of disorder for each residue in the target sequence, the green curve represents the determined disorder result by biological experiment for the target. In addition, the blue line y = 0.5 represents the threshold we chose to judge the probability of disorder for a residue.

Figure 2
figure2

Example output from PreDisorder showing probability of disorder for each residue in a sequence (CASP8 target T0470). The red curve represents predicted disorder probabilities. The green curve denotes real disorder annotations (1 - disorder; 0 - not disorder).

References

  1. 1.

    Tompa P: Intrinsically unstructured proteins. Trends Biochemistry Science 2002, 27: 527–533. 10.1016/S0968-0004(02)02169-2

    CAS  Article  Google Scholar 

  2. 2.

    Receveur-Bréchot V, Bourhis JM, Uversky VN, Canard B, Longhi S: Assessing protein disorder and induced folding. Proteins: Structure, Function, and Bioinformatics 2006, 62: 24–45. 10.1002/prot.20750

    Article  Google Scholar 

  3. 3.

    Dyson J, Wright P: Intrinsically unstructured proteins and their functions. Nature Reviews Molecular Cell Biology 2005, 6: 197–208. 10.1038/nrm1589

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Dunker AK, Obradovic Z: The protein trinity - linking function and disorder. Nature Biotechnology 2001, 19: 805–806. 10.1038/nbt0901-805

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z: Intrinsic disorder and protein function. Biochemestry 2002, 21: 6573–82. 10.1021/bi012159+

    Article  Google Scholar 

  6. 6.

    Cheng J, Sweredoski M, Baldi P: Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data. Data Mining and Knowledge Discovery 2005, 11: 213–222. 10.1007/s10618-005-0001-y

    Article  Google Scholar 

  7. 7.

    Bordoli L, Kiefer F, Schwede T: Assessment of disorder predictions in CASP7. Proteins: Structure, Function, and Bioinformatics 2007, 69(Suppl 8):129–136. 10.1002/prot.21671

    CAS  Article  Google Scholar 

  8. 8.

    Ferron F, Longhi S, Canard B, Karlin D: A Practical Overview of Protein Disorder Prediction Methods. Proteins: Structure, Function, and Bioinformatics 2006, 65: 1–14. 10.1002/prot.21075

    CAS  Article  Google Scholar 

  9. 9.

    Su CT, Chen CY, Ou YY: Protein disorder prediction by condensed PSSM considering propensity for order or disorder. BMC Bioinformatics 2006, 7: 319. 10.1186/1471-2105-7-319

    PubMed Central  Article  PubMed  Google Scholar 

  10. 10.

    Yang ZR, Thomson R, McNeil P, Esnouf RM: RONN: the biobasis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 2005, 21: 3369–3376. 10.1093/bioinformatics/bti534

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Coeytaux K, Poupon A: Prediction of unfolded segments in a protein sequence based on amino acid composition. Bioinformatics 2005, 21: 1891–1900. 10.1093/bioinformatics/bti266

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Melamud E, Moult J: Evaluation of disorder predictions in CASP5. Proteins 2003, 53: 561–565. 10.1002/prot.10533

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Oldfield CJ, Cheng Y, Cortese MS, Brown CJ, Uversky VN, Dunker AK: Comparing and combining predictors of mostly disordered proteins. Biochemistry, 44, 1989–2000. Proteins 2005, 61: 167–175.

    Google Scholar 

  14. 14.

    Ward JJ, McGuffin LJ, Bryson K, Buxton BF, Jones DT: The DISOPRED server for the prediction of protein disorder. Bioinformatics 2004, 20: 2138–2139. 10.1093/bioinformatics/bth195

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z: Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 2006, 7: 208. 10.1186/1471-2105-7-208

    PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    Vullo A, Bortolami O, Pollastri G, Tosatto S: Spitz.: A server for the prediction of intrinsically disordered regions in protein sequences using kernel machines. Nucleic Acids Research 2006, 34: W164-W168. 10.1093/nar/gkl166

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  17. 17.

    Obradovic Z, Peng K, Vucetic S, Radivojac P, Dunker A: Exploiting Heterogeneous Sequence Properties Improves Prediction of Protein Disorder. Proteins 2005, 61(suppl1):176–182. 10.1002/prot.20735

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Yang M, Yang J: IUP: Intrinsically Unstructured Protein predictor - A Software Tool for Analyzing Poly-Peptide Sequences. Proceeding of Sixth Symposium on Bioinformatics. Bioengineering (IEEE BIBE 2006) IEEE Computer Society 1–11.

  19. 19.

    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Willer W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 1997, 25: 3389–3402. 10.1093/nar/25.17.3389

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  20. 20.

    Cheng J, Randall A, Sweredoski M, Baldi P: SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Research 2005, 33: w72–76. 10.1093/nar/gki396

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  21. 21.

    Pollastri G, Przybylski D, Rost B, Bald P: Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 2002, 47: 228–235. 10.1002/prot.10082

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Pollastri G, Baldi P, Fariselli P, Casadio R: Prediction of Coordination Number and Relative Solvent Accessibility in Proteins. Proteins 2002, 47: 142–153. 10.1002/prot.10069

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Hecker J, Yang J, Cheng J: Protein Disorder Prediction at Multiple Levels of Sensitivity and Specificity. BMC Genomics 2008, 9(Suppl 1):S9. 10.1186/1471-2164-9-S1-S9

    PubMed Central  Article  PubMed  Google Scholar 

  24. 24.

    Meta server[http://meta.bioinfo.pl/submit_wizard.pl]

  25. 25.

    Laszlo K, Leszek R: Evaluation of 3D-Jury on CASP7 models. Bioinformatics 2007, 8: 304. 10.1186/1471-2105-8-304

    Google Scholar 

  26. 26.

    Ginalski K, Elofsson A, Fischer D, Rychlewski L: 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 2003, 22: 1015–1018. 10.1093/bioinformatics/btg124

    Article  Google Scholar 

  27. 27.

    CASP8 web site[http://predictioncenter.org/download_area/CASP8/predictions/]

  28. 28.

    The disorder annotations for the targets curated by Dr.McGuffin[http://www.reading.ac.uk/bioinf/CASP8/index.html]

  29. 29.

    Noivirt-Brik O, Prilusky J, Sussman JL: Assessment of disorder predictions in CASP8. Proteins: Structure, Function, and Bioinformatics 2009., 9999(9999):

  30. 30.

    Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT: Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 2004, 337: 635–645. 10.1016/j.jmb.2004.02.002

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Jin Y, Dunbrack RL Jr: Assessment of disorder predictions in CASP6. Proteins 2005, 61(Suppl 7):167–175. 10.1002/prot.20734

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    F-measure[http://en.wikipedia.org/wiki/F1_score]

  33. 33.

    McGuffin LJ: Intrinsic disorder prediction from the analysis of multiple protein fold recognition models. Bioinformatics 2008, 24: 1798–1804. 10.1093/bioinformatics/btn326

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Mohan A, Uversky VN, Radivojac P: Influence of sequence changes and environment on intrinsically disorder proteins. PLoS Comput Biol 2009., 5(Suppl 9):

Download references

Acknowledgements

This work was supported in part by a UM research board grant and a MU research council grant to JC.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jianlin Cheng.

Additional information

Authors' contributions

JC designed and implemented the disorder prediction methods and conducted CASP8 experiments. XD evaluated the predictors. XD and JE wrote the first draft of the manuscript. DX, JE and JC set up the web server. All the authors edited the manuscript and approved it.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Deng, X., Eickholt, J. & Cheng, J. PreDisorder: ab initio sequence-based prediction of protein disordered regions. BMC Bioinformatics 10, 436 (2009). https://doi.org/10.1186/1471-2105-10-436

Download citation

Keywords

  • Protein Structure Prediction
  • Disorder Prediction
  • Positive Sensitivity
  • Negative Sensitivity
  • Disorder Predictor