On the Encoding of Proteins for Disordered Regions Prediction

Bioinformatics/computer Applications in The Biosciences, 2007

Motivation: Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. Results: We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects.

Structure, 2003

It is becoming increasingly clear that many functionally important protein segments occur outside of globu-Biocomputing Unit Meyerhofstr 1 lar domains (Wright and Dyson, 1999; Dunker et al., 2002). Protein structure and function space is parti-D-69117 Heidelberg Germany tioned in two subspaces. The first consist of globular units with binding pockets, active sites, and interaction 2 Max-Delbrü ck-Centre fü r Molecular Medicine Robert-Rö ssle-Strasse 10 surfaces. The second subspace contains nonglobular segments such as sorting signals, posttranslational modi-D-13092 Berlin Germany fication sites, and protein ligands (e.g., SH3 ligands). Globular units are built of regular secondary structure 3 CellZome GmbH Meyerhofstr 1 elements and contribute the majority of the structural data deposited in PDB. In contrast, the nonglobular sub-D-69117 Heidelberg Germany space encompasses disordered, unstructured and flexible regions without regular secondary structure. Functional sites within the nonglobular space are known as linear motifs (cataloged by ELM [http://elm.eu.org]) Summary (Puntervoll et al., 2003). There are also many recent reports of Intrinsically A great challenge in the proteomics and structural genomics era is to predict protein structure and func-Disordered Proteins (IDPs, also known as Intrinsically Unstructured Proteins). These are proteins or domains tion, including identification of those proteins that are partially or wholly unstructured. Disordered regions in that, in their native state, are either completely disordered or contain large disordered regions. More than proteins often contain short linear peptide motifs (e.g., SH3 ligands and targeting signals) that are important 100 such proteins are known including Tau, Prions, Bcl-2, p53, 4E-BP1, and eIF1A (see Figure 4) (Tompa, for protein function. We present here DisEMBL, a computational tool for prediction of disordered/unstruc-2002; Uversky, 2002). Protein disorder is important for understanding pro-tured regions within a protein sequence. As no clear definition of disorder exists, we have developed pa-tein function as well as protein folding pathways (Plaxco and Gross, 2001; Verkhivker et al., 2003). Although little rameters based on several alternative definitions and introduced a new one based on the concept of "hot is understood about the cellular and structural meaning of IDPs, they are thought to become ordered only when loops," i.e., coils with high temperature factors. Avoiding potentially disordered segments in protein expression bound to another molecule (e.g., CREB-CBP complex [Radhakrishnan et al., 1997]) or owing to changes in constructs can increase expression, foldability, and stability of the expressed protein. DisEMBL is thus the biochemical environment (Dunker et al., 2001, 2002; Uversky, 2002). useful for target selection and the design of constructs as needed for many biochemical studies, particularly The current view on disorder is that disordered proteins are disordered to allow for more interaction part-structural biology and structural genomics projects. The tool is freely available via a web interface (http:// ners and modification sites (Wright and Dyson, 1999; Liu et al., 2002; Tompa, 2002). It has also been suggested dis.embl.de) and can be downloaded for use in largescale studies. that disordered proteins exist to provide a simple solution to having large intermolecular interfaces while keeping smaller protein, genome and cell sizes (Gunasekaran

Journal of Computer-Aided Molecular Design, 2017

residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus oper-andi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these pro-tean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents 'Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55 vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible 'disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.

2009

Intrinsically unstructured or disordered proteins are common and functionally important. Prediction of disordered regions in proteins can provide useful information for understanding protein function and for high-throughput determination of protein structures.

BMC bioinformatics, 2009

Many proteins contain disordered regions that lack fixed three-dimensional (3D) structure under physiological conditions but have important biological functions. Prediction of disordered regions in protein sequences is important for understanding protein function and in ...

BMC Bioinformatics, 2013

Background: Molecular recognition features (MoRFs) are short binding regions located in longer intrinsically disordered protein regions. Although these short regions lack a stable structure in the natural state, they readily undergo disorder-to-order transitions upon binding to their partner molecules. MoRFs play critical roles in the molecular interaction network of a cell, and are associated with many human genetic diseases. Therefore, identification of MoRFs is an important step in understanding functional aspects of these proteins and in finding applications in drug design. Results: Here, we propose a novel method for identifying MoRFs, named as MFSPSSMpred (Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based Predictor). Firstly, a masking method is used to calculate the average local conservation scores of residues within a masking-window length in the position-specific scoring matrix (PSSM). Then, the scores below the average are filtered out. Finally, a smoothing method is used to incorporate the features of flanking regions for each residue to prepare the feature sets for prediction. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the PSSM of sequence only. Experimental results show that, comparing with other methods tested on the same datasets, our method achieves the best performance: achieving 0.004~0.079 higher AUC than other methods when tested on TEST419, and achieving 0.045~0.212 higher AUC than other methods when tested on TEST2012. In addition, when tested on an independent membrane proteins-related dataset, MFSPSSMpred significantly outperformed the existing predictor MoRFpred. Conclusions: This study suggests that: 1) amino acid composition and physicochemical properties in the flanking regions of MoRFs are very different from those in the general non-MoRF regions; 2) MoRFs contain both highly conserved residues and highly variable residues and, on the whole, are highly locally conserved; and 3) combining contextual information with local conservation information of residues facilitates the prediction of MoRFs.

Proteins-structure Function and Bioinformatics - PROTEINS, 2006

In the past few years there has been a growing awareness that a large number,of proteins contain long disordered (unstructured) re- gions that often play a functional role. However, these disordered regions are still poorly detected. Recognition of disordered regions in a protein is important for two main reasons: reducing bias in sequence similarity analysis by avoiding alignment of disordered regions against ordered ones, and helping to delineate boundaries of protein domains to guide structural and functional studies. As none of the available method for disorder prediction can be taken as fully reliable on its own, we present an overview of the methods currently employed high- lighting their advantages and drawbacks. We show a few practical examples of how they can be com- bined to avoid pitfalls and to achieve more reliable predictions. Proteins 2006;65:1–14. © 2006 Wiley-Liss, Inc. Key words: protein disorder; prediction methods

Current Protein & Peptide Science, 2007

Intrinsically disordered/unstructured proteins exist in a highly flexible conformational state largely devoid of secondary structural elements and tertiary contacts. Despite their lack of a well defined structure, these proteins often fulfill essential regulatory functions. The intrinsic lack of structure confers functional advantages on these proteins, allowing them to adopt multiple conformations and to bind to different binding partners. The structural flexibility of disordered regions hampers efforts solving structures at high resolution by X-ray crystallography and/or NMR. Removing such proteins/regions from high-throughput structural genomics pipelines would be of significant benefit in terms of cost and success rate. In this paper we outline the theoretical background of structural disorder, and review bioinformatic predictors that can be used to delineate regions most likely to be amenable for structure determination. The primary focus of our review is the interpretation of prediction results in a way that enables segmentation of proteins to separate ordered domains from disordered regions.

BMC bioinformatics, 2011

Background: Intrinsically disordered proteins play important roles in various cellular activities and their prevalence was implicated in a number of human diseases. The knowledge of the content of the intrinsic disorder in proteins is useful for a variety of studies including estimation of the abundance of disorder in protein families, classes, and complete proteomes, and for the analysis of disorder-related protein functions. The above investigations currently utilize the disorder content derived from the per-residue disorder predictions. We show that these predictions may over-or under-predict the overall amount of disorder, which motivates development of novel tools for direct and accurate sequence-based prediction of the disorder content. Results: We hypothesize that sequence-level aggregation of input information may provide more accurate content prediction when compared with the content extracted from the local window-based residue-level disorder predictors. We propose a novel predictor, DisCon, that takes advantage of a small set of 29 custom-designed descriptors that aggregate and hybridize information concerning sequence, evolutionary profiles, and predicted secondary structure, solvent accessibility, flexibility, and annotation of globular domains. Using these descriptors and a ridge regression model, DisCon predicts the content with low, 0.05, mean squared error and high, 0.68, Pearson correlation. This is a statistically significant improvement over the content computed from outputs of ten modern disorder predictors on a test dataset with proteins that share low sequence identity with the training sequences. The proposed predictive model is analyzed to discuss factors related to the prediction of the disorder content.

1998

Neural network predictors of protein disorder using primary sequence information were developed and applied to the Swiss Protein Database. More than 15,000 proteins were predicted to contain disordered regions of at least 40 consecutive amino acids, with more than 1,000 having especially high scores indicating disorder. These results support proposals that consideration of structure-activity relationships in proteins need to be broadened to include unfolded or disordered protein.