Why are ?natively unfolded? proteins unstructured under physiologic conditions?

Uversky, Vladimir N.; Gillespie, Joel R.; Fink, Anthony L.

doi:10.1002/1097-0134(20001115)41:3<415::aid-prot130>3.0.co;2-7

Cited by 1,971 publications

(1,307 citation statements)

References 79 publications

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“…A large portion of the sequences of intrinsically disordered proteins contain segments of low complexity and high predicted flexibility [31−38]. It also has been indicated that a combination of low overall hydrophobicity and a large net charge represent a structural feature of intrinsically disordered proteins in comparison with small globular proteins [39,40]. There are currently several widely used methods for prediction of disordered regions: GlobPlot [41], a simple propensity-based approach for evaluating the tendency of residues to be in a regular secondary structure; PONDR VL3H [37], which is able to distinguish experimentally verified disordered proteins from globular proteins by various machine learning approaches; DISOPRED [42], in which the definition of disorder is restrained to regions that are missing from X-ray structures but are specifically recognized by a support vector machine in the DISOPRED model; and IUPred [43], which assigns the order/disorder status to residues on the basis of their ability to form favorable pairwise contacts.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Amyloidogenic and Disordered Regions in Protein Chains

2006

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The determination of factors that influence protein conformational changes is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for eight of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of amyloid fibril formation is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value with which one can predict both intrinsically disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding.

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Section: Introductionmentioning

confidence: 99%

Prediction of Amyloidogenic and Disordered Regions in Protein Chains

2006

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“…Fast methods identify regions with high net charge and low hydrophobicity [14,15], monitor the differences in amino acid propensities between unstructured and other regions (GlobPlot) [16], or identify motifs associated with regions depleted of regular structure [17,18]. Most methods are based on a different definition of disordered region that has been introduced by the Dunker group [19]: residues for which X-ray structures do not have coordinates are considered as disordered.…”

Section: Introductionmentioning

confidence: 99%

Natively Unstructured Loops Differ from Other Loops

2007

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Natively unstructured or disordered protein regions may increase the functional complexity of an organism; they are particularly abundant in eukaryotes and often evade structure determination. Many computational methods predict unstructured regions by training on outliers in otherwise well-ordered structures. Here, we introduce an approach that uses a neural network in a very different and novel way. We hypothesize that very long contiguous segments with nonregular secondary structure (NORS regions) differ significantly from regular, well-structured loops, and that a method detecting such features could predict natively unstructured regions. Training our new method, NORSnet, on predicted information rather than on experimental data yielded three major advantages: it removed the overlap between testing and training, it systematically covered entire proteomes, and it explicitly focused on one particular aspect of unstructured regions with a simple structural interpretation, namely that they are loops. Our hypothesis was correct: well-structured and unstructured loops differ so substantially that NORSnet succeeded in their distinction. Benchmarks on previously used and new experimental data of unstructured regions revealed that NORSnet performed very well. Although it was not the best single prediction method, NORSnet was sufficiently accurate to flag unstructured regions in proteins that were previously not annotated. In one application, NORSnet revealed previously undetected unstructured regions in putative targets for structural genomics and may thereby contribute to increasing structural coverage of large eukaryotic families. NORSnet found unstructured regions more often in domain boundaries than expected at random. In another application, we estimated that 50%–70% of all worm proteins observed to have more than seven protein–protein interaction partners have unstructured regions. The comparative analysis between NORSnet and DISOPRED2 suggested that long unstructured loops are a major part of unstructured regions in molecular networks.

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“…Some methods utilize machine learning approaches while others are based on simple biophysical considerations. The simplest methods, however, rely on a single amino acid scale [38,19,39]. In general, properties strongly correlating with hydrophobicity, such as flexibility and coordination number, had the highest discriminatory power among various amino acid properties [40,41].…”

Section: Overview Of Protein Disorder Prediction Techniquesmentioning

confidence: 99%

“…It was suggested that disordered proteins can be identified based on the combination of low hydrophobicity and high net charge [38,19]. The rationale behind this approach is that high net charge leads to charge-charge repulsion and low hydrophobicity means less driving force for a compact structure.…”

Section: Incorporating Physical Principles Into Disorder Predictionmentioning

confidence: 99%

Bioinformatical Approaches to Unstructured/Disordered Proteins and Their Interactions

Mészáros

Dosztányi

Magyar

et al. 2014

Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes

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Abstract. Intrinsically unstructured/disordered proteins (IUPs/IDPs) exist as highly flexible conformational ensembles without adopting a stable three-dimensional structure. Experimental and bioinformatical studies in the past two decades have shown that these proteins play a central role in various signaling and regulatory processes. Accordingly, their frequency in higher eukaryotes reaches high proportions and their malfunction can be connected to a wide variety of diseases. Recognizing the biological importance of these proteins motivated researchers to understand various aspects of disordered proteins and protein segments from the viewpoint of biochemistry, molecular biology and pharmacology. In general, IDPs are difficult to study experimentally because of the lack of a unique structure in the isolated form. Nevertheless, various bioinformatics tools developed over the last few years enable their identification and characterization using only the amino acid sequence. In this chapter -after a brief introduction to IDPs in generalwe present a small survey of current methods aimed at identifying disordered proteins or protein segments, focusing on those that are publicly available as web servers. We also discuss in more detail approaches that predict disordered regions and specific regions involved in protein binding by modeling the physical background of protein disorder. Furthermore, we argue that the heterogeneity of disordered segments needs to be taken into account for a better understanding of protein disorder and the correct use and interpretation of the output of disorder prediction algorithms.

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Why are ?natively unfolded? proteins unstructured under physiologic conditions?

Cited by 1,971 publications

References 79 publications

Prediction of Amyloidogenic and Disordered Regions in Protein Chains

Prediction of Amyloidogenic and Disordered Regions in Protein Chains

Natively Unstructured Loops Differ from Other Loops

Bioinformatical Approaches to Unstructured/Disordered Proteins and Their Interactions

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