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Natively unstructured loops differ from other loops.

ABSTRACT: 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.

SUBMITTER: Schlessinger A

PROVIDER: S-EPMC1924875 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Proresilin is the precursor protein for resilin, an extremely elastic, hydrated, cross-linked insoluble protein found in insects. We investigated the secondary-structure distribution in solution of a synthetic proresilin (AN16), based on 16 units of the consensus proresilin repeat from Anopheles gambiae. Raman spectroscopy was used to verify that the secondary-structure distributions in cross-linked AN16 resilin and in AN16 proresilin are similar, and hence that solution techniques (such as NMR and circular dichroism) may be used to gain information about the structure of the cross-linked solid. The synthetic proresilin AN16 is an intrinsically unstructured protein, displaying under native conditions many of the characteristics normally observed in denatured proteins. There are no apparent alpha-helical or beta-sheet features in the NMR spectra, and the majority of backbone protons and carbons exhibit chemical shifts characteristic of random-coil configurations. Relatively few peaks are observed in the nuclear Overhauser effect spectra, indicating that overall the protein is dynamic and unstructured. The radius of gyration of AN16 corresponds to the value expected for a denatured protein of similar chain length. This high degree of disorder is also consistent with observed circular dichroism and Raman spectra. The temperature dependences of the NH proton chemical shifts were also measured. Most values were indicative of protons exposed to water, although smaller dependences were observed for glycine and alanine within the Tyr-Gly-Ala-Pro sequence conserved in all resilins found to date, which is the site of dityrosine cross-link formation. This result implies that these residues are involved in hydrogen bonds, possibly to enable efficient self-association and subsequent cross-linking. The beta-spiral model for elastic proteins, where the protein is itself shaped like a spring, is not supported by the results for AN16. Both the random-network elastomer model and the sliding beta-turn model are consistent with the data. The results indicate a flat energy landscape for AN16, with very little energy required to switch between conformations. This ease of switching is likely to lead to the extremely low energy loss on deformation of resilin.

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Natively unstructured loops differ from other loops.

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