Dataset Information

Order, Disorder, and Temperature-Driven Compaction in a Designed Elastin Protein.

ABSTRACT: Artificial minielastin constructs have been designed that replicate the structure and function of natural elastins in a simpler context, allowing the NMR observation of structure and dynamics of elastin-like proteins with complete residue-specific resolution. We find that the alanine-rich cross-linking domains of elastin have a partially helical structure, but only when capped by proline-rich hydrophobic domains. We also find that the hydrophobic domains, composed of prominent 6-residue repeats VPGVGG and APGVGV found in natural elastins, appear random coil by both NMR chemical shift analysis and circular dichroism. However, these elastin hydrophobic domains exhibit structural bias for a dynamically disordered conformation that is neither helical nor ? sheet with a degree of nonrandom structural bias which is dependent on residue type and position in the sequence. Another nonrandom-coil aspect of hydrophobic domain structure lies in the fact that, in contrast to other intrinsically disordered proteins, these hydrophobic domains retain a relatively condensed conformation whether attached to cross-linking domains or not. Importantly, these domains and the proteins containing them constrict with increasing temperature by up to 30% in volume without becoming more ordered. This property is often observed in nonbiological polymers and suggests that temperature-driven constriction is a new type of protein structural change that is linked to elastin's biological functions of coacervation-driven assembly and elastic recoil.

SUBMITTER: Greenland KN

PROVIDER: S-EPMC6588176 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Order, Disorder, and Temperature-Driven Compaction in a Designed Elastin Protein.

Greenland Kelly N KN Carvajal Ma Faye Charmagne A MFCA Preston Jonathan M JM Ekblad Siri S Dean William L WL Chiang Jeff Y JY Koder Ronald L RL Wittebort Richard J RJ

The journal of physical chemistry. B 20180306 10

Artificial minielastin constructs have been designed that replicate the structure and function of natural elastins in a simpler context, allowing the NMR observation of structure and dynamics of elastin-like proteins with complete residue-specific resolution. We find that the alanine-rich cross-linking domains of elastin have a partially helical structure, but only when capped by proline-rich hydrophobic domains. We also find that the hydrophobic domains, composed of prominent 6-residue repeats ...[more]

PMID: 29461832

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Project description:BackgroundMore and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.ResultsThis paper first studies the effect of a condensed position specific scoring matrix with respect to physicochemical properties (PSSMP) on the prediction accuracy, where the PSSMP is derived by merging several amino acid columns of a PSSM belonging to a certain property into a single column. Next, we decompose each conventional physicochemical property of amino acids into two disjoint groups which have a propensity for order and disorder respectively, and show by experiments that some of the new properties perform better than their parent properties in predicting protein disorder. In order to get an effective and compact feature set on this problem, we propose a hybrid feature selection method that inherits the efficiency of uni-variant analysis and the effectiveness of the stepwise feature selection that explores combinations of multiple features. The experimental results show that the selected feature set improves the performance of a classifier built with Radial Basis Function Networks (RBFN) in comparison with the feature set constructed with PSSMs or PSSMPs that adopt simply the conventional physicochemical properties.ConclusionDistinguishing disordered regions from ordered regions in protein sequences facilitates the exploration of protein structures and functions. Results based on independent testing data reveal that the proposed predicting model DisPSSMP performs the best among several of the existing packages doing similar tasks, without either under-predicting or over-predicting the disordered regions. Furthermore, the selected properties are demonstrated to be useful in finding discriminating patterns for order/disorder classification.

Dataset Information

Order, Disorder, and Temperature-Driven Compaction in a Designed Elastin Protein.

Publications

Order, Disorder, and Temperature-Driven Compaction in a Designed Elastin Protein.

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