Dataset Information

Structural approaches to understanding retinal proteins needed for vision.

ABSTRACT: The past decade has witnessed an impressive expansion of our knowledge of retinal photoreceptor signal transduction and the regulation of the visual cycle required for normal eyesight. Progress in human genetics and next generation sequencing technologies have revealed the complexity behind many inherited retinal diseases. Structural studies have markedly increased our understanding of the visual process. Moreover, technical innovations and improved methodologies in proteomics, macromolecular crystallization and high resolution imaging at different levels set the scene for even greater advances. Pharmacology combined with structural biology of membrane proteins holds great promise for developing innovative accessible therapies for millions robbed of their sight or progressing toward blindness.

SUBMITTER: Orban T

PROVIDER: S-EPMC3971393 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Structural approaches to understanding retinal proteins needed for vision.

Orban Tivadar T Jastrzebska Beata B Palczewski Krzysztof K

Current opinion in cell biology 20131128

The past decade has witnessed an impressive expansion of our knowledge of retinal photoreceptor signal transduction and the regulation of the visual cycle required for normal eyesight. Progress in human genetics and next generation sequencing technologies have revealed the complexity behind many inherited retinal diseases. Structural studies have markedly increased our understanding of the visual process. Moreover, technical innovations and improved methodologies in proteomics, macromolecular cr ...[more]

PMID: 24680428

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Project description:Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.

Dataset Information

Structural approaches to understanding retinal proteins needed for vision.

Publications

Structural approaches to understanding retinal proteins needed for vision.

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