Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane).
Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane).
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DOI：
10.1093/bioinformatics/btu457
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发表时间：
2014-09-01
期刊：
影响因子：
--
通讯作者：
Schwede T
中科院分区：
文献类型：
--
作者：
Studer G;Biasini M;Schwede T
动机：膜蛋白是一类重要的生物大分子，参与许多细胞关键过程，包括信号和运输。它们占人类基因组基因的三分之一，占目前药物靶标的50%。尽管它们很重要，但实验结构数据是稀疏的，这导致对计算建模工具的期望很高，以帮助填补这一空白。然而，由于许多经验方法都是基于实验结构数据进行训练的，这些方法偏向于可溶性球状蛋白，因此它们对跨膜蛋白的准确性往往是有限的。结果：通过结合膜蛋白结构的统计势和按残基加权的方法，我们提出了一种膜蛋白的局部模型质量估计方法(‘QMEANBrane’)。越来越多的可用的实验膜蛋白结构使我们能够训练接近统计饱和的膜特定统计势能。我们证明了膜蛋白模型的可靠的局部质量估计是可能的，从而将局部质量估计扩展到这些生物相关的分子。可获得性和实施：源代码和数据集可根据要求提供。补充信息：补充数据可在BioInformation Online上获得。
Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited. Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules. Availability and implementation: Source code and datasets are available on request. Contact: torsten.schwede@unibas.ch Supplementary Information: Supplementary data are available at Bioinformatics online.
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影响因子：
3.5
作者：
Heim, Andrew J.;Li, Zhijun
通讯作者：
Li, Zhijun
影响因子：
3
作者：
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通讯作者：
Nikiforovich, Gregory V.
DOI：
10.1107/s0907444913007051
发表时间：
2013-05
期刊：
Acta crystallographica. Section D, Biological crystallography
影响因子：
--
作者：
Biasini M;Schmidt T;Bienert S;Mariani V;Studer G;Haas J;Johner N;Schenk AD;Philippsen A;Schwede T
通讯作者：
Schwede T
影响因子：
3
作者：
Hauser M;Mayer CE;Söding J
通讯作者：
Söding J