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Massive non-natural proteins structure prediction using grid technologies

机译:使用网格技术预测大规模非天然蛋白质的结构

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Background The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of proteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is their ability to form a well defined three-dimensional structure. Thus, the structural study of NBPs can help to understand if natural protein sequences were selected for their peculiar properties or if they are just one of the possible stable and functional ensembles. Methods The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 × 104 sequences) was generated, discarding amino acid sequences with significant similarity to natural proteins, and the corresponding structures were predicted using Rosetta. Given the highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc. Results The vast majority of NBPs, according to the Rosetta model, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in α helix-β strands relative content and in hydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins. Conclusion The results obtained suggest that the ability to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to adopt α helical folds indicate that all-α proteins may have emerged early in pre-biotic evolution. Further, the lower percentage of aromatic residues observed in natural proteins has important evolutionary implications as far as tolerance to mutations is concerned.
机译:背景技术天然蛋白质的数量仅占所有可能蛋白质序列的一小部分,并且有大量蛋白质从未被自然界采样过,即所谓的“永生蛋白质”(NBP)。在这方面的一个基本问题是,天然蛋白的整体是否具有独特的化学和物理特性,或者仅仅是偶然性与功能选择相结合的产物。天然蛋白质的关键特征是其形成定义明确的三维结构的能力。因此,对NBP的结构研究可以帮助了解是否选择了天然蛋白质序列作为其独特特性,或者它们仅仅是可能的稳定和功能性整合体之一。方法大量随机蛋白的结构表征无法通过实验进行,因此该问题已使用计算方法解决。生成了一个大的随机蛋白质序列库(2×10 4 序列),丢弃了与天然蛋白质具有显着相似性的氨基酸序列,并使用Rosetta预测了相应的结构。鉴于计算量很大的问题,Rosetta已移植到网格中,并在GENIUS Grid Portal中开发了用户友好的作业提交环境。根据净电荷,二级结构含量,表面/体积比,疏水核成分等对生成的蛋白质结构进行了分析。结果根据Rosetta模型,绝大多数NBP具有紧密的三维结构特征,并具有高二级结构含量。结构紧密度和表面极性与天然蛋白相当,表明相似的稳定性和溶解性。观察到α螺旋-β链的相对含量和疏水核心组成存在偏差,因为相对于天然蛋白质,NBP似乎在螺旋结构和芳香族氨基酸方面更丰富。结论得出的结果表明形成紧密,有序和水溶性结构的能力是多肽的固有特性。随机序列采用α螺旋折叠的趋势表明,全α蛋白可能已在益生元进化的早期出现。此外,就突变耐受性而言,天然蛋白质中观察到的较低的芳香族残基百分比具有重要的进化意义。

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