Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.
Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.
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DOI：
10.1371/journal.pcbi.1004419
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发表时间：
2015-08
影响因子：
4.3
通讯作者：
Fiser A
中科院分区：
文献类型：
--
作者：
Vallat B;Madrid-Aliste C;Fiser A
从蛋白质的氨基酸序列预测蛋白质的三维结构仍然是分子生物学中的一个具有挑战性的问题。虽然目前蛋白质的结构覆盖几乎完全由基于模板的技术提供，但其余蛋白质序列的建模越来越需要无模板的方法。然而，无模板建模方法的可靠性要低得多，而且通常适用于较小的蛋白质，因此有很大的改进空间。我们在这里提出了一种新的计算方法，该方法使用超二级结构片段库（称为Smotifs）来模拟蛋白质结构。Smotifs库随着时间的推移已经饱和，为高效建模提供了理论基础。该方法依赖于来自远程相关蛋白结构的弱序列信号来创建特定于目标蛋白序列的Smotif片段库。在片段组装协议中利用该Smotif库对诱饵进行采样，并通过复合评分函数对诱饵进行评估。由于smotiff片段比其他基于片段的方法中使用的片段更大，因此所提出的建模算法SmotifTF可以在诱饵组装期间采用详尽采样。SmotifTF在大约50%的测试案例中成功地预测了目标蛋白的整体折叠，与其他最先进的预测方法相比，其表现具有竞争力，特别是当远程同源物的序列信号减少时。基于smotifs的建模是对现有预测方法的补充，为解决结构预测问题提供了一个有希望的方向，特别是当针对较大的蛋白质进行建模时。每种蛋白质折叠成独特的三维结构，使其能够发挥其生物功能。因此，了解蛋白质结构的原子细节是了解其功能的关键。高通量实验技术的进步导致已知蛋白质序列的可用性呈指数增长。尽管在蛋白质结构的实验测定方面取得了很大的进展，但99%以上的结构信息仍然是由计算建模方法提供的。我们在这里描述了一种新的结构预测方法，SmotifTF，它使用一个独特的已知蛋白质片段库来组装序列的三维结构。随着时间的推移，片段库已经饱和，因此提供了一套完整的模型构建所需的构建块。与现有的结构预测方法相比，该方法具有很强的竞争力。
Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling. Each protein folds into a unique three-dimensional structure that enables it to carry out its biological function. Knowledge of the atomic details of protein structures is therefore a key to understanding their function. Advances in high throughput experimental technologies have lead to an exponential increase in the availability of known protein sequences. Although strong progress has been made in experimental protein structure determination, it remains a fact that more than 99% of structural information is provided by computational modeling methods. We describe here a novel structure prediction method, SmotifTF, which uses a unique library of known protein fragments to assemble the three-dimensional structure of a sequence. The fragment library has saturated over time and therefore provides a complete set of building blocks required for model building. The method performs competitively compared to existing methods of structure prediction.