Equipping Decoy Generation Algorithms for Template-free Protein Structure Prediction with Maps of the Protein Conformation Space
Equipping Decoy Generation Algorithms for Template-free Protein Structure Prediction with Maps of the Protein Conformation Space
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DOI：
10.29007/j5p9
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Ahmed Bin Zaman;Amarda Shehu
中科院分区：
文献类型：
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作者：
Ahmed Bin Zaman;Amarda Shehu
无模板蛋白质结构预测的一个核心挑战是控制计算的三级结构的质量，也称为诱饵。鉴于蛋白质结构空间的大小、维度和固有特征，这不是微不足道的。当前诱饵生成算法使用的机制依赖于生成尽可能多的诱饵。这是不切实际的，而且不受诱饵数据集上任何感兴趣的指标的影响。在本文中，我们提出用蛋白质结构空间的进化映射来装备一个诱饵生成算法。MAP利用蛋白质结构的低维表示，并充当其粒度可控的记忆。对不同目标序列的评估表明，急剧减少的存储并不会牺牲诱饵质量，这表明所提出的诱饵生成算法在无模板蛋白质结构预测中的前景。
A central challenge in template-free protein structure prediction is controlling the quality of computed tertiary structures also known as decoys. Given the size, dimensionality, and inherent characteristics of the protein structure space, this is non-trivial. The current mechanism employed by decoy generation algorithms relies on generating as many decoys as can be afforded. This is impractical and uninformed by any metrics of interest on a decoy dataset. In this paper, we propose to equip a decoy generation algorithm with an evolving map of the protein structure space. The map utilizes low-dimensional representations of protein structure and serves as a memory whose granularity can be controlled. Evaluations on diverse target sequences show that drastic reductions in storage do not sacrifice decoy quality, indicating the promise of the proposed mechanism for decoy generation algorithms in template-free protein structure prediction.