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Efficient Algorithms to Explore Conformation Spaces of Flexible Protein Loops

机译:探索柔性蛋白质环构象空间的有效算法

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摘要

Several applications in biology—e.g., incorporation of protein flexibility in ligand docking algorithms, interpretation of fuzzy X-ray crystallographic data, and homology modeling—require computing the internal parameters of a flexible fragment (usually, a loop) of a protein in order to connect its termini to the rest of the protein without causing any steric clash inside the loop and with the rest of the protein. One must often sample many such conformations in order to explore and adequately represent the conformational range of the studied loop. While sampling must be fast, it is made difficult by the fact that two conflicting constraints—kinematic closure and clash avoidance—must be satisfied concurrently. This paper describes two efficient and complementary sampling algorithms to explore the space of closed clash-free conformations of a flexible protein loop. The “seed sampling” algorithm samples broadly from this space, while the “deformation sampling” algorithm uses seed conformations as starting points to explore the conformation space around them at a finer grain. Computational results are presented for various loops ranging from 5 to 25 residues. More specific results also show that the combination of the sampling algorithms with a functional site prediction software (FEATURE) makes it possible to compute and recognize calcium-binding loop conformations. The sampling algorithms are implemented in a toolkit, called LoopTK, which is available at .
机译:生物学中的几种应用(例如,将蛋白质的柔韧性纳入配体对接算法,解释模糊的X射线晶体学数据和同源性建模)需要计算蛋白质的柔韧性片段(通常是环)的内部参数,以便将其末端连接至蛋白质的其余部分,而不会在环内以及蛋白质的其余部分引起任何空间冲突。为了探索并充分代表所研究环的构象范围,必须经常对许多这样的构象进行采样。尽管采样必须快速,但由于必须同时满足两个相互矛盾的约束条件(运动闭合和避免碰撞),因此很难进行采样。本文介绍了两种有效且互补的采样算法,以探索柔性蛋白质环的闭合,无碰撞构象的空间。 “种子采样”算法从该空间进行广泛采样,而“变形采样”算法以种子构象为起点,以更细的颗粒探索周围的构象空间。给出了从5到25个残基的各种循环的计算结果。更具体的结果还表明,采样算法与功能性位点预测软件(FEATURE)的组合使计算和识别钙结合环构象成为可能。采样算法是在名为LoopTK的工具包中实现的,该工具包可在上获得。

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