Tracing conformational changes in proteins.
Tracing conformational changes in proteins.
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DOI：
10.1186/1472-6807-10-s1-s1
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发表时间：
2010-05-17
影响因子：
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通讯作者：
Kavraki LE
中科院分区：
文献类型：
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作者：
Haspel N;Moll M;Baker ML;Chiu W;Kavraki LE
作为其功能的一部分，许多蛋白质经历了广泛的构象变化。追踪这些变化对于理解这些蛋白质的功能是很重要的。传统的基于生物物理学的构象搜索方法需要大量的计算，难以应用于大规模的构象运动。在这项工作中，我们研究了机器人启发方法的应用，使用主链和有限侧链表示以及粗粒度能量函数来跟踪大规模构象运动。我们在四种已知的大中型蛋白质上测试了该算法，结果表明，即使信息相对较少，我们也能够有效地追踪低能构象途径。我们的方法产生的构象途径可以进一步过滤和细化，以产生更多有用的信息，蛋白质在生理条件下的功能。所提出的方法有效地捕获了大规模的构象变化，并产生了与实验数据和其他计算研究一致的路径。该方法代表了迈向更复杂生物系统的更大规模建模的重要的第一步。
Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to large-scale conformational motions. In this work we investigate the application of a robotics-inspired method, using backbone and limited side chain representation and a coarse grained energy function to trace large-scale conformational motions. We tested the algorithm on four well known medium to large proteins and we show that even with relatively little information we are able to trace low-energy conformational pathways efficiently. The conformational pathways produced by our methods can be further filtered and refined to produce more useful information on the way proteins function under physiological conditions. The proposed method effectively captures large-scale conformational changes and produces pathways that are consistent with experimental data and other computational studies. The method represents an important first step towards a larger scale modeling of more complex biological systems.