Combining experiments and simulations using the maximum entropy principle.
Combining experiments and simulations using the maximum entropy principle.
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DOI：
10.1371/journal.pcbi.1003406
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发表时间：
2014-02
影响因子：
4.3
通讯作者：
Lindorff-Larsen K
中科院分区：
文献类型：
--
作者：
Boomsma W;Ferkinghoff-Borg J;Lindorff-Larsen K
计算生物学的一个关键组成部分是将计算机建模的结果与实验测量结果进行比较。尽管在计算生物学的许多领域中使用的模型和算法取得了重大进展，但这种比较有时会显示计算与实验数据并不定量一致。最大熵原理是根据新数据构建概率分布的一般程序，当初始模型提供的结果与实验不一致时，它是一个自然的工具。近年来，最大熵在我们领域的应用数量稳步增长，在序列分析，结构建模和神经生物学等领域。在这篇Perspectives文章中，我们对该方法进行了广泛的介绍，试图鼓励其进一步采用。一般的程序是在一个简单的例子的背景下解释，之后，我们继续在分子模拟，最大熵程序最近提供了新的见解领域的现实世界中的应用。由于力场的精确度有限，大分子模拟有时会产生与实验不完全和定量一致的结果。这个问题的一个常见的解决方案是明确地确保两者之间的协议，通过扰动势能函数对实验数据。到目前为止，对于如何实施这种扰动还没有达成普遍共识。最近的三篇论文使用最大熵方法探讨了这个问题，为这个问题提供了新的理论和实践见解。我们依次强调每一项贡献，最后讨论剩余的挑战。
A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.
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影响因子：
5.7
作者：
Levin, Elena J.;Kondrashov, Dmitry A.;Phillips, George N., Jr.
通讯作者：
Phillips, George N., Jr.
DOI：
10.1107/s0108767384000866
发表时间：
1984-01-01
影响因子：
1.8
作者：
BRICOGNE, G
通讯作者：
BRICOGNE, G
影响因子：
56.9
作者：
Burling, FT;Weis, WI;Brunger, AT
通讯作者：
Brunger, AT
影响因子：
64.8
作者：
Lindorff-Larsen, K;Best, RB;Vendruscolo, M
通讯作者：
Vendruscolo, M
影响因子：
--
作者：
KULLBACK, S;LEIBLER, RA
通讯作者：
LEIBLER, RA