Conformational variability and heterogeneity are crucial determinants of the function of biological macromolecules. The possibility of accessing this information experimentally suffers from severe under‐determination of the problem, since there are a few experimental observables to be accounted for by a (potentially) infinite number of available conformational states. Several computational methods have been proposed over the years in order to circumvent this theoretically insurmountable obstacle. A large share of these strategies is based on reweighting an initial conformational ensemble which arises from, for example, molecular simulations of different qualities and levels of theory. In this work, we compare the outcome of three reweighting approaches based on radically different views of the conformational heterogeneity problem, namely Maximum Entropy, Maximum Parsimony and Maximum Occurrence, and we do so using the same experimental data. In this comparison we find both expected as well as unexpected similarities.

中文翻译：

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从分子模拟计算大分子构象变异性的不同加权方法比较

构象变异性和异质性是决定生物大分子功能的关键因素。实验上访问此信息的可能性严重困扰着该问题的确定性，因为（可能）无限数量的可用构象状态会解释一些实验可观察到的问题。多年来，已经提出了几种计算方法来规避这一理论上无法克服的障碍。这些策略中的很大一部分是基于对初始构象集合进行加权的，这是由例如不同质量和理论水平的分子模拟产生的。在这项工作中，我们根据构象异质性问题的根本不同观点，比较了三种重新加权方法的结果，即最大熵，最大简约和最大出现次数，并且我们使用相同的实验数据来进行此操作。在此比较中，我们发现了预期的相似性和意外的相似性。