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Minimalistmodels for proteins :a comparative analysis

机译：蛋白质的极简模型：比较分析

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The last decade has witnessed a renewed interest in the coarse-grained (CG) models for biopolymers, also stimulated by the needs of modern molecular biology, dealing with nano- to micro-sized bio-molecular systems and larger than microsecond timescale. This combination of size and timescale is, in fact, hard to access by atomic-based simulations. Coarse graining the system is a route to be followed to overcome these limits, but the ways of practically implementing it are many and different, making the landscape of CG models very vast and complex. In this paper, the CG models are reviewed and their features, applications and performances compared. This analysis, restricted to proteins, focuses on the minimalist models, namely those reducing at minimum the number of degrees of freedom without losing the possibility of explicitly describing the secondary structures. This class includes models using a single or a few interacting centers (beads) for each amino acid. From this analysis several issues emerge. The difficulty in building these models r esides in theneed for combining transferability/predictive power with the capability of accurately reproducing the structures. It is shown that these aspects could be optimized by accurately choosing the force field (FF) terms and functional forms, and combining different parameterization procedures. In addition, in spite of the variety of the minimalist models, regularities can be found in the parameters values and in FF terms. These are outlined and schematically presented with the aid of a generic phase diagram of the polypeptide in the parameter space and, hopefully, could serve as guidelines for the development of minimalist models incorporating the maximum possible level of predictive power and structural accuracy.
机译：在过去的十年中，人们对生物聚合物的粗粒（CG）模型重新产生了兴趣，这也受到现代分子生物学的需求的刺激，涉及纳米级到微米级的生物分子系统以及大于微秒级的时标。实际上，这种大小和时标的组合很难通过基于原子的模拟来访问。粗粒度系统是克服这些限制的一种遵循的途径，但是实际实施该系统的方式很多且不同，这使得CG模型的前景变得非常广阔和复杂。本文对CG模型进行了回顾，并比较了它们的功能，应用和性能。这种仅限于蛋白质的分析着眼于极简主义模型，即那些在不损失明确描述二级结构的可能性的情况下，将自由度最小化的模型。此类包括针对每个氨基酸使用单个或几个相互作用中心（珠子）的模型。通过这种分析，出现了几个问题。建立这些模型的困难在于将可传递性/预测能力与精确再现结构的能力相结合的需要。结果表明，可以通过准确选择力场（FF）项和功能形式并组合不同的参数化过程来优化这些方面。此外，尽管极简主义模型多种多样，但可以在参数值和FF项中找到规律性。这些是在参数空间中借助多肽的通用相图进行概述和示意性呈现的，希望可以作为开发纳入最大可能水平的预测能力和结构准确性的极简模型的指南。

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