The protein side-chain packing problem (PSCPP) is a central task in computational protein design. The problem is usually modeled as a combinatorial optimization problem, which consists of searching for a set of rotamers, from a given rotamer library, that minimizes a scoring function (SF). The SF is a weighted sum of terms, that can be decomposed in physics-based and knowledge-based terms. Although there are many methods to obtain approximate solutions for this problem, all of them have similar performances and there has not been a significant improvement in recent years. Studies on protein structure prediction and protein design revealed the limitations of current SFs to achieve further improvements for these two problems. In the same line, a recent work reported a similar result for the PSCPP. In this work, we ask whether or not this negative result regarding further improvements in performance is due to (i) an incorrect weighting of the SFs terms or (ii) the constrained conformation resulting from the protein crystallization process. To analyze these questions, we (i) model the PSCPP as a bi-objective combinatorial optimization problem, optimizing, at the same time, the two most important terms of two SFs of state-of-the-art algorithms and (ii) performed a preprocessing relaxation of the crystal structure through molecular dynamics to simulate the protein in the solvent and evaluated the performance of these two state-of-the-art SFs under these conditions. Our results indicate that (i) no matter what combination of weight factors we use the current SFs will not lead to better performances and (ii) the evaluated SFs will not be able to improve performance on relaxed structures. Furthermore, the experiments revealed that the SFs and the methods are biased toward crystallized structures.

中文翻译：

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侧链填料评分：重量因素和分子动力学结构分析

蛋白质侧链包装问题（PSCPP）是计算蛋白质设计中的核心任务。通常将此问题建模为组合优化问题，该问题包括从给定的rotamer库中搜索一组rotamer，以将评分功能（SF）最小化。SF是术语的加权总和，可以分解为基于物理学的术语和基于知识的术语。尽管有很多方法可以解决该问题，但是它们都具有相似的性能，并且近年来没有显着的改进。对蛋白质结构预测和蛋白质设计的研究揭示了当前SF的局限性，无法对这两个问题进行进一步的改进。在同一行中，最近的一项工作报告了PSCPP的类似结果。在这项工作中，我们询问有关性能进一步改善的负面结果是否是由于（i）SFs术语的权重错误或（ii）蛋白质结晶过程导致的受约束构象。为了分析这些问题，我们（i）将PSCPP建模为双目标组合优化问题，同时优化两个最先进算法的SF的两个最重要的条件，以及（ii）执行通过分子动力学对晶体结构进行预处理弛豫，以模拟溶剂中的蛋白质，并评估了这两种最先进的SF在这些条件下的性能。我们的结果表明：（i）无论我们使用当前SF的重量因子的哪种组合，都不会导致更好的性能，并且（ii）评估的SF不能改善松弛结构的性能。此外，实验表明，SF和方法偏向结晶结构。