Suppr超能文献

使用物理能量函数评估CASP4预测结果。

Evaluating CASP4 predictions with physical energy functions.

作者信息

Feig Michael, Brooks Charles L

机构信息

Department of Molecular Biology, TPC6, The Scripps Research Institute, La Jolla, California 92037, USA.

出版信息

Proteins. 2002 Nov 1;49(2):232-45. doi: 10.1002/prot.10217.

Abstract

Physical energy scoring functions based on implicit solvation models are tested by evaluating predictions from the most recent CASP4 competition. The best performing scoring functions are identified along with the best protocol for preparing structures before energies are evaluated. Ranking of structures with the best scoring functions is compared across CASP4 targets to establish when physical scoring functions can be expected to reliably distinguish structures that are most similar to the native fold in a set of misfolded or unfolded protein conformations. The results are used to interpret previous studies where scoring functions were tested on the standard decoy sets by Park, Levitt, and Baker. We show that the best physical scoring functions can be applied successfully in automated consensus scoring applications where a single best conformation has to be selected from a set of structures from different sources. Finally, the potential for better protein structure scoring functions is discussed with a suggestion for an empirically parameterized linear combination of energy components.

摘要

通过评估最近的CASP4竞赛中的预测结果,对基于隐式溶剂化模型的物理能量评分函数进行了测试。确定了性能最佳的评分函数以及在评估能量之前准备结构的最佳方案。在CASP4的各个目标中,比较了使用最佳评分函数对结构的排名,以确定何时可以预期物理评分函数能够可靠地区分一组错误折叠或未折叠的蛋白质构象中与天然折叠最相似的结构。这些结果用于解释之前Park、Levitt和Baker在标准诱饵集上测试评分函数的研究。我们表明,最佳的物理评分函数可以成功应用于自动一致性评分应用中,即在必须从一组来自不同来源的结构中选择单个最佳构象的情况下。最后,讨论了改进蛋白质结构评分函数的潜力,并提出了一种对能量成分进行经验参数化线性组合的建议。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验