Robson Barry, Mordasini Tiziana, Curioni Alessandro
IBM Research, T. J. Watson Research Laboratory, Yorktown Heights, New York 10598, USA.
J Proteome Res. 2002 Mar-Apr;1(2):115-33. doi: 10.1021/pr0155228.
A diagnostic for assessing the quality of a fold has been developed to which further criteria can be progressively added. The goal is to create a measure that can follow the status of a protein structure in a simulation or modeling process, when the answer (the experimental structure) is not known in advance, rather than simply reject deliberate misfolds. This places greater emphasis on the need to study, and calibrate against, marginal cases, i.e., unusual native structures, incomplete structures, partially erroneous X-ray structures, good models, poor models, and the effect of cofactors. The first three terms introduced in the diagnostic are appropriate core-forming properties or noncore properties of residues in relation to tertiary structure, appropriate neighboring structure density for each residue in relation to tertiary structure, and secondary structure consistency. While the method emerges as a useful simulation analysis tool, we find a need for further fine-tuning to diminish sensitivity to minor conformational changes that retain essential features of the fold, balanced against the need to obtain a more sensitive response when a conformational change involves less physically meaningful interatomic interactions. This dual utility is difficult to obtain: the investigation highlights some of the issues. Initial attempts to obtain it have led to terms in the diagnostic that are admittedly complex: simplifications must also be explored.
已开发出一种用于评估折叠质量的诊断方法,可逐步添加更多标准。目标是创建一种度量方法,在模拟或建模过程中,当答案(实验结构)事先未知时,能够跟踪蛋白质结构的状态,而不是简单地拒绝故意错误折叠。这更加突出了研究边缘情况并针对其进行校准的必要性,即不寻常的天然结构、不完整结构、部分错误的X射线结构、好的模型、差的模型以及辅因子的影响。诊断方法中引入的前三个术语分别是与三级结构相关的残基合适的核心形成特性或非核心特性、与三级结构相关的每个残基合适的相邻结构密度以及二级结构一致性。虽然该方法成为一种有用的模拟分析工具,但我们发现需要进一步微调,以降低对保留折叠基本特征的微小构象变化的敏感性,同时要兼顾当构象变化涉及较少物理意义的原子间相互作用时获得更敏感响应的需求。这种双重效用很难实现:该研究突出了一些问题。最初实现它的尝试导致诊断方法中的术语公认很复杂:还必须探索简化方法。