Sippl M J
Center for Applied Molecular Engineering, University of Salzburg, Austria.
Proteins. 1993 Dec;17(4):355-62. doi: 10.1002/prot.340170404.
A major problem in the determination of the three-dimensional structure of proteins concerns the quality of the structural models obtained from the interpretation of experimental data. New developments in X-ray crystallography and nuclear magnetic resonance spectroscopy have accelerated the process of structure determination and the biological community is confronted with a steadily increasing number of experimentally determined protein folds. However, in the recent past several experimentally determined protein structures have been proven to contain major errors, indicating that in some cases the interpretation of experimental data is difficult and may yield incorrect models. Such problems can be avoided when computational methods are employed which complement experimental structure determinations. A prerequisite of such computational tools is that they are independent of the parameters obtained from a particular experiment. In addition such techniques are able to support and accelerate experimental structure determinations. Here we present techniques based on knowledge based mean fields which can be used to judge the quality of protein folds. The methods can be used to identify misfolded structures as well as faulty parts of structural models. The techniques are even applicable in cases where only the C alpha trace of a protein conformation is available. The capabilities of the technique are demonstrated using correct and incorrect protein folds.
确定蛋白质三维结构时的一个主要问题涉及从实验数据解释中获得的结构模型的质量。X射线晶体学和核磁共振光谱学的新进展加速了结构确定过程，生物学界面临着实验确定的蛋白质折叠数量不断增加的情况。然而，最近有几个实验确定的蛋白质结构被证明包含重大错误，这表明在某些情况下，实验数据的解释很困难，可能会产生不正确的模型。当采用补充实验结构确定的计算方法时，可以避免此类问题。此类计算工具的一个先决条件是它们独立于从特定实验获得的参数。此外，此类技术能够支持并加速实验结构确定。在此，我们介绍基于知识的平均场技术，可用于判断蛋白质折叠的质量。这些方法可用于识别错误折叠的结构以及结构模型中的错误部分。该技术甚至适用于仅获得蛋白质构象的Cα轨迹的情况。使用正确和错误的蛋白质折叠展示了该技术的能力。
