Skolnick J, Kolinski A, Ortiz A R
Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
J Biomol Struct Dyn. 1998 Oct;16(2):381-96. doi: 10.1080/07391102.1998.10508255.
One of the most important unsolved problems of computational biology is prediction of the three-dimensional structure of a protein from its amino acid sequence. In practice, the solution to the protein folding problem demands that two interrelated problems be simultaneously addressed. Potentials that recognize the native state from the myriad of misfolded conformations are required, and the multiple minima conformational search problem must be solved. A means of partly surmounting both problems is to use reduced protein models and knowledge-based potentials. Such models have been employed to elucidate a number of general features of protein folding, including the nature of the energy landscape, the factors responsible for the uniqueness of the native state and the origin of the two-state thermodynamic behavior of globular proteins. Reduced models have also been used to predict protein tertiary and quaternary structure. When combined with a limited amount of experimental information about secondary and tertiary structure, molecules of substantial complexity can be assembled. If predicted secondary structure and tertiary restraints are employed, low resolution models of single domain proteins can be successfully predicted. Thus, simplified protein models have played an important role in furthering the understanding of the physical properties of proteins.
计算生物学中最重要的未解决问题之一是根据蛋白质的氨基酸序列预测其三维结构。实际上，解决蛋白质折叠问题需要同时解决两个相互关联的问题。需要有能从大量错误折叠构象中识别天然状态的势能，并且必须解决多极小值构象搜索问题。部分克服这两个问题的一种方法是使用简化的蛋白质模型和基于知识的势能。此类模型已被用于阐明蛋白质折叠的一些一般特征，包括能量景观的性质、导致天然状态唯一性的因素以及球状蛋白质两态热力学行为的起源。简化模型也已用于预测蛋白质的三级和四级结构。当与关于二级和三级结构的有限实验信息相结合时，可以组装出相当复杂的分子。如果采用预测的二级结构和三级限制条件，则可以成功预测单结构域蛋白质的低分辨率模型。因此，简化的蛋白质模型在增进对蛋白质物理性质的理解方面发挥了重要作用。
