L. H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa 50010, USA.
J Chem Phys. 2011 Jun 21;134(23):235101. doi: 10.1063/1.3596947.
Protein structures are evolutionarily more conserved than sequences, and sequences with very low sequence identity frequently share the same fold. This leads to the concept of protein designability. Some folds are more designable and lots of sequences can assume that fold. Elucidating the relationship between protein sequence and the three-dimensional (3D) structure that the sequence folds into is an important problem in computational structural biology. Lattice models have been utilized in numerous studies to model protein folds and predict the designability of certain folds. In this study, all possible compact conformations within a set of two-dimensional and 3D lattice spaces are explored. Complementary interaction graphs are then generated for each conformation and are described using a set of graph features. The full HP sequence space for each lattice model is generated and contact energies are calculated by threading each sequence onto all the possible conformations. Unique conformation giving minimum energy is identified for each sequence and the number of sequences folding to each conformation (designability) is obtained. Machine learning algorithms are used to predict the designability of each conformation. We find that the highly designable structures can be distinguished from other non-designable conformations based on certain graphical geometric features of the interactions. This finding confirms the fact that the topology of a conformation is an important determinant of the extent of its designability and suggests that the interactions themselves are important for determining the designability.
蛋白质结构在进化上比序列更保守，而序列相似度非常低的情况下，它们通常具有相同的折叠结构。这就引出了蛋白质可设计性的概念。有些折叠结构更具可设计性，许多序列都可以采用这种折叠结构。阐明蛋白质序列与序列折叠成的三维（3D）结构之间的关系是计算结构生物学中的一个重要问题。格点模型在许多研究中被用于模拟蛋白质折叠并预测某些折叠的可设计性。在这项研究中，我们探索了二维和三维格点空间中一组可能的紧凑构象。然后为每个构象生成互补的相互作用图，并使用一组图特征来描述它们。为每个格点模型生成完整的 HP 序列空间，并通过将每个序列穿线到所有可能的构象来计算接触能。为每个序列确定给出最小能量的唯一构象，并获得每个构象折叠的序列数量（可设计性）。然后使用机器学习算法来预测每个构象的可设计性。我们发现，高度可设计的结构可以根据相互作用的某些图形几何特征与其他不可设计的构象区分开来。这一发现证实了构象的拓扑结构是其可设计性程度的重要决定因素的事实，并表明相互作用本身对于确定可设计性很重要。
