Department of Physics and Institute of Complex Systems, University of Strathclyde, Glasgow, United Kingdom.
Biophys J. 2010 Mar 3;98(5):890-900. doi: 10.1016/j.bpj.2009.11.017.
Residue networks representing 595 nonhomologous proteins are studied. These networks exhibit universal topological characteristics as they belong to the topological class of modular networks formed by several highly interconnected clusters separated by topological cavities. There are some networks that tend to deviate from this universality. These networks represent small-size proteins having <200 residues. This article explains such differences in terms of the domain structure of these proteins. On the other hand, the topological cavities characterizing proteins residue networks match very well with protein binding sites. This study investigates the effect of the cutoff value used in building the residue network. For small cutoff values, <5 A, the cavities found are very large corresponding almost to the whole protein surface. On the contrary, for large cutoff value, >10.0 A, only very large cavities are detected and the networks look very homogeneous. These findings are useful for practical purposes as well as for identifying protein-like complex networks. Finally, this article shows that the main topological class of residue networks is not reproduced by random networks growing according to Erdös-Rényi model or the preferential attachment method of Barabási-Albert. However, the Watts-Strogatz model reproduces very well the topological class as well as other topological properties of residue network. A more biologically appealing modification of the Watts-Strogatz model to describe residue networks is proposed.
研究了代表 595 个非同源蛋白质的残基网络。这些网络表现出普遍的拓扑特征,因为它们属于由几个高度相互连接的簇组成的模块化网络拓扑类,这些簇由拓扑腔隔开。有些网络往往偏离这种普遍性。这些网络代表的是小尺寸的蛋白质,其残基数小于 200。本文从这些蛋白质的结构域结构方面解释了这些差异。另一方面,残基网络中蛋白质拓扑腔的特征与蛋白质结合位点非常匹配。本研究调查了在构建残基网络时使用的截止值的影响。对于较小的截止值,<5 A,发现的腔非常大,几乎对应整个蛋白质表面。相反,对于较大的截止值,>10.0 A,仅检测到非常大的腔,并且网络看起来非常均匀。这些发现对于实际目的以及识别蛋白质样复杂网络都很有用。最后,本文表明,根据 Erdös-Rényi 模型或 Barabási-Albert 的优先附着方法生长的随机网络无法再现残基网络的主要拓扑类。然而,Watts-Strogatz 模型很好地再现了残基网络的拓扑类以及其他拓扑性质。提出了一种更具生物学吸引力的 Watts-Strogatz 模型修改,用于描述残基网络。