Abstract
To quantitatively categorize protein structures, we developed a quantitative coarse-grained model of protein structures with a novel amino acid network, the interaction selective network (ISN), characterized by the links based on interactions in both the main and side chains. We found that the ISN is a novel robust network model to show the higher classification probability in the plots of average vertex degree (k) versus average clustering coefficient (C), both of which are typical network parameters for protein structures, and successfully distinguished between “all-α” and “all-β” proteins. On the other hand, one of the typical conventional networks, the α-carbon network (CAN), was found to be less robust than the ISN, and another typical network, atomic distance network (ADN), failed to distinguish between these two protein structures. Considering that the links in the CAN and ADN are defined by the interactions only between the main chain atoms and by the distance of the closest atom pair between the two amino acid residues, respectively, we can conclude that reflecting structural information from both secondary and tertiary structures in the network parameters improves the quantitative evaluation and robustness in network models, resulting in a quantitative and more robust description of three-dimensional protein structures in the ISN.
Highlights
To quantitatively categorize protein structures, we developed a quantitative coarse-grained model of protein structures with a novel amino acid network, the interaction selective network (ISN), characterized by the links based on interactions in both the main and side chains
As shown in the results, significant differences between all-α and all-β proteins were detected in average vertex degree, k, and average cluster coefficient, C, in the ISN and the carbon network (CAN)
To confirm the validity of the classification based on these network models, the logistic regression[27], which is one of widely used statistical methods to examine the validity of the classification and discrimination, was applied to obtain an optimum classification line which does not so much affected by outlier values
Summary
To quantitatively categorize protein structures, we developed a quantitative coarse-grained model of protein structures with a novel amino acid network, the interaction selective network (ISN), characterized by the links based on interactions in both the main and side chains. The relationship between the ratio of the secondary structure contents and the protein classes remains unclear Because these databases characterize the protein structures by separating several hierarchies, understanding the protein 3D structures by integrating both the secondary and tertiary structures becomes difficult. Compared with previous classification approaches based on the protein secondary structure contents, AAN enables the quantitative characterization of protein geometry using the network parameters without estimating the secondary structure contents. Such AAN allows us to quantitatively argue and is not for analysis of protein secondary structure components, but for quantitative characterization of whole protein structures
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