Local pre-processing for node classification in networks : application in protein-protein interaction

Network modelling provides an increasingly popular conceptualisation in a wide range of domains, including the analysis of protein structure. Typical approaches to analysis model parameter values at nodes within the network. The spherical locality around a node provides a microenvironment that can be used to characterise an area of a network rather than a particular point within it. Microenvironments that centre on the nodes in a protein chain can be used to quantify parameters that are related to protein functionality. They also permit particular patterns of such parameters in node-centred microenvironments to be used to locate sites of particular interest. This paper evaluates an approach to index generation that seeks to rapidly construct microenvironment data. The results show that index generation performs best when the radius of microenvironments matches the granularity of the index. Results are presented to show that such microenvironments improve the utility of protein chain parameters in classifying the structural characteristics of nodes using both support vector machines and neural networks

Using microenvironments to identify allosteric binding sites

Protein amino acid residues can be classified by their chemical properties and data mining can be used to make predictions about their structure and function. However, the properties of the surrounding residues contribute to the overall chemical context. This paper defines microenvironments as the spherical volume around a point in space and uses these volumes to determine average properties of the encompassed residues. The approach to index generation rapidly constructs microenvironment data. The averaged chemical properties are then employed in allosteric site prediction using support vector machines and neural networks. The results show that index generation performs best when microenvironment radius matches the granularity of the index and that microenvironments improve the classification accuracy