Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

Table 1 Degree of all proteins, drug targets only and non-drug targets only for the full PIN and various subsets

Protein interaction network	Nodes examined	Mean degree	Median degree	Degree standard deviation	Degree skewness	Degree kurtosis	Max degree
full PIN -spoke	all	14.2	4	28.9	6.6	86.1	789
	drug targets in full PIN	22.5	8	44.8	6.8	84.2	789
	non-drug targets in full PIN	13.5	4	27.1	6.0	66.5	615
drug target subnetwork -spoke		1.7	1	44.7	3.2	15.8	23
non-drug target subnetwork -spoke		12.7	4	26.3	6.8	90.3	709
BioGRID only –spoke	all	7.5	3	13.5	7.9	133.0	395
	drug targets in BioGRID only	9.0	3	18.6	5.5	41.3	203
Rual + Stelzl papers only -spoke	all	4.3	2	8.8	7.5	81.6	158
	drug targets in Rual + Stelzl only	3.7	2	5.5	6.0	54.7	60
Rual paper only –spoke	all	3.8	2	8.4	9.4	127.5	158
	drug targets in Rual only	2.2	1	2.5	3.5	19.4	15

Statistical descriptors of the degree distribution of 11 different PINs whose protein complexes have all been represented as spoke models (i.e. any N-ary data is included by a spoke-model representation). Drug targets have a higher degree on average, even though the standard deviations are equally higher. Degree distribution of drug targets are also more skewed and peaked than non-drug targets. This is different in distributions like the BioGRID database or the Rual and Stelzl papers, where the numerical values are not only significantly smaller but the conclusions might be even the contrary, such as drug targets having a lower degree for Rual and Stelzl. The values of the drug target subnetwork show that interactions between drug targets are scarce and, therefore, the average higher degree of drug targets represent interactions between drug targets and non-drug targets. BioGRID was used by [2], Rual+Stelzl was used by [1] and Rual-only was used by [18].

ISSN: 1471-2105