Systematic Analyses and Prediction of Human Drug Side Effect Associated Proteins from the Perspective of Protein Evolution

Abstract

Identification of various factors involved in adverse drug reactions in target proteins to develop therapeutic drugs with minimal/no side effect is very important. In this context, we have performed a comparative evolutionary rate analyses between the genes exhibiting drug side-effect(s) (SET) and genes showing no side effect (NSET) with an aim to increase the prediction accuracy of SET/NSET proteins using evolutionary rate determinants. We found that SET proteins are more conserved than the NSET proteins. The rates of evolution between SET and NSET protein primarily depend upon their noncomplex (protein complex association number = 0) forming nature, phylogenetic age, multifunctionality, membrane localization, and transmembrane helix content irrespective of their essentiality, total druggability (total number of drugs/target), m-RNA expression level, and tissue expression breadth. We also introduced two novel terms-killer druggability (number of drugs with killing side effect(s)/target), essential druggability (number of drugs targeting essential proteins/target) to explain the evolutionary rate variation between SET and NSET proteins. Interestingly, we noticed that SET proteins are younger than NSET proteins and multifunctional younger SET proteins are candidates of acquiring killing side effects. We provide evidence that higher killer druggability, multifunctionality, and transmembrane helices support the conservation of SET proteins over NSET proteins in spite of their recent origin. By employing all these entities, our Support Vector Machine model predicts human SET/NSET proteins to a high degree of accuracy (∼86%).

Keywords: essential druggability; killer druggability; nonside effect associated drug target (NSET); protein evolutionary rates; side effect associated drug target (SET); support vector machine (SVM).

Fig. 1.—

The relationships between protein evolutionary rates (dN) and target drug numbers of human SET vs. NSET proteins in different bins. P_MWT < 0.05 denotes significant difference between groups.

Fig. 2.—

The impact of complex forming ability on dN/dS of SET and NSET proteins. The bar graphs demonstrate the difference in the distribution of dN/dS between SET and NSET proteins in complex/noncomplex forming groups considering (a) all protein complexes (b) large protein complexes (size ≥ 5) of CORUM database. P_MWT < 0.05 between groups was used to represent a statistically significant difference. Error bars signify 95% confidence interval.

Fig. 3.—

Comparisons of evolutionary rates (dN) of SET vs. NSET proteins within different age groups. In the figure, (a) the categorical age data are provided by Domazet-Lošo and Tautz (2008); (b) the numerical protein age data were obtained from ProteinHistorian (Capra et al. 2012). For numerical data, we considered proteins with age ≤ 500 Ma as “new” and rest as “old” proteins. The plots showing the importance of young/new gene age in the disparity of evolutionary rates of SET and NSET proteins. Error bars represent 95% confidence interval.