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%).

Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

“…Indeed, we see that this is a strong correlation within our dataset (Supplementary Figure 1); e.g., drugs with blood-related indications are nearly ten times more likely to have targets with Mendelian genetic syndromes involving blood. Other confounders include variables that have been previously linked to side effects: properties of the protein such as its cross-tissue expression pattern 15 , properties of the gene such as evolutionary constraint 37 and corresponding intolerance of population genetic variation 27 , or properties of the drug such as modality or delivery route. To explore the relationship of genetics and side effects with these potential confounders, we gathered additional drug and gene information and built a dataset for regression modeling, to predict side effects in each organ system as dependent variables.…”

Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs

“…It is noteworthy that viruses may hijack these EGEPs, leading to severe or lethal symptoms. Drugs that directly target them may cause serious side effects or specific toxicity [ 35 ]. Therefore, we examined the enrichment of EGEPs in known drug targets and VTHPs of the 35 viruses.…”

Tissue-specific genetic features inform prediction of drug side effects in clinical trials

“…A cutoff of the upper bound of the OE confidence interval <0.35 was used as suggested by Karczewski et al ( 31 ). Constraint was examined because previous studies have shown it to be predictive of genes exhibiting drug side effects ( 32 ).…”

“…We defined a genetic feature called “coloc2 phenotype,” which is a drug target gene with a GWA phenotype that is driven by gene expression regulation through colocalization. We selected this genetic feature because previous studies have shown GWA loci phenotypes to be predictive of genes exhibiting drug side effects ( 32 ).…”