Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

Abstract

The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in < 50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction, and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects, and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly.

Biological and functional relevance of CASP predictions

“…We evaluated the physical features of the predicted structure sites and the degree to which they shared similarity with the experimental structure sites. We previously developed PocketFEATURE (PF), an algorithm that evaluates similarity between two functional sites in terms of their physicochemical features . As part of this work, we applied the PF algorithm to assess the extent to which physicochemical features that are observed in experimental structures can be replicated by predicted structures.…”

“…We previously developed PocketFEATURE (PF), an algorithm that evaluates similarity between two functional sites in terms of their physicochemical features. [9][10][11][12] As part of this work, we applied the PF algorithm to assess the extent to which physicochemical features that are observed in experimental structures can be replicated by predicted structures. We also analyzed features of quaternary structure assemblies in two oligomeric proteins and disease-causing variants, which often play an important role in protein function.…”

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

“…Although, target essentiality (the target protein is essential in nature) is claimed to play lead role in drug side effects (Wang et al. 2013a, 2013b; Liu and Altman 2015), we observed that the variation in evolutionary rates between SET and NSET proteins does not solely depend on target essentiality. We also used three druggability measures of targets: (i) total druggability, essential druggability, and killer druggability to explain the rate discrepancy of SET vs. NSET proteins.…”

Evolutionarily Conserved Roles for Blood-Brain Barrier Xenobiotic Transporters in Endogenous Steroid Partitioning and Behavior

“…Competitive binding of Mdr1-modulating drugs may prevent Mdr1’s interaction with its typical substrates, leading to adverse effects. To investigate the possible role of human Mdr1 in regulating anxiety-related behaviors, we used an established analytical approach (Liu and Altman, 2015, Lounkine et al, 2012) to calculate Mdr1’s enrichment factor (EF) against each of 10,098 possible human adverse drug reactions (ADRs) gathered from the OFFSIDES collection of FDA drug reports (Tatonetti et al, 2012); Figure 6A). …”