Office of Research and Development Publications

Comparing and contrasting the coverage of publicly available structural alerts for protein binding

Citation:

Nelms, M., R. Lougee, D. Roberts, A. Richard, AND G. Patlewicz. Comparing and contrasting the coverage of publicly available structural alerts for protein binding. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 12:100100, (2019). https://doi.org/10.1016/j.comtox.2019.100100

Impact/Purpose:

This was an exercise to evaluate several publicly available structural alert schemes that predict potential reactivity. This is an important consideration for grouping chemicals for read-across as well as explaining potential confounding factors in HTS assay results.

Description:

The initiating steps for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides the starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTs patterns and their associated chemistry reaction mechanistic domains have been compared. The goals are 1) to understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each consensus reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.