Dataset Information

In Silico Site-Directed Mutagenesis Informs Species-Specific Predictions of Chemical Susceptibility Derived From the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Tool.

ABSTRACT: Chemical hazard assessment requires extrapolation of information from model organisms to all species of concern. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed as a rapid, cost-effective method to aid cross-species extrapolation of susceptibility to chemicals acting on specific protein targets through evaluation of protein structural similarities and differences. The greatest resolution for extrapolation of chemical susceptibility across species involves comparisons of individual amino acid residues at key positions involved in protein-chemical interactions. However, a lack of understanding of whether specific amino acid substitutions among species at key positions in proteins affect interaction with chemicals made manual interpretation of alignments time consuming and potentially inconsistent. Therefore, this study used in silico site-directed mutagenesis coupled with docking simulations of computational models for acetylcholinesterase (AChE) and ecdysone receptor (EcR) to investigate how specific amino acid substitutions impact protein-chemical interaction. This study found that computationally derived substitutions in identities of key amino acids caused no change in protein-chemical interaction if residues share the same side chain functional properties and have comparable molecular dimensions, while differences in these characteristics can change protein-chemical interaction. These findings were considered in the development of capabilities for automatically generated species-specific predictions of chemical susceptibility in SeqAPASS. These predictions for AChE and EcR were shown to agree with SeqAPASS predictions comparing the primary sequence and functional domain sequence of proteins for more than 90% of the investigated species, but also identified dramatic species-specific differences in chemical susceptibility that align with results from standard toxicity tests. These results provide a compelling line of evidence for use of SeqAPASS in deriving screening level, species-specific, susceptibility predictions across broad taxonomic groups for application to human and ecological hazard assessment.

SUBMITTER: Doering JA

PROVIDER: S-EPMC6390969 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

In Silico Site-Directed Mutagenesis Informs Species-Specific Predictions of Chemical Susceptibility Derived From the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Tool.

Doering Jon A JA Lee Sehan S Kristiansen Kurt K Evenseth Linn L Barron Mace G MG Sylte Ingebrigt I LaLone Carlie A CA

Toxicological sciences : an official journal of the Society of Toxicology 20181101 1

Chemical hazard assessment requires extrapolation of information from model organisms to all species of concern. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed as a rapid, cost-effective method to aid cross-species extrapolation of susceptibility to chemicals acting on specific protein targets through evaluation of protein structural similarities and differences. The greatest resolution for extrapolation of chemical susceptibility across species inv ...[more]

PMID: 30060110

Similar Datasets

Project description:Computational algorithms are often used to assess pathogenicity of Variants of Uncertain Significance (VUS) that are found in disease-associated genes. Most computational methods include analysis of protein multiple sequence alignments (PMSA), assessing interspecies variation. Careful validation of PMSA-based methods has been done for relatively few genes, partially because creation of curated PMSAs is labor-intensive. We assessed how PMSA-based computational tools predict the effects of the missense changes in the APC gene, in which pathogenic variants cause Familial Adenomatous Polyposis. Most Pathogenic or Likely Pathogenic APC variants are protein-truncating changes. However, public databases now contain thousands of variants reported as missense. We created a curated APC PMSA that contained >3 substitutions/site, which is large enough for statistically robust in silico analysis. The creation of the PMSA was not easily automated, requiring significant querying and computational analysis of protein and genome sequences. Of 1924 missense APC variants in the NCBI ClinVar database, 1800 (93.5%) are reported as VUS. All but two missense variants listed as P/LP occur at canonical splice or Exonic Splice Enhancer sites. Pathogenicity predictions by five computational tools (Align-GVGD, SIFT, PolyPhen2, MAPP, REVEL) differed widely in their predictions of Pathogenic/Likely Pathogenic (range 17.5-75.0%) and Benign/Likely Benign (range 25.0-82.5%) for APC missense variants in ClinVar. When applied to 21 missense variants reported in ClinVar and securely classified as Benign, the five methods ranged in accuracy from 76.2-100%. Computational PMSA-based methods can be an excellent classifier for variants of some hereditary cancer genes. However, there may be characteristics of the APC gene and protein that confound the results of in silico algorithms. A systematic study of these features could greatly improve the automation of alignment-based techniques and the use of predictive algorithms in hereditary cancer genes.

Dataset Information

In Silico Site-Directed Mutagenesis Informs Species-Specific Predictions of Chemical Susceptibility Derived From the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Tool.

Publications

In Silico Site-Directed Mutagenesis Informs Species-Specific Predictions of Chemical Susceptibility Derived From the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Tool.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets