Literature search – Exploring in silico protein toxicity prediction methods to support the food and feed risk assessment
Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.
This report is the outcome of an EFSA procurement (NP/EFSA/GMO/2018/01) reviewing relevant scientific information on in silico prediction methods for protein toxicity, that could support the food and feed risk assessment. Several proteins are associated with adverse (toxic) effects in humans and animals, by a variety of mechanisms. These are produced by plants, animals and bacteria to prevail in hostile environments. In the present report, we present an integrated pipeline to perform a comprehensive literature and database search applied to proteins with toxic effects. “Toxin activity” and “toxin‐antitoxin” system strings were used as inputs for this pipeline. UniProtKB was considered as the reference database, and only the UniProtKB curator‐reviewedproteins were considered in the pipeline. Experimentally‐determinedstructures and homology‐based in silico3D models were retrieved from protein structures repositories; family‐, domain‐, motif‐ and other molecular signature‐related information was also obtained from specific databases which are part of the InterPro consortium. Protein aggregation associated with adverse effects was also investigated using different search strategies.This work can serve as the basis for further exploring novel risk assessment strategies for new proteins using in silico predictive methods.