Computer-aided Discovery of Potential Inhibitors for Transthyretin-related Amyloidosis

Halogen Bonding Interactions of Polychlorinated Biphenyls and the Potential for Thyroid Disruption

Polychlorinated biphenyl (PCB) flame retardants are persistent pollutants and inhibit neurodevelopment, particularly in the early stages of life. Halogen bonding (XB) to the iodothyronine deiodinases (Dio) that modulate thyroid hormones (THs) is a potential mechanism for endocrine disruption. Cl⋅⋅⋅Se XB interactions of PCBs with SeMe- , a small model of the Dio active site selenocysteine, are compared with previous results on polybrominated diphenylethers (PBDEs) and THs using density functional theory. PCBs generally display weaker XB interactions compared to PBDEs and THs, consistent with the dependence of XB strength on the size of the halogen (I>Br>Cl). PCBs also do not meet a proposed energy threshold for substrates to undergo dehalogenation, suggesting they may behave as competitive inhibitors of Dio in addition to other mechanisms of endocrine disruption. XB interactions in PCBs are position-dependent, with ortho interactions slightly more favorable than meta and para interactions, suggesting that PCBs may have a greater effect on certain classes of Dio. Flexibility of PCBs around the biphenyl C-C bond is limited by ortho substitutions relative to the biphenyl linkage, which may contribute to the ability to inhibit Dio and other TH-related proteins.

Automated reaction database and reaction network analysis: extraction of reaction templates using cheminformatics

Both the automated generation of reaction networks and the automated prediction of synthetic trees require, in one way or another, the definition of possible transformations a molecule can undergo. One way of doing this is by using reaction templates. In view of the expanding amount of known reactions, it has become more and more difficult to envision all possible transformations that could occur in a studied system. Nonetheless, most reaction network generation tools rely on user-defined reaction templates. Not only does this limit the amount of chemistry that can be accounted for in the reaction networks, it also confines the wide-spread use of the tools by a broad public. In retrosynthetic analysis, the quality of the analysis depends on what percentage of the known chemistry is accounted for. Using databases to identify templates is therefore crucial in this respect. For this purpose, an algorithm has been developed to extract reaction templates from various types of chemical databases. Some databases such as the Kyoto Encyclopedia for Genes and Genomes and RMG do not report an atom-atom mapping (AAM) for the reactions. This makes the extraction of a template non-straightforward. If no mapping is available, it is calculated by the Reaction Decoder Tool (RDT). With a correct AAM-either calculated by RDT or specified-the algorithm consistently extracts a correct template for a wide variety of reactions, both elementary and non-elementary. The developed algorithm is a first step towards data-driven generation of synthetic trees or reaction networks, and a greater accessibility for non-expert users.