Toward in Silico Modeling of Dynamic Combinatorial Libraries.
Fiche publication
Date publication
juin 2022
Journal
ACS central science
Auteurs
Membres identifiés du Cancéropôle Est :
Pr LEHN Jean-Marie
Tous les auteurs :
Casciuc I, Osypenko A, Kozibroda B, Horvath D, Marcou G, Bonachera F, Varnek A, Lehn JM
Lien Pubmed
Résumé
Dynamic combinatorial libraries (DCLs) display adaptive behavior, enabled by the reversible generation of their molecular constituents from building blocks, in response to external effectors, e.g., protein receptors. So far, chemoinformatics has not yet been used for the design of DCLs-which comprise a radically different set of challenges compared to classical library design. Here, we propose a chemoinformatic model for theoretically assessing the composition of DCLs in the presence and the absence of an effector. An imine-based DCL in interaction with the effector human carbonic anhydrase II (CA II) served as a case study. Support vector regression models for the imine formation constants and imine-CA II binding were derived from, respectively, a set of 276 imines synthesized and experimentally studied in this work and 4350 inhibitors of CA II from ChEMBL. These models predict constants for all DCL constituents, to feed software assessing equilibrium concentrations. They are publicly available on the dedicated website. Models rationally selected two amines and two aldehydes predicted to yield stable imines with high affinity for CA II and provided a virtual illustration on how effector affinity regulates DCL members.
Référence
ACS Cent Sci. 2022 06 22;8(6):804-813