Theoretical models of inhibitory activity for inhibitors of protein–protein interactions: targeting menin–mixed lineage leukemia with small molecules† †Electronic Supplementary Information (ESI) available: Detailed characterization of the compound collection; structural characteristics of the menin–MI-2-2 complex; and additional computational results. See DOI: 10.1039/c7md00170c
| Autor: | Jedwabny, Wiktoria, Kłossowski, Szymon, Purohit, Trupta, Cierpicki, Tomasz, Grembecka, Jolanta, Dyguda-Kazimierowicz, Edyta |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Medchemcomm |
| ISSN: | 2040-2511 2040-2503 |
| Popis: | A computationally affordable, non-empirical model based on electrostatic multipole and dispersion terms successfully predicts the binding affinity of inhibitors of menin–MLL protein–protein interactions. Development and binding affinity predictions of inhibitors targeting protein–protein interactions (PPI) still represent a major challenge in drug discovery efforts. This work reports application of a predictive non-empirical model of inhibitory activity for PPI inhibitors, exemplified here for small molecules targeting the menin–mixed lineage leukemia (MLL) interaction. Systematic ab initio analysis of menin–inhibitor complexes was performed, revealing the physical nature of these interactions. Notably, the non-empirical protein–ligand interaction energy comprising electrostatic multipole and approximate dispersion terms (E(10)El,MTP + EDas) produced a remarkable correlation with experimentally measured inhibitory activities and enabled accurate activity prediction for new menin–MLL inhibitors. Importantly, this relatively simple and computationally affordable non-empirical interaction energy model outperformed binding affinity predictions derived from commonly used empirical scoring functions. This study demonstrates high relevance of the non-empirical model we developed for binding affinity prediction of inhibitors targeting protein–protein interactions that are difficult to predict using empirical scoring functions. |
| Databáze: | OpenAIRE |
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