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1.
J Chem Inf Model ; 63(10): 2918-2927, 2023 05 22.
Article in English | MEDLINE | ID: mdl-37150933

ABSTRACT

A drug discovery and development pipeline is a prolonged and complex process that remains challenging for both computational methods and medicinal chemists and has not been able to be resolved using computational methods. Deep learning has been utilized in various fields and achieved tremendous success in designing novel molecules in the pharmaceutical industry. Herein, we use state-of-the-art techniques to propose a deep neural network, AIMLinker, to rapidly design and generate meaningful drug-like proteolysis targeting chimeras (PROTACs) analogs. The model extracts the structural information from the input fragments and generates linkers to incorporate them. We integrate filters in the model to exclude nondruggable structures guided via protein-protein complexes while retaining molecules with potent chemical properties. The novel PROTACs subsequently pass through molecular docking, taking root-mean-square deviation (RMSD), relative Gibbs free energy (ΔΔGbinding), molecular dynamics (MD) simulation, and free energy perturbation (FEP) calculations as the measurement criteria for testing the robustness and feasibility of the model. The generated novel PROTACs molecules possess similar structural information with superior binding affinity to the binding pockets compared to the existing CRBN-dBET6-BRD4 ternary complexes. We demonstrate the effectiveness of the methodology of leveraging AIMLinker to design novel compounds for PROTACs molecules exhibiting better chemical properties compared to the dBET6 crystal pose.


Subject(s)
Drug Design , Molecular Docking Simulation , Proteolysis , Molecular Dynamics Simulation
2.
Bioorg Med Chem ; 78: 117129, 2023 01 15.
Article in English | MEDLINE | ID: mdl-36542959

ABSTRACT

To discover small molecules as acid alpha-glucosidase (GAA) stabilizers for potential benefits of the exogenous enzyme treatment toward Pompe disease cells, we started from the initial screening of the unique chemical space, consisting of sixteen stereoisomers of 2-aminomethyl polyhydroxylated pyrrolidines (ADMDPs) to find out two primary stabilizers 17 and 18. Further external or internal structural modifications of 17 and 18 were performed to increase structural diversity, followed by the protein thermal shift study to evaluate the GAA stabilizing ability. Fortunately, pyrrolidine 21, possessing an l-arabino-typed configuration pattern, was identified as a specific potent rh-GAA stabilizer, enabling the suppression of rh-GAA protein denaturation. In a cell-based Pompe model, co-administration of 21 with rh-GAA protein significantly improved enzymatic activity (up to 5-fold) compared to administration of enzyme alone. Potentially, pyrrolidine 21 enables the direct increase of ERT (enzyme replacement therapy) efficacy in cellulo and in vivo.


Subject(s)
Glycogen Storage Disease Type II , Humans , Glycogen Storage Disease Type II/drug therapy , Glycogen Storage Disease Type II/diagnosis , alpha-Glucosidases , Enzyme Replacement Therapy
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