Do Thi Mai Dung

Understanding the inhibitor–HER2 interaction mechanism remains a critical challenge in combating breast cancer. In the present work, the role of five critical residues that are deeply located in the HER2 active site was recognized. To win the race against time in learning the activities of the HER2 tyrosine kinase protein, we employed a stepwise computational procedure including a machine learning predictive regression model, atomistic molecular dynamics (MD) simulations, and the umbrella sampling MD method. A systematic mining of a data set of 8 million chemical compounds allowed us to finally identify 13 candidates whose capacities as anti-HER2 have not been reported before. Based on the computed results, a benchmark for the strength of the ligand–HER2 interaction has been established. Although van der Waals potential energy tends to stabilize ligand–protein associations, the ligand that electrostatically interacts with five residues, Lys753, Leu796, Thr798, Asp863, and Asp880, is a key factor in deciding the inhibitor strength. Significantly, the strong binding of compound lig233 was exemplified by its ability to form hydrogen bonds with Asp863 and Asp880 and maintain exceptionally short distances to many key residues, indicating the formation of strong chemical bonds. Lig233 also exhibits a binding free energy of −47 kcal/mol, two times as large as that of −21 kcal/mol for the known drug lapatinib. The fresh understanding achieved in the present study can lead to the necessary adjustments in the experimental development of HER2 inhibitors.

The inhibition of β-site amyloid precursor protein-cleaving enzyme 1 presents a promising therapeutic strategy for treating Alzheimer’s disease by reducing amyloid-β (Aβ) production. This paper employed a computational approach that combined machine learning (ML) and atomistic simulations to accelerate the discovery of potential BACE1 inhibitors. Our ML models, trained on a set of ligands with experimental binding affinity, showed high accuracy when tested on a holdout test set. The best model was used to screen more than two million compounds in the CHEMBL33 chemical library to obtain a short list of top-hit compounds, which were further analyzed using molecular docking and fast pulling of ligand (FPL) simulations. The insights into structure and binding energetics obtained from FPL simulations elucidate the stability and interaction mechanisms of the BACE1-ligand bound state, providing data useful for the rational design of novel AD therapeutics.

The major side effects of in-use drugs, such as gefitinib, erlotinib and osimetinib, have led to inherent limitations and considerable concerns regarding the use of tyrosine kinase inhibitors (TKIs) in cancer treatment.

Histon deacetylase 2 (HDAC2) là một mục tiêu phân tử hấp dẫn trong thiết kế các thuốc chống ung thư. Trong nghiên cứu này, cấu trúc của 2.809 chất cùng kết quả thực nghiệm được dùng để xây dựng các mô hình học máy với mục đích nhận diện các hợp chất có tiềm năng ức chế HDAC2. Kết quả sàng lọc trên 140 triệu cấu trúc xác định được 88 chất có tiềm năng ức chế HDAC2 tốt hơn SAHA (độ chính xác của dự đoán trên 85% với cả 4 mô hình). Phân tích mô phỏng docking, 60/88 chất có tiềm năng tốt hơn chất đối chiếu, ΔG khoảng -12,3 đến -16,5 kcal/mol. Từ cấu trúc của 88 chất, 16 cấu trúc được thiết kế mới để gia tăng khả năng liên kết với trung tâm hoạt động của HDAC2. Áp dụng quy trình sàng lọc ảo gồm 4 mô hình học máy kết hợp docking, kết quả thiết kế được 2 cấu trúc (chất I, II). Phân tích tương tác chỉ ra chất I và II có thể tạo thêm các liên kết mới với trung tâm hoạt động, đồng thời duy trì khả năng tạo phức với ion kẽm. Nghiên cứu đã xây đựng được mô hình học máy và đưa ra quy trình sàng lọc kết hợp docking tìm ra các cấu trúc hợp chất mới có tiềm năng ức chế tốt. Quy trình này không chỉ phù hợp với HDAC2 mà có thể áp dụng cho nhiều mục tiêu sinh học khác.

A series of novel 5′(7′)-substituted-2′-oxospiro[1,3]dioxolane-2,3′-indoline-based N-hydroxypropenamides were designed, synthesized and evaluated for histone deacetylase (HDAC) inhibition and cytotoxicity. It was found that the compounds in this series displayed potent inhibitory effects against HDAC2 with IC50 values as low as 0.284μM, almost comparable to that of SAHA (IC50, 0.265μM), a positive control. In Western blot analysis, these compounds also exhibited noted inhibition toward histone deacetylation and this inhibition was found to correlate well with the cytotoxicity of the compounds in three human cancer cell lines. Docking studies indicated the compounds in this series bound to HDAC2 with high binding affinities (∼−9.8kcal/mol) compared to SAHA (−7.4kcal/mol)

Since the first histone deacetylase (HDAC) inhibitor (Zolinza®, widely known as suberoylanilide hydroxamic acid; SAHA) was approved by the Food and Drug Administration for the treatment of T-cell lymphoma in 2006, the search for newer HDAC inhibitors has attracted a great deal of interest of medicinal chemists worldwide. As a continuity of our ongoing research in this area, we designed and synthesized a series of 5-substitutedphenyl-1,3,4-thiadiazole-based hydroxamic acids as analogues of SAHA and evaluated their biological activities. A number of compounds in this series, for example, N(1)-hydroxy-N(8)-(5-(2-chlorophenyl)-1,3,4-thiadiazol-2-yl)octandiamide (5b), N(1)-hydroxy-N(8)-(5-(3-chlorophenyl-1,3,4-thiadiazol-2-yl)octandiamide (5c) and N(1)-hydroxy-N(8)-(5-(4-chlorophenyl)-1,3,4-thiadiazol-2-yl)octandiamide (5d), were found to possess potent anticancer cytotoxicity and HDAC inhibition effects. Compounds 5b-d were generally two- to five-fold more potent in terms of cytotoxicity compared to SAHA against five cancer cell lines tested. Docking studies revealed that these hydroxamic acid displayed higher affinities than SAHA toward HDAC8.