Development of human lactate dehydrogenase a inhibitors: high-throughput screening, molecular dynamics simulation and enzyme activity assay.

Lactate dehydrogenase A (LDHA) is highly expressed in many tumor cells and promotes the conversion of pyruvate to lactic acid in the glucose pathway, providing energy and synthetic precursors for rapid proliferation of tumor cells. Therefore, inhibition of LDHA has become a widely concerned tumor treatment strategy. However, the research and development of highly efficient and low toxic LDHA small molecule inhibitors still faces challenges. To discover potential inhibitors against LDHA, virtual screening based on molecular docking techniques was performed from Specs database of more than 260,000 compounds and Chemdiv-smart database of more than 1,000 compounds. Through molecular dynamics (MD) simulation studies, we identified 12 potential LDHA inhibitors, all of which can stably bind to human LDHA protein and form multiple interactions with its active central residues. In order to verify the inhibitory activities of these compounds, we established an enzyme activity assay system and measured their inhibitory effects on recombinant human LDHA. The results showed that Compound 6 could inhibit the catalytic effect of LDHA on pyruvate in a dose-dependent manner with an EC50 value of 14.54 ± 0.83 µM. Further in vitro experiments showed that Compound 6 could significantly inhibit the proliferation of various tumor cell lines such as pancreatic cancer cells and lung cancer cells, reduce intracellular lactic acid content and increase intracellular reactive oxygen species (ROS) level. In summary, through virtual screening and in vitro validation, we found that Compound 6 is a small molecule inhibitor for LDHA, providing a good lead compound for the research and development of LDHA related targeted anti-tumor drugs.

Discovery of the Inhibitor Targeting the SLC7A11/xCT Axis through In Silico and In Vitro Experiments.

In the development and progression of cervical cancer, oxidative stress plays an important role within the cells. Among them, Solute Carrier Family 7 Member 11 (SLC7A11/xCT) is crucial for maintaining the synthesis of glutathione and the antioxidant system in cervical cancer cells. In various tumor cells, studies have shown that SLC7A11 inhibits ferroptosis, a form of cell death, by mediating cystine uptake and maintaining glutathione synthesis. Additionally, SLC7A11 is also involved in promoting tumor metastasis and immune evasion. Therefore, inhibiting the SLC7A11/xCT axis has become a potential therapeutic strategy for cervical cancer. In this study, through structure-based high-throughput virtual screening, a compound targeting the SLC7A11/xCT axis named compound 1 (PubChem CID: 3492258) was discovered. In vitro experiments using HeLa cervical cancer cells as the experimental cell model showed that compound 1 could reduce intracellular glutathione levels, increase glutamate and reactive oxygen species (ROS) levels, disrupt the oxidative balance within HeLa cells, and induce cell death. Furthermore, molecular dynamics simulation results showed that compound 1 has a stronger binding affinity with SLC7A11 compared to the positive control erastin. Overall, all the results mentioned above indicate the potential of compound 1 in targeting the SLC7A11/xCT axis and treating cervical cancer both in vitro and in silico.

Discovery of potential antidiabetic peptides using deep learning.

Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.

Targeting the PDK/PDH axis to reverse metabolic abnormalities by structure-based virtual screening with in vitro and in vivo experiments.

In humans and animals, the pyruvate dehydrogenase kinase (PDK) family proteins (PDKs 1-4) are excessively activated in metabolic disorders such as obesity, diabetes, and cancer, inhibiting the activity of pyruvate dehydrogenase (PDH) which plays a crucial role in energy and fatty acid metabolism and impairing its function. Intervention and regulation of PDH activity have become important research approaches for the treatment of various metabolic disorders. In this study, a small molecule (g25) targeting PDKs and activating PDH, was identified through multi-level computational screening methods. In vivo and in vitro experiments have shown that g25 activated the activity of PDH and reduced plasma lactate and triglyceride level. Besides, g25 significantly decreased hepatic fat deposition in a diet-induced obesity mouse model. Furthermore, g25 enhanced the tumor-inhibiting activity of cisplatin when used in combination. Molecular dynamics simulations and in vitro kinase assay also revealed the specificity of g25 towards PDK2. Overall, these findings emphasize the importance of targeting the PDK/PDH axis to regulate PDH enzyme activity in the treatment of metabolic disorders, providing directions for future related research. This study provides a possible lead compound for the PDK/PDH axis related diseases and offers insights into the regulatory mechanisms of this pathway in diseases.

Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments.

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC50s are 7.86 ± 1.25 and 24.72 ± 1.94 µM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

Discovering inhibitors of TEAD palmitate binding pocket through virtual screening and molecular dynamics simulation.

Transcriptional enhanced associate domain (TEAD) proteins bind to YAP/TAZ and mediate YAP/TAZ-induced gene expression. TEADs are not only the key transcription factors and final effector of the Hippo signaling pathway, but also the proteins that regulate cell proliferation and apoptosis. Disorders of Hippo signaling pathway occur in liver cancer, breast cancer, colon cancer and other cancers. S-palmitylation can stabilize the structure of TEADs and is also a necessary condition for the binding of TEADs to YAP/TAZ. The absence of TEAD palmitoylation prevents TEADs from binding to chromatin, thereby inhibiting the transcription and expression of downstream target genes in the Hippo pathway through a dominant-negative mechanism. Therefore, disrupting the S-palmitylation of TEADs has become an attractive and very feasible method in cancer treatment. The palmitate binding pockets of TEADs are conservative, and the crystal structures of TEAD2-palmitoylation inhibitor complexes and the potential TEAD2 inhibitors are more than other TEADs, TEAD2 can be selected to be the target receptor. In this study, structure-based and ligand-based virtual screening, molecular dynamics simulations, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) calculations, residue decomposition binding energy calculations, and ADME predictions have been performed to discover 11 potential TEAD2 S-palmitylation inhibitors. ChEBML196567 and ZINC000013942794 are the most recommended, because they formed strong binding energies and stable hydrogen bonds with TEAD2 and have good drugbility and high human oral absorption. We found that it was easier to find the targeting small molecules using a combination of structure-based and ligand-based virtual screening methods. Besides, a new core structure has been found in the selected small molecules. In addition, we analyzed the binding modes of these small molecules to TEAD2, and confirmed the hot spot residues Cys380, Ser345, Tyr426, Phe428, Ile408, and Met379. AVAILABILITY OF DATA AND MATERIAL: Supplementary materials are available online.