Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Targeting PI4KA sensitizes refractory leukemia to chemotherapy by modulating the ERK/AMPK/OXPHOS axis.

Background: The emergence of chemoresistance in leukemia markedly impedes chemotherapeutic efficacy and dictates poor prognosis. Recent evidence has revealed that phosphatidylinositol 4 kinase-IIIα (PI4KA) plays a critical role in tumorigenesis. However, the molecular mechanisms of PI4KA-regulated chemoresistance and leukemogenesis remain largely unknown. Methods: Liquid chromatography-mass spectrometry (LC-MS), patient samples and leukemia xenograft mouse models were used to investigate whether PI4KA was an effective target to overcome chemoresistance in leukemia. Enzyme-linked immunosorbent assay (ELISA) and molecular mechanics/generalized born surface area (MM/GBSA) method were employed to identify cepharanthine (CEP) as a novel PI4KA inhibitor. Results: High expression of PI4KA was observed in drug-resistant leukemia cells or in relapsed leukemia patients, which was correlated with poor overall survival. Depletion of PI4KA sensitized drug-resistant leukemia cells to chemotherapeutic drugs in vitro and in vivo by regulating ERK/AMPK/OXPHOS axis. We also identified cepharanthine (CEP) as a novel PI4KA inhibitor, which could undermine the stability of the PI4KA/TTC7/FAM126 complex, enhancing the sensitivity of drug-resistant leukemia cells to chemotherapeutic drugs in vitro and in vivo. Conclusions: Our study underscored the potential of therapeutic targeting of PI4KA to overcome chemoresistance in leukemia. A combination of the PI4KA inhibitor with classic chemotherapeutic agents could represent a novel therapeutic strategy for the treatment of refractory leukemia.

Follistatin-like 1 (FSTL1) interacts with Wnt ligands and Frizzled receptors to enhance Wnt/ß-catenin signaling in obstructed kidneys in vivo.

Follistatin (FS)-like 1 (FSTL1) is a member of the FS-SPARC (secreted protein, acidic and rich in cysteine) family of secreted and extracellular matrix proteins. The functions of FSTL1 have been studied in heart and lung injury as well as in wound healing; however, the role of FSTL1 in the kidney is largely unknown. Here, we show using single-cell RNA-Seq that Fstl1 was enriched in stromal cells in obstructed mouse kidneys. In addition, immunofluorescence demonstrated that FSTL1 expression was induced in fibroblasts during kidney fibrogenesis in mice and human patients. We demonstrate that FSTL1 overexpression increased renal fibrosis and activated the Wnt/ß-catenin signaling pathway, known to promote kidney fibrosis, but not the transforming growth factor ß (TGF-ß), Notch, Hedgehog, or Yes-associated protein (YAP) signaling pathways in obstructed mouse kidneys, whereas inhibition of FSTL1 lowered Wnt/ß-catenin signaling. Importantly, we show that FSTL1 interacted with Wnt ligands and the Frizzled (FZD) receptors but not the coreceptor lipoprotein receptor-related protein 6 (LRP6). Specifically, we found FSTL1 interacted with Wnt3a through its extracellular calcium-binding (EC) domain and von Willebrand factor type C-like (VWC) domain, and with FZD4 through its EC domain. Furthermore, we show that FSTL1 increased the association of Wnt3a with FZD4 and promoted Wnt/ß-catenin signaling and fibrogenesis. The EC domain interacting with both Wnt3a and FZD4 also enhanced Wnt3a signaling. Therefore, we conclude that FSTL1 is a novel extracellular enhancer of the Wnt/ß-catenin pathway.

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics.

Glioblastoma (GBM) is the most common and aggressive intracranial malignant brain tumor, and the abnormal expression of HDAC1 is closely correlated to the progression, recurrence and metastasis of GBM cells, making selective inhibition of HDAC1 a promising strategy for GBM treatments. Among all available selective HDAC1 inhibitors, the macrocyclic peptides have gained great attention due to their remarkable inhibitory selectivity on HDAC1. However, the binding mechanism underlying this selectivity is still elusive, which increases the difficulty of designing and synthesizing the macrocyclic peptide-based anti-GBM drug. Herein, multiple computational approaches were employed to explore the binding behaviors of a typical macrocyclic peptide FK228 in both HDAC1 and HDAC6. Starting from the docking conformations of FK228 in the binding pockets of HDAC1&6, relatively long MD simulation (500 ns) shown that the hydrophobic interaction and hydrogen bonding of E91 and D92 in the Loop2 of HDAC1 with the Cap had a certain traction effect on FK228, and the sub-pocket formed by Loop1 and Loop2 in HDAC1 could better accommodate the Cap group, which had a positive effect on maintaining the active conformation of FK228. While the weakening of the interactions between FK228 and the residues in the Loop2 of HDAC6 during the MD simulation led to the large deflection of FK228 in the binding site, which also resulted in the decrease in the interactions between the Linker region of FK228 and the previously identified key amino acids (H134, F143, H174, and F203). Therefore, the residues located in Loop1 and Loop2 contributed in maintaining the active conformation of FK228, which would provide valuable hints for the discovery and design of novel macrocyclic polypeptide HDAC inhibitors.

Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder.

Amitifadine, the only drug ever clinically tested in Phase 3 for treating depression, is a triple reuptake inhibitor (TRI) that simultaneously interacts with human monoamine transporters (MATs) including hSERT, hNET and hDAT. This novel multi-target strategy improves drug efficacy and reduces the toxic side effects of drugs. However, the binding modes accounting for amitifadine's polypharmacological mode of action are still elusive, and extensive exploration of the amitifadine-target interactions between amitifadine and MATs is urgently needed. In this study, a total of 0.63 µs molecular dynamics (MD) simulations with an explicit solvent as well as endpoint binding free energy (BFE) calculation were carried out. MD simulation results identified a shared binding mode involving eleven key residues at the S1 site of MATs for the binding of amitifadine, and the results of the BFE calculations were in good agreement with experimental reports. Moreover, by analyzing the per-residue energy contribution variation at the S1 site of three MATs and additional cross-mutagenesis simulations, the variation in the inhibition ratio of amitifadine between hSERT and two other MATs was discovered to mainly come from non-conserved residues (Y95, I172 and T439 in hNET and Y95, I172, A169 and T439 in hDAT). As the rational inhibition ratio of multi-target drugs among various therapeutic targets was found to be the key to their safety and tolerance, the findings of this study may further facilitate the rational design of more potent but less toxic multi-target antidepressant drugs.