An Integrated in silico Approach and in vitro Study for the Discovery of Small-Molecule USP7 Inhibitors as Potential Cancer Therapies

Role of antidiarrheal agents nifuroxazide in antitumor multi‑target anticancer, multi‑mechanism anticancer drug (Review)

Nifuroxazide (NFZ) is an antimicrobial drug, which has been found to be a promising antitumor agent in recent years. In addition to being a classic STAT3 inhibitor, NFZ can also act on IL-6 and exert an anti-tumor role through inflammatory factor pathways. It can also bind to target proteins of aldehyde dehydrogenase 1, one of the families of E-twenty-six transcription factors and ubiquitin-specific protease 21 to play an anti-tumor role in different pathways. NFZ is able to act on the tumor cell microenvironment to inhibit tumor angiogenesis and tumor cell migration, enhance tumor immune cells, increase the cytotoxicity of tumor cells and enhance the anti-tumor effect of other drugs. Furthermore, it has high safety with few toxic side effects. The anti-tumor mechanisms of NFZ were described in the current review, aiming to provide insight and a reference for future studies promoting the implementation of NFZ as an anti-tumor drug in the clinic.

Efficient, rapid, and high-yield synthesis of aryl Schiff base derivatives and their in vitro and in silico inhibition studies of hCA I, hCA II, AChE, and BuChE

This study reports a rapid and efficient synthesis of four novel aryl Schiff base derivatives. Biological activity and molecular modeling studies were conducted to evaluate the inhibitory effects of these compounds on human carbonic anhydrases (hCA) and cholinesterases. The results indicate that the triazole-ring-containing compounds have strong inhibitory effects on hCA I, hCA II, acetylcholinesterase (AChE), and butyrylcholinesterase (BuChE) targets. Besides comparing the Schiff bases synthesized in our study to reference molecules, we conducted in silico investigations to examine how these compounds interact with their targets. Our studies revealed that these compounds can occupy binding sites and establish interactions with crucial residues, thus inhibiting the functions of the targets. These findings have significant implications as they can be utilized to develop more potent compounds for treating the diseases that these target proteins play crucial roles in or to obtain drug precursors with enhanced efficacy.

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

Identifying Potential SOS1 Inhibitors via Virtual Screening of Multiple Small Molecule Libraries against KRAS-SOS1 Interaction

The RAS-MAPK signaling pathway, crucial for cell proliferation and differentiation, involves key proteins KRAS and SOS1. Mutations in the KRAS and SOS1 genes are implicated in various cancer types, including pancreatic, lung, and juvenile myelomonocytic leukemia. There is considerable interest in identifying inhibitors targeting KRAS and SOS1 to explore potential therapeutic strategies for cancer treatment. In this study, advanced in silico techniques were employed to screen small molecule libraries at this interface, leading to the identification of promising lead compounds as potential SOS1 inhibitors. Comparative analysis of the average binding free energies of these predicted potent compounds with known SOS1 small molecule inhibitors revealed that the identified compounds display similar or even superior predicted binding affinities compared to the known inhibitors. These findings offer valuable insights into the potential of these compounds as candidates for further development as effective anti-cancer agents.

Utilizing machine learning to identify nifuroxazide as an inhibitor of ubiquitin-specific protease 21 in a drug repositioning strategy

Ubiquitin-specific protease (USP), an enzyme catalyzing protein deubiquitination, is involved in biological processes related to metabolic disorders and cancer proliferation. We focused on constructing predictive models tailored to unveil compounds boasting USP21 inhibitory attributes. Six models, Extra Trees Classifier, Random Forest Classifier, LightGBM Classifier, XGBoost Classifier, Bagging Classifier, and a convolutional neural network harnessed from empirical data were selected for the screening process. These models guided our selection of 26 compounds from the FDA-approved drug library for further evaluation. Notably, nifuroxazide emerged as the most potent inhibitor, with a half-maximal inhibitory concentration of 14.9 ± 1.63 μM. The stability of protein-ligand complexes was confirmed using molecular modeling. Furthermore, nifuroxazide treatment of HepG2 cells not only inhibited USP21 and its established substrate ACLY but also elevated p-AMPKα, a downstream functional target of USP21. Intriguingly, we unveiled the previously unknown capacity of nifuroxazide to increase the levels of miR-4458, which was identified as downregulating USP21. This discovery was substantiated by manipulating miR-4458 levels in HepG2 cells, resulting in corresponding changes in USP21 protein levels in line with its predicted interaction with ACLY. Lastly, we confirmed the in vivo efficacy of nifuroxazide in inhibiting USP21 in mice livers, observing concurrent alterations in ACLY and p-AMPKα levels. Collectively, our study establishes nifuroxazide as a promising USP21 inhibitor with potential implications for addressing metabolic disorders and cancer proliferation. This multidimensional investigation sheds light on the intricate regulatory mechanisms involving USP21 and its downstream effects, paving the way for further exploration and therapeutic development.

Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison

Ubiquitin-specific-processing protease 7 (USP7) is a promising target protein for cancer therapy, and great attention has been given to the identification of USP7 inhibitors. Traditional virtual screening methods have now been successfully applied to discover USP7 inhibitors aiming at reducing costs and speeding up time in several studies. However, due to their unsatisfactory accuracy, it is still a difficult task to develop USP7 inhibitors. In this study, multiple supervised learning classifiers were built to distinguish active USP7 inhibitors from inactive ligands. Physicochemical descriptors, MACCS keys, ECFP4 fingerprints and SMILES were first calculated to represent the compounds in our in-house dataset. Two deep learning (DL) models and nine classical machine learning (ML) models were then constructed based on different combinations of the above molecular representations under three activity cutoff values, and a total of 15 groups of experiments (75 experiments) were implemented. The performance of the models in these experiments was evaluated, compared and discussed using a variety of metrics. The optimal models are ensemble learning models when the dataset is balanced or severely imbalanced, and SMILES-based DL performs the best when the dataset is slightly imbalanced. Meanwhile, multimodal data fusion in some cases can improve the performance of ML and DL models. In addition, SMOTE, unbiased decoy selection and SMILES enumeration can improve the performance of ML and DL models when the dataset is severely imbalanced, and SMOTE works the best. Our study established highly accurate supervised learning classification models, which would accelerate the development of USP7 inhibitors. Some guidance was also provided for drug researchers in selecting supervised models and molecular representations as well as handling imbalanced datasets.

Ubiquitin specific protease 46 potentiates triple negative breast cancer development by stabilizing PGAM1-mediated glycolysis

Triple-negative breast cancer (TNBC) is a malignancy with high metastasis rate and poor prognosis. Limited drugs are effective for the treatment of TNBC patients. Ubiquitin specific proteases (USPs) are important posttranscription modulators that promote protein stability by reducing the ubiquitination of the proteins. Aberrant expression of USPs is involved in the development of numerous cancers. However, it remains poorly understood on the role of USP46 in TNBC growth and metastasis. In this study, we explored the clinical relevance, function and molecular mechanisms of USP46 in TNBC. USP46 expression was increased in breast cancer tissues. High expression of USP46 was associated with the poorer prognosis of the patients. Overexpression and knockdown experiments demonstrated that USP46 was critical for TNBC cell growth, migration, and tumorigenesis. Mechanistically, USP46 enhanced the protein stability of phosphoglycerate mutase 1 (PGAM1) via direct interaction. Importantly, USP46 stimulated the glycolysis and promoted the malignant growth of TNBC cells through upregulation of PGAM1. Our study reveals that USP46/PGAM1 axis contributes to TNBC progression and is a potential target for the treatment of TNBC patients.

Genomics-based strategies toward the identification of a Z-ISO carotenoid biosynthetic enzyme suitable for structural studies

Over the past 20years, structural genomics efforts have proven enormously successful for the determination of integral membrane protein structures, particularly for those of prokaryotic origin. However, traditional genomic expansion screens have included up to hundreds of targets, necessitating the use of robotics and other automation not available to most laboratories. Moreover, such large-scale screens of eukaryotic targets are not easily performed at such a scale. To have broader appeal, traditional structural genomic approaches need to be modified and improved such that they are feasible for most laboratories and especially so for proteins from eukaryotic organisms. One such refinement, termed "microgenomic expansion," has been recently described. This approach improves the process of target selection by making target screening a two-step process, with a minimal number of targets tested at each step. Microgenomic expansion methods are applied here theoretically to a project that has the objective of acquiring a structure for the plant 15-cis-ζ-carotene isomerase, Z-ISO.

Screening of the small molecule library of Meinox enables the identification of anticancer compounds in pathologically distinct cancers

Small molecules are widely used for the modulation of the molecular basis of diseases. This makes them the perfect tool for discovering and developing new therapeutics. In this work, we have established a library of small molecules in house and characterized its molecular and druglike properties. We have shown that most small molecules have molecular weights less than 450. They have pharmaceutically relevant cLogP, cLogS, and druglikeness value distributions. In addition, Meinox’s small molecule library contained small molecules with polar surface areas that are less than 60 square angstroms, suggesting their potent ability to cross the blood-brain barrier. Meinox’s small molecule library was also tested in vitro for pathologically distinct forms of cancer, including pancreatic adenocarcinoma PANC1, breast carcinoma MCF7, and lymphoblastic carcinoma RS4-11 cell lines. Analysis of this library at a dose of 1 μM allowed the discovery of potent, specific or broadly active anticancer compounds against pathologically distinct cancers. This study shows that in vitro analysis of different cancers or other phenotypic assays with Meinox small molecule library may generate novel and potent bioassay-specific compounds.

Binary-QSAR guided virtual screening of FDA approved drugs and compounds in clinical investigation against SARS-CoV-2 main protease

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the antiviral therapeutic activity binary QSAR model of the MetaCore/MetaDrug platform. Among 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 main protease (M^pro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docking scored compounds and following molecular mechanics generalized born surface area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivoxetil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 M^pro.