Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2

ATLAS: protein flexibility description from atomistic molecular dynamics simulations

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Repurposing Drugs for Inhibition against ALDH2 via a 2D/3D Ligand-Based Similarity Search and Molecular Simulation

Aldehyde dehydrogenase-2 (ALDH2) is a crucial enzyme participating in intracellular aldehyde metabolism and is acknowledged as a potential therapeutic target for the treatment of alcohol use disorder and other addictive behaviors. Using previously reported ALDH2 inhibitors of Daidzin, CVT-10216, and CHEMBL114083 as reference molecules, here we perform a ligand-based virtual screening of world-approved drugs via 2D/3D similarity search methods, followed by the assessments of molecular docking, toxicity prediction, molecular simulation, and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis. The 2D molecular fingerprinting of ECFP4 and FCFP4 and 3D molecule-shape-based USRCAT methods show good performances in selecting compounds with a strong binding behavior with ALDH2. Three compounds of Zeaxanthin (q = 0), Troglitazone (q = 0), and Sequinavir (q = +1 e) are singled out as potential inhibitors; Zeaxanthin can only be hit via USRCAT. These drugs displayed a stronger binding strength compared to the reported potent inhibitor CVT-10216. Sarizotan (q = +1 e) and Netarsudil (q = 0/+1 e) displayed a strong binding strength with ALDH2 as well, whereas they displayed a shallow penetration into the substrate-binding tunnel of ALDH2 and could not fully occupy it. This likely left a space for substrate binding, and thus they were not ideal inhibitors. The MM-PBSA results indicate that the selected negatively charged compounds from the similarity search and Vina scoring are thermodynamically unfavorable, mainly due to electrostatic repulsion with the receptor (q = -6 e for ALDH2). The electrostatic attraction with positively charged compounds, however, yielded very strong binding results with ALDH2. These findings reveal a deficiency in the modeling of electrostatic interactions (in particular, between charged moieties) in the virtual screening via the 2D/3D similarity search and molecular docking with the Vina scoring system.

A patent review on aldehyde dehydrogenase inhibitors: an overview of small molecule inhibitors from the last decade

INTRODUCTION

Physiological and pathophysiological effects arising from detoxification of aldehydes in humans implicate the enzyme aldehyde dehydrogenase (ALDH) gene family comprising of 19 isoforms. The main function of this enzyme family is to metabolize reactive aldehydes to carboxylic acids. Dysregulation of ALDH activity has been associated with various diseases. Extensive research has since gone into studying ALHD isozymes, their structural biology and developing small-molecule inhibitors. Novel chemical strategies to enhance the selectivity of ALDH inhibitors have now appeared.

AREAS COVERED

A comprehensive review of patent literature related to aldehyde dehydrogenase inhibitors in the last decade and half (2007-2022) is provided.

EXPERT OPINION

Aldehyde dehydrogenase (ALDH) is an important enzyme that metabolizes reactive exogenous and endogenous aldehydes in the body through NAD(P)±dependent oxidation. Hence this family of enzymes possess important physiological as well as toxicological roles in human body. Significant efforts in the field have led to potent inhibitors with approved clinical agents for alcohol use disorder therapy. Further clinical translation of novel compounds targeting ALDH inhibition will validate the promised therapeutic potential in treating many human diseases.The scientific/patent literature has been searched on SciFinder-n, Reaxys, PubMed, Espacenet and Google Patents. The search terms used were 'ALDH inhibitors', 'Aldehyde Dehydrogenase Inhibitors'.

Human Aldehyde Dehydrogenases: A Superfamily of Similar Yet Different Proteins Highly Related to Cancer

The superfamily of human aldehyde dehydrogenases (hALDHs) consists of 19 isoenzymes which are critical for several physiological and biosynthetic processes and play a major role in the organism's detoxification via the NAD(P) dependent oxidation of numerous endogenous and exogenous aldehyde substrates to their corresponding carboxylic acids. Over the last decades, ALDHs have been the subject of several studies as it was revealed that their differential expression patterns in various cancer types are associated either with carcinogenesis or promotion of cell survival. Here, we attempt to provide a thorough review of hALDHs' diverse functions and 3D structures with particular emphasis on their role in cancer pathology and resistance to chemotherapy. We are especially interested in findings regarding the association of structural features and their changes with effects on enzymes' functionalities. Moreover, we provide an updated outline of the hALDHs inhibitors utilized in experimental or clinical settings for cancer therapy. Overall, this review aims to provide a better understanding of the impact of ALDHs in cancer pathology and therapy from a structural perspective.

Aldehyde dehydrogenase in solid tumors and other diseases: Potential biomarkers and therapeutic targets

The family of aldehyde dehydrogenases (ALDHs) contains 19 isozymes and is involved in the oxidation of endogenous and exogenous aldehydes to carboxylic acids, which contributes to cellular and tissue homeostasis. ALDHs play essential parts in detoxification, biosynthesis, and antioxidants, which are of important value for cell proliferation, differentiation, and survival in normal body tissues. However, ALDHs are frequently dysregulated and associated with various diseases like Alzheimer's disease, Parkinson's disease, and especially solid tumors. Notably, the involvement of the ALDHs in tumor progression is responsible for the maintenance of the stem-cell-like phenotype, triggering rapid and aggressive clinical progressions. ALDHs have captured increasing attention as biomarkers for disease diagnosis and prognosis. Nevertheless, these require further longitudinal clinical studies in large populations for broad application. This review summarizes our current knowledge regarding ALDHs as potential biomarkers in tumors and several non-tumor diseases, as well as recent advances in our understanding of the functions and underlying molecular mechanisms of ALDHs in disease development. Finally, we discuss the therapeutic potential of ALDHs in diseases, especially in tumor therapy with an emphasis on their clinical implications.

Virtual Screening of FDA-Approved Drugs for Enhanced Binding with Mitochondrial Aldehyde Dehydrogenase

Mitochondrial aldehyde dehydrogenase (ALDH2) is a potential target for the treatment of substance use disorders such as alcohol addiction. Here, we adopted computational methods of molecular dynamics (MD) simulation, docking, and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis to perform a virtual screening of FDA-approved drugs, hitting potent inhibitors against ALDH2. Using MD-derived conformations as receptors, butenafine (net charge q = +1 e) and olaparib (q = 0) were selected as promising compounds with a low toxicity and a binding strength equal to or stronger than previously reported potent inhibitors of daidzin and CVT-10216. A few negatively charged compounds were also hit from the docking with the Autodock Vina software, while the MM-PBSA analysis yielded positive binding energies (unfavorable binding) for these compounds, mainly owing to electrostatic repulsion in association with a negatively charged receptor (q = -6 e for ALDH2 plus the cofactor NAD⁺). This revealed a deficiency of the Vina scoring in dealing with strong charge-charge interactions between binding partners, due to its built-in protocol of not using atomic charges for electrostatic interactions. These observations indicated a requirement of further verification using MD and/or MM-PBSA after docking prediction. The identification of key residues for the binding implied that the receptor residues at the bottom and entrance of the substrate-binding hydrophobic tunnel were able to offer additional interactions with different inhibitors such as π-π, π-alkyl, van der Waals contacts, and polar interactions, and that the rational use of these interactions is beneficial to the design of potent inhibitors against ALDH2.

Computational Investigation of Structural Basis for Enhanced Binding of Isoflavone Analogues with Mitochondrial Aldehyde Dehydrogenase

Isoflavone compounds are potent inhibitors against mitochondrial aldehyde dehydrogenase (ALDH2) for the treatment of alcoholism and drug addiction, and an in-depth understanding of the underlying structural basis helps design new inhibitors for enhanced binding. Here, we investigated the binding poses and strengths of eight isoflavone analogues (including CVT-10216 and daidzin) with ALDH2 via computational methods of molecular docking, molecular dynamics (MD) simulation, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), steered MD, and umbrella sampling. Neither the Vina scoring of docked and MD-sampled complexes nor the nonbonded protein-inhibitor interaction energy from MD simulations is able to reproduce the relative binding strength of the inhibitors compared to experimental IC₅₀ values. Considering the solvation contribution, MM-PBSA and relatively expensive umbrella sampling yield good performance for the relative binding (free) energies. The isoflavone skeleton prefers to form π-π stacking, π-sulfur, and π-alkyl interactions with planar (Phe and Trp) or sulfur-containing (Cys and Met) residues. The enhanced inhibition of CVT-10216 originates from both end groups of the isoflavone skeleton offering strong van der Waals contacts and from the methylsulfonamide group at the 4' position by hydrogen bonding (HB) with neighboring receptor residues. These results indicate that the hydrophobic binding tunnel of ALDH2 is larger than the isoflavone skeleton in length and thus an extended hydrophobic core is likely a premise for potent inhibitors.

Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking

Estrogen-related receptor α (ERRα), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERRα have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERRα determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERRα agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERRα with EC50 values ranging from 1.11 to 21.70 μM, with novel scaffolds different from known ERRα agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERRα with an IC50 of 0.82 μM. The predicted binding modes showed that these compounds were anchored in ERRα-LBP via interactions with several residues of ERRα. Overall, this study not only identified the novel potent ERRα agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERRα agonists.

Aldehyde dehydrogenase in regulatory T-cell development, immunity and cancer

The role of aldehyde dehydrogenase (ALDH) in carcinogenesis and resistance to cancer therapies is well known. Mounting evidence also suggests a potentially important role for ALDH in the induction and function of regulatory T (Treg) cells. Treg cells are important cells of the immune system involved in promoting immune tolerance and preventing aberrant immune responses to beneficial or non-harmful antigens. However, Treg cells also impair tumor immunity, leading to the progression of various carcinomas. ALDH expression and the subsequent production of retinoic acid by numerous cells, including dendritic cells, macrophages, eosinophils and epithelial cells, seems important in Treg induction and function in multiple organ systems. This is particularly evident in the gastrointestinal tract, pulmonary tract and skin, which are exposed to a myriad of environmental antigens and represent interfaces between the human body and the outside world. Expression of ALDH in Treg cells themselves may also be involved in the proliferation of these cells and resistance to certain cytotoxic therapies. Hence, inhibition of ALDH expression may be useful to treat cancer. Besides the direct effect of ALDH inhibition on carcinogenesis and resistance to cancer therapies, inhibition of ALDH could potentially augment the immune response to tumor antigens by inhibiting Treg induction, function and ability to promote immune tolerance to tumor cells in multiple cancer types.

Structural ensemble-based docking simulation and biophysical studies discovered new inhibitors of Hsp90 N-terminal domain

Heat shock protein 90 (Hsp90) is one of the most abundant cellular proteins and plays a substantial role in the folding of client proteins. The inhibition of Hsp90 has been regarded as an attractive therapeutic strategy for treating cancer because many oncogenic kinases are Hsp90 client proteins. In this study, we report new inhibitors that directly bind to N-terminal ATP-binding pocket of Hsp90. Optimized structure-based virtual screening predicted candidate molecules, which was followed by confirmation using biophysical and cell-based assays. Among the reported crystal structures, we chose the two structures that show the most favourable early enrichments of true-positives in the receiver operating characteristic curve. Four molecules showed significant changes in the signals of 2D [¹H, ¹⁵N] correlation NMR spectroscopy. Differential scanning calorimetry analysis supported the results indicating direct binding. Quantified dissociation constant values of the molecules, determined by a series of 2D NMR experiments, lie in the range of 0.1–33 μM. Growth inhibition assay with breast and lung cancer cells confirmed the cellular activities of the molecules. Cheminformatics revealed that the molecules share limited chemical similarities with known inhibitors. Molecular dynamics simulations detailed the putative binding modes of the inhibitors.

The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) has become an indispensable technique for hit identification at the early stage of drug discovery. However, the accuracy of current scoring functions is not high enough to confer success to every target and thus remains to be improved. Previously, we had developed binary pose filters (PFs) using knowledge derived from the protein-ligand interface of a single X-ray structure of a specific target. This novel approach had been validated as an effective way to improve ligand enrichment. Continuing from it, in the present work we attempted to incorporate knowledge collected from diverse protein-ligand interfaces of multiple crystal structures of the same target to build PF ensembles (PFEs). Toward this end, we first constructed a comprehensive data set to meet the requirements of ensemble modeling and validation. This set contains 10 diverse targets, 118 well-prepared X-ray structures of protein-ligand complexes, and large benchmarking actives/decoys sets. Notably, we designed a unique workflow of two-layer classifiers based on the concept of ensemble learning and applied it to the construction of PFEs for all of the targets. Through extensive benchmarking studies, we demonstrated that (1) coupling PFE with Chemgauss4 significantly improves the early enrichment of Chemgauss4 itself and (2) PFEs show greater consistency in boosting early enrichment and larger overall enrichment than our prior PFs. In addition, we analyzed the pairwise topological similarities among cognate ligands used to construct PFEs and found that it is the higher chemical diversity of the cognate ligands that leads to the improved performance of PFEs. Taken together, the results so far prove that the incorporation of knowledge from diverse protein-ligand interfaces by ensemble modeling is able to enhance the screening competence of SBVS scoring functions.

Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations

In silico virtual screening (VS) is a powerful hit identification technique used in drug discovery projects that aims to effectively distinguish true actives from inactive or decoy molecules. To better capture the dynamic behavior of protein drug targets, compound databases may be screened against an ensemble of protein conformations, which may be experimentally determined or generated computationally, i.e. via molecular dynamics (MD) simulations. Several studies have shown that conformations generated by MD are useful in identifying novel hit compounds, in part because structural rearrangements sampled during MD can provide novel targetable areas. However, it remains difficult to predict a priori when an MD conformation will outperform a VS against the crystal structure alone. Here, we assess whether MD conformations result in improved VS performance for six protein kinases. MD conformations are selected using three different methods, and their VS performances are compared to the corresponding crystal structures. Additionally, these conformations are used to train ensembles, and their VS performance is compared to the individual MD conformations and the corresponding crystal structures using receiver operating characteristic curve (ROC) metrics. We show that performing MD results in at least one conformation that offers better VS performance than the crystal structure, and that, while it is possible to train ensembles to outperform the crystal structure alone, the extent of this enhancement is target dependent. Lastly, we show that the optimal structural selection method is also target dependent and recommend optimizing virtual screens on a kinase-by-kinase basis to improve the likelihood of success.

Statistical analysis of EGFR structures' performance in virtual screening

In this work the ability of EGFR structures to distinguish true inhibitors from decoys in docking and MM-PBSA is assessed by statistical procedures. The docking performance depends critically on the receptor conformation and bound state. The enrichment of known inhibitors is well correlated with the difference between EGFR structures rather than the bound-ligand property. The optimal structures for virtual screening can be selected based purely on the complex information. And the mixed combination of distinct EGFR conformations is recommended for ensemble docking. In MM-PBSA, a variety of EGFR structures have identically good performance in the scoring and ranking of known inhibitors, indicating that the choice of the receptor structure has little effect on the screening.