MeLAD: an integrated resource for metalloenzyme-ligand associations

Twenty years in metalloprotein bioinformatics: A short history of a long journey

The study of the structure and function of metalloproteins is a central subject of inorganic biochemistry. Starting from the 2000s, computational methods have flanked experimental research by exploiting the ever-increasing computing power and the huge amount of data produced by omics technologies. In this article, we retrace the major advancements that brought bioinformatics from being of minor relevance to being an essential tool for today's inorganic biochemists, focusing on the contributions coming from the Magnetic Resonance Center (CERM) of Florence, where we have been developing for twenty years methods and resources to investigate metalloproteins with computational approaches.

Computational approaches for the identification of novel metal-binding pharmacophores: advances and challenges

Metalloenzymes are important therapeutic targets for a variety of human diseases. Computational approaches have recently emerged as effective tools to understand metal-ligand interactions and expand the structural diversity of both metalloenzyme inhibitors (MIs) and metal-binding pharmacophores (MBPs). In this review, we highlight key advances in currently available fine-tuning modeling methods and data-driven cheminformatic approaches. We also discuss major challenges to the recognition of novel MBPs and MIs. The evidence provided herein could expedite future computational efforts to guide metalloenzyme-based drug discovery.

On the hunt for metalloenzyme inhibitors: Investigating the presence of metal-coordinating compounds in screening libraries and chemical spaces

Metalloenzymes play vital roles in various biological processes, requiring the search for inhibitors to develop treatment options for diverse diseases. While compound library screening is a conventional approach, the exploration of virtual chemical spaces housing trillions of compounds has emerged as an alternative strategy. In this study, we investigated the suitability of selected screening libraries and chemical spaces for discovering inhibitors of metalloenzymes featuring common ions (Mg2+, Mn2+, and Zn2+). First, metal-coordinating groups from ligands interacting with ions in the Protein Data Bank were extracted. Subsequently, the prevalence of these groups in two focused screening libraries (Life Chemicals' chelator library, comprising 6,428 compounds, and Otava's chelator fragment library, with 1,784 fragments) as well as two chemical spaces (GalaXi and REAL space, containing billions of virtual products) was investigated. In total, 1,223 metal-coordinating groups were identified, with about a quarter of these groups found within the examined libraries and spaces. Our results indicate that these can serve as valuable starting points for drug discovery targeting metalloenzymes. In addition, this study suggests ways to improve libraries and spaces for better success in finding potential inhibitors for metalloenzymes.

Discovery of Novel Metalloenzyme Inhibitors Based on Property Characterization: Strategy and Application for HDAC1 Inhibitors

Metalloenzymes are ubiquitously present in the human body and are relevant to a variety of diseases. However, the development of metalloenzyme inhibitors is limited by low specificity and poor drug-likeness associated with metal-binding fragments (MBFs). A generalized drug discovery strategy was established, which is characterized by the property characterization of zinc-dependent metalloenzyme inhibitors (ZnMIs). Fifteen potential Zn2+-binding fragments (ZnBFs) were identified, and a customized pharmacophore feature was defined based on these ZnBFs. The customized feature was set as a required feature and applied to a search for novel inhibitors for histone deacetylase 1 (HDAC1). Ten potential HDAC1 inhibitors were recognized, and one of them (compound 9) was a known potent HDAC1 inhibitor. The results demonstrated the effectiveness of our strategy to identify novel inhibitors for zinc-dependent metalloenzymes.

Design, Synthesis and Bioactivity Evaluation of Heterocycle-Containing Mono- and Bisphosphonic Acid Compounds

Fosmidomycin (FOS) is a naturally occurring compound active against the 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) enzyme in the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, and using it as a template for lead structure design is an effective strategy to develop new active compounds. In this work, by replacing the hydroxamate unit of FOS with pyrazole, isoxazole and the related heterocycles that also have metal ion binding affinity, while retaining the monophosphonic acid in FOS or replacing it with a bisphosphonic acid group, heterocycle-containing mono- and bisphosphonic acid compounds as FOS analogs were designed. The key steps involved in the facile synthesis of these FOS analogs included the Michael addition of diethyl vinylphosphonate or tetraethyl vinylidenebisphosphonate to β-dicarbonyl compounds and the subsequent cyclic condensation with hydrazine or hydroxylamine. Two additional isoxazolinone-bearing FOS analogs were synthesized via the Michaelis-Becker reaction with diethyl phosphite as a key step. The bioactivity evaluation on model plants demonstrated that several compounds have better herbicidal activities compared to FOS, with the most active compound showing a 3.7-fold inhibitory activity on Arabidopsis thaliana, while on the roots and stalks of Brassica napus L. and Echinochloa crus-galli in a pre-emergence inhibitory activity test, the activities of this compound were found to be 3.2- and 14.3-fold and 5.4- and 9.4-fold, respectively, and in a post-emergency activity test on Amaranthus retroflexus and Echinochloa crus-galli, 2.2- and 2.0-fold inhibition activities were displayed. Despite the significant herbicidal activity, this compound exhibited a DXR inhibitory activity lower than that of FOS but comparable to that of other non-hydroxamate DXR inhibitors, and the dimethylallyl pyrophosphate rescue assay gave no statistical significance, suggesting that a different target might be involved in the inhibiting process. This work demonstrates that using bioisosteric replacement can be considered as a valuable strategy to discover new FOS analogs that may have high herbicidal activities.

Controllable Synthesis of Hemoglobin-Metal Phosphate Organic-Inorganic Hybrid Nanoflowers and Their Applications in Biocatalysis

In recent years, organic-inorganic hybrid nanoflower technology has become an effective method for enzyme immobilization. Here, seven hierarchical flower-like hemoglobin-phosphate organic-inorganic hybrid nanomaterials (Hb-M3(PO4)2·nH2O HNFs) were synthesized through an improved universal one-pot wet-chemical method, with Ca2+, Mn2+, Fe2+, Co2+, Ni2+, Cu2+ and Zn2+ as inorganic components. In this synthesis process, the metal cations are successively involved in the coordination reaction with Hb and the metathesis reaction to generate phosphate precipitation. The coordination ability of metal cations and the generation rate of phosphate precipitations were evaluated, then the progress of the two chemical reactions was controlled synchronously by adjusting the phosphate buffer (PB) concentration, and finally a flower-like structure conducive to substrate diffusion and transport was obtained. Due to the conformational transformation of hemoglobin and the abundant Cu2+/Fe3+ active sites, the hemoglobin-Cu3(PO4)2·3H2O nanoflowers have extremely high catalytic activity, which is ∼14 times that of Hb. Importantly, this method is suitable for the monometallic-ionic, polymetallic-ionic and polyvalent metal-ion nanoflowers, which broadens the chemical composition and structural diversity of nanoflowers.

Metal binding pharmacophore click-derived discovery of new broad-spectrum metallo-β-lactamase inhibitors

The emergence of metallo-β-lactamases (MBLs) confers resistance to nearly all the β-lactam antibiotics, including carbapenems. Currently, there is a lack of clinically useful MBL inhibitors, making it crucial to discover new inhibitor chemotypes that can potently target multiple clinically relevant MBLs. Herein we report a strategy that utilizes a metal binding pharmacophore (MBP) click approach to identify new broad-spectrum MBL inhibitors. Our initial investigation identified several MBPs including phthalic acid, phenylboronic acid and benzyl phosphoric acid, which were subjected to structural transformations using azide-alkyne click reactions. Subsequent structure-activity relationship analyses led to the identification of several potent broad-spectrum MBL inhibitors, including 73 that manifested IC₅₀ values ranging from 0.00012 μM to 0.64 μM against multiple MBLs. Co-crystallographic studies demonstrated the importance of MBPs in engaging with the MBL active site anchor pharmacophore features, and revealed the unusual two-molecule binding modes with IMP-1, highlighting the critical role of flexible active site loops in recognizing structurally diverse substrates/inhibitors. Our work provides new chemotypes for MBL inhibition and establishes a MBP click-derived paradigm for inhibitor discovery targeting MBLs as well as other metalloenzymes.

MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions

Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

MeCOM: A Method for Comparing Three-Dimensional Metalloenzyme Active Sites

Since metalloenzymes are a large collection of metal ion(s) dependent enzymes, comparison analyses of metalloenzyme active sites are critical for metalloenzyme de novo design, function investigation, and inhibitor development. Here, we report a method named MeCOM for comparing metalloenzyme active sites. It is characterized by metal ion(s) centric active site recognition and three-dimensional superimposition using α-carbon or pharmacophore features. The test results revealed that for the given metalloenzymes, MeCOM could effectively recognize the active sites, extract active site features, and superimpose the active sites; it also could correctly identify similar active sites, differentiate dissimilar active sites, and evaluate the similarity degree. Moreover, MeCOM showed potential to establish new associations between structurally distinct metalloenzymes by active site comparison. MeCOM is freely available at https://mecom.ddtmlab.org.

Targeting Metalloenzymes by Boron-Containing Metal-Binding Pharmacophores

Metalloenzymes have critical roles in a wide range of biological processes and are directly involved in many human diseases; hence, they are considered as important targets for therapeutic intervention. The specific characteristics of metal ion(s)-containing active sites make exploitation of metal-binding pharmacophores (MBPs) critical to inhibitor development targeting metalloenzymes. This Perspective focuses on boron-containing MBPs, which display unique binding modes with metalloenzyme active sites, particularly via mimicking native substrates or tetrahedral transition states. The design concepts regarding boron-containing MBPs are highlighted through the case analyses on five distinct classes of clinically relevant nucleophilic metalloenzymes from medicinal chemistry perspectives. The challenges (e.g., selectivity) faced by some boron-containing MBPs and possible strategies (e.g., bioisosteres) for metalloenzyme inhibitor transformation are also discussed.

The Mechanism of Metal Homeostasis in Plants: A New View on the Synergistic Regulation Pathway of Membrane Proteins, Lipids and Metal Ions

Heavy metal stress (HMS) is one of the most destructive abiotic stresses which seriously affects the growth and development of plants. Recent studies have shown significant progress in understanding the molecular mechanisms underlying plant tolerance to HMS. In general, three core signals are involved in plants' responses to HMS; these are mitogen-activated protein kinase (MAPK), calcium, and hormonal (abscisic acid) signals. In addition to these signal components, other regulatory factors, such as microRNAs and membrane proteins, also play an important role in regulating HMS responses in plants. Membrane proteins interact with the highly complex and heterogeneous lipids in the plant cell environment. The function of membrane proteins is affected by the interactions between lipids and lipid-membrane proteins. Our review findings also indicate the possibility of membrane protein-lipid-metal ion interactions in regulating metal homeostasis in plant cells. In this review, we investigated the role of membrane proteins with specific substrate recognition in regulating cell metal homeostasis. The understanding of the possible interaction networks and upstream and downstream pathways is developed. In addition, possible interactions between membrane proteins, metal ions, and lipids are discussed to provide new ideas for studying metal homeostasis in plant cells.

AncPhore: A versatile tool for anchor pharmacophore steered drug discovery with applications in discovery of new inhibitors targeting metallo-β-lactamases and indoleamine/tryptophan 2,3-dioxygenases

We herein describe AncPhore, a versatile tool for drug discovery, which is characterized by pharmacophore feature analysis and anchor pharmacophore (i.e., most important pharmacophore features) steered molecular fitting and virtual screening. Comparative analyses of numerous protein–ligand complexes using AncPhore revealed that anchor pharmacophore features are biologically important, commonly associated with protein conservative characteristics, and have significant contributions to the binding affinity. Performance evaluation of AncPhore showed that it had substantially improved prediction ability on different types of target proteins including metalloenzymes by considering the specific contributions and diversity of anchor pharmacophore features. To demonstrate the practicability of AncPhore, we screened commercially available chemical compounds and discovered a set of structurally diverse inhibitors for clinically relevant metallo-β-lactamases (MBLs); of them, 4 and 6 manifested potent inhibitory activity to VIM-2, NDM-1 and IMP-1 MBLs. Crystallographic analyses of VIM-2:4 complex revealed the precise inhibition mode of 4 with VIM-2, highly consistent with the defined anchor pharmacophore features. Besides, we also identified new hit compounds by using AncPhore for indoleamine/tryptophan 2,3-dioxygenases (IDO/TDO), another class of clinically relevant metalloenzymes. This work reveals anchor pharmacophore as a valuable concept for target-centered drug discovery and illustrates the potential of AncPhore to efficiently identify new inhibitors for different types of protein targets.

X-ray Structure-Guided Discovery of a Potent, Orally Bioavailable, Dual Human Indoleamine/Tryptophan 2,3-Dioxygenase (hIDO/hTDO) Inhibitor That Shows Activity in a Mouse Model of Parkinson's Disease

Human indoleamine 2,3-dioxygenase 1 (hIDO1) and tryptophan 2,3-dioxygenase (hTDO) have been closely linked to the pathogenesis of Parkinson's disease (PD); nevertheless, development of dual hIDO1 and hTDO inhibitors to evaluate their potential efficacy against PD is still lacking. Here, we report biochemical, biophysical, and computational analyses revealing that 1H-indazole-4-amines inhibit both hIDO1 and hTDO by a mechanism involving direct coordination with the heme ferrous and ferric states. Crystal structure-guided optimization led to 23, which manifested IC50 values of 0.64 and 0.04 μM to hIDO1 and hTDO, respectively, and had good pharmacokinetic properties and brain penetration in mice. 23 showed efficacy against the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced mouse motor coordination deficits, comparable to Madopar, an anti-PD medicine. Further studies revealed that different from Madopar, 23 likely has specific anti-PD mechanisms involving lowering IDO1 expression, alleviating dopaminergic neurodegeneration, reducing inflammatory cytokines and quinolinic acid in mouse brain, and increasing kynurenic acid in mouse blood.

Discovery of [1,2,4]Triazole Derivatives as New Metallo-β-Lactamase Inhibitors

The emergence and spread of metallo-β-lactamase (MBL)-mediated resistance to β-lactam antibacterials has already threatened the global public health. A clinically useful MBL inhibitor that can reverse β-lactam resistance has not been established yet. We here report a series of [1,2,4]triazole derivatives and analogs, which displayed inhibition to the clinically relevant subclass B1 (Verona integron-encoded MBL-2) VIM-2. 3-(4-Bromophenyl)-6,7-dihydro-5H-[1,2,4]triazolo [3,4-b][1,3]thiazine (5l) manifested the most potent inhibition with an IC₅₀ (half-maximal inhibitory concentration) value of 38.36 μM. Investigations of 5l against other B1 MBLs and the serine β-lactamases (SBLs) revealed the selectivity to VIM-2. Molecular docking analyses suggested that 5l bound to the VIM-2 active site via the triazole involving zinc coordination and made hydrophobic interactions with the residues Phe61 and Tyr67 on the flexible L1 loop. This work provided new triazole-based MBL inhibitors and may aid efforts to develop new types of inhibitors combating MBL-mediated resistance.