Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins

DisDock: A Deep Learning Method for Metal Ion-Protein Redocking

The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a metalloprotein binds and the binding structure are still not readily available, even with the predicted protein structure. In this study, we present DisDock, a deep learning method for predicting protein-metal docking. DisDock takes distogram of randomly initialized protein-ligand configuration as input and outputs the distogram of the predicted binding complex. It combines the U-net architecture with self-attention modules to enhance model performance. Taking inspiration from the physical principle that atoms in closer proximity display a stronger mutual attraction, this predictor capitalizes on geometric information to uncover latent characteristics indicative of atom interactions. To train our model, we employ a high-quality metalloprotein dataset sourced from the Mother of All Databases (MOAD). Experimental results demonstrate that our approach outperforms other existing methods in prediction accuracy for various types of metal ions.

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.

Studying Peptide-Metal Ion Complex Structures by Solution-State NMR

Metal chelation can provide structural stability and form reactive centers in metalloproteins. Approximately one third of known protein structures are metalloproteins, and metal binding, or the lack thereof, is often implicated in disease, making it necessary to be able to study these systems in detail. Peptide-metal complexes are both present in nature and can provide a means to focus on the binding region of a protein and control experimental variables to a high degree. Structural studies of peptide complexes with metal ions by nuclear magnetic resonance (NMR) were surveyed for all the essential metal complexes and many non-essential metal complexes. The various methods used to study each metal ion are presented together with examples of recent research. Many of these metal systems have been individually reviewed and this current overview of NMR studies of metallopeptide complexes aims to provide a basis for inspiration from structural studies and methodology applied in the field.

A reinforcement learning approach for protein-ligand binding pose prediction

Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu²⁺), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO₄^2-), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.

An Effective Swarm Intelligence Optimization Algorithm for Flexible Ligand Docking

In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites.

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.

Bioluminescence Color-Tuning Firefly Luciferases: Engineering and Prospects for Real-Time Intracellular pH Imaging and Heavy Metal Biosensing

Firefly luciferases catalyze the efficient production of yellow-green light under normal physiological conditions, having been extensively used for bioanalytical purposes for over 5 decades. Under acidic conditions, high temperatures and the presence of heavy metals, they produce red light, a property that is called pH-sensitivity or pH-dependency. Despite the demand for physiological intracellular biosensors for pH and heavy metals, firefly luciferase pH and metal sensitivities were considered drawbacks in analytical assays. We first demonstrated that firefly luciferases and their pH and metal sensitivities can be harnessed to estimate intracellular pH variations and toxic metal concentrations through ratiometric analysis. Using Macrolampis sp2 firefly luciferase, the intracellular pH could be ratiometrically estimated in bacteria and then in mammalian cells. The luciferases of Macrolampis sp2 and Cratomorphus distinctus fireflies were also harnessed to ratiometrically estimate zinc, mercury and other toxic metal concentrations in the micromolar range. The temperature was also ratiometrically estimated using firefly luciferases. The identification and engineering of metal-binding sites have allowed the development of novel luciferases that are more specific to certain metals. The luciferase of the Amydetes viviani firefly was selected for its special sensitivity to cadmium and mercury, and for its stability at higher temperatures. These color-tuning luciferases can potentially be used with smartphones for hands-on field analysis of water contamination and biochemistry teaching assays. Thus, firefly luciferases are novel color-tuning sensors for intracellular pH and toxic metals. Furthermore, a single luciferase gene is potentially useful as a dual bioluminescent reporter to simultaneously report intracellular ATP and/or luciferase concentrations luminometrically, and pH or metal concentrations ratiometrically, providing a useful tool for real-time imaging of intracellular dynamics and stress.

GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms

Metals are present in >30% of proteins found in nature and assist them to perform important biological functions, including storage, transport, signal transduction and enzymatic activity. Traditional and experimental techniques for metal-binding site prediction are usually costly and time-consuming, making computational tools that can assist in these predictions of significant importance. Here we present Genetic Active Site Search (GASS)-Metal, a new method for protein metal-binding site prediction. The method relies on a parallel genetic algorithm to find candidate metal-binding sites that are structurally similar to curated templates from M-CSA and MetalPDB. GASS-Metal was thoroughly validated using homologous proteins and conservative mutations of residues, showing a robust performance. The ability of GASS-Metal to identify metal-binding sites was also compared with state-of-the-art methods, outperforming similar methods and achieving an MCC of up to 0.57 and detecting up to 96.1% of the sites correctly. GASS-Metal is freely available at https://gassmetal.unifei.edu.br. The GASS-Metal source code is available at https://github.com/sandroizidoro/gassmetal-local.

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.

Successes and challenges in multiscale modelling of artificial metalloenzymes: the case study of POP-Rh2 cyclopropanase

Molecular modelling applications in metalloenzyme design are still scarce due to a series of challenges. On top of that, the simulations of metal-mediated binding and the identification of catalytic competent geometries require both large conformational exploration and simulation of fine electronic properties. Here, we demonstrate how the incorporation of new tools in multiscale strategies, namely substrate diffusion exploration, allows taking a step further. As a showcase, the enantioselective profiles of the most outstanding variants of an artificial Rh₂-based cyclopropanase (GSH, HFF and RFY) developed by Lewis and co-workers (Nat. Commun., 2015, 6, 7789 and Nat. Chem., 2018, 10, 318-324) have been rationalized. DFT calculations on the free-cofactor-mediated process identify the carbene insertion and the cyclopropanoid formation as crucial events, the latter being the enantiodetermining step, which displays up to 8 competitive orientations easily altered by the protein environment. The key intermediates of the reaction were docked into the protein scaffold showing that some mutated residues have direct interaction with the cofactor and/or the co-substrate. These interactions take the form of a direct coordination of Rh in GSH and HFF and a strong hydrophobic patch with the carbene moiety in RFY. Posterior molecular dynamics sustain that the cofactor induces global re-arrangements of the protein. Finally, massive exploration of substrate diffusion, based on the GPathFinder approach, defines this event as the origin of the enantioselectivity in GSH and RFY. For HFF, fine molecular dockings suggest that it is likely related to local interactions upon diffusion. This work shows how modelling of long-range mutations on the catalytic profiles of metalloenzymes may be unavoidable and software simulating substrate diffusion should be applied.

Integrated experimental/computational approaches to characterize the systems formed by vanadium with proteins and enzymes

An integrated instrumental/computational approach to characterize metallodrug–protein adducts at the molecular level is reviewed. A series of applications are described, focusing on potential vanadium drugs with a generalization to other metals.

BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

With a large amount of research dedicated to decoding how metallic species bind to proteins, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal-binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parameterization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 90 metal-binding X-ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems whose structures (either experimental or theoretical) are not optimal for metal-binding sites. We report here its application on three different challenging cases: (i) the modulation of metal-binding sites during conformational transition in human serum albumin, (ii) the identification of possible routes of metal migration in hemocyanins, and (iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. BioMetAll is an open-source application available at https://github.com/insilichem/biometall.

New VIV, VIVO, VVO, and VVO2 Systems: Exploring their Interconversion in Solution, Protein Interactions, and Cytotoxicity

The synthesis and characterization of one oxidoethoxidovanadium(V) [V^VO(L¹)(OEt)] (1) and two nonoxidovanadium(IV) complexes, [V^IV(L^2-3)₂] (2 and 3), with aroylhydrazone ligands incorporating naphthalene moieties, are reported. The synthesized oxido and nonoxido vanadium complexes are characterized by various physicochemical techniques, and their molecular structures are solved by single crystal X-ray diffraction (SC-XRD). This revealed that in 1 the geometry around the vanadium atom corresponds to a distorted square pyramid, with a O₄N coordination sphere, whereas that of the two nonoxido V^IV complexes 2 and 3 corresponds to a distorted trigonal prismatic arrangement with a O₄N₂ coordination sphere around each "bare" vanadium center. In aqueous solution, the V^VO moiety of 1 undergoes a change to V^VO₂ species_, yielding [V^VO₂(L¹)]^- (1'), while the nonoxido V^IV-compounds 2 and 3 are partly converted into their corresponding V^IVO complexes, [V^IVO(L^2-3)(H₂O)] (2' and 3'). Interaction of these V^VO₂, V^IVO, and V^IV systems with two model proteins, ubiquitin (Ub) and lysozyme (Lyz), is investigated through docking approaches, which suggest the potential binding sites: the interaction is covalent for species 2' and 3', with the binding to Glu16, Glu18, and Asp21 for Ub, and His15 for Lyz, and it is noncovalent for species 1', 2, and 3, with the surface residues of the proteins. The ligand precursors and complexes are also evaluated for their in vitro antiproliferative activity against ovarian (A2780) and prostate (PC3) human cancer cells and in normal fibroblasts (V79) to check the selectivity of the compounds for cancer cells.

Biospeciation of Potential Vanadium Drugs of Acetylacetonate in the Presence of Proteins

Among vanadium compounds with potential medicinal applications, [V^IVO(acac)₂] is one of the most promising for its antidiabetic and anticancer activity. In the organism, however, interconversion of the oxidation state to +III and +V and binding to proteins are possible. In this report, the transformation of V^III(acac)₃, V^IVO(acac)₂, and V^VO₂(acac)2- after the interaction with two model proteins, lysozyme (Lyz) and ubiquitin (Ub), was studied with ESI-MS (ElectroSpray Ionization-Mass Spectroscopy), EPR (Electron Paramagnetic Resonance), and computational (docking) techniques. It was shown that, in the metal concentration range close to that found in the organism (15–250 μM), V^III(acac)₃ is oxidized to V^IVO(acac)⁺ and V^IVO(acac)₂, which—in their turn—interact with proteins to give n[V^IVO(acac)]–Protein and n[V^IVO(acac)₂]–Protein adducts. Similarly, the complex in the +IV oxidation state, V^IVO(acac)₂, dissociates to the mono-chelated species V^IVO(acac)⁺ which binds to Lyz and Ub. Finally, V^VO₂(acac)2- undergoes complete dissociation to give the 'bare' V^VO2+ ion that forms adducts n[V^VO₂]–Protein with n = 1–3. Docking calculations allowed the prediction of the residues involved in the metal binding. The results suggest that only the V^IVO complex of acetylacetonate survives in the presence of proteins and that its adducts could be the species responsible of the observed pharmacological activity, suggesting that in these systems V^IVO²⁺ ion should be used in the design of potential vanadium drugs. If V^III or V^VO₂ potential active complexes had to be designed, the features of the organic ligand must be adequately modulated to obtain species with high redox and thermodynamic stability to prevent oxidation and dissociation.

Molecular Modeling for Artificial Metalloenzyme Design and Optimization

Artificial metalloenzymes (ArMs) are obtained by inserting homogeneous catalysts into biological scaffolds and are among the most promising strategies in the quest for new-to-nature biocatalysts. The quality of their design strongly depends on how three partners interact: the biological host, the "artificial cofactor," and the substrate. However, structural characterization of functional artificial metalloenzymes by X-ray or NMR is often partial, elusive, or absent. How the cofactor binds to the protein, how the receptor reorganizes upon the binding of the cofactor and the substrate, and which are the binding mode(s) of the substrate for the reaction to proceed are key questions that are frequently unresolved yet crucial for ArM design. Such questions may eventually be solved by molecular modeling but require a step change beyond the current state-of-the-art methodologies.Here, we summarize our efforts in the study of ArMs, presenting both the development of computational strategies and their application. We first focus on our integrative computational framework that incorporates a variety of methods such as protein-ligand docking, classical molecular dynamics (MD), and pure quantum mechanical (QM) methods, which, when properly combined, are able to depict questions that range from host-cofactor binding predictions to simulations of entire catalytic mechanisms. We also pay particular attention to the protein-ligand docking strategies that we have developed to accurately predict the binding of transition metal-containing molecules to proteins. While this aspect is fundamental to many bioinorganic fields beyond ArMs, it has been disregarded from the molecular modeling landscape until very recently.Next we describe how to apply this computational framework to particular ArMs including systems previously characterized experimentally as well as others where computation served to guide the design. We start with the prediction of the interactions between homogeneous catalysts and biological hosts. Protein-ligand docking is pivotal at that stage, but it needs to be combined with QM/MM or MD approaches when the binding of the cofactor implies significant conformational changes of the protein or involve changes of the electronic state of the metal.Then, we summarize molecular modeling studies aimed at identifying cofactor-substrate arrangements inside the ArM active pocket that are consistent with its reactivity. These calculations stand on "Theozyme"-like dockings, MD-refined or not, which provide molecular rationale of the catalytic profiles of the artificial systems.In the third section, we present case studies to decode the entire catalytic mechanism of two ArMs: (1) an iridium based asymmetric transfer hydrogenase obtained by insertion of Noyori's catalyst into streptavidin and (2) a metallohydrolase achieved by including a receptor. Transition states, second coordination sphere effects, as well as motions of the cofactors are identified as drivers of the enantiomeric profiles.Finally, we report computer-aided designs of ArMs to guide experiments toward chemical and mutational changes that improve their activity and/or enantioselective profiles and expand toward future directions.

GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm

Protein-ligand docking is a widely used method to generate solutions for the binding of a small molecule with its target in a short amount of time. However, these methods provide identification of physically sound protein-ligand complexes without a complete view of the binding process dynamics, which has been recognized to be a major discriminant in binding affinity and ligand selectivity. In this paper, a novel piece of open-source software to approach this problem is presented, called GPathFinder. It is built as an extension of the modular GaudiMM platform and is able to simulate ligand diffusion pathways at atomistic level. The method has been benchmarked on a set of 20 systems whose ligand-binding routes were studied by other computational tools or suggested from experimental "snapshots". In all of this set, GPathFinder identifies those channels that were already reported in the literature. Interestingly, the low-energy pathways in some cases indicate novel possible binding routes. To show the usefulness of GPathFinder, the analysis of three case systems is reported. We believe that GPathFinder is a software solution with a good balance between accuracy and computational cost, and represents a step forward in extending protein-ligand docking capacities, with implications in several fields such as drug or enzyme design.

Integrated ESI-MS/EPR/computational characterization of the binding of metal species to proteins: vanadium drug–myoglobin application

An integrated strategy based on ESI-MS spectrometry, EPR spectroscopy and docking/QM computational methods is applied to the systems formed by V^IVO²⁺ ions and four potential V^IVOL₂ drugs and myoglobin. This approach is generizable to other metals and proteins.