Metal Binding Sites In Proteins Research Articles

MetalTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in Protein Metal-Binding Sites.

The critical importance of accurately predicting mutations in protein metal-binding sites for advancing drug discovery and enhancing disease diagnostic processes cannot be overstated. In response to this imperative, MetalTrans emerges as an accurate predictor for disease-associated mutations in protein metal-binding sites. The core innovation of MetalTrans lies in its seamless integration of multifeature splicing with the Transformer framework, a strategy that ensures exhaustive feature extraction. Central to MetalTrans's effectiveness is its deep feature combination strategy, which merges evolutionary-scale modeling amino acid embeddings with ProtTrans embeddings, thus shedding light on the biochemical properties of proteins. Employing the Transformer component, MetalTrans leverages the self-attention mechanism to delve into higher-level representations. Utilizing mutation site information for feature fusion not only enriches the feature set but also sidesteps the common pitfall of overestimation linked to protein sequence-based predictions. This nuanced approach to feature fusion is a key differentiator, enabling MetalTrans to outperform existing methods significantly, as evidenced by comparative analyses. Our evaluations across varied metal binding site data sets (specifically Zn, Ca, Mg, and Mix) underscore MetalTrans's superior performance, which achieved the average AUC values of 0.971, 0.965, 0.980, and 0.945 on multiple 5-fold cross-validation, respectively. Remarkably, against the multichannel convolutional neural network method on a benchmark independent test set, MetalTrans demonstrated unparalleled robustness and superiority, boasting the AUC score of 0.998 on multiple 5-fold cross-validation. Our comprehensive examination of the predicted outcomes further confirms the effectiveness of the model. The source codes, data sets, and prediction results for MetalTrans can be accessed for academic usage at https://github.com/EduardWang/MetalTrans.

A database overview of metal-coordination distances in metalloproteins.

Metalloproteins are ubiquitous in all living organisms and take part in a very wide range of biological processes. For this reason, their experimental characterization is crucial to obtain improved knowledge of their structure and biological functions. The three-dimensional structure represents highly relevant information since it provides insight into the interaction between the metal ion(s) and the protein fold. Such interactions determine the chemical reactivity of the bound metal. The available PDB structures can contain errors due to experimental factors such as poor resolution and radiation damage. A lack of use of distance restraints during the refinement and validation process also impacts the structure quality. Here, the aim was to obtain a thorough overview of the distribution of the distances between metal ions and their donor atoms through the statistical analysis of a data set based on more than 115 000 metal-binding sites in proteins. This analysis not only produced reference data that can be used by experimentalists to support the structure-determination process, for example as refinement restraints, but also resulted in an improved insight into how protein coordination occurs for different metals and the nature of their binding interactions. In particular, the features of carboxylate coordination were inspected, which is the only type of interaction that is commonly present for nearly all metals.

Metal-binding amino acid ligands commonly found in metalloproteins differentially fractionate copper isotopes

Copper (Cu) is a cofactor in numerous key proteins and, thus, an essential element for life. In biological systems, Cu isotope abundances shift with metabolic and homeostatic state. However, the mechanisms underpinning these isotopic shifts remain poorly understood, hampering use of Cu isotopes as biomarkers. Computational predictions suggest that isotope fractionation occurs when proteins bind Cu, with the magnitude of this effect dependent on the identity and arrangement of the coordinating amino acids. This study sought to constrain equilibrium isotope fractionation values for Cu bound by common amino acids at protein metal-binding sites. Free and bound metal ions were separated via Donnan dialysis using a cation-permeable membrane. Isotope ratios of pre- and post-dialysis solutions were measured by MC-ICP-MS following purification. Sulfur ligands (cysteine) preferentially bound the light isotope (63Cu) relative to water (Δ65Cucomplex-free = − 0.48 ± 0.18‰) while oxygen ligands favored the heavy isotope (65Cu; + 0.26 ± 0.04‰ for glutamate and + 0.16 ± 0.10‰ for aspartate). Binding by nitrogen ligands (histidine) imparted no isotope effect (− 0.01 ± 0.04‰). This experimental work unequivocally demonstrates that amino acids differentially fractionate Cu isotopes and supports the hypothesis that metalloprotein biosynthesis affects the distribution of transition metal isotopes in biological systems.

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.

An ensemble 3D deep-learning model to predict protein metal-binding site

Predicting metal-binding sites in proteins is critical for understanding the protein’s biological function. Here, we develop an ensemble deep convolutional neural network (CNN) method for predicting metal-binding sites based on their three-dimensional (3D) structure. We build multi-channel 3D voxels based on biophysical characteristics obtained from raw atom coordinates of each protein-binding pocket. Then, we use these 3D voxels as the input of an ensemble 3D CNN model. We train and evaluate the model using a curated dataset of 3D protein structures. Our proposed model shows high performance in predicting metal-binding sites for Zn, Fe, Mg, Mn, Ca, and Na. Our approach offers a framework to use 3D spatial features to train 3D-CNN, which may be used to predict complicated metal-binding sites directly from their biophysical characteristics. The source code and webserver of the model are publicly available.

Comparative cisplatin reactivity towards human Zn7-metallothionein-2 and MTF-1 zinc fingers: potential implications in anticancer drug resistance.

Cis-diamminedichloroplatinum(II) (cisplatin) is a widely used metal-based chemotherapeutic drug for the treatment of cancers. However, intrinsic and acquired drug resistance limit the efficacy of cisplatin-based treatments. Increased production of intracellular thiol-rich molecules, in particular metallothioneins (MTs), which form stable coordination complexes with the electrophilic cisplatin, results in cisplatin sequestration leading to pre-target resistance. MT-1/-2 are overexpressed in cancer cells, and their expression is controlled by the metal response element (MRE)-binding transcription factor-1 (MTF-1), featuring six Cys2His2-type zinc fingers which, upon zinc metalation, recognize specific MRE sequences in the promoter region of MT genes triggering their expression. Cisplatin can efficiently react with protein metal binding sites featuring nucleophilic cysteine and/or histidine residues, including MTs and zinc fingers proteins, but the preferential reactivity towards specific targets with competing binding sites cannot be easily predicted. In this work, by in vitro competition reactions, we investigated the thermodynamic and kinetic preferential reactivity of cisplatin towards human Zn7MT-2, each of the six MTF-1 zinc fingers, and the entire human MTF-1 zinc finger domain. By spectroscopic, spectrometric, and electrophoretic mobility shift assays (EMSA), we demonstrated that cisplatin preferentially reacts with Zn7MT-2 to form Cys4-Pt(II) complexes, resulting in zinc release from MT-2. Zinc transfer from MT-2 to the MTF-1 triggers MTF-1 metalation, activation, and binding to target MRE sequences, as demonstrated by EMSA with DNA oligonucleotides. The cisplatin-dependent MT-mediated MTF-1 activation leading to apo-MT overexpression potentially establishes one of the molecular mechanisms underlying the development and potentiation of MT-mediated pre-target resistance.

Incorporation of metal-chelating unnatural amino acids into halotag for allylic deamination

The potential of artificial metalloenzymes has led to an increase in interest for the design of novel metal-binding sites in proteins. Metal-chelating unnatural amino acids offer an auspicious solution to engineer active metal sites in a defined way. Herein, we describe the introduction of four metal-chelating unnatural amino acids into HaloTag, an attractive scaffold for the assembly of functional artificial metalloenzymes. HaloTag, engineered with 2-amino-3-(8-hydroxyquinolin-5-yl)propanoic acid (HQ-Ala-1) was used to assemble an artificial metalloenzyme for improved allylic deamination upon complementation with [(η5-C5H5)Ru(MeCN)3]+.

Identifying metal binding amino acids based on backbone geometries as a tool for metalloprotein engineering.

Metal cofactors within proteins perform a versatile set of essential cellular functions. In order to take advantage of the diverse functionality of metalloproteins, researchers have been working to design or modify metal binding sites in proteins to rationally tune the function or activity of the metal cofactor. This study has performed an analysis on the backbone atom geometries of metal-binding amino acids among 10 different metal binding sites within the entire protein data bank. A set of 13 geometric parameters (features) was identified that is capable of predicting the presence of a metal cofactor in the protein structure with overall accuracies of up to 97% given only the relative positions of their backbone atoms. The decision tree machine-learning algorithm used can quickly analyze an entire protein structure for the presence of sets of primary metal coordination spheres upon mutagenesis, independent of their original amino acid identities. The methodology was designed for application in the field of metalloprotein engineering. A cluster analysis using the data set was also performed and demonstrated that the features chosen are useful for identifying clusters of structurally similar metal-binding sites.

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.

Molecular Mechanisms and Aspects on the Role of Neuropeptide Y as a Zn2+ and Cu2+ Chelator.

The concept of metal chelation is based on simple coordination chemistry. The development of an ideal metal chelator that completely and selectively removes toxic metals from a specific metal binding site in proteins is required to prevent and or inhibit a variety of diseases, among them neurodegenerative diseases. This work examines neuropeptide Y (NPY) as a Zn2+ and Cu2+ chelator agent. NPY is a natural peptide that is produced in the human body; therefore, it is not a toxic agent and the complex that it forms is not toxic as well. Our simulations reveal that NPY has an efficient Zn2+ chelation activity but is less effective in chelating Cu2+. Moreover, while NPY demonstrates several conformations, the metal chelation occurs more efficiently in its native structure. Beyond the exploration of the activity of NPY as a Zn2+ and Cu2+ chelator agent, this work provides an insight into the molecular mechanisms of the chelation of these metals at the molecular level. The outcomes from this work may guide future experimental studies to examine NPY in metal chelation therapy for neurodegenerative diseases.

Mass Spectrometry-Based Structural Analysis of Cysteine-Rich Metal-Binding Sites in Proteins with MetaOdysseus R Software.

Identification of metal-binding sites in proteins and understanding metal-coupled protein folding mechanisms are aspects of high importance for the structure-to-function relationship. Mass spectrometry (MS) has brought a powerful adjunct perspective to structural biology, obtaining from metal-to-protein stoichiometry to quaternary structure information. Currently, the different experimental and/or instrumental setups usually require the use of multiple data analysis software, and in some cases, they lack some of the main data analysis steps (MS processing, scoring, identification). Here, we present a comprehensive data analysis pipeline that addresses charge-state deconvolution, statistical scoring, and mass assignment for native MS, bottom-up, and native top-down with emphasis on metal–protein complexes. We have evaluated all of the approaches using assemblies of increasing complexity, including free and chemically labeled proteins, from low- to high-resolution MS. In all cases, the results have been compared with common software and proved how MetaOdysseus outperformed them.

Metallomics reveals a persisting impact of cadmium on the evolution of metal-selective snail metallothioneins.

The tiny contribution of cadmium (Cd) to the composition of the earth's crust contrasts with its high biological significance, owing mainly to the competition of Cd with the essential zinc (Zn) for suitable metal binding sites in proteins. In this context it was speculated that in several animal lineages, the protein family of metallothioneins (MTs) has evolved to specifically detoxify Cd. Although the multi-functionality and heterometallic composition of MTs in most animal species does not support such an assumption, there are some exceptions to this role, particularly in animal lineages at the roots of animal evolution. In order to substantiate this hypothesis and to further understand MT evolution, we have studied MTs of different snails that exhibit clear Cd-binding preferences in a lineage-specific manner. By applying a metallomics approach including 74 MT sequences from 47 gastropod species, and by combining phylogenomic methods with molecular, biochemical, and spectroscopic techniques, we show that Cd selectivity of snail MTs has resulted from convergent evolution of metal-binding domains that significantly differ in their primary structure. We also demonstrate how their Cd selectivity and specificity has been optimized by the persistent impact of Cd through 430 million years of MT evolution, modifying them upon lineage-specific adaptation of snails to different habitats. Overall, our results support the role of Cd for MT evolution in snails, and provide an interesting example of a vestigial abiotic factor directly driving gene evolution. Finally, we discuss the potential implications of our findings for studies devoted to the understanding of mechanisms leading to metal specificity in proteins, which is important when designing metal-selective peptides.

How mechanical forces can modulate the metal affinity and selectivity of metal binding sites in proteins.

Mechanical forces play a key role in essential biological processes including cell growth, division, deformation, adhesion, migration and intra-cell interactions. The effect of mechanical forces in modulating the structure and properties of metal-occupied protein binding sites has not been fully understood. Here, by employing a combination of density functional theory (DFT) calculations and polarizable continuum model (PCM) computations applied on model metal-loaded EF-hand binding sites, we shed light on the intimate mechanism of the Mg2+/Ca2+ competition impacted by the application of mechanical stimuli. Applying mechanical force with a specific directionality and magnitude may shift the balance between the competing metal cations in favor of a given contestant depending on the composition and strength of the coordinative bonds and robustness of the metal binding site. Furthermore, the calculations help to determine the range of mechanical rupture forces typical for these structures: these range from 0.4 to 1.5 nN depending on the nature of the metal and amino acid residue. This positions the strength of the Mg2+-O and Ca2+-O coordinative bonds between that of typical covalent and hydrogen bonds. The bonds between the metal cation and the charged amino acid residue rupture at higher forces (∼1.2-1.5 nN) relative to those of their metal-noncharged counterparts which dissociate at ∼0.2-0.4 nN.

Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach

Metalloproteins play important roles in many biological processes. Mutations at the metal-binding sites may functionally disrupt metalloproteins, initiating severe diseases; however, there seemed to be no effective approach to predict such mutations until now. Here we develop a deep learning approach to successfully predict disease-associated mutations that occur at the metal-binding sites of metalloproteins. We generate energy-based affinity grid maps and physiochemical features of the metal-binding pockets (obtained from different databases as spatial and sequential features) and subsequently implement these features into a multichannel convolutional neural network. After training the model, the multichannel convolutional neural network can successfully predict disease-associated mutations that occur at the first and second coordination spheres of zinc-binding sites with an area under the curve of 0.90 and an accuracy of 0.82. Our approach stands for the first deep learning approach for the prediction of disease-associated metal-relevant site mutations in metalloproteins, providing a new platform to tackle human diseases. Metals can bind to proteins to fulfil important biological functions. Predicting the features of mutated binding sites can thus help us understand the connection between specific mutations and their role in diseases.

Probing Metal Ion Discrimination in a Protein Designed to Bind Uranyl Cation With Femtomolar Affinity.

The design of metal-binding sites in proteins that combine high affinity with high selectivity for the desired metal ion remains a challenging goal. Recently, a protein designed to display femtomolar affinity for , dubbed “Super Uranyl-binding Protein” (SUP), was described, with potential applications for removing in water. Although it discriminated most metal ions present in seawater, the protein showed a surprisingly high affinity for Cu2+ ions. Here, we have investigated Cu2+ binding to SUP using a combination of electron paramagnetic resonance, fluorescence and circular dichroism spectroscopies. Our results provide evidence for two Cu2+ binding sites on SUP that are distinct from the binding site, but one of which interferes with binding. They further suggest that in solution the protein's secondary structure changes significantly in response to binding ; in contrast, the crystal structures of the apo- and holo-protein are almost superimposable. These results provide insights for further improving the selectivity of SUP for , paving the way toward protein-based biomaterials for decontamination and/or recovery of uranium.

First studies on the interactions of the C-terminal cystatin C fragment 85–94 with Cu(II) ions

The hypothesis connecting dyshomeostasis of selected metal ions in the brain with increased probability of the development of severe neurodegenerative diseases is gaining increasing attention. The mechanisms of metal toxicity can be studied using different methodologies. One of the approaches is based on peptides as mimetics of the metal binding sites in proteins. In our studies, we have focused on human cystatin C (hCC) as one of the proteins involved in neurodegeneration. Physiologically, it is an important inhibitor of cysteine proteases and modulator of many physiological and pathological states. Native hCC is present at particularly high concentrations in cerebrospinal fluid (CSF) and was shown to possess both neurodegenerative and neuroprotective propensities. We have studied the copper(II) binding to its C-terminal fragment containing two histidine residues as potential site(s) for metal coordination. Our data indicate that the hCC fragment is fully capable of effective metal binding. Moreover, two potential metal binding sites can be proposed for the studied ligand. At the physiological range of pH formation of two types of complexes was observed: with imidazole and one/two amide donors in the coordination sphere of the metal ion and the final species characterized by the 4N-type of coordination.

Prediction of Protein Metal Binding Sites Using Deep Neural Networks.

Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which vital function is fulfilled. The prediction of metal-binding in proteins will be considered as a step-in function assignment for new proteins, which helps to obtain functional proteins in genomic studies, is critical to protein function annotation and drug discovery. Computational predictions made by using machine learning methods from the data obtained from amino acid sequences are widely used in the protein metal-binding and various bioinformatics fields. In this work, we present three different deep learning architectures for prediction of metal-binding of Histidines (HIS) and Cysteines (CYS) amino acids. These architectures are as follows: 2D Convolutional Neural Network, Long-Short Term Memory and Recurrent Neural Network. Their comparison is carried out on the three different sets of attributes derived from a public dataset of protein sequences. These three sets of features extracted from the protein sequence were obtained using the PAM scoring matrix, protein composition server, and binary representation methods. The results show that a better performance for prediction of protein metal- binding sites is obtained through Convolutional Neural Network architecture.

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

With more than a third of the genome encoding for metal-containing biomolecules, the in silico prediction of how metal ions bind to proteins is crucial in chemistry, biology, and medicine. To date, algorithms for metal-binding site prediction are mainly based on sequence analysis. Those methods have reached enough quality to predict the correct region of the protein and the coordinating residues involved in metal-binding, but they do not provide three-dimensional (3D) models. On the contrary, the prediction of accurate 3D models for protein–metal adducts by structural bioinformatics and molecular modeling techniques is still a challenge. Here, we present an update of our multipurpose molecular modeling suite, GaudiMM, to locate metal-binding sites in proteins. The approach is benchmarked on 105 X-ray structures with resolution lower than 2.0 Å. Results predict the correct binding site of the metal in the biological scaffold for all the entries in the data set. Generated 3D models of the protein–metal coordination complexes reach root-mean-square deviation values under 1.0 Å between calculated and experimental structures. The whole process is purely based on finding poses that satisfy metal-derived geometrical rules without needing sequence or fine electronic inputs. Additional post-optimizations, including receptor flexibility, have been tested and suggest that more extensive searches, required when the host structures present a low level of pre-organization, are also possible. With this new update, GaudiMM is now able to look for metal-binding sites in biological scaffolds and clearly shows how explicitly considering the geometric particularities of the first coordination sphere of the metal in a docking process provides excellent results.

How Pb2+ Binds and Modulates Properties of Ca2+-Signaling Proteins

Abiogenic lead (Pb2+), present in the environment in elevated levels due to human activities, has detrimental effects on human health. Metal-binding sites in proteins have been identified as primary targets for lead substitution resulting in malfunction of the host protein. Although Pb2+ is known to be a potent competitor of Ca2+ in protein binding sites, why/how Pb2+ can compete with Ca2+ in proteins remains unclear, raising multiple outstanding questions, including the following: (1) What are the physicochemical factors governing the competition between Pb2+ and Ca2+? (2) Which Ca2+-binding sites in terms of the structure, composition, overall charge, flexibility, and solvent exposure are the most likely targets for Pb2+ attack? Using density functional theory combined with polarizable continuum model calculations, we address these questions by studying the thermodynamic outcome of the competition between Pb2+ and Ca2+ in various model Ca2+-binding sites, including those modeling voltage-gated calcium channel selectivity filters and EF-hand and non-EF-hand Ca2+-binding sites. The results, which are in good agreement with experiment, reveal that the metal site's flexibility and number of amino acid ligands dictate the outcome of the competition between Pb2+ and Ca2+: If the Ca2+-binding site is relatively rigid and crowded with protein ligands, then Pb2+, upon binding, preserves the native metal-binding site geometry and at low concentrations, can act as an activator of the host protein. If the Ca2+-binding site is flexible and consists of only a few protein ligands, then Pb2+ can displace Ca2+ and deform the native metal-binding site geometry, resulting in protein malfunction.

Characterizing metal-binding sites in proteins with X-ray crystallography.

Metals have crucial roles in many physiological, pathological, toxicological, pharmaceutical, and diagnostic processes. Proper handling of metal-containing macromolecule samples for structural studies is not trivial, and failure to handle them properly is often a source of irreproducibility caused by issues such as pH changes, incorporation of unexpected metals, or oxidization/reduction of the metal. This protocol outlines the guidelines and best practices for characterizing metal-binding sites in protein structures and alerts experimenters to potential pitfalls during the preparation and handling of metal-containing protein samples for X-ray crystallography studies. The protocol features strategies for controlling the sample pH and the metal oxidation state, recording X-ray fluorescence (XRF) spectra, and collecting diffraction data sets above and below the corresponding metal absorption edges. This protocol should allow experimenters to gather sufficient evidence to unambiguously determine the identity and location of the metal of interest, as well as to accurately characterize the coordinating ligands in the metal binding environment within the protein. Meticulous handling of metal-containing macromolecule samples as described in this protocol should enhance experimental reproducibility in biomedical sciences, especially in X-ray macromolecular crystallography. For most samples, the protocol can be completed within a period of 7-190 d, most of which (2-180 d) is devoted to growing the crystal. The protocol should be readily understandable to structural biologists, particularly protein crystallographers with an intermediate level of experience.