Identification of metal ion binding sites based on amino acid sequences

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings

MOTIVATION

Understanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal-binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (pLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the pLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal binding.

RESULTS

On independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall = 84.6%) in distinguishing metal binding from non-binding proteins compared to AUROC of 0.74 (recall = 61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e. Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the pLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties.

AVAILABILITY AND IMPLEMENTATION

M-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). The GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data.

Amodiaquine drug pressure selects nonsynonymous mutations in pantothenate kinase 1, diacylglycerol kinase, and phosphatidylinositol-4 kinase in Plasmodium berghei ANKA

Background

Lumefantrine (LM), piperaquine (PQ), and amodiaquine (AQ), the long-acting components of the artemisinin-based combination therapies (ACTs), are a cornerstone of malaria treatment in Africa. Studies have shown that PQ, AQ, and LM resistance may arise independently of predicted modes of action. Protein kinases have emerged as mediators of drug action and efficacy in malaria parasites; however, the link between top druggable Plasmodium kinases with LM, PQ, and AQ resistance remains unclear. Using LM, PQ, or AQ-resistant Plasmodium berghei parasites, we have evaluated the association of choline kinase (CK), pantothenate kinase 1 (PANK1), diacylglycerol kinase (DAGK), and phosphatidylinositol-4 kinase (PI4Kβ), and calcium-dependent protein kinase 1 (CDPK1) with LM, PQ, and AQ resistance in Plasmodium berghei ANKA.

Methods

We used in silico bioinformatics tools to identify ligand-binding motifs, active sites, and sequence conservation across the different parasites. We then used PCR and sequencing analysis to probe for single nucleotide polymorphisms (SNPs) within the predicted functional motifs in the CK, PANK1, DAGK, PI4Kβ, and CDPK1. Using qPCR analysis, we measured the mRNA amount of PANK1, DAGK, and PI4Kβ at trophozoites and schizonts stages.

Results

We reveal sequence conservation and unique ligand-binding motifs in the CK, PANK1, DAGK, PI4Kβ, and CDPK1 across malaria species. DAGK, PANK1, and PI4Kβ possessed nonsynonymous mutations; surprisingly, the mutations only occurred in the AQr parasites. PANK1 acquired Asn394His, while DAGK contained K270R and K292R mutations. PI4Kβ had Asp366Asn, Ser1367Arg, Tyr1394Asn and Asp1423Asn. We show downregulation of PANK1, DAGK, and PI4Kβ in the trophozoites but upregulation at the schizonts stages in the AQr parasites.

Conclusions

The selective acquisition of the mutations and the differential gene expression in AQ-resistant parasites may signify proteins under AQ pressure. The role of the mutations in the resistant parasites and their impact on drug responses require investigations using reverse genetics techniques in malaria parasites.

Prediction of Protein Ion-Ligand Binding Sites with ELECTRA

Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it is becoming crucial to identify ion-binding sites. The accurate identification of protein-ion binding sites helps us to understand proteins' biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion-binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32-, SO42-, PO43-, NO2-). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46%, respectively, in the F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools, producing prediction results for a hundred sequences for a specific ion in under ten minutes.

Differences in pork myosin solubility and structure with various chloride salts and their property of pork gel

The solubility and structure of myosin and the properties of pork gel with NaCl, KCl, CaCl2, and MgCl2 were investigated. Myofibrillar proteins (MPs) with phosphate were more solubilized with NaCl than with KCl (p < 0.05). CaCl2 and MgCl2 showed lower MP solubilities than those of NaCl and KCl (p < 0.05). The α-helix content of myosin was lower in KCl, CaCl2, and MgCl2 than in NaCl (p < 0.05). The pH of pork batter decreased in the order of KCl, NaCl, MgCl2, and CaCl2 (p < 0.05). The cooking yield of the pork gel manufactured with monovalent salts was higher than that of the pork gel manufactured with divalent salts (p < 0.05). The pork gel manufactured with KCl and MgCl2 showed lower hardness than that of the pork gel manufactured with NaCl. The solubility and structure of myosin were different with the different chloride salts and those led the different quality properties of pork gel. Therefore, the results of this study can be helpful for understanding the quality properties of low-slat meat products manufactured by replacing sodium chloride with different chloride salts.

A systematic in silico report on iron and zinc proteome of Zea mays

Zea mays is an essential staple food crop across the globe. Maize contains macro and micronutrients but is limited in essential mineral micronutrients such as Fe and Zn. Worldwide, serious health concerns have risen due to the deficiencies of essential nutrients in human diets, which rigorously jeopardizes economic development. In the present study, the systematic in silico approach has been used to predict Fe and Zn binding proteins from the whole proteome of maize. A total of 356 and 546 putative proteins have been predicted, which contain sequence and structural motifs for Fe and Zn ions, respectively. Furthermore, the functional annotation of these predicted proteins, based on their domains, subcellular localization, gene ontology, and literature support, showed their roles in distinct cellular and biological processes, such as metabolism, gene expression and regulation, transport, stress response, protein folding, and proteolysis. The versatile roles of these shortlisted putative Fe and Zn binding proteins of maize could be used to manipulate many facets of maize physiology. Moreover, in the future, the predicted Fe and Zn binding proteins may act as relevant, novel, and economical markers for various crop improvement programs.

SCAMPER: Accurate Type-Specific Prediction of Calcium-Binding Residues Using Sequence-Derived Features

Understanding molecular mechanisms involved in calcium-protein interactions and modeling corresponding docking rely on the accurate identification of calcium-binding residues (CaBRs). The defects of experimentally annotating protein functions enhances the development of computational approaches that correctly identify calcium-binding interactions. Studies have reported that current methods severely cross-predict residues that interact with other types of molecules (e.g., nucleic acids, proteins, and small ligands) as CaBRs. In this study, a novel predictor named SCAMPER (Selective CAlciuM-binding PrEdictoR) is proposed for the accurate and specific prediction of CaBRs. SCAMPER is designed using newly compiled dataset with complete UniProt sequences and annotations, which include calcium-binding, nucleic acid-binding, protein-binding, and small ligand-binding residues. We use a novel designed two-layer scheme to perform predictions as well as penalize cross-predictions. Empirical tests on an independent test dataset reveals that the proposed method significantly outperforms state-of-the-art predictors. SCAMPER is proved to be capable of distinguishing CaBRs from different types of metal-ion binding residues. We further perform CaBRs predictions on the whole human proteome, and use the results to hypothesize calcium-binding proteins (CaBPs). The latest experimental verified CaBPs and GO analysis prove the accuracy of our predictions. We implement the proposed method and share the data at http://www.inforstation.com/webservers/SCAMPER/.

Gene expression profiling of nasal inflammation induced by diesel particles using an in vivo system

Korean diesel particulate matter 20 (KDP20) is a pollutant comprising a complex mixture of carbon and chemical irritants. Although particulate matter and nasal inflammation are strongly associated, the underlying molecular mechanism based on systematic transcriptome analysis remains unknown. In this study, genome-wide gene expression profiles of mouse nasal tissues were determined following exposure to KDP20 for 5 and 10 days and compared with those of the control (n = 4/group). We identified 758 significant differentially expressed genes (DEGs) and classified them as 5-day-specific, 10-day-specific, and common among groups based on their expression patterns. The terms "regulation of alpha-beta T cell differentiation," "macrophage differentiation," and "cell adhesion mediated by integrin" were significantly enriched in each group. Receiver operating characteristic analysis revealed six genes as potential predictive biomarkers. The differential expression of these six genes was validated using quantitative RT-PCR (n = 3/group). Furthermore, a possible mechanism for nasal inflammation was suggested through the binding analysis between metal ions and genes. The genes identified in this study may play important roles in regulating the mechanism of nasal inflammation induced by diesel particles, especially immune cell regulation, and may function as markers for diesel particle-induced nasal inflammation.

Bioinformatics and computational analyses of kidney stone modulatory proteins lead to solid experimental evidence and therapeutic potential

In recent biomedical research, bioinformatics and computational analyses have played essential roles for examining experimental findings and database information. Several bioinformatic tools have been developed and made publicly available for analyzing protein sequence, structure, functional motif/domain, and interactions network. Such properties are very helpful to define biochemical and functional roles of the protein(s) of interest. During the past few decades, bioinformatics and computational biotechnology have been widely applied to kidney stone research. This review summarizes commonly used tools and evidence of bioinformatics and computational biotechnology applied to kidney stone disease (KSD) with special emphasis on analyses of the stone modulatory proteins that play critical roles in kidney stone formation. Such analyses lead to solid experimental evidence to demonstrate mechanisms underlying their stone modulatory activities. The findings obtained from such analyses may also lead to better understanding of KSD pathogenesis and to further development of new therapeutic and preventive strategies.

In Silico Characterization of the Physicochemical and Biological Properties of the Pink (Pleurotus djamor var. salmoneostramineus) Oyster Mushroom Chromoprotein

Cap color is an important commercial trait for oyster mushrooms. Various pigment constituents determine a diverse color. However, the pigments of oyster mushrooms are still ambiguous. The pink oyster mushroom (Pleurotus salmoneostramineus or Pleurotus djamor) chromoprotein is one of the few proteins belonging to this fungus that has a record of its sequence of amino acid residues. However, even though there are studies about this chromoprotein isolation, purification, and crystallization, the current information focused on its 3-dimensional model and the cofactor and prosthetic group (3H-indol-3-one) binding sites is unreliable and fragmented. Therefore, in this study, using free online servers such as Prot pi, GalaxyWEB, MIB, and CB-Dock2, a structural analysis and the prediction of its physicochemical and biological properties were conducted, to understand the possible function of this chromoprotein. The obtained results showed that this molecule is a protein with a molecular weight of 23 712.5 Da, an isoelectric point of 7.505, with oligomerization capacity in a dimer and glycation in the Ser6 residue. In addition, the participation of the residues Leu5, Leu8, Lys211, Ala214, and Gln215 in the binding of the prosthetic group to the protein was highlighted; as well as Ser6 and Pro7 are important residues for the interaction of the Mg²⁺ ion and eumelanin. Likewise, morphological changes based on different culture conditions (light/dark) showed that this protein is constitutive expressed and independent of blue light. The findings in this study demonstrate that pink chromoprotein is a melanosomal protein, and it possibly has a critical role in melanogenesis and the melanin polymerization. However, more experimental studies are needed to predict a possible mechanism of action and type of enzymatic activity.

Unveiling the Metal-Dependent Aggregation Properties of the C-terminal Region of Amyloidogenic Intrinsically Disordered Protein Isoforms DPF3b and DPF3a

Double-PHD fingers 3 (DPF3) is a BAF-associated human epigenetic regulator, which is increasingly recognised as a major contributor to various pathological contexts, such as cardiac defects, cancer, and neurodegenerative diseases. Recently, we unveiled that its two isoforms (DPF3b and DPF3a) are amyloidogenic intrinsically disordered proteins. DPF3 isoforms differ from their C-terminal region (C-TERb and C-TERa), containing zinc fingers and disordered domains. Herein, we investigated the disorder aggregation properties of C-TER isoforms. In agreement with the predictions, spectroscopy highlighted a lack of a highly ordered structure, especially for C-TERa. Over a few days, both C-TERs were shown to spontaneously assemble into similar antiparallel and parallel β-sheet-rich fibrils. Altered metal homeostasis being a neurodegeneration hallmark, we also assessed the influence of divalent metal cations, namely Cu²⁺, Mg²⁺, Ni²⁺, and Zn²⁺, on the C-TER aggregation pathway. Circular dichroism revealed that metal binding does not impair the formation of β-sheets, though metal-specific tertiary structure modifications were observed. Through intrinsic and extrinsic fluorescence, we found that metal cations differently affect C-TERb and C-TERa. Cu²⁺ and Ni²⁺ have a strong inhibitory effect on the aggregation of both isoforms, whereas Mg²⁺ impedes C-TERb fibrillation and, on the contrary, enhances that of C-TERa. Upon Zn²⁺ binding, C-TERb aggregation is also hindered, and the amyloid autofluorescence of C-TERa is remarkably red-shifted. Using electron microscopy, we confirmed that the metal-induced spectral changes are related to the morphological diversity of the aggregates. While metal-treated C-TERb formed breakable and fragmented filaments, C-TERa fibrils retained their flexibility and packing properties in the presence of Mg²⁺ and Zn²⁺ cations.

Prediction of metal ion ligand binding residues by adding disorder value and propensity factors based on deep learning algorithm

Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge. In this study, we selected Zn²⁺, Cu²⁺, Fe²⁺, Fe³⁺, Co²⁺, Mn²⁺, Ca²⁺ and Mg²⁺ metal ion ligands from the BioLip database as the research objects. Based on the amino acids, the physicochemical properties and predicted structural information, we introduced the disorder value as the feature parameter. In addition, based on the component information, position weight matrix and information entropy, we introduced the propensity factor as prediction parameters. Then, we used the deep neural network algorithm for the prediction. Furtherly, we made an optimization for the hyper-parameters of the deep learning algorithm and obtained improved results than the previous IonSeq method.

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. These models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline allows to retrieve the experimental structural dynamics experimentally represented by different X-ray conformations for the same sequence as well the conformational space observed with the MD simulations. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.

mebipred: identifying metal binding potential in protein sequence

Motivation

metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability.

Results

we developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred’s utility in analyzing microbiome metal requirements.

Availability and implementation

mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Ambient Availability of Amino Acids, Proteins, and Iron Impacts Copper Resistance of Aspergillus fumigatus

The transition metals iron and copper are required by virtually all organisms but are toxic in excess. Acquisition of both metals and resistance to copper excess have previously been shown to be important for virulence of the most common airborne human mold pathogen, Aspergillus fumigatus. Here we demonstrate that the ambient availability of amino acids and proteins increases the copper resistance of A. fumigatus wild type and particularly of the ΔcrpA mutant that lacks export-mediated copper detoxification. The highest-protecting activity was found for L-histidine followed by L-asparagine, L-aspartate, L-serine, L-threonine, and L-tyrosine. Other amino acids and proteins also displayed significant but lower protection. The protecting activity of non-proteinogenic D-histidine, L-histidine-mediated growth inhibition in the absence of high-affinity copper uptake, determination of cellular metal contents, and expression analysis of copper-regulated genes suggested that histidine inhibits low-affinity but not high-affinity copper acquisition by extracellular copper complexation. An increase in the cellular copper content was found to be accompanied by an increase in the iron content, and, in agreement, iron starvation increased copper susceptibility, which underlines the importance of cellular metal balancing. Due to the role of iron and copper in nutritional immunity, these findings are likely to play an important role in the host niche.

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.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn²⁺, Cu²⁺, Fe²⁺, Fe³⁺, Co²⁺, Mn²⁺, Ca²⁺, Mg²⁺, Na⁺, and K⁺). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the S_n and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm

Background

Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.

Results

In this paper, Mg²⁺ and Ca²⁺ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm.

Conclusions

An efficient method for predicting Mg²⁺ and Ca²⁺ ligand binding sites was presented.

Development of Cobalt-Binding Peptide Chelate from Human Serum Albumin: Cobalt-Binding Properties and Stability

Radioactive isotopes are used as drugs or contrast agents in the medical field after being conjugated with chelates such as DOTA, NOTA, DTPA, TETA, CyDTA, TRITA, and DPDP. The N-terminal sequence of human serum albumin (HSA) is known as a metal binding site, such as for Co2+, Cu2+, and Ni2+. For this study, we designed and synthesized wAlb12 peptide from the N-terminal region of HSA, which can bind to cobalt, to develop a peptide-based chelate. The wAlb12 with a random coil structure tightly binds to the Co(II) ion. Moreover, the binding property of wAlb12 toward Co(II) was confirmed using various spectroscopic experiments. To identify the binding site of wAlb12, the analogs were synthesized by alanine scanning mutagenesis. Among them, H3A and Ac-wAlb12 did not bind to Co(II). The analysis of the binding regions confirmed that the His3 and α-amino group of the N-terminal region are important for Co(II) binding. The wAlb12 bound to Co(II) with Kd of 75 μM determined by isothermal titration calorimetry when analyzed by a single-site binding model. For the use of wAlb12 as a chelate in humans, its cytotoxicity and stability were investigated. Trypsin stability showed that the wAlb12 - Co(II) complex was more stable than wAlb12 alone. Furthermore, the cell viability analysis showed wAlb12 and wAlb12 + Co(II) to be non-toxic to the Raw 264.7 and HEK 293T cell lines. Therefore, a hot radioactive isotope such as cobalt-57 will have the same effect as a stable isotope cobalt. Accordingly, we expect wAlb12 to be used as a peptide chelate that binds with radioactive isotopes.

Physicochemical and biological impact of metal-catalyzed oxidation of IgG1 monoclonal antibodies and antibody-drug conjugates via reactive oxygen species

Biotherapeutics are exposed to common transition metal ions such as Cu(II) and Fe(II) during manufacturing processes and storage. IgG1 biotherapeutics are vulnerable to reactive oxygen species (ROS) generated via the metal-catalyzed oxidation reactions. Exposure to these metal ions can lead to potential changes to structure and function, ultimately influencing efficacy, potency, and potential immunogenicity of the molecules. Here, we stress four biotherapeutics of the IgG1 subclass (trastuzumab, trastuzumab emtansine, anti-NaPi2b, and anti-NaPi2b-vc-MMAE) with two common pharmaceutically relevant metal-induced oxidizing systems, Cu(II)/ ascorbic acid and Fe(II)/ H₂O₂, and evaluated oxidation, size distribution, carbonylation, Fc effector functions, antibody-dependent cellular cytotoxicity (ADCC) activity, cell anti-proliferation and autophaghic flux. Our study demonstrates that the extent of oxidation was metal ion-dependent and site-specific, leading to decreased FcγRIIIa and FcRn receptor binding and subsequently potentially reduced bioactivity, though antigen binding was not affected to a great extent. In general, the monoclonal antibody (mAb) and corresponding antibody-drug conjugate (ADC) showed similar impacts to product quality when exposed to the same metal ion, either Cu(II) or Fe(II). Our study clearly demonstrates that transition metal ion binding to therapeutic IgG1 mAbs and ADCs is not random and that oxidation products show unique structural and functional ramifications. A critical outcome from this study is our highlighting of key process parameters, route of degradation, especially oxidation (metal catalyzed or via ROS), on the CH1 and Fc region of full-length mAbs and ADCs.

Abbreviations: DNPH 2,4-dinitrophenylhydrazine; ADC Antibody drug conjugate; ADCC Antibody-dependent cellular cytotoxicity; CDR Complementary determining region; DTT Dithiothreitol; HMWF high molecular weight form; LC-MS Liquid chromatography–mass spectrometry; LMWF low molecular weight forms; MOA Mechanism of action; MCO Metal-catalyzed oxidation; MetO Methionine sulfoxide; mAbs Monoclonal antibodies; MyBPC Myosin binding protein C; ROS Reactive oxygen species; SEC Size exclusion chromatography

Prediction of Metal Ion Binding Sites of Transmembrane Proteins

The metal ion binding of transmembrane proteins (TMPs) plays a fundamental role in biological processes, pharmaceutics, and medicine, but it is hard to extract enough TMP structures in experimental techniques to discover their binding mechanism comprehensively. To predict the metal ion binding sites for TMPs on a large scale, we present a simple and effective two-stage prediction method TMP-MIBS, to identify the corresponding binding residues using TMP sequences. At present, there is no specific research on the metal ion binding prediction of TMPs. Thereby, we compared our model with the published tools which do not distinguish TMPs from water-soluble proteins. The results in the independent verification dataset show that TMP-MIBS has superior performance. This paper explores the interaction mechanism between TMPs and metal ions, which is helpful to understand the structure and function of TMPs and is of great significance to further construct transport mechanisms and identify potential drug targets.

Forced Biomineralization: A Review

Biologically induced and controlled mineralization of metals promotes the development of protective structures to shield cells from thermal, chemical, and ultraviolet stresses. Metal biomineralization is widely considered to have been relevant for the survival of life in the environmental conditions of ancient terrestrial oceans. Similar behavior is seen among extremophilic biomineralizers today, which have evolved to inhabit a variety of industrial aqueous environments with elevated metal concentrations. As an example of extreme biomineralization, we introduce the category of "forced biomineralization", which we use to refer to the biologically mediated sequestration of dissolved metals and metalloids into minerals. We discuss forced mineralization as it is known to be carried out by a variety of organisms, including polyextremophiles in a range of psychrophilic, thermophilic, anaerobic, alkaliphilic, acidophilic, and halophilic conditions, as well as in environments with very high or toxic metal ion concentrations. While much additional work lies ahead to characterize the various pathways by which these biominerals form, forced biomineralization has been shown to provide insights for the progression of extreme biomimetics, allowing for promising new forays into creating the next generation of composites using organic-templating approaches under biologically extreme laboratory conditions relevant to a wide range of industrial conditions.

Artificial intelligence in drug design: algorithms, applications, challenges and ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

Copper bioavailability, uptake, toxicity and tolerance in plants: A comprehensive review

Copper (Cu) is an essential element for humans and plants when present in lesser amount, while in excessive amounts it exerts detrimental effects. There subsists a narrow difference amid the indispensable, positive and detrimental concentration of Cu in living system, which substantially alters with Cu speciation, and form of living organisms. Consequently, it is vital to monitor its bioavailability, speciation, exposure levels and routes in the living organisms. The ingestion of Cu-laced food crops is the key source of this heavy metal toxicity in humans. Hence, it is necessary to appraise the biogeochemical behaviour of Cu in soil-plant system with esteem to their quantity and speciation. On the basis of existing research, this appraisal traces a probable connexion midst: Cu levels, sources, chemistry, speciation and bioavailability in the soil. Besides, the functions of protein transporters in soil-plant Cu transport, and the detrimental effect of Cu on morphological, physiological and nutrient uptake in plants has also been discussed in the current manuscript. Mechanisms related to detoxification strategies like antioxidative response and generation of glutathione and phytochelatins to combat Cu-induced toxicity in plants is discussed as well. We also delimits the Cu accretion in food crops and allied health perils from soils encompassing less or high Cu quantity. Finally, an overview of various techniques involved in the reclamation and restoration of Cu-contaminated soils has been provided.

Application of Next Generation Sequencing (NGS) in Phage Displayed Peptide Selection to Support the Identification of Arsenic-Binding Motifs

Next generation sequencing (NGS) in combination with phage surface display (PSD) are powerful tools in the newly equipped molecular biology toolbox for the identification of specific target binding biomolecules. Application of PSD led to the discovery of manifold ligands in clinical and material research. However, limitations of traditional phage display hinder the identification process. Growth-based library biases and target-unrelated peptides often result in the dominance of parasitic sequences and the collapse of library diversity. This study describes the effective enrichment of specific peptide motifs potentially binding to arsenic as proof-of-concept using the combination of PSD and NGS. Arsenic is an environmental toxin, which is applied in various semiconductors as gallium arsenide and selective recovery of this element is crucial for recycling and remediation. The development of biomolecules as specific arsenic-binding sorbents is a new approach for its recovery. Usage of NGS for all biopanning fractions allowed for evaluation of motif enrichment, in-depth insight into the selection process and the discrimination of biopanning artefacts, e.g., the amplification-induced library-wide reduction in hydrophobic amino acid proportion. Application of bioinformatics tools led to the identification of an SxHS and a carboxy-terminal QxQ motif, which are potentially involved in the binding of arsenic. To the best of our knowledge, this is the first report of PSD combined with NGS of all relevant biopanning fractions.

Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle

The prediction of ion ligand–binding residues in protein sequences is a challenging work that contributes to understand the specific functions of proteins in life processes. In this article, we selected binding residues of 14 ion ligands as research objects, including four acid radical ion ligands and 10 metal ion ligands. Based on the amino acid sequence information, we selected the composition and position conservation information of amino acids, the predicted structural information, and physicochemical properties of amino acids as basic feature parameters. We then performed a statistical analysis and reclassification for dihedral angle and proposed new methods on the extraction of feature parameters. The methods mainly included applying information entropy on the extraction of polarization charge and hydrophilic–hydrophobic information of amino acids and using position weight matrices on the extraction of position conservation information. In the prediction model, we used the random forest algorithm and obtained better prediction results than previous works. With the independent test, the Matthew's correlation coefficient and accuracy of 10 metal ion ligand–binding residues were larger than 0.07 and 52%, respectively; the corresponding evaluation values of four acid radical ion ligand–binding residues were larger than 0.15 and 86%, respectively. Further, we classified and combined the phi and psi angles and optimized prediction model for each ion ligand–binding residue.

funtrp: identifying protein positions for variation driven functional tuning

Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. An in-depth understanding of sequence changes is also fundamental for synthetic protein design and stability assessments. However, the variant effect predictor performance gain observed in recent years has not kept up with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene and protein features for modeling variant effects, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the variation observable in vivo is arguably weaker in its impact, thus requiring evaluation at a higher level of resolution. Here, we describe functionNeutral/Toggle/Rheostatpredictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types can improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility

Many proteins realize their special functions by binding with specific metal ion ligands during a cell's life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work. We have presented an improved computational approach for predicting the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Ca2+, Mg2+, Mn2+, Na+, and K+) by adding reclassified relative solvent accessibility (RSA). The best accuracy of fivefold cross-validation was higher than 77.9%, which was about 16% higher than the previous result on the same dataset. It was found that different reclassification of the RSA information can make different contributions to the identification of specific ligand binding residues. Our study has provided an additional understanding of the effect of the RSA on the identification of metal ion ligand binding residues.

Recognizing ion ligand binding sites by SMO algorithm

Background

In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function.

Results

In this study, four acid radical ion ligands (NO₂⁻,CO₃²⁻,SO₄²⁻,PO₄³⁻) and ten metal ion ligands (Zn²⁺,Cu²⁺,Fe²⁺,Fe³⁺,Ca²⁺,Mg²⁺,Mn²⁺,Na⁺,K⁺,Co²⁺) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation.

Conclusions

An efficient method for predicting ion ligand binding sites was presented.

Prediction of acid radical ion binding residues by K-nearest neighbors classifier

Background

Proteins perform their functions by interacting with acid radical ions. Recently, it was a challenging work to precisely predict the binding residues of acid radical ion ligands in the research field of molecular drug design.

Results

In this study, we proposed an improved method to predict the acid radical ion binding residues by using K-nearest Neighbors classifier. Meanwhile, we constructed datasets of four acid radical ion ligand (NO₂⁻, CO₃²⁻, SO₄²⁻, PO₄³⁻) binding residues from BioLip database. Then, based on the optimal window length for each acid radical ion ligand, we refined composition information and position conservative information and extracted them as feature parameters for K-nearest Neighbors classifier. In the results of 5-fold cross-validation, the Matthew’s correlation coefficient was higher than 0.45, the values of accuracy, sensitivity and specificity were all higher than 69.2%, and the false positive rate was lower than 30.8%. Further, we also performed an independent test to test the practicability of the proposed method. In the obtained results, the sensitivity was higher than 40.9%, the values of accuracy and specificity were higher than 84.2%, the Matthew’s correlation coefficient was higher than 0.116, and the false positive rate was lower than 15.4%. Finally, we identified binding residues of the six metal ion ligands. In the predicted results, the values of accuracy, sensitivity and specificity were all higher than 77.6%, the Matthew’s correlation coefficient was higher than 0.6, and the false positive rate was lower than 19.6%.

Conclusions

Taken together, the good results of our prediction method added new insights in the prediction of the binding residues of acid radical ion ligands.

Cobalt(ii) cation binding by proteins

Herein, a set of non-homologous proteins (238) that could bind the cobalt(ii) cations was selected from all the available Protein Data Bank structures with Co²⁺ cations.