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Essien C, Jiang L, Wang D, Xu D. Prediction of Protein Ion-Ligand Binding Sites with ELECTRA. Molecules 2023; 28:6793. [PMID: 37836636 PMCID: PMC10574437 DOI: 10.3390/molecules28196793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
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.
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Affiliation(s)
| | | | | | - Dong Xu
- Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA; (C.E.); (L.J.); (D.W.)
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2
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Ravnik V, Jukič M, Bren U. Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach. J Chem Inf Model 2023; 63:5204-5219. [PMID: 37557084 PMCID: PMC10466382 DOI: 10.1021/acs.jcim.3c00558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Indexed: 08/11/2023]
Abstract
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.
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Affiliation(s)
- Vid Ravnik
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
| | - Marko Jukič
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
- The
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper SI-6000, Slovenia
- Institute
for Environmental Protection and Sensors, Beloruska ulica 7, Maribor SI-2000, Slovenia
| | - Urban Bren
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
- The
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper SI-6000, Slovenia
- Institute
for Environmental Protection and Sensors, Beloruska ulica 7, Maribor SI-2000, Slovenia
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3
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Paiva VA, Mendonça MV, Silveira SA, Ascher DB, Pires DEV, Izidoro SC. GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms. Brief Bioinform 2022; 23:6590153. [PMID: 35595534 DOI: 10.1093/bib/bbac178] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Vinícius A Paiva
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Murillo V Mendonça
- Institute of Technological Sciences, Campus Theodomiro Carneiro Santiago, Universidade Federal de Itajubá, Itabira, Brazil
| | - Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Sandro C Izidoro
- Institute of Technological Sciences, Campus Theodomiro Carneiro Santiago, Universidade Federal de Itajubá, Itabira, Brazil
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4
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You X, Hu X, Feng Z, Wang Z, Hao S, Yang C. Recognizing Protein-metal Ion Ligands Binding Residues by Random Forest Algorithm with Adding Orthogonal Properties. Comput Biol Chem 2022; 98:107693. [DOI: 10.1016/j.compbiolchem.2022.107693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 11/16/2022]
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5
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A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8965712. [PMID: 35402609 PMCID: PMC8989566 DOI: 10.1155/2022/8965712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 03/04/2022] [Indexed: 12/29/2022]
Abstract
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.
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6
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Fleminger G, Dayan A. The moonlighting activities of dihydrolipoamide dehydrogenase: Biotechnological and biomedical applications. J Mol Recognit 2021; 34:e2924. [PMID: 34164859 DOI: 10.1002/jmr.2924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 06/13/2021] [Indexed: 12/13/2022]
Abstract
Dihydrolipoamide dehydrogenase (DLDH) is a homodimeric flavin-dependent enzyme that catalyzes the NAD+ -dependent oxidation of dihydrolipoamide. The enzyme is part of several multi-enzyme complexes such as the Pyruvate Dehydrogenase system that transforms pyruvate into acetyl-co-A. Concomitantly with its redox activity, DLDH produces Reactive Oxygen Species (ROS), which are involved in cellular apoptotic processes. DLDH possesses several moonlighting functions. One of these is the capacity to adhere to metal-oxides surfaces. This was first exemplified by the presence of an exocellular form of the enzyme on the cell-wall surface of Rhodococcus ruber. This capability was evolutionarily conserved and identified in the human, mitochondrial, DLDH. The enzyme was modified with Arg-Gly-Asp (RGD) groups, which enabled its interaction with integrin-rich cancer cells followed by "integrin-assisted-endocytosis." This allowed harnessing the enzyme for cancer therapy. Combining the TiO2 -binding property with DLDH's ROS-production, enabled us to develop several medical applications including improving oesseointegration of TiO2 -based implants and photodynamic treatment for melanoma. The TiO2 -binding sites of both the bacterial and human DLDH's were identified on the proteins' molecules at regions that overlap with the binding site of E3-binding protein (E3BP). This protein is essential in forming the multiunit structure of PDC. Another moonlighting activity of DLDH, which is described in this Review, is its DNA-binding capacity that may affect DNA chelation and shredding leading to apoptotic processes in living cells. The typical ROS-generation by DLDH, which occurs in association with its enzymatic activity and its implications in cancer and apoptotic cell death are also discussed.
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Affiliation(s)
- Gideon Fleminger
- The Shmunis School of Biomedicine and Cancer Research, The George Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel
| | - Avraham Dayan
- The Shmunis School of Biomedicine and Cancer Research, The George Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel
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Nallapareddy V, Bogam S, Devarakonda H, Paliwal S, Bandyopadhyay D. DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains. Proteins 2021; 89:745-761. [PMID: 33580578 DOI: 10.1002/prot.26056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 01/31/2021] [Indexed: 12/29/2022]
Abstract
Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scarce, in contrast to specific function prediction algorithms. Here we present a deep neural network-based multiple Cys function prediction, available on web-server (DeepCys) (https://deepcys.herokuapp.com/). DeepCys model was trained and tested on two independent datasets curated from protein crystal structures. This prediction method requires three inputs, namely, PDB identifier (ID), chain ID and residue ID for a given Cys and outputs the probabilities of four cysteine functions, namely, disulphide, metal-binding, thioether and sulphenylation and predicts the most probable Cys function. The algorithm exploits the local and global protein properties, like, sequence and secondary structure motifs, buried fractions, microenvironments and protein/enzyme class. DeepCys outperformed most of the multiple and specific Cys function algorithms. This method can predict maximum number of cysteine functions. Moreover, for the first time, explicitly predicts thioether function. This tool was used to elucidate the cysteine functions on domains of unknown functions belonging to cytochrome C oxidase subunit-II like transmembrane domains. Apart from the web-server, a standalone program is also available on GitHub (https://github.com/vam-sin/deepcys).
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Affiliation(s)
- Vamsi Nallapareddy
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Shubham Bogam
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Himaja Devarakonda
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Shubham Paliwal
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Debashree Bandyopadhyay
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
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8
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Hu X, Feng Z, Zhang X, Liu L, Wang S. The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility. Front Genet 2020; 11:214. [PMID: 32265982 PMCID: PMC7096583 DOI: 10.3389/fgene.2020.00214] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/24/2020] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
| | - Zhenxing Feng
- College of Sciences, Inner Mongolla University of Technology, Hohhot, China
| | - Xiaojin Zhang
- College of Sciences, Inner Mongolla University of Technology, Hohhot, China
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Wang S, Hu X, Feng Z, Zhang X, Liu L, Sun K, Xu S. Recognizing ion ligand binding sites by SMO algorithm. BMC Mol Cell Biol 2019; 20:53. [PMID: 31823742 PMCID: PMC6905020 DOI: 10.1186/s12860-019-0237-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
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.
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Affiliation(s)
- Shan Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China.
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Xiaojin Zhang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Liu Liu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Shuang Xu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
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10
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Cao X, Hu X, Zhang X, Gao S, Ding C, Feng Y, Bao W. Identification of metal ion binding sites based on amino acid sequences. PLoS One 2017; 12:e0183756. [PMID: 28854211 PMCID: PMC5576659 DOI: 10.1371/journal.pone.0183756] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Accepted: 08/10/2017] [Indexed: 11/26/2022] Open
Abstract
The identification of metal ion binding sites is important for protein function annotation and the design of new drug molecules. This study presents an effective method of analyzing and identifying the binding residues of metal ions based solely on sequence information. Ten metal ions were extracted from the BioLip database: Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+. The analysis showed that Zn2+, Cu2+, Fe2+, Fe3+, and Co2+ were sensitive to the conservation of amino acids at binding sites, and promising results can be achieved using the Position Weight Scoring Matrix algorithm, with an accuracy of over 79.9% and a Matthews correlation coefficient of over 0.6. The binding sites of other metals can also be accurately identified using the Support Vector Machine algorithm with multifeature parameters as input. In addition, we found that Ca2+ was insensitive to hydrophobicity and hydrophilicity information and Mn2+ was insensitive to polarization charge information. An online server was constructed based on the framework of the proposed method and is freely available at http://60.31.198.140:8081/metal/HomePage/HomePage.html.
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Affiliation(s)
- Xiaoyong Cao
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Xiaojin Zhang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Sujuan Gao
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
- College of Sciences, Inner Mongolia Agricultural University, Hohhot, 010021, China
| | - Changjiang Ding
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Yonge Feng
- College of Sciences, Inner Mongolia Agricultural University, Hohhot, 010021, China
| | - Weihua Bao
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
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11
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Azia A, Levy R, Unger R, Edelman M, Sobolev V. Genome-wide computational determination of the human metalloproteome. Proteins 2015; 83:931-9. [PMID: 25739467 DOI: 10.1002/prot.24790] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 02/06/2015] [Accepted: 02/25/2015] [Indexed: 11/08/2022]
Abstract
Accurate prediction of protein function in humans is important for understanding biological processes at the molecular level in biomedicine and drug design. Over a third of proteins are commonly held to bind metal, and ∼10% of human proteins, to bind zinc. Therefore, an initial step in protein function prediction frequently involves predicting metal ion binding. In recent years, methods have been developed to predict a set of residues in 3D space forming the metal-ion binding site, often with a high degree of accuracy. Here, using extensions of these methods, we provide an extensive list of human proteins and their putative metal ion binding site residues, using translated gene sequences derived from the complete, resolved human genome. Under conditions of ∼90% selectivity, over 900 new human putative metal ion binding proteins are identified. A statistical analysis of resolved metal ion binding sites in the human metalloproteome is furnished and the importance of remote homology analysis is demonstrated. As an example, a novel metal-ion binding site involving a complex of a botulinum substrate with its inhibitor is presented. On the basis of the location of the predicted site and the interactions of the contacting residues at the complex interface, we postulate that metal ion binding in this region could influence complex formation and, consequently, the functioning of the protein. Thus, this work provides testable hypotheses about novel functions of known proteins.
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Affiliation(s)
- Ariel Azia
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
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