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Jia P, Zhang F, Wu C, Li M. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. Brief Bioinform 2024; 25:bbae162. [PMID: 38739759 PMCID: PMC11089422 DOI: 10.1093/bib/bbae162] [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: 01/01/2024] [Revised: 02/17/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
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Affiliation(s)
- Pengzhen Jia
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
- College of Information Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China
| | - Chaojin Wu
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
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Zhang J, Zhou F, Liang X, Yang G. SCAMPER: Accurate Type-Specific Prediction of Calcium-Binding Residues Using Sequence-Derived Features. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1406-1416. [PMID: 35536812 DOI: 10.1109/tcbb.2022.3173437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
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/.
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Hao S, Hu X, Feng Z, Sun K, You X, Wang Z, Yang C. Prediction of metal ion ligand binding residues by adding disorder value and propensity factors based on deep learning algorithm. Front Genet 2022; 13:969412. [PMID: 36035120 PMCID: PMC9402973 DOI: 10.3389/fgene.2022.969412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Sixi Hao
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
- Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
- Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
- *Correspondence: Xiuzhen Hu, ; Zhenxing Feng,
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
- Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
- *Correspondence: Xiuzhen Hu, ; Zhenxing Feng,
| | - Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
- Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Xiaoxiao You
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
- Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Ziyang Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
- Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Caiyun Yang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
- Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
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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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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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Xu S, Hu X, Feng Z, Pang J, Sun K, You X, Wang Z. Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors. Front Genet 2022; 12:793800. [PMID: 35058970 PMCID: PMC8764267 DOI: 10.3389/fgene.2021.793800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
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 Sn 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.
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Affiliation(s)
- Shuang Xu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Jing Pang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Xiaoxiao You
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Ziyang Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
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Sun K, Hu X, Feng Z, Wang H, Lv H, Wang Z, Zhang G, Xu S, You X. Predicting Ca 2+ and Mg 2+ ligand binding sites by deep neural network algorithm. BMC Bioinformatics 2022; 22:324. [PMID: 35045825 PMCID: PMC8772041 DOI: 10.1186/s12859-021-04250-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 06/09/2021] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China. .,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China.
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China
| | - Hongbin Wang
- College of Data Science and Application, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China
| | - Haotian Lv
- College of Data Science and Application, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China
| | - Ziyang Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China
| | - Gaimei Zhang
- Hohhot First Hospital, Hohhot, 010051, People's Republic of China
| | - Shuang Xu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China
| | - Xiaoxiao You
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China
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Liu L, Hu X, Feng Z, Wang S, Sun K, Xu S. Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle. Front Bioeng Biotechnol 2020; 8:493. [PMID: 32596216 PMCID: PMC7303464 DOI: 10.3389/fbioe.2020.00493] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 04/28/2020] [Indexed: 11/26/2022] Open
Abstract
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.
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Affiliation(s)
- Liu Liu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Shan Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Shuang Xu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China
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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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