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Yu H, Luo X. ThermoFinder: A sequence-based thermophilic proteins prediction framework. Int J Biol Macromol 2024; 270:132469. [PMID: 38761901 DOI: 10.1016/j.ijbiomac.2024.132469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
Thermophilic proteins are important for academic research and industrial processes, and various computational methods have been developed to identify and screen them. However, their performance has been limited due to the lack of high-quality labeled data and efficient models for representing protein. Here, we proposed a novel sequence-based thermophilic proteins prediction framework, called ThermoFinder. The results demonstrated that ThermoFinder outperforms previous state-of-the-art tools on two benchmark datasets, and feature ablation experiments confirmed the effectiveness of our approach. Additionally, ThermoFinder exhibited exceptional performance and consistency across two newly constructed datasets, one of these was specifically constructed for the regression-based prediction of temperature optimum values directly derived from protein sequences. The feature importance analysis, using shapley additive explanations, further validated the advantages of ThermoFinder. We believe that ThermoFinder will be a valuable and comprehensive framework for predicting thermophilic proteins, and we have made our model open source and available on Github at https://github.com/Luo-SynBioLab/ThermoFinder.
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Affiliation(s)
- Han Yu
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiaozhou Luo
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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2
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Huang A, Chen Z, Wu X, Yan W, Lu F, Liu F. Improving the thermal stability and catalytic activity of ulvan lyase by the combination of FoldX and KnowVolution campaign. Int J Biol Macromol 2024; 257:128577. [PMID: 38070809 DOI: 10.1016/j.ijbiomac.2023.128577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/22/2023] [Accepted: 12/01/2023] [Indexed: 01/26/2024]
Abstract
Thermal stability is one of the most important properties of ulvan lyases for their application in algae biomass degradation. The Knowledge gaining directed eVolution (KnowVolution) protein engineering strategy could be employed to improve thermostability of ulvan lyase with less screening effort. Herein, the unfolding free energies (ΔΔG) of the loop region were calculated using FoldX and four sites (D103, G104, T113, Q229) were selected for saturation mutagenesis, resulting in the identification of a favorable single-site mutant Q229M. Subsequently, iteration mutation was carried out with the mutant N57P (previously obtained by our group) to further enhance the performance of ulvan lyase. The results showed that the most beneficial variant N57P/Q229M exhibited a 1.67-fold and 2-fold increase in residual activity compared to the wild type after incubation at 40 °C and 50 °C for 1 h, respectively. In addition, the variant produced 1.06 mg/mL of reducing sugar in 2 h, which was almost four times as much as the wild type. Molecular dynamics simulations revealed that N57P/Q229M mutant enhanced the structural rigidity by augmenting intramolecular hydrogen bonds. Meanwhile, the shorter proton transmission distance between the general base of the enzyme and the substrate contributed to the glycosidic bond breakage. Our research showed that in silico saturation mutagenesis using position scan module in FoldX allowed for faster screening of mutants with improved thermal stability, and combining it with KnowVolution enabled a balanced effect of thermal stability and enzyme activity in protein engineering.
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Affiliation(s)
- Ailan Huang
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China
| | - Zhengqi Chen
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China
| | - Xinming Wu
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China
| | - Wenxing Yan
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China
| | - Fuping Lu
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China; Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, Tianjin, PR China
| | - Fufeng Liu
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China; Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, Tianjin, PR China.
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3
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Li J, He X, Gao S, Liang Y, Qi Z, Xi Q, Zuo Y, Xing Y. The Metal-binding Protein Atlas (MbPA): an integrated database for curating metalloproteins in all aspects. J Mol Biol 2023:168117. [PMID: 37086947 DOI: 10.1016/j.jmb.2023.168117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 04/24/2023]
Abstract
Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106373 entries and 440187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.
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Affiliation(s)
- Jinzhao Li
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Xiang He
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Shuang Gao
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Yuchao Liang
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Zhi Qi
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China; Key Laboratory of Forage and Endemic Crop Biotechnology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Qilemuge Xi
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Yongchun Zuo
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China.
| | - Yongqiang Xing
- The Inner Mongolia Key Laboratory of Functional Genome Bioinformatics, School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou 014010, China.
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4
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Converting the genomic knowledge base to build protein specific machine learning prediction models; a classification study on thermophilic serine protease. Biologia (Bratisl) 2022. [DOI: 10.1007/s11756-022-01214-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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5
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SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Comput Biol Med 2022; 146:105704. [PMID: 35690478 DOI: 10.1016/j.compbiomed.2022.105704] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/15/2022] [Accepted: 06/04/2022] [Indexed: 11/22/2022]
Abstract
Thermophilic proteins (TPPs) are important in the field of protein biochemistry and development of new enzymes. Thus, computational methods must be urgently developed to accurately and rapidly identify TPPs. To date, several computational methods have been developed for TPP identification; however, few limitations in terms of performance and utility remain. In this study, we present a novel computational method, SAPPHIRE, to achieve more accurate identification of TPPs using only sequence information without any need for structural information. We combined twelve different feature encodings representing different perspectives and six popular machine learning algorithms to train 72 baseline models and extract the key information of TPPs. Subsequently, the informative predicted probabilities from the baseline models were mined and selected using a genetic algorithm in conjunction with a self-assessment-report approach. Finally, the final meta-predictor, SAPPHIRE, was built and optimized by applying an optimal feature set. The performance of SAPPHIRE in the 10-fold cross-validation test showed that a superior predictive performance compared with several baseline models could be achieved. Moreover, SAPPHIRE yielded an accuracy of 0.942 and Matthew's coefficient correlation of 0.884, which were 7.68 and 5.12% higher than those of the current existing methods, respectively, as indicated by the independent test. The proposed computational approach is anticipated to facilitate large-scale identification of TPPs and accelerate their applications in the food industry. The codes and datasets are available at https://github.com/plenoi/SAPPHIRE.
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Charoenkwan P, Schaduangrat N, Hasan MM, Moni MA, Lió P, Shoombuatong W. Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins. EXCLI JOURNAL 2022; 21:554-570. [PMID: 35651661 PMCID: PMC9150013 DOI: 10.17179/excli2022-4723] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, the expeditious identification of novel TPPs through computational models from protein sequences is very desirable. Over the last few decades, a number of computational methods, especially machine learning (ML)-based methods, for in silico prediction of TPPs have been developed. Therefore, it is desirable to revisit these methods and summarize their advantages and disadvantages in order to further develop new computational approaches to achieve more accurate and improved prediction of TPPs. With this goal in mind, we comprehensively investigate a large collection of fourteen state-of-the-art TPP predictors in terms of their dataset size, feature encoding schemes, feature selection strategies, ML algorithms, evaluation strategies and web server/software usability. To the best of our knowledge, this article represents the first comprehensive review on the development of ML-based methods for in silico prediction of TPPs. Among these TPP predictors, they can be classified into two groups according to the interpretability of ML algorithms employed (i.e., computational black-box methods and computational white-box methods). In order to perform the comparative analysis, we conducted a comparative study on several currently available TPP predictors based on two benchmark datasets. Finally, we provide future perspectives for the design and development of new computational models for TPP prediction. We hope that this comprehensive review will facilitate researchers in selecting an appropriate TPP predictor that is the most suitable one to deal with their purposes and provide useful perspectives for the development of more effective and accurate TPP predictors.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand, 50200
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
| | - Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, the University of Queensland, St Lucia, QLD 4072, Australia
| | - Pietro Lió
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
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7
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Ahmed Z, Zulfiqar H, Khan AA, Gul I, Dao FY, Zhang ZY, Yu XL, Tang L. iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy. Front Microbiol 2022; 13:790063. [PMID: 35273581 PMCID: PMC8902591 DOI: 10.3389/fmicb.2022.790063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/10/2022] [Indexed: 01/20/2023] Open
Abstract
Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.
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Affiliation(s)
- Zahoor Ahmed
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Abdullah Aman Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.,Sichuan Artificial Intelligence Research Institute, Yibin, China
| | - Ijaz Gul
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Tsinghua Shenzhen International Graduate School, Institute of Biopharmaceutical and Health Engineering, Tsinghua University, Shenzhen, China
| | - Fu-Ying Dao
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou, China
| | - Lixia Tang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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8
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Charoenkwan P, Chotpatiwetchkul W, Lee VS, Nantasenamat C, Shoombuatong W. A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides. Sci Rep 2021; 11:23782. [PMID: 34893688 PMCID: PMC8664844 DOI: 10.1038/s41598-021-03293-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/01/2021] [Indexed: 02/08/2023] Open
Abstract
Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.
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Affiliation(s)
- Phasit Charoenkwan
- grid.7132.70000 0000 9039 7662Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200 Thailand
| | - Warot Chotpatiwetchkul
- grid.419784.70000 0001 0816 7508Applied Computational Chemistry Research Unit, Department of Chemistry, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520 Thailand
| | - Vannajan Sanghiran Lee
- grid.10347.310000 0001 2308 5949Department of Chemistry, Centre of Theoretical and Computational Physics, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Chanin Nantasenamat
- grid.10223.320000 0004 1937 0490Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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9
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Wang H, Xi Q, Liang P, Zheng L, Hong Y, Zuo Y. IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy. Amino Acids 2021; 53:239-251. [PMID: 33486591 DOI: 10.1007/s00726-021-02941-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 01/11/2021] [Indexed: 12/18/2022]
Abstract
Enzymes have been proven to play considerable roles in disease diagnosis and biological functions. The feature extraction that truly reflects the intrinsic properties of protein is the most critical step for the automatic identification of enzymes. Although lots of feature extraction methods have been proposed, some challenges remain. In this study, we developed a predictor called IHEC_RAAC, which has the capability to identify whether a protein is a human enzyme and distinguish the function of the human enzyme. To improve the feature representation ability, protein sequences were encoded by a new feature-vector called 'reduced amino acid cluster'. We calculated 673 amino acid reduction alphabets to determine the optimal feature representative scheme. The tenfold cross-validation test showed that the accuracy of IHEC_RAAC to identify human enzymes was 74.66% and further discriminate the human enzyme classes with an accuracy of 54.78%, which was 2.06% and 8.68% higher than the state-of-the-art predictors, respectively. Additionally, the results from the independent dataset indicated that IHEC_RAAC can effectively predict human enzymes and human enzyme classes to further provide guidance for protein research. A user-friendly web server, IHEC_RAAC, is freely accessible at http://bioinfor.imu.edu.cn/ihecraac .
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Affiliation(s)
- Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Qilemuge Xi
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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Abstract
Background:
Thermophilic proteins can maintain good activity under high temperature,
therefore, it is important to study thermophilic proteins for the thermal stability of proteins.
Objective:
In order to solve the problem of low precision and low efficiency in predicting
thermophilic proteins, a prediction method based on feature fusion and machine learning was
proposed in this paper.
Methods:
For the selected thermophilic data sets, firstly, the thermophilic protein sequence was
characterized based on feature fusion by the combination of g-gap dipeptide, entropy density and
autocorrelation coefficient. Then, Kernel Principal Component Analysis (KPCA) was used to reduce
the dimension of the expressed protein sequence features in order to reduce the training time and
improve efficiency. Finally, the classification model was designed by using the classification
algorithm.
Results:
A variety of classification algorithms was used to train and test on the selected thermophilic
dataset. By comparison, the accuracy of the Support Vector Machine (SVM) under the jackknife
method was over 92%. The combination of other evaluation indicators also proved that the SVM
performance was the best.
Conclusion:
Because of choosing an effectively feature representation method and a robust
classifier, the proposed method is suitable for predicting thermophilic proteins and is superior to
most reported methods.
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Affiliation(s)
- Xian-Fang Wang
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Yi-Feng Liu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Hong-Fei Li
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Fan Lu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
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11
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Sun Z, Huang S, Zheng L, Liang P, Yang W, Zuo Y. ICTC-RAAC: An improved web predictor for identifying the types of ion channel-targeted conotoxins by using reduced amino acid cluster descriptors. Comput Biol Chem 2020; 89:107371. [PMID: 32950852 DOI: 10.1016/j.compbiolchem.2020.107371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 12/27/2022]
Abstract
Conotoxins are small peptide toxins which are rich in disulfide and have the unique diversity of sequences. It is significant to correctly identify the types of ion channel-targeted conotoxins because that they are considered as the optimal pharmacological candidate medicine in drug design owing to their ability specifically binding to ion channels and interfering with neural transmission. Comparing with other feature extracting methods, the reduced amino acid cluster (RAAC) better resolved in simplifying protein complexity and identifying functional conserved regions. Thus, in our study, 673 RAACs generated from 74 types of reduced amino acid alphabet were comprehensively assessed to establish a state-of-the-art predictor for predicting ion channel-targeted conotoxins. The results showed Type 20, Cluster 9 (T = 20, C = 9) in the tripeptide composition (N = 3) achieved the best accuracy, 89.3%, which was based on the algorithm of amino acids reduction of variance maximization. Further, the ANOVA with incremental feature selection (IFS) was used for feature selection to improve prediction performance. Finally, the cross-validation results showed that the best overall accuracy we calculated was 96.4% and 1.8% higher than the best accuracy of previous studies. Based on the predictor we proposed, a user-friendly webserver was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ictcraac.
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Affiliation(s)
- Zijie Sun
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; School of Mathematical Sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Wuritu Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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12
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Wu L, Hu Y, Zhang X, Chen W, Yu ASL, Kellum JA, Waitman LR, Liu M. Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study. BMC Nephrol 2020; 21:321. [PMID: 32741377 PMCID: PMC7397647 DOI: 10.1186/s12882-020-01980-w] [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: 01/04/2020] [Accepted: 07/23/2020] [Indexed: 12/14/2022] Open
Abstract
Background Likelihood of developing acute kidney injury (AKI) increases with age. We aimed to explore whether the predictability of AKI varies between age groups and assess the volatility of risk factors using electronic medical records (EMR). Methods We constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital and stratified it into four age groups: 18–35, 36–55, 56–65, and > 65. Potential risk factors collected from EMR for the study cohort included demographics, vital signs, medications, laboratory values, past medical diagnoses, and admission diagnoses. AKI was defined based on the Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria. We analyzed relative importance of the risk factors in predicting AKI using Gradient Boosting Machine algorithm and explored the predictability of AKI across age groups using multiple machine learning models. Results In our cohort, older patients showed a significantly higher incidence of AKI than younger adults: 18–35 (7.29%), 36–55 (8.82%), 56–65 (10.53%), and > 65 (10.55%) (p < 0.001). However, the predictability of AKI decreased with age, where the best cross-validated area under the receiver operating characteristic curve (AUROC) achieved for age groups 18–35, 36–55, 56–65, and > 65 were 0.784 (95% CI, 0.769–0.800), 0.766 (95% CI, 0.754–0.777), 0.754 (95% CI, 0.741–0.768), and 0.725 (95% CI, 0.709–0.737), respectively. We also observed that the relative risk of AKI predictors fluctuated between age groups. Conclusions As complexity of the cases increases with age, it is more difficult to quantify AKI risk for older adults in inpatient population.
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Affiliation(s)
- Lijuan Wu
- Big Data Decision Institute (BDDI), Jinan University, Guangzhou, 510632, China.,Guangdong Engineering Technology Research Center for Big Data Precision Healthcare, Guangzhou, 510632, China
| | - Yong Hu
- Big Data Decision Institute (BDDI), Jinan University, Guangzhou, 510632, China.,Guangdong Engineering Technology Research Center for Big Data Precision Healthcare, Guangzhou, 510632, China
| | - Xiangzhou Zhang
- Big Data Decision Institute (BDDI), Jinan University, Guangzhou, 510632, China.,Guangdong Engineering Technology Research Center for Big Data Precision Healthcare, Guangzhou, 510632, China
| | - Weiqi Chen
- Big Data Decision Institute (BDDI), Jinan University, Guangzhou, 510632, China.,Guangdong Engineering Technology Research Center for Big Data Precision Healthcare, Guangzhou, 510632, China
| | - Alan S L Yu
- Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, 66160, USA
| | - John A Kellum
- Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, 15260, USA
| | - Lemuel R Waitman
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, 66160, USA
| | - Mei Liu
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, 66160, USA.
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13
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Gado JE, Beckham GT, Payne CM. Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning. J Chem Inf Model 2020; 60:4098-4107. [DOI: 10.1021/acs.jcim.0c00489] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Japheth E. Gado
- Department of Chemical and Materials Engineering, University of Kentucky, Lexington, Kentucky 40506, United States
- National Bioenergy Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Gregg T. Beckham
- National Bioenergy Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Christina M. Payne
- Department of Chemical and Materials Engineering, University of Kentucky, Lexington, Kentucky 40506, United States
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14
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Feng C, Ma Z, Yang D, Li X, Zhang J, Li Y. A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features. Front Bioeng Biotechnol 2020; 8:285. [PMID: 32432088 PMCID: PMC7214540 DOI: 10.3389/fbioe.2020.00285] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/18/2020] [Indexed: 11/13/2022] Open
Abstract
The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.
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Affiliation(s)
- Changli Feng
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Zhaogui Ma
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Deyun Yang
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Xin Li
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Jun Zhang
- Department of Rehabilitation, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yanjuan Li
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
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15
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Fu X, Yang Y. WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning. QUANTITATIVE BIOLOGY 2019. [DOI: 10.1007/s40484-019-0184-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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16
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Fang X, Huang J, Zhang R, Wang F, Zhang Q, Li G, Yan J, Zhang H, Yan Y, Xu L. Convolution Neural Network-Based Prediction of Protein Thermostability. J Chem Inf Model 2019; 59:4833-4843. [PMID: 31657922 DOI: 10.1021/acs.jcim.9b00220] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Most natural proteins exhibit poor thermostability, which limits their industrial application. Computer-aided rational design is an efficient purpose-oriented method that can improve protein thermostability. Numerous machine-learning-based methods have been designed to predict the changes in protein thermostability induced by mutations. However, all of these methods have certain limitations due to existing mutation coding methods that overlook protein sequence features. Here we propose a method to predict protein thermostability using convolutional neural networks based on an in-depth study of thermostability-related protein properties. This method comprises a three-dimensional coding algorithm, including protein mutation information and a strategy to extract neighboring features at protein mutation sites based on multiscale convolution. The accuracies on the S1615 and S388 data sets, which are widely used for protein thermostability predictions, reached 86.4 and 87%, respectively. The Matthews correlation coefficient was nearly double those produced using other methods. Furthermore, a model was constructed to predict the thermostability of Rhizomucor miehei lipase mutants based on the S3661 data set, a single amino acid mutation data set screened from the ProTherm protein thermodynamics database. Compared with the RIF strategy, which consists of three algorithms, i.e., Rosetta ddg monomer, I Mutant 3.0, and FoldX, the accuracy of the proposed method was higher (75.0 vs 66.7%), and the negative sample resolution was simultaneously enhanced. These results indicate that our prediction method more effectively assessed the protein thermostability and distinguished its features, making it a powerful tool to devise mutations that enhance the thermostability of proteins, particularly enzymes.
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Affiliation(s)
- Xingrong Fang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Jinsha Huang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Rui Zhang
- Editorial Board of the Journal of Wuhan Institute of Technology , Wuhan Institute of Technology , Wuhan 430074 , P. R. China
| | - Fei Wang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Qiuyu Zhang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Guanlin Li
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Jinyong Yan
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Houjin Zhang
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Yunjun Yan
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
| | - Li Xu
- Key Laboratory of Molecular Biophysics, Ministry of Education, College of Life Science and Technology , Huazhong University of Science and Technology , Wuhan 430074 , P. R. China
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17
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Zuo Y, Chang Y, Huang S, Zheng L, Yang L, Cao G. iDEF-PseRAAC: Identifying the Defensin Peptide by Using Reduced Amino Acid Composition Descriptor. Evol Bioinform Online 2019; 15:1176934319867088. [PMID: 31391777 PMCID: PMC6669840 DOI: 10.1177/1176934319867088] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 07/08/2019] [Indexed: 11/18/2022] Open
Abstract
Defensins as 1 of major classes of host defense peptides play a significant role in the innate immunity, which are extremely evolved in almost all living organisms. Developing high-throughput computational methods can accurately help in designing drugs or medical means to defense against pathogens. To take up such a challenge, an up-to-date server based on rigorous benchmark dataset, referred to as iDEF-PseRAAC, was designed for predicting the defensin family in this study. By extracting primary sequence compositions based on different types of reduced amino acid alphabet, it was calculated that the best overall accuracy of the selected feature subset was achieved to 92.38%. Therefore, we can conclude that the information provided by abundant types of amino acid reduction will provide efficient and rational methodology for defensin identification. And, a free online server is freely available for academic users at http://bioinfor.imu.edu.cn/idpf. We hold expectations that iDEF-PseRAAC may be a promising weapon for the function annotation about the defensins protein.
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Affiliation(s)
- Yongchun Zuo
- College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot, China.,State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yu Chang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Guifang Cao
- College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot, China
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18
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Pan Z, Zhang H, Liang C, Li G, Xiao Q, Ding P, Luo J. Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction. MOLECULAR THERAPY-NUCLEIC ACIDS 2019; 17:414-423. [PMID: 31319245 PMCID: PMC6637211 DOI: 10.1016/j.omtn.2019.06.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 05/22/2019] [Accepted: 06/12/2019] [Indexed: 11/23/2022]
Abstract
Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction.
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Affiliation(s)
- Zhenxia Pan
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Huaxiang Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
| | - Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha 410006, China
| | - Pingjian Ding
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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19
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Panja AS, Nag A, Bandopadhyay B, Maiti S. Protein Stability Determination (PSD): A Tool for Proteomics Analysis. Curr Bioinform 2018. [DOI: 10.2174/1574893613666180315121614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Protein Stability Determination (PSD) is a sequence-based bioinformatics tool which was developed by utilizing a large input of datasets of protein sequences in FASTA format. The PSD can be used to analyze the meta-proteomics data which will help to predict and design thermozyme and mesozyme for academic and industrial purposes. The PSD also can be utilized to analyze the protein sequence and to predict whether it will be stable in thermophilic or in the mesophilic environment. </P><P> Method and Results: This tool which is supported by any operating system is designed in Java and it provides a user-friendly graphical interface. It is a simple programme and can predict the thermostability nature of proteins with >90% accuracy. The PSD can also predict the nature of constituent amino acids i.e. acidic or basic and polar or nonpolar etc.Conclusion:PSD is highly capable to determine the thermostability status of a protein of hypothetical or unknown peptides as well as meta-proteomics data from any established database. The utilities of the PSD driven analyses include predictions on the functional assignment to a protein. The PSD also helps in designing peptides having flexible combinations of amino acids for functional stability. PSD is freely available at https://sourceforge.net/projects/protein-sequence-determination.
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Affiliation(s)
- Anindya Sundar Panja
- Post Graduate Department of Biotechnology, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, West Bengal, India
| | - Akash Nag
- Department of Computer science, University of Burdwan, India
| | - Bidyut Bandopadhyay
- Post Graduate Department of Biotechnology, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, West Bengal, India
| | - Smarajit Maiti
- Post Graduate Department of Biochemistry and Biotechnology, Cell and Molecular Therapeutics Laboratory, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, West Bengal, India
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20
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Lai HY, Chen XX, Chen W, Tang H, Lin H. Sequence-based predictive modeling to identify cancerlectins. Oncotarget 2018; 8:28169-28175. [PMID: 28423655 PMCID: PMC5438640 DOI: 10.18632/oncotarget.15963] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 02/24/2017] [Indexed: 11/25/2022] Open
Abstract
Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools.
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Affiliation(s)
- Hong-Yan Lai
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin-Xin Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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21
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Tang H, Cao RZ, Wang W, Liu TS, Wang LM, He CM. A two-step discriminated method to identify thermophilic proteins. INT J BIOMATH 2017. [DOI: 10.1142/s1793524517500504] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Improving thermostability of an enzyme can accelerate the relevant chemical reaction. Thus, the analysis and prediction of thermophilic proteins are conducive to protein engineering and enzyme design. In this study, a novel method based on two-step discrimination was proposed to distinguish between thermophilic and non-thermophilic proteins. The model was rigorously benchmarked on an objective dataset including 915 thermophilic proteins and 793 non-thermophilic proteins. Results showed that the overall accuracy of our method is 94.44% in 5-fold cross-validation, which is higher than those of other published methods. We believe that the two-step discriminated strategy will become a promising method in the relevant field of protein bioinformatics.
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Affiliation(s)
- Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, P. R. China
| | - Ren-Zhi Cao
- Computer Science Department, Pacific Lutheran University, Tacoma WA 98447, USA
| | - Wen Wang
- Computer Science Department, Pacific Lutheran University, Tacoma WA 98447, USA
| | - Tie-Shan Liu
- Maize Institute, Shandong Academy of Agricultural Science, Jinan 250100, P. R. China
| | - Li-Ming Wang
- Maize Institute, Shandong Academy of Agricultural Science, Jinan 250100, P. R. China
| | - Chun-Mei He
- Maize Institute, Shandong Academy of Agricultural Science, Jinan 250100, P. R. China
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22
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Fan GL, Liu YL, Wang H. Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition. J Theor Biol 2016; 407:138-142. [DOI: 10.1016/j.jtbi.2016.07.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Revised: 06/24/2016] [Accepted: 07/07/2016] [Indexed: 10/21/2022]
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23
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iDPF-PseRAAAC: A Web-Server for Identifying the Defensin Peptide Family and Subfamily Using Pseudo Reduced Amino Acid Alphabet Composition. PLoS One 2015; 10:e0145541. [PMID: 26713618 PMCID: PMC4694767 DOI: 10.1371/journal.pone.0145541] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 12/04/2015] [Indexed: 11/29/2022] Open
Abstract
Defensins as one of the most abundant classes of antimicrobial peptides are an essential part of the innate immunity that has evolved in most living organisms from lower organisms to humans. To identify specific defensins as interesting antifungal leads, in this study, we constructed a more rigorous benchmark dataset and the iDPF-PseRAAAC server was developed to predict the defensin family and subfamily. Using reduced dipeptide compositions were used, the overall accuracy of proposed method increased to 95.10% for the defensin family, and 98.39% for the vertebrate subfamily, which is higher than the accuracy from other methods. The jackknife test shows that more than 4% improvement was obtained comparing with the previous method. A free online server was further established for the convenience of most experimental scientists at http://wlxy.imu.edu.cn/college/biostation/fuwu/iDPF-PseRAAAC/index.asp. A friendly guide is provided to describe how to use the web server. We anticipate that iDPF-PseRAAAC may become a useful high-throughput tool for both basic research and drug design.
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24
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Zuo YC, Su WX, Zhang SH, Wang SS, Wu CY, Yang L, Li GP. Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure. MOLECULAR BIOSYSTEMS 2015; 11:950-7. [PMID: 25607774 DOI: 10.1039/c4mb00681j] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Membrane transporters play crucial roles in the fundamental cellular processes of living organisms. Computational techniques are very necessary to annotate the transporter functions. In this study, a multi-class K nearest neighbor classifier based on the increment of diversity (KNN-ID) was developed to discriminate the membrane transporter types when the increment of diversity (ID) was introduced as one of the novel similarity distances. Comparisons with multiple recently published methods showed that the proposed KNN-ID method outperformed the other methods, obtaining more than 20% improvement for overall accuracy. The overall prediction accuracy reached was 83.1%, when the K was selected as 2. The prediction sensitivity achieved 76.7%, 89.1%, 80.1% for channels/pores, electrochemical potential-driven transporters, primary active transporters, respectively. Discrimination and comparison between any two different classes of transporters further demonstrated that the proposed method is a potential classifier and will play a complementary role for facilitating the functional assignment of transporters.
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Affiliation(s)
- Yong-Chun Zuo
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of Life Sciences, Inner Mongolia University, Hohhot, 010021, China.
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25
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Zuo YC, Peng Y, Liu L, Chen W, Yang L, Fan GL. Predicting peroxidase subcellular location by hybridizing different descriptors of Chou’ pseudo amino acid patterns. Anal Biochem 2014; 458:14-9. [DOI: 10.1016/j.ab.2014.04.032] [Citation(s) in RCA: 81] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2014] [Revised: 04/22/2014] [Accepted: 04/25/2014] [Indexed: 11/28/2022]
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26
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Wang L, Li C. Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification. Biotechnol Lett 2014; 36:1963-9. [PMID: 24930111 DOI: 10.1007/s10529-014-1577-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Accepted: 05/28/2014] [Indexed: 10/25/2022]
Abstract
A genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to extract useful features from amino acids and g-gap dipeptides for distinguishing between thermophilic and non-thermophilic proteins. The method was trained by a benchmark dataset of 915 thermophilic and 793 non-thermophilic proteins. The method reached an overall accuracy of 95.4 % in a Jackknife test using nine amino acids, 38 0-gap dipeptides and 29 1-gap dipeptides. The accuracy as a function of protein size ranged between 85.8 and 96.9 %. The overall accuracies of three independent tests were 93, 93.4 and 91.8 %. The observed results of detecting thermophilic proteins suggest that the GA-MLR approach described herein should be a powerful method for selecting features that describe thermostabile machines and be an aid in the design of more stable proteins.
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Affiliation(s)
- LiQiang Wang
- Department of Biochemistry and Molecular Biology, College of Life Science, Nankai University, Weijin Road 94, Tianjin, 300071, China,
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27
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Human proteins characterization with subcellular localizations. J Theor Biol 2014; 358:61-73. [PMID: 24862400 DOI: 10.1016/j.jtbi.2014.05.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2014] [Revised: 05/04/2014] [Accepted: 05/05/2014] [Indexed: 11/20/2022]
Abstract
Proteins are responsible for performing the vast majority of cellular functions which are critical to a cell's survival. The knowledge of the subcellular localization of proteins can provide valuable information about their molecular functions. Therefore, one of the fundamental goals in cell biology and proteomics is to analyze the subcellular localizations and functions of these proteins. Recent large-scale human genomics and proteomics studies have made it possible to characterize human proteins at a subcellular localization level. In this study, according to the annotation in Swiss-Prot, 8842 human proteins were classified into seven subcellular localizations. Human proteins in the seven subcellular localizations were compared by using topological properties, biological properties, codon usage indices, mRNA expression levels, protein complexity and physicochemical properties. All these properties were found to be significantly different in the seven categories. In addition, based on these properties and pseudo-amino acid compositions, a machine learning classifier was built for the prediction of protein subcellular localization. The study presented here was an attempt to address the aforementioned properties for comparing human proteins of different subcellular localizations. We hope our findings presented in this study may provide important help for the prediction of protein subcellular localization and for understanding the general function of human proteins in cells.
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