1
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Xiao SR, Zhang YK, Liu KY, Huang YX, Liu R. PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity. BMC Biol 2024; 22:203. [PMID: 39256728 PMCID: PMC11389284 DOI: 10.1186/s12915-024-02006-9] [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: 12/24/2023] [Accepted: 09/03/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND Mutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types. RESULTS Here, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks. CONCLUSIONS PNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA.
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Affiliation(s)
- Si-Rui Xiao
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yao-Kun Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Kai-Yu Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yu-Xiang Huang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Rong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
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2
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Li X, Wang GA, Wei Z, Wang H, Zhu X. Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features. Comput Biol Chem 2023; 107:107970. [PMID: 37866116 DOI: 10.1016/j.compbiolchem.2023.107970] [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: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/24/2023]
Abstract
The identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues. Several computational methods have been developed, however, they are mainly based on hand-crafted features which may not be able to represent all the information of proteins. In this regard, we propose a model called PDH-EH, which utilizes fused features of embeddings extracted from a protein language model (PLM) and handcrafted features. After we extracted the total 1141 dimensional features, we used mRMR to select the optimal feature subset. Based on the optimal feature subset, several different learning algorithms such as Random Forest, Support Vector Machine, and XGBoost were used to build the models. The cross-validation results on the training dataset show that the model built by using Random Forest achieves the highest AUROC. Further evaluation on the independent test set shows that our model outperforms the existing state-of-the-art models. Moreover, the effectiveness and interpretability of embeddings extracted from PLM were demonstrated in our analysis. The codes and datasets used in this study are available at: https://github.com/lixiangli01/PDH-EH.
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Affiliation(s)
- Xiang Li
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Gang-Ao Wang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Zhuoyu Wei
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Hong Wang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China.
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3
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Chandra A, Sharma A, Dehzangi I, Tsunoda T, Sattar A. PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features. Sci Rep 2023; 13:20882. [PMID: 38016996 PMCID: PMC10684570 DOI: 10.1038/s41598-023-47624-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/16/2023] [Indexed: 11/30/2023] Open
Abstract
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .
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Affiliation(s)
- Abel Chandra
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, USA
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Abdul Sattar
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia
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4
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Pan Y, Li R, Li W, Lv L, Guan J, Zhou S. HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:976-990. [PMID: 37730114 PMCID: PMC10928439 DOI: 10.1016/j.gpb.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 09/22/2023]
Abstract
A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.
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Affiliation(s)
- Yuliang Pan
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
| | - Ruiyi Li
- Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China
| | - Wengen Li
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
| | - Liuzhenghao Lv
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
| | - Jihong Guan
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
| | - Shuigeng Zhou
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China.
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5
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Durairaj J, de Ridder D, van Dijk AD. Beyond sequence: Structure-based machine learning. Comput Struct Biotechnol J 2022; 21:630-643. [PMID: 36659927 PMCID: PMC9826903 DOI: 10.1016/j.csbj.2022.12.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/31/2022] Open
Abstract
Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.
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Affiliation(s)
- Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Aalt D.J. van Dijk
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
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6
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Leander M, Liu Z, Cui Q, Raman S. Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins. eLife 2022; 11:e79932. [PMID: 36226916 PMCID: PMC9662819 DOI: 10.7554/elife.79932] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/13/2022] [Indexed: 01/29/2023] Open
Abstract
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.
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Affiliation(s)
- Megan Leander
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Zhuang Liu
- Department of Physics, Boston UniversityBostonUnited States
| | - Qiang Cui
- Department of Physics, Boston UniversityBostonUnited States
- Department of Chemistry, Boston UniversityBostonUnited States
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
- Department of Bacteriology, University of Wisconsin-MadisonMadisonUnited States
- Department of Chemical and Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
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7
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Williams ME, Cloete R. Molecular Modeling of Subtype-Specific Tat Protein Signatures to Predict Tat-TAR Interactions That May Be Involved in HIV-Associated Neurocognitive Disorders. Front Microbiol 2022; 13:866611. [PMID: 35464972 PMCID: PMC9021916 DOI: 10.3389/fmicb.2022.866611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/16/2022] [Indexed: 12/30/2022] Open
Abstract
HIV-1 is responsible for a spectrum of neurocognitive deficits defined as HIV-associated neurocognitive disorders (HAND). The HIV transactivator of transcription (Tat) protein plays a key role in the neuropathophysiology of HAND. The Tat protein functions by transactivation of viral genes through its interaction with the transactivation response (TAR) RNA element. Subtype-specific Tat protein signatures including C31S, R57S and Q63E present in Tat subtype C has previously been linked to a lowered neuropathophysiology compared to Tat subtype B. In this study, we attempted to understand the molecular mechanism by which Tat subtype-specific variation, particularly, C31S, R57S, and Q63E influence the Tat-TAR interaction. We performed molecular modeling to generate accurate three-dimensional protein structures of the HIV-1 Tat subtypes C and B using the Swiss model webserver. Thereafter, we performed a molecular docking of the TAR RNA element to each of the Tat subtypes B and C protein structures using the HDOCK webserver. Our findings indicate that Tat subtype B had a higher affinity for the TAR RNA element compared to Tat subtype C based on a higher docking score of −187.37, a higher binding free energy value of −9834.63 ± 216.17 kJ/mol, and a higher number of protein–nucleotide interactions of 26. Furthermore, Tat subtype B displayed more flexible regions when bound to the TAR element and this flexibility could account for the stronger affinity of Tat subtype B to TAR. From the Tat signatures linked to neuropathogenesis, only R57/R57S are involved in Tat-TAR interaction. Due to the lack of electrostatic interactions observed between Tat subtype C and TAR, weaker affinity is observed, and this may contribute to a lower level of neuropathophysiology observed in subtype C infection.
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Affiliation(s)
- Monray E. Williams
- Human Metabolomics, North-West University, Potchefstroom, South Africa
- *Correspondence: Monray E. Williams,
| | - Ruben Cloete
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
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8
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Zhou T, Rong J, Liu Y, Gong W, Li C. An ensemble approach to predict binding hotspots in protein-RNA interactions based on SMOTE data balancing and random grouping feature selection strategies. Bioinformatics 2022; 38:2452-2458. [PMID: 35253843 DOI: 10.1093/bioinformatics/btac138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/15/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The identification of binding hotspots in protein-RNA interactions is crucial for understanding their potential recognition mechanisms and drug design. The experimental methods have many limitations, since they are usually time-consuming and labor-intensive. Thus, developing an effective and efficient theoretical method is urgently needed. RESULTS Here we present SREPRHot, a method to predict hotspots, defined as the residues whose mutation to alanine generate a binding free energy change ≥ 2.0 kcal/mol, while others use a cutoff of 1.0 kcal/mol to obtain balanced datasets. To deal with the dataset imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is utilized to generate minority samples to achieve a dataset balance. Additionally, besides conventional features, we use two types of new features, residue interface propensity previously developed by us, and topological features obtained using node-weighted networks, and propose an effective Random Grouping feature selection strategy combined with a two-step method to determine an optimal feature set. Finally, a stacking ensemble classifier is adopted to build our model. The results show SREPRHot achieves a good performance with SEN, MCC and AUC of 0.900, 0.557 and 0.829 on the independent testing dataset. The comparison study indicates SREPRHot shows a promising performance. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/ChunhuaLiLab/SREPRHot. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tong Zhou
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Jie Rong
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Yang Liu
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Weikang Gong
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Chunhua Li
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
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9
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Nguyen TB, Myung Y, de Sá AGC, Pires DEV, Ascher DB. mmCSM-NA: accurately predicting effects of single and multiple mutations on protein-nucleic acid binding affinity. NAR Genom Bioinform 2021; 3:lqab109. [PMID: 34805992 PMCID: PMC8600011 DOI: 10.1093/nargab/lqab109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 09/20/2021] [Accepted: 10/27/2021] [Indexed: 02/02/2023] Open
Abstract
While protein-nucleic acid interactions are pivotal for many crucial biological processes, limited experimental data has made the development of computational approaches to characterise these interactions a challenge. Consequently, most approaches to understand the effects of missense mutations on protein-nucleic acid affinity have focused on single-point mutations and have presented a limited performance on independent data sets. To overcome this, we have curated the largest dataset of experimentally measured effects of mutations on nucleic acid binding affinity to date, encompassing 856 single-point mutations and 141 multiple-point mutations across 155 experimentally solved complexes. This was used in combination with an optimized version of our graph-based signatures to develop mmCSM-NA (http://biosig.unimelb.edu.au/mmcsm_na), the first scalable method capable of quantitatively and accurately predicting the effects of multiple-point mutations on nucleic acid binding affinities. mmCSM-NA obtained a Pearson's correlation of up to 0.67 (RMSE of 1.06 Kcal/mol) on single-point mutations under cross-validation, and up to 0.65 on independent non-redundant datasets of multiple-point mutations (RMSE of 1.12 kcal/mol), outperforming similar tools. mmCSM-NA is freely available as an easy-to-use web-server and API. We believe it will be an invaluable tool to shed light on the role of mutations affecting protein-nucleic acid interactions in diseases.
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Affiliation(s)
- Thanh Binh Nguyen
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Yoochan Myung
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Alex G C de Sá
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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10
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Zhang S, Zhao L, Zheng CH, Xia J. A feature-based approach to predict hot spots in protein-DNA binding interfaces. Brief Bioinform 2021; 21:1038-1046. [PMID: 30957840 DOI: 10.1093/bib/bbz037] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 02/20/2019] [Accepted: 03/07/2019] [Indexed: 12/21/2022] Open
Abstract
DNA-binding hot spot residues of proteins are dominant and fundamental interface residues that contribute most of the binding free energy of protein-DNA interfaces. As experimental methods for identifying hot spots are expensive and time consuming, computational approaches are urgently required in predicting hot spots on a large scale. In this work, we systematically assessed a wide variety of 114 features from a combination of the protein sequence, structure, network and solvent accessible information and their combinations along with various feature selection strategies for hot spot prediction. We then trained and compared four commonly used machine learning models, namely, support vector machine (SVM), random forest, Naïve Bayes and k-nearest neighbor, for the identification of hot spots using 10-fold cross-validation and the independent test set. Our results show that (1) features based on the solvent accessible surface area have significant effect on hot spot prediction; (2) different but complementary features generally enhance the prediction performance; and (3) SVM outperforms other machine learning methods on both training and independent test sets. In an effort to improve predictive performance, we developed a feature-based method, namely, PrPDH (Prediction of Protein-DNA binding Hot spots), for the prediction of hot spots in protein-DNA binding interfaces using SVM based on the selected 10 optimal features. Comparative results on benchmark data sets indicate that our predictor is able to achieve generally better performance in predicting hot spots compared to the state-of-the-art predictors. A user-friendly web server for PrPDH is well established and is freely available at http://bioinfo.ahu.edu.cn:8080/PrPDH.
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Affiliation(s)
- Sijia Zhang
- Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
| | - Le Zhao
- Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
| | - Chun-Hou Zheng
- Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
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11
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Mei LC, Wang YL, Wu FX, Wang F, Hao GF, Yang GF. HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study. Brief Bioinform 2021; 22:bbaa373. [PMID: 33406224 PMCID: PMC7929440 DOI: 10.1093/bib/bbaa373] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/19/2020] [Accepted: 11/23/2020] [Indexed: 01/18/2023] Open
Abstract
Protein-nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein-nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein-nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein-nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/.
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Affiliation(s)
- Long-Can Mei
- College of Chemistry, Central China Normal University
| | | | | | | | | | - Guang-Fu Yang
- Pesticide Science from Nankai University, Tianjin, China
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12
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Liu J, Liu S, Liu C, Zhang Y, Pan Y, Wang Z, Wang J, Wen T, Deng L. Nabe: an energetic database of amino acid mutations in protein-nucleic acid binding interfaces. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6352208. [PMID: 34389843 PMCID: PMC8363842 DOI: 10.1093/database/baab050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/23/2021] [Accepted: 07/29/2021] [Indexed: 12/17/2022]
Abstract
Protein–nucleic acid complexes play essential roles in regulating transcription, translation, DNA replication, repair and recombination, RNA processing and translocation. Site-directed mutagenesis has been extremely useful in understanding the principles of protein–DNA and protein–RNA interactions, and experimentally determined mutagenesis data are prerequisites for designing effective algorithms for predicting the binding affinity change upon mutation. However, a vital challenge in this area is the lack of sufficient public experimentally recognized mutation data, which leads to difficulties in developing computational prediction methods. In this article, we present Nabe, an integrated database of amino acid mutations and their effects on the binding free energy in protein–DNA and protein–RNA interactions for which binding affinities have been experimentally determined. Compared with existing databases and data sets, Nabe is the largest protein–nucleic acid mutation database, containing 2506 mutations in 473 protein–DNA and protein–RNA complexes, and of that 1751 are alanine mutations in 405 protein–nucleic acid complexes. For researchers to conveniently utilize the data, Nabe assembles protein–DNA and protein–RNA benchmark databases by adopting the data-processing procedures in the majority of models. To further facilitate users to query data, Nabe provides a searchable and graphical web page. Database URL: http://nabe.denglab.org
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Affiliation(s)
- Junyi Liu
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China.,Viterbi School of Engineering, University of Southern California, 3650 McClintock Ave. OHE 106, Los Angeles, CA 90089, USA
| | - Siyu Liu
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
| | - Chenzhe Liu
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
| | - Yaping Zhang
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
| | - Yuliang Pan
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
| | - Zixiang Wang
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
| | - Jiacheng Wang
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
| | - Ting Wen
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, 22 Shaoshan South Road, Changsha 410075, China
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13
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Deng L, Li W, Zhang J. LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1572-1581. [PMID: 31725386 DOI: 10.1109/tcbb.2019.2946257] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Accumulating evidence has demonstrated dysfunctions of long non-coding RNAs (lncRNAs) are involved in various complex human diseases. However, even today, the relationships between lncRNAs and diseases remain unknown in most cases. Developing effective computational approaches to identify potential lncRNA-disease associations has become a hot topic. Existing network-based approaches are usually focused on the intrinsic features of lncRNAs and diseases but ignore the heterogeneous information of biological networks. Considering the limitations in previous methods, we propose LDAH2V, an efficient computational framework for predicting potential lncRNA-disease associations. LDAH2V uses the HIN2Vec to calculate the meta-path and feature vector for each lncRNA-disease pair in the heterogeneous information network (HIN), which consists of lncRNA similarity network, disease similarity network, miRNA similarity network, and the associations between them. Then, a Gradient Boosting Tree (GBT) classifier to predict lncRNA-disease associations is built with the feature vectors. The results show that LDAH2V performs significantly better than the four existing state-of-the-art methods and gains an AUC of 0.97 in the 10-fold cross-validation test. Furthermore, case studies of colon cancer and ovarian cancer-related lncRNAs have been confirmed in related databases and medical literature.
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14
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Mei LC, Hao GF, Yang GF. Computational methods for predicting hotspots at protein-RNA interfaces. WILEY INTERDISCIPLINARY REVIEWS-RNA 2021; 13:e1675. [PMID: 34080311 DOI: 10.1002/wrna.1675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 11/10/2022]
Abstract
Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been produced for hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Long-Can Mei
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China
| | - Ge-Fei Hao
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China.,State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
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15
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Zhang S, Wang L, Zhao L, Li M, Liu M, Li K, Bin Y, Xia J. An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties. BMC Bioinformatics 2021; 22:253. [PMID: 34000983 PMCID: PMC8130120 DOI: 10.1186/s12859-020-03871-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 11/09/2020] [Indexed: 11/29/2022] Open
Abstract
Background DNA-binding hot spots are dominant and fundamental residues that contribute most of the binding free energy yet accounting for a small portion of protein–DNA interfaces. As experimental methods for identifying hot spots are time-consuming and costly, high-efficiency computational approaches are emerging as alternative pathways to experimental methods. Results Herein, we present a new computational method, termed inpPDH, for hot spot prediction. To improve the prediction performance, we extract hybrid features which incorporate traditional features and new interfacial neighbor properties. To remove redundant and irrelevant features, feature selection is employed using a two-step feature selection strategy. Finally, a subset of 7 optimal features are chosen to construct the predictor using support vector machine. The results on the benchmark dataset show that this proposed method yields significantly better prediction accuracy than those previously published methods in the literature. Moreover, a user-friendly web server for inpPDH is well established and is freely available at http://bioinfo.ahu.edu.cn/inpPDH. Conclusions We have developed an accurate improved prediction model, inpPDH, for hot spot residues in protein–DNA binding interfaces by given the structure of a protein–DNA complex. Moreover, we identify a comprehensive and useful feature subset including the proposed interfacial neighbor features that has an important strength for identifying hot spot residues. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of interfacial neighbor features and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues in protein–DNA complexes. Supplementary information Supplementary information accompanies this paper at 10.1186/s12859-020-03871-1.
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Affiliation(s)
- Sijia Zhang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Lihua Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Le Zhao
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Menglu Li
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Mengya Liu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Ke Li
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
| | - Junfeng Xia
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
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16
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Yin J, Li X, Li F, Lu Y, Zeng S, Zhu F. Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease. Comput Struct Biotechnol J 2021; 19:2318-2328. [PMID: 33995923 PMCID: PMC8105181 DOI: 10.1016/j.csbj.2021.04.035] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 12/14/2022] Open
Abstract
An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.
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Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaoxu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yinjing Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China.,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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17
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Jiang Y, Liu HF, Liu R. Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions. PLoS Comput Biol 2021; 17:e1008951. [PMID: 33872313 PMCID: PMC8084330 DOI: 10.1371/journal.pcbi.1008951] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/29/2021] [Accepted: 04/08/2021] [Indexed: 12/30/2022] Open
Abstract
The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI. Protein-nucleic acid interactions play important roles in various cellular processes. Missense mutations occurring in DNA- or RNA-binding proteins (termed MPDs and MPRs, respectively) could change the binding affinities of these interactions. Previous studies have compared protein-DNA and protein-RNA interactions from multifaceted viewpoints, but less attention has been given to the similarities and specific differences between the effects of MPDs and MPRs and between the methodologies for predicting the affinity changes induced by the two mutation classes. Therefore, we systematically compared their impacts and demonstrated that MPDs and MPRs could have specific preferences for binding affinity changes. These observations motivated us to construct regression models separately for MPDs and MPRs by introducing novel energy and nonenergy descriptors. Although similar frameworks were developed to estimate these two categories of mutation effects, different descriptors were selected in the regression models and further revealed the specificity of mutation classes. The interplay between the energy and nonenergy modules effectively improved prediction performance. Our algorithm can also be adopted to disentangle mutations significantly decreasing binding affinities from other mutations.
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Affiliation(s)
- Yao Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Hui-Fang Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Rong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
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18
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Peng J, Lu G, Shang X. A Survey of Network Representation Learning Methods for Link Prediction in Biological Network. Curr Pharm Des 2021; 26:3076-3084. [PMID: 31951161 DOI: 10.2174/1381612826666200116145057] [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: 12/12/2019] [Accepted: 01/09/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Networks are powerful resources for describing complex systems. Link prediction is an important issue in network analysis and has important practical application value. Network representation learning has proven to be useful for network analysis, especially for link prediction tasks. OBJECTIVE To review the application of network representation learning on link prediction in a biological network, we summarize recent methods for link prediction in a biological network and discuss the application and significance of network representation learning in link prediction task. METHOD & RESULTS We first introduce the widely used link prediction algorithms, then briefly introduce the development of network representation learning methods, focusing on a few widely used methods, and their application in biological network link prediction. Existing studies demonstrate that using network representation learning to predict links in biological networks can achieve better performance. In the end, some possible future directions have been discussed.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Guilin Lu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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19
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Guo Z, Wang P, Liu Z, Zhao Y. Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction. Front Bioeng Biotechnol 2020; 8:584807. [PMID: 33195148 PMCID: PMC7642589 DOI: 10.3389/fbioe.2020.584807] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 09/11/2020] [Indexed: 01/19/2023] Open
Abstract
Thermophilicity is a very important property of proteins, as it sometimes determines denaturation and cell death. Thus, methods for predicting thermophilic proteins and non-thermophilic proteins are of interest and can contribute to the design and engineering of proteins. In this article, we describe the use of feature dimension reduction technology and LIBSVM to identify thermophilic proteins. The highest accuracy obtained by cross-validation was 96.02% with 119 parameters. When using only 16 features, we obtained an accuracy of 93.33%. We discuss the importance of the different characteristics in identification and report a comparison of the performance of support vector machine to that of other methods.
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Affiliation(s)
- Zifan Guo
- School of Aeronautics and Astronautic, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Zhendong Liu
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Yuming Zhao
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
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20
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Predicting Hot Spot Residues at Protein-DNA Binding Interfaces Based on Sequence Information. Interdiscip Sci 2020; 13:1-11. [PMID: 33068261 DOI: 10.1007/s12539-020-00399-z] [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: 07/08/2020] [Revised: 09/27/2020] [Accepted: 10/01/2020] [Indexed: 10/23/2022]
Abstract
Hot spot residues at protein-DNA binding interfaces are hugely important for investigating the underlying mechanism of molecular recognition. Currently, there are a few tools available for identifying the hot spot residues in the protein-DNA complexes. In addition, the three-dimensional protein structures are needed in these tools. However, it is well known that the three-dimensional structures are unavailable for most proteins. Considering the limitation, we proposed a method, named SPDH, for predicting hot spot residues only based on protein sequences. Firstly, we obtained 133 features from physicochemical property, conservation, predicted solvent accessible surface area and structure. Then, we systematically assessed these features based on various feature selection methods to obtain the optimal feature subset and compared the models using four classical machine learning algorithms (support vector machine, random forest, logistic regression, and k-nearest neighbor) on the training dataset. We found that the variability of physicochemical property features between wild and mutative types was important on improving the performance of the prediction model. On the independent test set, our method achieved the performance with AUC of 0.760 and sensitivity of 0.808, and outperformed other methods. The data and source code can be downloaded at https://github.com/xialab-ahu/SPDH .
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21
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Pan Y, Zhou S, Guan J. Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach. BMC Bioinformatics 2020; 21:384. [PMID: 32938375 PMCID: PMC7495898 DOI: 10.1186/s12859-020-03675-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods. RESULTS Here, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones. CONCLUSIONS PreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .
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Affiliation(s)
- Yuliang Pan
- Department of Computer Science and Technology, Tongji University, No. 4800 Caoan Road, Shanghai, 201804, China
| | - Shuigeng Zhou
- Shanghai Key Laboratory of Intelligent Information Processing, and School of Computer Science, Fudan University, No. 220 Handan Road, Shanghai, 200433, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, No. 4800 Caoan Road, Shanghai, 201804, China.
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22
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Li K, Zhang S, Yan D, Bin Y, Xia J. Prediction of hot spots in protein-DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting. BMC Bioinformatics 2020; 21:381. [PMID: 32938395 PMCID: PMC7495874 DOI: 10.1186/s12859-020-03683-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale. RESULTS Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP. CONCLUSION Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.
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Affiliation(s)
- Ke Li
- School of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.,Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Sijia Zhang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Di Yan
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.,School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Junfeng Xia
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
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23
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Zhuang H, Zhang Y, Yang S, Cheng L, Liu SL. A Mendelian Randomization Study on Infant Length and Type 2 Diabetes Mellitus Risk. Curr Gene Ther 2020; 19:224-231. [PMID: 31553296 DOI: 10.2174/1566523219666190925115535] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/15/2019] [Accepted: 06/16/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Infant length (IL) is a positively associated phenotype of type 2 diabetes mellitus (T2DM), but the causal relationship of which is still unclear. Here, we applied a Mendelian randomization (MR) study to explore the causal relationship between IL and T2DM, which has the potential to provide guidance for assessing T2DM activity and T2DM- prevention in young at-risk populations. MATERIALS AND METHODS To classify the study, a two-sample MR, using genetic instrumental variables (IVs) to explore the causal effect was applied to test the influence of IL on the risk of T2DM. In this study, MR was carried out on GWAS data using 8 independent IL SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated by the inverse-variance weighted method for the assessment of the risk the shorter IL brings to T2DM. Sensitivity validation was conducted to identify the effect of individual SNPs. MR-Egger regression was used to detect pleiotropic bias of IVs. RESULTS The pooled odds ratio from the IVW method was 1.03 (95% CI 0.89-1.18, P = 0.0785), low intercept was -0.477, P = 0.252, and small fluctuation of ORs ranged from -0.062 ((0.966 - 1.03) / 1.03) to 0.05 ((1.081 - 1.03) / 1.03) in leave-one-out validation. CONCLUSION We validated that the shorter IL causes no additional risk to T2DM. The sensitivity analysis and the MR-Egger regression analysis also provided adequate evidence that the above result was not due to any heterogeneity or pleiotropic effect of IVs.
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Affiliation(s)
- He Zhuang
- Systemomics Center, College of Pharmacy, and Genomics Research Center (State-Province Key Laboratories of Biomedicine- Pharmaceutics of China), Harbin Medical University, Harbin, China.,HMU-UCFM Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, 150001, Harbin, China
| | - Shuo Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shu-Lin Liu
- Systemomics Center, College of Pharmacy, and Genomics Research Center (State-Province Key Laboratories of Biomedicine- Pharmaceutics of China), Harbin Medical University, Harbin, China.,HMU-UCFM Centre for Infection and Genomics, Harbin Medical University, Harbin, China.,Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Canada.,Department of Infectious Diseases, The First Affiliated Hospital, Harbin Medical University, Harbin, China.,Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China
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24
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Zhang N, Lu H, Chen Y, Zhu Z, Yang Q, Wang S, Li M. PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions. Int J Mol Sci 2020; 21:ijms21155560. [PMID: 32756481 PMCID: PMC7432928 DOI: 10.3390/ijms21155560] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 12/23/2022] Open
Abstract
Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol−1, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.
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25
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Wahab A, Mahmoudi O, Kim J, Chong KT. DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning. Cells 2020; 9:E1756. [PMID: 32707969 PMCID: PMC7465362 DOI: 10.3390/cells9081756] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/17/2020] [Accepted: 07/17/2020] [Indexed: 11/24/2022] Open
Abstract
N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.
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Affiliation(s)
- Abdul Wahab
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea; (A.W.); (O.M.)
| | - Omid Mahmoudi
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea; (A.W.); (O.M.)
| | - Jeehong Kim
- Department of New & Renewable Energy, VISION College of Jeonju, Jeonju 55069, Korea
| | - Kil To Chong
- Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Korea
- Advance Electronics & Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
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Zhu X, Liu L, He J, Fang T, Xiong Y, Mitchell JC. iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots. BMC Bioinformatics 2020; 21:289. [PMID: 32631222 PMCID: PMC7336410 DOI: 10.1186/s12859-020-03636-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 06/25/2020] [Indexed: 12/20/2022] Open
Abstract
Background The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. Results In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ∆SASsa1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Conclusion In this study, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.
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Affiliation(s)
- Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui, China. .,School of Life Sciences, Anhui University, Hefei, Anhui, China.
| | - Ling Liu
- School of Life Sciences, Anhui University, Hefei, Anhui, China
| | - Jingjing He
- School of Life Sciences, Anhui University, Hefei, Anhui, China
| | - Ting Fang
- School of Life Sciences, Anhui University, Hefei, Anhui, China
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Julie C Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
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Deng S, Sun Y, Zhao T, Hu Y, Zang T. A Review of Drug Side Effect Identification Methods. Curr Pharm Des 2020; 26:3096-3104. [PMID: 32532187 DOI: 10.2174/1381612826666200612163819] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/18/2020] [Indexed: 11/22/2022]
Abstract
Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.
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Affiliation(s)
- Shuai Deng
- College of Science, Beijing Forestry University, Beijing, China
| | - Yige Sun
- Microbiology Department, Harbin Medical University, Harbin, 150081, China
| | - Tianyi Zhao
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Yang Hu
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Tianyi Zang
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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Li F, Fan C, Marquez-Lago TT, Leier A, Revote J, Jia C, Zhu Y, Smith AI, Webb GI, Liu Q, Wei L, Li J, Song J. PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. Brief Bioinform 2020; 21:1069-1079. [PMID: 31161204 PMCID: PMC7299293 DOI: 10.1093/bib/bbz050] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 03/26/2019] [Accepted: 03/29/2019] [Indexed: 12/26/2022] Open
Abstract
Post-translational modifications (PTMs) play very important roles in various cell signaling pathways and biological process. Due to PTMs' extremely important roles, many major PTMs have been studied, while the functional and mechanical characterization of major PTMs is well documented in several databases. However, most currently available databases mainly focus on protein sequences, while the real 3D structures of PTMs have been largely ignored. Therefore, studies of PTMs 3D structural signatures have been severely limited by the deficiency of the data. Here, we develop PRISMOID, a novel publicly available and free 3D structure database for a wide range of PTMs. PRISMOID represents an up-to-date and interactive online knowledge base with specific focus on 3D structural contexts of PTMs sites and mutations that occur on PTMs and in the close proximity of PTM sites with functional impact. The first version of PRISMOID encompasses 17 145 non-redundant modification sites on 3919 related protein 3D structure entries pertaining to 37 different types of PTMs. Our entry web page is organized in a comprehensive manner, including detailed PTM annotation on the 3D structure and biological information in terms of mutations affecting PTMs, secondary structure features and per-residue solvent accessibility features of PTM sites, domain context, predicted natively disordered regions and sequence alignments. In addition, high-definition JavaScript packages are employed to enhance information visualization in PRISMOID. PRISMOID equips a variety of interactive and customizable search options and data browsing functions; these capabilities allow users to access data via keyword, ID and advanced options combination search in an efficient and user-friendly way. A download page is also provided to enable users to download the SQL file, computational structural features and PTM sites' data. We anticipate PRISMOID will swiftly become an invaluable online resource, assisting both biologists and bioinformaticians to conduct experiments and develop applications supporting discovery efforts in the sequence-structural-functional relationship of PTMs and providing important insight into mutations and PTM sites interaction mechanisms. The PRISMOID database is freely accessible at http://prismoid.erc.monash.edu/. The database and web interface are implemented in MySQL, JSP, JavaScript and HTML with all major browsers supported.
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Affiliation(s)
- Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Cunshuo Fan
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Tatiana T Marquez-Lago
- Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - André Leier
- Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Jerico Revote
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia
| | - Cangzhi Jia
- College of Science, Dalian Maritime University, Dalian, China
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yan Zhu
- Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, Victoria, Australia
| | - A Ian Smith
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Quanzhong Liu
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Leyi Wei
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jian Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
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Zeng F, Fang G, Yao L. A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites. Front Genet 2020; 11:209. [PMID: 32211035 PMCID: PMC7067889 DOI: 10.3389/fgene.2020.00209] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 02/21/2020] [Indexed: 11/25/2022] Open
Abstract
Motivation: N4-methylcytosine (4mC) plays an important role in host defense and transcriptional regulation. Accurate identification of 4mc sites provides a more comprehensive understanding of its biological effects. At present, the traditional machine learning algorithms are used in the research on 4mC sites prediction, but the complexity of the algorithms is relatively high, which is not suitable for the processing of large data sets, and the accuracy of prediction needs to be improved. Therefore, it is necessary to develop a new and effective method to accurately identify 4mC sites. Results: In this work, we found a large number of 4mC sites and non 4mC sites of Caenorhabditis elegans (C. elegans) from the latest MethSMRT website, which greatly expanded the dataset of C. elegans, and developed a hybrid deep neural network framework named 4mcDeep-CBI, aiming to identify 4mC sites. In order to obtain the high latitude information of the feature, we input the preliminary extracted features into the Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory network (BLSTM) to generate advanced features. Taking the advanced features as algorithm input, we have proposed an integrated algorithm to improve feature representation. Experimental results on large new dataset show that the proposed predictor is able to achieve generally better performance in identifying 4mC sites as compared to the state-of-art predictor. Notably, this is the first study of identifying 4mC sites using deep neural network. Moreover, our model runs much faster than the state-of-art predictor.
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Affiliation(s)
- Feng Zeng
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Guanyun Fang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lan Yao
- College of Mathematics and Econometrics, Hunan University, Changsha, China
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30
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Huang Q, Zhang J, Wei L, Guo F, Zou Q. 6mA-RicePred: A Method for Identifying DNA N 6-Methyladenine Sites in the Rice Genome Based on Feature Fusion. FRONTIERS IN PLANT SCIENCE 2020; 11:4. [PMID: 32076430 PMCID: PMC7006724 DOI: 10.3389/fpls.2020.00004] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 01/06/2020] [Indexed: 06/01/2023]
Abstract
MOTIVATION The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. RESULTS In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.
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Affiliation(s)
- Qianfei Huang
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jun Zhang
- Rehabilitation Department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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31
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Wang C, Zhao N, Yuan L, Liu X. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers. Cells 2020; 9:E326. [PMID: 32019269 PMCID: PMC7072524 DOI: 10.3390/cells9020326] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 01/28/2020] [Accepted: 01/28/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.
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Affiliation(s)
- Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Ning Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China;
| | - Linlin Yuan
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
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32
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PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity. Sci Rep 2020; 10:1278. [PMID: 31992738 PMCID: PMC6987227 DOI: 10.1038/s41598-020-57778-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 01/06/2020] [Indexed: 11/17/2022] Open
Abstract
The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.
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33
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Tan C, Wang T, Yang W, Deng L. PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction. Molecules 2019; 25:molecules25010098. [PMID: 31888057 PMCID: PMC6982935 DOI: 10.3390/molecules25010098] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 12/20/2019] [Accepted: 12/21/2019] [Indexed: 11/16/2022] Open
Abstract
Interactions between proteins and DNAs play essential roles in many biological processes. DNA binding proteins can be classified into two categories. Double-stranded DNA-binding proteins (DSBs) bind to double-stranded DNA and are involved in a series of cell functions such as gene expression and regulation. Single-stranded DNA-binding proteins (SSBs) are necessary for DNA replication, recombination, and repair and are responsible for binding to the single-stranded DNA. Therefore, the effective classification of DNA-binding proteins is helpful for functional annotations of proteins. In this work, we propose PredPSD, a computational method based on sequence information that accurately predicts SSBs and DSBs. It introduces three novel feature extraction algorithms. In particular, we use the autocross-covariance (ACC) transformation to transform feature matrices into fixed-length vectors. Then, we put the optimal feature subset obtained by the minimal-redundancy-maximal-relevance criterion (mRMR) feature selection algorithm into the gradient tree boosting (GTB). In 10-fold cross-validation based on a benchmark dataset, PredPSD achieves promising performances with an AUC score of 0.956 and an accuracy of 0.912, which are better than those of existing methods. Moreover, our method has significantly improved the prediction accuracy in independent testing. The experimental results show that PredPSD can significantly recognize the binding specificity and differentiate DSBs and SSBs.
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Affiliation(s)
- Changgeng Tan
- School of Computer Science and Engineering, Central South University, Changsha 410075, China; (C.T.); (T.W.); (W.Y.)
| | - Tong Wang
- School of Computer Science and Engineering, Central South University, Changsha 410075, China; (C.T.); (T.W.); (W.Y.)
| | - Wenyi Yang
- School of Computer Science and Engineering, Central South University, Changsha 410075, China; (C.T.); (T.W.); (W.Y.)
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China; (C.T.); (T.W.); (W.Y.)
- School of Software, Xinjiang University, Urumqi 830008, China
- Correspondence: ; Tel.: +86-731-82539736
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Deng L, Zhong G, Liu C, Luo J, Liu H. MADOKA: an ultra-fast approach for large-scale protein structure similarity searching. BMC Bioinformatics 2019; 20:662. [PMID: 31870277 PMCID: PMC6929402 DOI: 10.1186/s12859-019-3235-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 11/14/2019] [Indexed: 01/22/2023] Open
Abstract
Background Protein comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging. Results Here, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. Then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6–100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database (About 360,000 protein chains in total), as well as additional features such as 3D structure alignment visualization. The MADOKA web server is freely available at: http://madoka.denglab.org/ Conclusions MADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, 410075, China
| | - Guolun Zhong
- School of Computer Science and Engineering, Central South University, Changsha, 410075, China
| | - Chenzhe Liu
- School of Computer Science and Engineering, Central South University, Changsha, 410075, China
| | - Judong Luo
- Department of Radiation Oncology, the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
| | - Hui Liu
- Lab of Information Management, Changzhou University, Changzhou, 213164, China.
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35
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Nithin C, Mukherjee S, Bahadur RP. A structure-based model for the prediction of protein-RNA binding affinity. RNA (NEW YORK, N.Y.) 2019; 25:1628-1645. [PMID: 31395671 PMCID: PMC6859855 DOI: 10.1261/rna.071779.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/05/2019] [Indexed: 05/28/2023]
Abstract
Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.
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Affiliation(s)
- Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
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Zheng N, Wang K, Zhan W, Deng L. Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches. Curr Drug Metab 2019; 20:177-184. [PMID: 30156155 DOI: 10.2174/1389200219666180829121038] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 05/21/2018] [Accepted: 08/02/2018] [Indexed: 01/15/2023]
Abstract
BACKGROUND Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions. METHODS In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods. RESULTS We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions. CONCLUSION The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.
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Affiliation(s)
- Nantao Zheng
- School of Software, Central South University, Changsha, 410075, China
| | - Kairou Wang
- School of Software, Central South University, Changsha, 410075, China
| | - Weihua Zhan
- School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, China
| | - Lei Deng
- School of Software, Central South University, Changsha, 410075, China.,Shanghai Key Lab of Intelligent Information Processing, Shanghai 200433, China
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37
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Wang X, Wang Y, Xu Z, Xiong Y, Wei DQ. ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method. Front Pharmacol 2019; 10:971. [PMID: 31543820 PMCID: PMC6739564 DOI: 10.3389/fphar.2019.00971] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 07/29/2019] [Indexed: 01/12/2023] Open
Abstract
Anatomical Therapeutic Chemical (ATC) classification system proposed by the World Health Organization is a widely accepted drug classification scheme in both academic and industrial realm. It is a multilabeling system which categorizes drugs into multiple classes according to their therapeutic, pharmacological, and chemical attributes. In this study, we adopted a data-driven network-based label space partition (NLSP) method for prediction of ATC classes of a given compound within the multilabel learning framework. The proposed method ATC-NLSP is trained on the similarity-based features such as chemical–chemical interaction and structural and fingerprint similarities of a compound to other compounds belonging to the different ATC categories. The NLSP method trains predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes the ensemble labels for a compound as final prediction. Experimental evaluation based on the jackknife test on the benchmark dataset demonstrated that our method has boosted the absolute true rate, which is the most stringent evaluation metrics in this study, from 0.6330 to 0.7497, in comparison to the state-of-the-art approaches. Moreover, the community structures of the label relation graph were detected through the label propagation method. The advantage of multilabel learning over the single-label models was shown by label-wise analysis. Our study indicated that the proposed method ATC-NLSP, which adopts ideas from network research community and captures the correlation of labels in a data driven manner, is the top-performing model in the ATC prediction task. We believed that the power of NLSP remains to be unleashed for the multilabel learning tasks in drug discovery. The source codes are freely available at https://github.com/dqwei-lab/ATC.
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Affiliation(s)
- Xiangeng Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhenyu Xu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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38
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Lv Z, Jin S, Ding H, Zou Q. A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features. Front Bioeng Biotechnol 2019; 7:215. [PMID: 31552241 PMCID: PMC6737778 DOI: 10.3389/fbioe.2019.00215] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 08/22/2019] [Indexed: 02/01/2023] Open
Abstract
To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.
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Affiliation(s)
- Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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39
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Deng L, Yang W, Liu H. PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees. Front Genet 2019; 10:637. [PMID: 31428122 PMCID: PMC6688581 DOI: 10.3389/fgene.2019.00637] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/18/2019] [Indexed: 01/24/2023] Open
Abstract
Protein-RNA interactions play essential roles in many biological aspects. Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanisms and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches. In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validation. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other typical regression methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. In addition, a user-friendly web server has been developed to predict the binding affinity of protein-RNA complexes. The PredPRBA webserver is freely available at http://PredPRBA.denglab.org/.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China.,School of Software, Xinjiang University, Urumqi, China
| | - Wenyi Yang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hui Liu
- Lab of Information Management, Changzhou University, Changzhou, China
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40
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Mabonga L, Kappo AP. Protein-protein interaction modulators: advances, successes and remaining challenges. Biophys Rev 2019; 11:559-581. [PMID: 31301019 PMCID: PMC6682198 DOI: 10.1007/s12551-019-00570-x] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 06/24/2019] [Indexed: 12/12/2022] Open
Abstract
Modulating disease-relevant protein-protein interactions (PPIs) using small-molecule inhibitors is a quite indispensable diagnostic and therapeutic strategy in averting pathophysiological cues and disease progression. Over the years, targeting intracellular PPIs as drug design targets has been a challenging task owing to their highly dynamic and expansive interfacial areas (flat, featureless and relatively large). However, advances in PPI-focused drug discovery technology have been reported and a few drugs are already on the market, with some potential drug-like candidates already in clinical trials. In this article, we review the advances, successes and remaining challenges in the application of small molecules as valuable PPI modulators in disease diagnosis and therapeutics.
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Affiliation(s)
- Lloyd Mabonga
- Biotechnology and Structural Biology (BSB) Group, Department of Biochemistry and Microbiology, University of Zululand, KwaDlangezwa, 3886, South Africa
| | - Abidemi Paul Kappo
- Biotechnology and Structural Biology (BSB) Group, Department of Biochemistry and Microbiology, University of Zululand, KwaDlangezwa, 3886, South Africa.
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41
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Cheng N, Li M, Zhao L, Zhang B, Yang Y, Zheng CH, Xia J. Comparison and integration of computational methods for deleterious synonymous mutation prediction. Brief Bioinform 2019; 21:970-981. [DOI: 10.1093/bib/bbz047] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 03/28/2019] [Accepted: 03/29/2019] [Indexed: 01/03/2023] Open
Abstract
Abstract
Synonymous mutations do not change the encoded amino acids but may alter the structure or function of an mRNA in ways that impact gene function. Advances in next generation sequencing technologies have detected numerous synonymous mutations in the human genome. Several computational models have been proposed to predict deleterious synonymous mutations, which have greatly facilitated the development of this important field. Consequently, there is an urgent need to assess the state-of-the-art computational methods for deleterious synonymous mutation prediction to further advance the existing methodologies and to improve performance. In this regard, we systematically compared a total of 10 computational methods (including specific method for deleterious synonymous mutation and general method for single nucleotide mutation) in terms of the algorithms used, calculated features, performance evaluation and software usability. In addition, we constructed two carefully curated independent test datasets and accordingly assessed the robustness and scalability of these different computational methods for the identification of deleterious synonymous mutations. In an effort to improve predictive performance, we established an ensemble model, named Prediction of Deleterious Synonymous Mutation (PrDSM), which averages the ratings generated by the three most accurate predictors. Our benchmark tests demonstrated that the ensemble model PrDSM outperformed the reviewed tools for the prediction of deleterious synonymous mutations. Using the ensemble model, we developed an accessible online predictor, PrDSM, available at http://bioinfo.ahu.edu.cn:8080/PrDSM/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for deleterious synonymous mutation prediction.
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Affiliation(s)
- Na Cheng
- Institutes of Physical Science and Information Technology, Anhui University
| | - Menglu Li
- School of Computer Science and Technology, Anhui University
| | - Le Zhao
- School of Computer Science and Technology, Anhui University
| | - Bo Zhang
- School of Computer Science and Technology, Anhui University
| | - Yuhua Yang
- School of Computer Science and Technology, Anhui University
| | - Chun-Hou Zheng
- School of Computer Science and Technology, Anhui University
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University
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42
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Deng L, Sui Y, Zhang J. XGBPRH: Prediction of Binding Hot Spots at Protein⁻RNA Interfaces Utilizing Extreme Gradient Boosting. Genes (Basel) 2019; 10:genes10030242. [PMID: 30901953 PMCID: PMC6471955 DOI: 10.3390/genes10030242] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/14/2019] [Accepted: 03/15/2019] [Indexed: 01/24/2023] Open
Abstract
Hot spot residues at protein⁻RNA complexes are vitally important for investigating the underlying molecular recognition mechanism. Accurately identifying protein⁻RNA binding hot spots is critical for drug designing and protein engineering. Although some progress has been made by utilizing various available features and a series of machine learning approaches, these methods are still in the infant stage. In this paper, we present a new computational method named XGBPRH, which is based on an eXtreme Gradient Boosting (XGBoost) algorithm and can effectively predict hot spot residues in protein⁻RNA interfaces utilizing an optimal set of properties. Firstly, we download 47 protein⁻RNA complexes and calculate a total of 156 sequence, structure, exposure, and network features. Next, we adopt a two-step feature selection algorithm to extract a combination of 6 optimal features from the combination of these 156 features. Compared with the state-of-the-art approaches, XGBPRH achieves better performances with an area under the ROC curve (AUC) score of 0.817 and an F1-score of 0.802 on the independent test set. Meanwhile, we also apply XGBPRH to two case studies. The results demonstrate that the method can effectively identify novel energy hotspots.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.
| | - Yuanchao Sui
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.
| | - Jingpu Zhang
- School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan 467000, China.
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43
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Li Y, Niu M, Zou Q. ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm. J Proteome Res 2019; 18:1392-1401. [DOI: 10.1021/acs.jproteome.9b00012] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Yanjuan Li
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Mengting Niu
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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44
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Deng L, Wang J, Zhang J. Predicting Gene Ontology Function of Human MicroRNAs by Integrating Multiple Networks. Front Genet 2019; 10:3. [PMID: 30761178 PMCID: PMC6361788 DOI: 10.3389/fgene.2019.00003] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 01/07/2019] [Indexed: 12/15/2022] Open
Abstract
MicroRNAs (miRNAs) have been demonstrated to play significant biological roles in many human biological processes. Inferring the functions of miRNAs is an important strategy for understanding disease pathogenesis at the molecular level. In this paper, we propose an integrated model, PmiRGO, to infer the gene ontology (GO) functions of miRNAs by integrating multiple data sources, including the expression profiles of miRNAs, miRNA-target interactions, and protein-protein interactions (PPI). PmiRGO starts by building a global network consisting of three networks. Then, it employs DeepWalk to learn latent representations as network features of the global heterogeneous network. Finally, the SVM-based models are applied to label the GO terms of miRNAs. The experimental results show that PmiRGO has a significantly better performance than existing state-of-the-art methods in terms of F max . A case study further demonstrates the feasibility of PmiRGO to annotate the potential functions of miRNAs.
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Affiliation(s)
- Lei Deng
- School of Software, Central South University, Changsha, China
| | - Jiacheng Wang
- School of Software, Central South University, Changsha, China
| | - Jingpu Zhang
- School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, China
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45
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Qu K, Wei L, Yu J, Wang C. Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods. FRONTIERS IN PLANT SCIENCE 2019; 9:1961. [PMID: 30687359 PMCID: PMC6335366 DOI: 10.3389/fpls.2018.01961] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 12/17/2018] [Indexed: 05/04/2023]
Abstract
Motivation: Pentatricopeptide repeat (PPR) is a triangular pentapeptide repeat domain that plays a vital role in plant growth. In this study, we seek to identify PPR coding genes and proteins using a mixture of feature extraction methods. We use four single feature extraction methods focusing on the sequence, physical, and chemical properties as well as the amino acid composition, and mix the features. The Max-Relevant-Max-Distance (MRMD) technique is applied to reduce the feature dimension. Classification uses the random forest, J48, and naïve Bayes with 10-fold cross-validation. Results: Combining two of the feature extraction methods with the random forest classifier produces the highest area under the curve of 0.9848. Using MRMD to reduce the dimension improves this metric for J48 and naïve Bayes, but has little effect on the random forest results. Availability and Implementation: The webserver is available at: http://server.malab.cn/MixedPPR/index.jsp.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jiantao Yu
- College of Information Engineering, North-West A&F University, Yangling, China
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
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46
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Liu Q, Chen P, Wang B, Zhang J, Li J. Hot spot prediction in protein-protein interactions by an ensemble system. BMC SYSTEMS BIOLOGY 2018; 12:132. [PMID: 30598091 PMCID: PMC6311905 DOI: 10.1186/s12918-018-0665-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features. RESULTS This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance. CONCLUSION The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction. AVAILABILITY http://deeplearner.ahu.edu.cn/web/HotspotEL.htm .
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Affiliation(s)
- Quanya Liu
- Institute of Physical Science and Information Technology, Anhui University, Hefei, Anhui, 230601, China
| | - Peng Chen
- Institute of Physical Science and Information Technology, Anhui University, Hefei, Anhui, 230601, China.
| | - Bing Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, Anhui, 243032, China. .,School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, Anhui, 243032, China.
| | - Jun Zhang
- School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, 230601, China.
| | - Jinyan Li
- Advanced Analytics Institute and Centre for Health Technologies, University of Technology, Sydney, Sydney, Broadway, NSW, 2007, Australia
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47
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Deng L, Pan J, Xu X, Yang W, Liu C, Liu H. PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine. BMC Bioinformatics 2018; 19:522. [PMID: 30598073 PMCID: PMC6311926 DOI: 10.1186/s12859-018-2527-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability. Results Here, we present an efficient approach termed PDRLGB that uses a light gradient boosting machine (LightGBM) to predict binding residues in protein-DNA complexes. Initially, we extract a wide variety of 913 sequence and structure features with a sliding window of 11. Then, we apply the random forest algorithm to sort the features in descending order of importance and obtain the optimal subset of features using incremental feature selection. Based on the selected feature set, we use a light gradient boosting machine to build the prediction model for DNA-binding residues. Our PDRLGB method shows better overall predictive accuracy and relatively less training time than other widely used machine learning (ML) methods such as random forest (RF), Adaboost and support vector machine (SVM). We further compare PDRLGB with various existing approaches on the independent test datasets and show improvement in results over the existing state-of-the-art approaches. Conclusions PDRLGB is an efficient approach to predict specific residues for protein-DNA interactions.
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Affiliation(s)
- Lei Deng
- School of Software, Central South University, Changsha, 410075, China
| | - Juan Pan
- School of Software, Central South University, Changsha, 410075, China
| | - Xiaojie Xu
- School of Software, Central South University, Changsha, 410075, China
| | - Wenyi Yang
- School of Software, Central South University, Changsha, 410075, China
| | - Chuyao Liu
- School of Software, Central South University, Changsha, 410075, China
| | - Hui Liu
- Lab of Information Management, Changzhou University, Changzhou, 213164, China.
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48
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Gan Y, Li N, Zou G, Xin Y, Guan J. Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method. BMC Med Genomics 2018; 11:117. [PMID: 30598115 PMCID: PMC6311928 DOI: 10.1186/s12920-018-0433-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Background Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes. Methods In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters. Results Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes. Conclusions Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Ning Li
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yongchang Xin
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, Shanghai, China.
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49
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Integrating Multiple Interaction Networks for Gene Function Inference. Molecules 2018; 24:molecules24010030. [PMID: 30577643 PMCID: PMC6337127 DOI: 10.3390/molecules24010030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 12/19/2018] [Accepted: 12/20/2018] [Indexed: 01/17/2023] Open
Abstract
In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.
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50
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Zeng C, Zhan W, Deng L. SDADB: a functional annotation database of protein structural domains. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:5046758. [PMID: 29961821 PMCID: PMC6025185 DOI: 10.1093/database/bay064] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 06/04/2018] [Indexed: 12/27/2022]
Abstract
Annotating functional terms with individual domains is essential for understanding the functions of full-length proteins. We describe SDADB, a functional annotation database for structural domains. SDADB provides associations between gene ontology (GO) terms and SCOP domains calculated with an integrated framework. GO annotations are assigned probabilities of being correct, which are estimated with a Bayesian network by taking advantage of structural neighborhood mappings, SCOP-InterPro domain mapping information, position-specific scoring matrices (PSSMs) and sequence homolog features, with the most substantial contribution coming from high-coverage structure-based domain-protein mappings. The domain-protein mappings are computed using large-scale structure alignment. SDADB contains ontological terms with probabilistic scores for more than 214 000 distinct SCOP domains. It also provides additional features include 3D structure alignment visualization, GO hierarchical tree view, search, browse and download options. Database URL: http://sda.denglab.org
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Affiliation(s)
- Cheng Zeng
- School of Software, Central South University, Changsha 410075, China
| | - Weihua Zhan
- School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, China
| | - Lei Deng
- School of Software, Central South University, Changsha 410075, China.,Shanghai Key Lab of Intelligent Information Processing, Shanghai 200433, China
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