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Zeng H, Zhang Y, Liu N, Wei Q, Yang F, Li J. Stimulus-Responsive Nanodelivery and Release Systems for Cancer Gene Therapy: Efficacy Improvement Strategies. Int J Nanomedicine 2024; 19:7099-7121. [PMID: 39045344 PMCID: PMC11265383 DOI: 10.2147/ijn.s470637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/18/2024] [Indexed: 07/25/2024] Open
Abstract
Introduction of exogenous genes into target cells to overcome various tumor diseases caused by genetic defects or abnormalities and gene therapy, a new treatment method, provides a promising strategy for tumor treatment. Over the past decade, gene therapy has made exciting progress; however, it still faces the challenge of low nucleic acid delivery and release efficiencies. The emergence of nonviral vectors, primarily nanodelivery and release systems (NDRS), has resulted in a historic breakthrough in the application of gene therapy. NDRS, especially stimulus-responsive NDRS that can respond in a timely manner to changes in the internal and external microenvironment (eg, low pH, high concentration of glutathione/reactive oxygen species, overexpressed enzymes, temperature, light, ultrasound, and magnetic field), has shown excellent loading and release advantages in the precision and efficiency of tumor gene therapy and has been widely applied. The only disadvantage is that poor transfection efficiency limits the in-depth application of gene therapy in clinical practice, owing to the presence of biological barriers in the body. Therefore, this review first introduces the development history of gene therapy, the current obstacles faced by gene delivery, strategies to overcome these obstacles, and conventional vectors, and then focuses on the latest research progress in various stimulus-responsive NDRS for improving gene delivery efficiency. Finally, the future challenges and prospects that stimulus-responsive NDRS may face in clinical application and transformation are discussed to provide references for enhancing in-depth research on tumor gene therapy.
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Affiliation(s)
- Huamin Zeng
- Cancer Prevention and Treatment Institute of Chengdu, Department of Pathology, Chengdu Fifth People’s Hospital (The Second Clinical Medical College, Affiliated Fifth People’s Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, Sichuan, 611130, People’s Republic of China
| | - Yiran Zhang
- Cancer Prevention and Treatment Institute of Chengdu, Department of Pathology, Chengdu Fifth People’s Hospital (The Second Clinical Medical College, Affiliated Fifth People’s Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, Sichuan, 611130, People’s Republic of China
- School of Medical and Life Sciences, Chengdu University of traditional Chinese Medicine, Chengdu, Sichuan, 611137, People’s Republic of China
| | - Ningyi Liu
- Cancer Prevention and Treatment Institute of Chengdu, Department of Pathology, Chengdu Fifth People’s Hospital (The Second Clinical Medical College, Affiliated Fifth People’s Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, Sichuan, 611130, People’s Republic of China
- School of Basic Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637007, People’s Republic of China
| | - Qingqing Wei
- Cancer Prevention and Treatment Institute of Chengdu, Department of Pathology, Chengdu Fifth People’s Hospital (The Second Clinical Medical College, Affiliated Fifth People’s Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, Sichuan, 611130, People’s Republic of China
- School of Basic Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637007, People’s Republic of China
| | - Fan Yang
- Cancer Prevention and Treatment Institute of Chengdu, Department of Pathology, Chengdu Fifth People’s Hospital (The Second Clinical Medical College, Affiliated Fifth People’s Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, Sichuan, 611130, People’s Republic of China
| | - Jie Li
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 611137, People’s Republic of China
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2
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Fang Z, Li Z, Li M, Yue Z, Li K. Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning. Genes (Basel) 2024; 15:676. [PMID: 38927611 PMCID: PMC11202800 DOI: 10.3390/genes15060676] [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: 04/27/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/28/2024] Open
Abstract
Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.
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Affiliation(s)
- Zirui Fang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; (Z.F.); (Z.L.); (M.L.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China
| | - Zixuan Li
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; (Z.F.); (Z.L.); (M.L.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China
| | - Ming Li
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; (Z.F.); (Z.L.); (M.L.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China
| | - Zhenyu Yue
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; (Z.F.); (Z.L.); (M.L.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China
| | - Ke Li
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China; (Z.F.); (Z.L.); (M.L.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
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3
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Sabei A, Hognon C, Martin J, Frezza E. Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations. J Phys Chem B 2024; 128:4865-4886. [PMID: 38740056 DOI: 10.1021/acs.jpcb.3c07698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.
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Affiliation(s)
- Afra Sabei
- Université Paris Cité, CiTCoM, CNRS, Paris F-75006, France
| | - Cécilia Hognon
- Université Paris Cité, CiTCoM, CNRS, Paris F-75006, France
| | - Juliette Martin
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, UMR 5086 MMSB, Lyon 69367, France
- Laboratory of Biology and Modeling of the Cell, Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5239, Inserm U1293, Lyon 69367, France
| | - Elisa Frezza
- Université Paris Cité, CiTCoM, CNRS, Paris F-75006, France
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4
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Pandey U, Behara SM, Sharma S, Patil RS, Nambiar S, Koner D, Bhukya H. DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences. J Chem Inf Model 2024; 64:1806-1815. [PMID: 38458968 DOI: 10.1021/acs.jcim.3c01151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2024]
Abstract
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.
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Affiliation(s)
- Uddeshya Pandey
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Sasi M Behara
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Siddhant Sharma
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Rachit S Patil
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Souparnika Nambiar
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Debasish Koner
- Department of Chemistry, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Hussain Bhukya
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
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5
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Harini K, Sekijima M, Gromiha MM. PRA-Pred: Structure-based prediction of protein-RNA binding affinity. Int J Biol Macromol 2024; 259:129490. [PMID: 38224813 DOI: 10.1016/j.ijbiomac.2024.129490] [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: 12/10/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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6
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Yang E, Zhang H, Zang Z, Zhou Z, Wang S, Liu Z, Liu Y. GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction. Comput Biol Med 2023; 164:107246. [PMID: 37487383 DOI: 10.1016/j.compbiomed.2023.107246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/26/2023]
Abstract
RNA secondary structure is essential for predicting the tertiary structure and understanding RNA function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. Structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of RNA, such as stems, hairpin, and internal loops. Structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an RNA secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://github.com/EnbinYang/GCNfold.
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Affiliation(s)
- Enbin Yang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Hao Zhang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China
| | - Zinan Zang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhiyong Zhou
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Shuo Wang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhen Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Graduate School of Engineering, Nagasaki Institute of Applied Science, 536 Aba-machi, Nagasaki 851-0193, Japan
| | - Yuanning Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China.
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7
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Zhang X, Mei LC, Gao YY, Hao GF, Song BA. Web tools support predicting protein-nucleic acid complexes stability with affinity changes. WILEY INTERDISCIPLINARY REVIEWS. RNA 2023; 14:e1781. [PMID: 36693636 DOI: 10.1002/wrna.1781] [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: 09/14/2022] [Revised: 11/10/2022] [Accepted: 11/28/2022] [Indexed: 01/26/2023]
Abstract
Numerous biological processes, such as transcription, replication, and translation, rely on protein-nucleic acid interactions (PNIs). Demonstrating the binding stability of protein-nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web-based tools have been developed to attach importance to protein-nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein-nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein-nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web-based tools will facilitate the discovery of recognition mechanisms for protein-nucleic acid binding stability. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.
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Affiliation(s)
- Xiao Zhang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Long-Can Mei
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
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8
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Hong X, Tong X, Xie J, Liu P, Liu X, Song Q, Liu S, Liu S. An updated dataset and a structure-based prediction model for protein-RNA binding affinity. Proteins 2023; 91:1245-1253. [PMID: 37186412 DOI: 10.1002/prot.26503] [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: 06/30/2022] [Revised: 03/08/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023]
Abstract
Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.
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Affiliation(s)
- Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Pinyu Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Gromiha MM, Harini K. Comment on 'Thermodynamic database supports deciphering protein-nucleic acid interactions'. Trends Biotechnol 2023; 41:988-989. [PMID: 37117054 DOI: 10.1016/j.tibtech.2023.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/30/2023]
Abstract
Mei and colleagues reported a thermodynamic database, PNATDB for protein-nucleic acid interactions, which contains 12 635 experimentally determined thermodynamic parameters. They claimed that extracting data from existing databases is difficult. ProNAB, which has more than 20 000 experimental data points for binding affinities of protein-nucleic acid complexes and other information, was not discussed.
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Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
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10
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Mei LC, Hao GF, Yang GF. Protein-nucleic acid thermodynamic databases for specific uses. Trends Biotechnol 2023; 41:990-991. [PMID: 37045637 DOI: 10.1016/j.tibtech.2023.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 04/14/2023]
Abstract
In response to Gromiha and Harini, we review the currently available thermodynamic databases for protein-nucleic acid interactions. These databases are designed for particular uses. We give general comments on them to facilitate browsing and exploration.
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Affiliation(s)
- Long-Can Mei
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China; National Key Laboratory of Green Pesticide, 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 550000, China.
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
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11
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Pandey P, Panday SK, Rimal P, Ancona N, Alexov E. Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations. Int J Mol Sci 2023; 24:12073. [PMID: 37569449 PMCID: PMC10418460 DOI: 10.3390/ijms241512073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.
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Affiliation(s)
- Preeti Pandey
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Shailesh Kumar Panday
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Prawin Rimal
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Nicolas Ancona
- Department of Biological Sciences, Clemson University, Clemson, SC 29634, USA;
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
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12
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Yang S, Gong W, Zhou T, Sun X, Chen L, Zhou W, Li C. emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model. Brief Bioinform 2023:7165253. [PMID: 37193676 DOI: 10.1093/bib/bbad192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/26/2023] [Accepted: 04/29/2023] [Indexed: 05/18/2023] Open
Abstract
Protein-deoxyribonucleic acid (DNA) interactions are important in a variety of biological processes. Accurately predicting protein-DNA binding affinity has been one of the most attractive and challenging issues in computational biology. However, the existing approaches still have much room for improvement. In this work, we propose an ensemble model for Protein-DNA Binding Affinity prediction (emPDBA), which combines six base models with one meta-model. The complexes are classified into four types based on the DNA structure (double-stranded or other forms) and the percentage of interface residues. For each type, emPDBA is trained with the sequence-based, structure-based and energy features from binding partners and complex structures. Through feature selection by the sequential forward selection method, it is found that there do exist considerable differences in the key factors contributing to intermolecular binding affinity. The complex classification is beneficial for the important feature extraction for binding affinity prediction. The performance comparison of our method with other peer ones on the independent testing dataset shows that emPDBA outperforms the state-of-the-art methods with the Pearson correlation coefficient of 0.53 and the mean absolute error of 1.11 kcal/mol. The comprehensive results demonstrate that our method has a good performance for protein-DNA binding affinity prediction. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/emPDBA/.
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Affiliation(s)
- Shuang Yang
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Weikang Gong
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Tong Zhou
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Xiaohan Sun
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Lei Chen
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Wenxue Zhou
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
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13
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Harini K, Kihara D, Michael Gromiha M. PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods 2023; 213:10-17. [PMID: 36924867 PMCID: PMC10563387 DOI: 10.1016/j.ymeth.2023.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/17/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States; Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan.
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14
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David A, Sternberg MJE. Protein structure-based evaluation of missense variants: Resources, challenges and future directions. Curr Opin Struct Biol 2023; 80:102600. [PMID: 37126977 DOI: 10.1016/j.sbi.2023.102600] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 05/03/2023]
Abstract
We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.
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Affiliation(s)
- Alessia David
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
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15
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Sun Y, Wu H, Xu Z, Yue Z, Li K. Prediction of hot spots in protein-DNA binding interfaces based on discrete wavelet transform and wavelet packet transform. BMC Bioinformatics 2023; 24:129. [PMID: 37016308 PMCID: PMC10074722 DOI: 10.1186/s12859-023-05263-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/30/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Identification of hot spots in protein-DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein-DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein-DNA features to predict hot spots, unable to make full use of the effective information in the features. RESULTS In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model. CONCLUSIONS Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH .
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Affiliation(s)
- Yu Sun
- School of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Hongwei Wu
- School of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Zhengrong Xu
- School of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Zhenyu Yue
- School of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Ke Li
- School of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui, China.
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, Anhui, China.
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China.
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16
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Mei LC, Hao GF, Yang GF. Thermodynamic database supports deciphering protein-nucleic acid interactions. Trends Biotechnol 2023; 41:140-143. [PMID: 36272818 DOI: 10.1016/j.tibtech.2022.09.018] [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: 07/13/2022] [Revised: 09/16/2022] [Accepted: 09/27/2022] [Indexed: 01/11/2023]
Abstract
The thermodynamics of protein-nucleic acid interactions (PNIs) is crucial for elucidating the mechanisms of molecular recognition and pathological consequences. The Protein-Nucleic Acid Thermodynamics Database (PNATDB) is a database containing experimentally determined thermodynamic parameters along with sequence, structural, and function data, which is available free online.
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Affiliation(s)
- Long-Can Mei
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Ge-Fei Hao
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, 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 550000, China.
| | - Guang-Fu Yang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
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17
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Pal A, Chakrabarti P, Dey S. ProDFace: A web-tool for the dissection of protein-DNA interfaces. Front Mol Biosci 2022; 9:978310. [PMID: 36148013 PMCID: PMC9486321 DOI: 10.3389/fmolb.2022.978310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 08/09/2022] [Indexed: 11/30/2022] Open
Abstract
Protein-DNA interactions play a crucial role in gene expression and regulation. Identifying the DNA binding surface of proteins has long been a challenge–in comparison to protein-protein interactions, limited progress has been made in the development of efficient DNA binding site prediction and protein-DNA docking methods. Here we present ProDFace, a web tool that characterizes the binding region of a protein-DNA complex based on amino acid propensity, hydrogen bond (HB) donor capacity (number of solvent accessible HB donor groups), sequence conservation at the interface core and rim region, and geometry. The program takes as input the structure of a protein-DNA complex in PDB (Protein Data Bank) format, and outputs various physicochemical and geometric parameters of the interface, as well as conservation of the interface residues in the protein component. Values are provided for the whole interface, and after dissecting it into core and rim regions. Details of water mediated HBs between protein and DNA, potential HB donor groups present at the binding surface of protein, and conserved interface residues are also provided as downloadable text files. These parameters can be useful in evaluating and validating protein-DNA docking solutions, structures derived from simulation as well as solutions from the available prediction tools, and facilitate the development of more efficient prediction methods. The web-tool is freely available at structbioinfo.iitj.ac.in/resources/bioinfo/pd_interface.
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Affiliation(s)
- Arumay Pal
- School of Bioengineering, Vellore Institute of Technology, Bhopal, India
| | | | - Sucharita Dey
- Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur, Karwar, India
- *Correspondence: Sucharita Dey,
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18
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Flamand MN, Ke K, Tamming R, Meyer KD. Single-molecule identification of the target RNAs of different RNA binding proteins simultaneously in cells. Genes Dev 2022; 36:1002-1015. [PMID: 36302554 PMCID: PMC9732904 DOI: 10.1101/gad.349983.122] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/07/2022] [Indexed: 01/07/2023]
Abstract
RNA-binding proteins (RBPs) regulate nearly every aspect of mRNA processing and are important regulators of gene expression in cells. However, current methods for transcriptome-wide identification of RBP targets are limited, since they examine only a single RBP at a time and do not provide information on the individual RNA molecules that are bound by a given RBP. Here, we overcome these limitations by developing TRIBE-STAMP, an approach for single-molecule detection of the target RNAs of two RNA binding proteins simultaneously in cells. We applied TRIBE-STAMP to the cytoplasmic m6A reader proteins YTHDF1, YTHDF2, and YTHDF3 and discovered that individual mRNA molecules can be bound by more than one YTHDF protein throughout their lifetime, providing new insights into the function of YTHDF proteins in cells. TRIBE-STAMP is a highly versatile approach that enables single-molecule analysis of the targets of RBP pairs simultaneously in the same cells.
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Affiliation(s)
- Mathieu N. Flamand
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA
| | - Ke Ke
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA
| | - Renee Tamming
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA
| | - Kate D. Meyer
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA;,Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710, USA
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19
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DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features. Appl Bionics Biomech 2022; 2022:5483115. [PMID: 35465187 PMCID: PMC9020926 DOI: 10.1155/2022/5483115] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/25/2021] [Accepted: 02/05/2022] [Indexed: 12/29/2022] Open
Abstract
In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.
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20
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Rigden DJ, Fernández XM. The 2022 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2022; 50:D1-D10. [PMID: 34986604 PMCID: PMC8728296 DOI: 10.1093/nar/gkab1195] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.
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Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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