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Computational Molecular Biology 2024, Vol.14, No.5 http://bioscipublisher.com/index.php/cmb © 2024 BioSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. BioSci Publisher is an international Open Access publishing platform that publishes scientific journals in the field of bioscience registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. BioSci Publisher Publisher BioSci Publisher Editedby Editorial Team of Computational Molecular Biology Email: edit@cmb.bioscipublisher.com Website: http://bioscipublisher.com/index.php/cmb Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Computational Molecular Biology (ISSN 1927-5587) is an open access, peer reviewed journal published online by BioSciPublisher. The Journal is publishing all the latest and outstanding research articles, letters, methods, and reviews in all areas of computational molecular biology, covering new discoveries in molecular biology, from genes to genomes, using statistical, mathematical, and computational methods as well as new development of computational methods and databases in molecular and genome biology. The papers published in the journal are expected to be of interests to computational scientists, biologists and teachers/students/researchers engaged in biology. All the articles published in Computational Molecular Biology are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BioSciPublisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
Computational Molecular Biology (online), 2024, Vol. 14 ISSN 1927-6648 http://hortherbpublisher.com/index.php/cmb © 2024 BioSc iPublisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content 2024, Vol. 14, No.5 【Research Perspective】 Molecular Interactions in Biological Systems: A Systematic Review of Biophysical Approaches 182-190 Liangbing Chen, Xianle Ruan, Xinyi Li, Huanling Fu DOI: 10.5376/cmb.2024.14.0021 【Feature Review】 Protein-Protein Interaction Networks in Hybrid Japonica Rice under Drought Stress: Insights from Proteomics and Bioinformatics Analysis 191-201 Chunli Wang, Nant Nyein Zar Ni Naing, Cui Zhang, Junjie Li, Qian Zhu, Dongsun Lee, Lijuan Chen DOI: 10.5376/cmb.2024.14.0022 【Research Insight】 Unveiling the Patterns and Impact of New Gene Recruitment in Development and Evolution 202-210 Kaiwen Liang DOI: 10.5376/cmb.2024.14.0023 【Systematic Review】 Emerging Trends in Systems Biology: Multi-Omics Integration and Beyond 211-219 Ning Wang, Guocheng Zhang, Manman Li DOI: 10.5376/cmb.2024.14.0024 【Review and Progress】 Integrative Approaches in Computational Genomics: Combining Omics Data to Study Gene Evolution 220-228 Hongwei Liu DOI: 10.5376/cmb.2024.14.0025
Computational Molecular Biology 2024, Vol.14, No.5, 182-190 http://bioscipublisher.com/index.php/cmb 182 Research Perspective Open Access Molecular Interactions in Biological Systems: Technological Applications and Innovations Liangbing Chen , Xianle Ruan, Xinyi Li, Huanling Fu College of Life Sciences and Agronomy, Zhoukou Normal University, Zhoukou, 466000, Henan, China Corresponding author: chliangbing@163.com Computational Molecular Biology, 2024, Vol.14, No.5 doi: 10.5376/cmb.2024.14.0021 Received: 15 Jul., 2024 Accepted: 26 Aug., 2024 Published: 10 Sep., 2024 Copyright © 2024 Chen et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Chen et al., 2024, Molecular interactions in biological systems: technological applications and innovations, Computational Molecular Biology, 14(5): 182-190 (doi: 10.5376/cmb.2024.14.0021) Abstract This study reviews the complex interactions between proteins, DNA, RNA, lipids, and small molecules in biological systems, emphasizing the critical role of the "interactome" concept in understanding organismal functions. These interactions, including protein-protein and protein-RNA interactions, play significant roles in cellular processes such as recognition, regulation, and signaling, and have great potential in drug discovery. The research methods encompass various biophysical techniques, such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, and surface plasmon resonance (SPR), which are used to elucidate the structure and dynamics of molecular interactions. The results demonstrate that molecular interactions are key to developmental innovation, environmental adaptation, and disease mechanisms, particularly in revealing the interactions between biological membranes and small molecules, protein complex formation, and drug target discovery. The study highlights the importance of future improvements in research methods, especially through the integration of computational and experimental approaches, to better understand the dynamic molecular interactions in biological systems. Keywords Molecular interactions; Biophysical techniques; Protein-protein interactions; Molecular dynamics simulations; Drug discovery 1 Introduction Molecular interactions in biological systems are fundamental to the understanding of cellular functions and the molecular basis of diseases. These interactions encompass a wide range of biomolecules, including proteins, DNA, RNA, lipids, and small molecules, which interact in complex and dynamic ways to regulate cellular processes such as recognition, regulation, and signaling (Ardini et al., 2022). The concept of the "interactome" highlights the complexity of these interactions, suggesting that the functionality of an organism can be better understood by examining the network of interactions among its biomolecules rather than solely its genetic content. For instance, protein-protein interactions (PPIs) are crucial for cellular functions and have been extensively studied to map out the functional proteome and understand disease mechanisms (Havugimana et al., 2017). Additionally, the interactions between proteins and RNA play significant roles in cellular processes and are emerging as important targets for drug discovery (Steinmetz et al., 2023). The study of molecular interactions has been greatly advanced by various biophysical techniques. Mass spectrometry (MS) has become a pivotal tool for characterizing protein complexes and mapping interaction networks on a global scale. Techniques such as chemical cross-linking combined with MS provide structural insights into protein interactions that are difficult to study using conventional methods (Chavez and Bruce, 2019).Nuclear magnetic resonance (NMR) spectroscopy, electron paramagnetic resonance (EPR) spectroscopy, and fluorescence-based methods are also employed to gain detailed understanding of biomolecular interactions. Moreover, molecular dynamics (MD) simulations have revolutionized our understanding of how small molecules interact with biological membranes, providing atomic-level insights into the binding and permeation processes. The advent of deep learning methods, such as AlphaFold (Parker and Pratt, 2022), has further enhanced our ability to predict protein structures and their complexes, offering new avenues for exploring molecular interactions. This study provides a comprehensive overview of the current research status of molecular interactions in biological systems, with a focus on the technological applications and innovations that have emerged in recent
Computational Molecular Biology 2024, Vol.14, No.5, 182-190 http://bioscipublisher.com/index.php/cmb 183 years. We will explore various biophysical techniques for studying these interactions, emphasizing their contribution to our understanding of cellular processes and disease mechanisms. In addition, the impact of these interactions on drug discovery and the development of new therapeutic strategies will be discussed, aiming to provide a coherent understanding of the dynamic and complex nature of molecular interactions in biological systems and their technological applications. 2 Biophysical Techniques for Studying Molecular Interactions 2.1 X-ray crystallography X-ray crystallography is a cornerstone technique in structural biology, providing atomic-resolution structures of biomolecules. This method involves the crystallization of the molecule of interest and subsequent diffraction of X-rays through the crystal lattice. The resulting diffraction pattern is analyzed to determine the electron density and thus the three-dimensional structure of the molecule. X-ray crystallography has been instrumental in elucidating the structures of numerous proteins and nucleic acids, linking structural information to biological function and dynamics. 2.2 Nuclear magnetic resonance (NMR) spectroscopy NMR spectroscopy is another pivotal technique in structural biology, offering insights into the structure, dynamics, and interactions of biomolecules in solution. Unlike X-ray crystallography, NMR does not require crystallization, making it suitable for studying molecules in their native state. Recent advancements in solid-state NMR (ssNMR) have expanded its applications to include samples with static and dynamic disorder, such as lipid bilayers and protein aggregates. ssNMR provides complementary data to other structural techniques, enhancing our understanding of complex biological assemblies (Wel, 2018; Tsegaye et al., 2021). 2.3 Cryo-electron microscopy (Cryo-EM) Cryo-EM has emerged as a powerful tool for studying large macromolecular complexes that are challenging to analyze using X-ray crystallography or NMR. This technique involves flash-freezing samples in vitreous ice and imaging them using an electron microscope. Recent technological advancements have significantly improved the resolution of cryo-EM, allowing for near-atomic resolution structures. Cryo-EM is particularly valuable for studying dynamic and heterogeneous systems, providing detailed insights into the structural basis of biological mechanisms (Lerner et al., 2018; Tan and Carragher, 2020). 2.4 Surface plasmon resonance (SPR) SPR is an optical technique used to study ligand-analyte interactions in real-time without the need for labeling. It measures changes in the refractive index near a metal surface, which occur upon binding of molecules. SPR is widely used to investigate biomolecular interactions, including protein-protein, protein-DNA, and protein-membrane interactions. Recent advancements in SPR technology, such as multiplexed and regenerable biosensors, have enhanced its sensitivity and specificity, making it a valuable tool in both basic research and applied fields like drug discovery and GMO detection (Renaud et al., 2016). 2.5 Isothermal titration calorimetry (ITC) ITC is a thermodynamic technique that measures the heat change associated with molecular interactions, providing direct insights into binding affinities, stoichiometry, and thermodynamic parameters. It is a label-free method that can be used to study a wide range of interactions, including protein-ligand, protein-protein, and protein-DNA interactions. ITC is particularly useful in drug discovery for characterizing the binding properties of potential therapeutic compounds (Gavriilidou et al., 2022). 3 Applications of Biophysical Methods in Biological Research 3.1 Protein-protein interactions Protein-protein interactions (PPIs) are fundamental to numerous cellular processes, including signal transduction, cellular assembly, and enzymatic catalysis. Various biophysical methods have been developed to characterize these interactions, each with its strengths and limitations. Techniques such as mass spectrometry, nuclear magnetic resonance (NMR), and X-ray crystallography have been instrumental in mapping the interaction networks and
Computational Molecular Biology 2024, Vol.14, No.5, 182-190 http://bioscipublisher.com/index.php/cmb 184 understanding the structural basis of PPIs (Dobson, 2019). Additionally, computational methods like molecular dynamics (MD) simulations and docking studies have provided deeper insights into the dynamic nature of these interactions, especially in crowded cellular environments where weak interactions play a significant role (Corrales‐Guerrero et al., 2023). 3.2 Protein-nucleic acid interactions Protein-nucleic acid interactions are critical for processes such as DNA replication, transcription, and repair. Biophysical techniques like X-ray crystallography and NMR spectroscopy have been pivotal in determining the structures of protein-DNA and protein-RNA complexes, revealing the molecular mechanisms of these interactions. Advanced methods such as single-molecule spectroscopy and surface plasmon resonance (SPR) have further enhanced our ability to study these interactions in real-time, providing valuable kinetic and thermodynamic data (Biswas, 2018). 3.3 Protein-ligand interactions Understanding protein-ligand interactions is crucial for drug discovery and the development of therapeutic strategies. Biophysical methods such as isothermal titration calorimetry (ITC), differential scanning fluorimetry (DSF), and SPR are widely used to quantify the binding affinities and kinetics of protein-ligand interactions. MD simulations have also become a powerful tool in this field, offering detailed insights into the binding mechanisms and helping to predict binding free energies accurately (Arcon et al., 2017; Liu et al., 2018). These techniques have been successfully applied to study the interactions between proteins and various ligands, including small molecules, peptides, and other proteins (Figure 1), thereby facilitating the design of more effective drugs. Figure 1 Steps required to conduct an in silico study of food peptides (ligand) and proteins (receptor) (Adopted from Vidal-Limon et al., 2022)
Computational Molecular Biology 2024, Vol.14, No.5, 182-190 http://bioscipublisher.com/index.php/cmb 185 3.4 Viral and pathogen interactions Biophysical methods have significantly advanced our understanding of host-pathogen interactions, Techniques such as cryo-electron microscopy (cryo-EM) and X-ray crystallography have provided high-resolution structures of viral proteins and their complexes with host proteins, shedding light on the mechanisms of viral entry, replication, and immune evasion (Dobson, 2019). Computational approaches, including MD simulations and machine learning models, have further enhanced our ability to predict and analyze these interactions, offering new avenues for therapeutic intervention (Chen et al., 2018). These methods have been particularly useful in studying the structural principles of host-pathogen protein-protein interactions, providing insights into the design of novel antiviral and antibacterial agents. 4 Advancements and Innovations in Biophysical Techniques 4.1 Technological innovations in biophysics 4.1.1 Advances in imaging technologies Recent advancements in imaging technologies have significantly enhanced our ability to visualize and understand complex biological systems at the molecular level. Single-molecule imaging techniques, such as convex lens-induced confinement (CLiC) microscopy, have enabled researchers to observe molecular interactions with high precision and control under cell-like conditions, eliminating the biases associated with tethering molecules (Zhang, 2024). Additionally, the integration of super-resolution microscopy with other techniques, such as atomic force microscopy (AFM), has provided transformative insights into the dynamic processes of biomolecules, allowing for the investigation of molecular interactions closer to their native physiological states (Figure 2) (Haghizadeh et al., 2023). Figure 2 Understanding various DNA–protein interactions using correlated optical tweezers fluorescence microscopy (Adopted from Haghizadeh et al., 2023) Image caption: (a) Gene editing: a DNA molecule tethered between two optically trapped beads. (b) DNA organization: the left side depicts a cohesin bridge between two DNA molecules formed during the incubation of two DNA tethers in cohesin, ATP, and SCC2/4. (c) DNA replication: Schematic of DNA unwinding mechanism by BLM helicase. (d) DNA repair: an experimental schematic where an optical tweezers-based single-molecule technique was used to resolve individual RAD-51 filament growth and measure their growth rates in replacing RPA-covered resected DNA in the HR repair mechanism (Adopted from Haghizadeh et al., 2023)
Computational Molecular Biology 2024, Vol.14, No.5, 182-190 http://bioscipublisher.com/index.php/cmb 186 4.1.2 Innovations in spectroscopy and calorimetry techniques Spectroscopy and calorimetry techniques have also seen significant innovations, particularly in the realm of single-molecule studies. Atomic force microscopy-based force spectroscopy (AFM-FS) has emerged as a powerful tool for directly measuring interactions between biomolecules and material interfaces at the single-molecule level. This technique has been applied to both imaging and label-free sensing of various biomolecules, providing detailed insights into their interactions and functions (Li et al., 2016). Single-cell Raman spectroscopy (SCRS) has been integrated with advanced analytical techniques and modern data analytics to offer high-resolution, label-free, and non-invasive analysis of complex biological and environmental samples (Wang et al., 2020). 4.1.3 Development of label-free detection methods Label-free detection methods have gained prominence due to their ability to provide real-time, high-specificity measurements without the need for molecular labels. Single-molecule biosensors, including electrochemical, plasmonic, and spectroelectrochemical platforms, have been developed to detect individual biological molecules with high sensitivity and specificity. These advancements are crucial for early disease diagnosis and personalized medicine (Akkilic et al., 2020). AFM-FS has been utilized for label-free sensing of DNA, RNA, proteins, enzymes, and small molecules, further expanding the capabilities of biophysical techniques in understanding molecular interactions. 4.2 Integration of computational and experimental approaches The integration of computational and experimental approaches has revolutionized the field of biophysics, enabling a more comprehensive understanding of biological systems. Computational techniques, such as molecular dynamics simulations, complement experimental methods by providing detailed insights into the structures and dynamics of biomolecules. This combined approach has been particularly effective in studying complex biological systems, such as membrane proteins and their interactions with lipid molecules (Dobson, 2019). The use of correlative techniques, such as combining atomic force microscopy with fluorescence imaging, has allowed researchers to probe biological questions with greater accuracy and depth. 4.3 High-throughput biophysical screening High-throughput biophysical screening methods have become essential for rapidly analyzing large numbers of biological samples. These techniques leverage advancements in imaging, spectroscopy, and computational methods to provide detailed and quantitative measurements of molecular interactions. Single-molecule techniques, such as those combining optical tweezers with fluorescence microscopy, have enabled high-throughput analysis of dynamic biomolecular interactions, facilitating research in fields such as cell biology and nanomaterials (Haghizadeh et al., 2023). The development of high-throughput platforms for single-molecule detection and analysis continues to drive innovations in biophysical research, offering new opportunities for understanding and manipulating biological systems (Croop et al., 2019). 5 Challenges and Limitations of Biophysical Methods 5.1 Technical challenges in experimental design Biophysical methods have significantly advanced our understanding of molecular interactions in biological systems. However, these methods face several technical challenges in experimental design. One major challenge is the inherent complexity and heterogeneity of biological molecules, which can lead to loss of critical information in traditional ensemble-averaging techniques. Single-molecule methods, such as fluorescence microscopy, have been developed to address this issue by avoiding ensemble averaging and providing detailed insights into molecular dynamics (Miller et al., 2017). Despite these advancements, the sensitivity and speed of detectors, as well as the stability and efficiency of light sources and probes, remain critical factors that can limit the accuracy and resolution of these techniques. Another technical challenge is the accurate simulation of biological processes. Molecular dynamics (MD) simulations, for instance, require significant computational power and advanced algorithms to achieve the necessary time scales and spatial resolution. Recent developments have improved the efficiency of these simulations, but challenges remain in accurately reproducing experimental results and extending simulations to
Computational Molecular Biology 2024, Vol.14, No.5, 182-190 http://bioscipublisher.com/index.php/cmb 187 longer time scales (Nerenberg and Head‐Gordon, 2018). Additionally, the development of force fields for biomolecular simulations is an ongoing challenge, as it requires precise parameterization to accurately represent nonbonded interactions. 5.2 Limitations in data interpretation Interpreting data from biophysical experiments can be challenging due to the complexity of biological systems. For example, affinity purification-mass spectrometry (AP-MS) techniques used to identify protein complexes often suffer from high false positive and false negative rates, complicating the interpretation of protein interaction networks. Computational methods have been developed to filter and validate these interactions, but selecting the most appropriate method for a given experimental design remains a challenge (Meysman et al., 2017). The interpretation of data from single-molecule techniques can be complicated by the presence of multiple metastable states and complex inter-conversion kinetics in biological molecules. These factors can lead to difficulties in distinguishing between different molecular states and understanding their functional roles (Miller et al., 2017). Advanced simulation techniques, such as enhanced sampling and kinetic models, have been developed to address these issues, but their accuracy and reliability are still being evaluated. 5.3 Reproducibility and standardization Reproducibility and standardization are critical issues in biophysical research. The variability in experimental conditions, such as temperature, pH, and ionic strength, can lead to inconsistent results across different studies. For instance, the reproducibility of single-molecule imaging techniques can be affected by the precision and control of the experimental setup, as well as the potential biases introduced by tethering molecules (Leslie et al., 2019). Techniques like convex lens-induced confinement (CLiC) microscopy have been developed to mitigate these biases, but standardization across different laboratories remains a challenge. In computational biophysics, the reproducibility of MD simulations is influenced by the choice of force fields and simulation parameters. The development of standardized benchmarks and protocols for validating MD force fields is essential to ensure the reliability and reproducibility of simulation results (Nerenberg and Head‐Gordon, 2018). The integration of experimental and computational approaches can help to validate and tune simulation methodologies, but this requires careful coordination and standardization of experimental protocols. 6 Future Perspectives 6.1 Emerging techniques and technologies The future of molecular interactions in biological systems is poised to be revolutionized by several emerging techniques and technologies. One such advancement is the application of global "omics" technologies, which offer comprehensive mapping of biological networks and tissue-specific responses to various stimuli, such as exercise. These technologies are expected to uncover novel exercise-regulated targets, aiding in the development of precision exercise medicine. Additionally, the advent of single-molecule imaging techniques, such as convex lens-induced confinement (CLiC) microscopy, allows for the visualization of molecular interactions with unprecedented precision and control, emulating cell-like conditions without the biases of traditional tethering methods (Leslie et al., 2019). Another significant development is the use of deep learning and graph neural networks (GNNs) to analyze biological networks. These computational tools are being applied to predict protein functions, protein-protein interactions, and facilitate in silico drug discovery, thereby enhancing our understanding of complex biological processes (Muzio et al., 2020). Furthermore, the integration of small-molecule probes with advanced analytical technologies has opened new avenues for the molecular characterization of drug-target interactions, offering potential for whole-body imaging and tissue-based measurements. 6.2 Cross-disciplinary approaches Cross-disciplinary approaches are becoming increasingly vital in the study of molecular interactions. The integration of systems biology with high-throughput data analysis and mathematical modeling is one such approach that has shown promise in understanding host-pathogen interactions and predicting biomarkers for
Computational Molecular Biology 2024, Vol.14, No.5, 182-190 http://bioscipublisher.com/index.php/cmb 188 disease diagnosis and therapeutic decisions (Smith, 2024). Similarly, the field of synthetic biology, bolstered by advancements in genome editing and protein engineering, is enabling the design of sophisticated mammalian systems for applications in regenerative medicine and cancer immunotherapy. The use of affinity chromatography and high-performance affinity chromatography (HPAC) in conjunction with other techniques like mass spectrometry is enhancing the study of biochemical interactions, facilitating high-throughput drug screening and personalized medicine applications (Leslie et al., 2019). The convergence of these diverse fields is expected to drive significant innovations in the understanding and manipulation of molecular interactions. 6.3 Applications in personalized medicine and drug development The advancements in molecular interaction technologies are set to have profound implications for personalized medicine and drug development. The use of single-molecule biosensors, which offer real-time detection of individual biological molecules with high specificity, is crucial for early disease diagnosis and monitoring medical treatments. These biosensors hold great potential for developing point-of-care devices tailored to individual patient needs (Akkilic et al., 2020). In drug development, multiscale modeling methods that bridge chemical and biological complexity are emerging as powerful tools. These methods, driven by improved algorithms and rich datasets, enable detailed simulations of biochemical systems, facilitating the discovery and design of novel therapeutics. Additionally, the application of computational network biology is providing new insights into the interactions between genotypes, phenotypes, and environmental factors, furthering our understanding of human diseases and aiding in the development of targeted drug therapies (Liu et al., 2020). Acknowledgments The authors express deep gratitude to Prof. and Dr. Xuanjun Fang, Director of the Hainan Institute of Tropical Agricultural Resources and Director of the Hainan Provincal Key Laboratory of Crop Molecular Breeding for his thorough review of the manuscript and for providing comprehensive and systematic revision suggestions. The authors also extend thanks to the two anonymous peer reviewers for their valuable comments and constructive recommendations on this manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Akkilic N., Geschwindner S., and Höök F., 2020, Single-molecule biosensors: recent advances and applications, Biosensors and Bioelectronics, 151: 111944. https://doi.org/10.1016/j.bios.2019.111944 Arcon J., Defelipe L., Modenutti C., López E., Alvarez-Garcia D., Barril X., Turjanski A., and Marti M., 2017, Molecular dynamics in mixed solvents reveals protein-ligand interactions improves docking and allows accurate binding free energy predictions, Journal of Chemical Information and Modeling, 57(4): 846-863. https://doi.org/10.1021/acs.jcim.6b00678 Ardini M., Baiocco P., Matteo A., Giardina G., and Miele A.E., 2022, Editorial: tailored modulation of interactions between biomolecules: fundamentals and applications, Frontiers in Molecular Biosciences, 9: 961452. https://doi.org/10.3389/fmolb.2022.961452 Biswas P., 2018, Modern biophysical approaches to study protein–ligand interactions, Biophysical Reviews and Letters, 13(4): 133-155. https://doi.org/10.1142/S1793048018300013 Chavez J., and Bruce J., 2019, Chemical cross-linking with mass spectrometry: a tool for systems structural biology, Current opinion in Chemical Biology, 48: 8-18. https://doi.org/10.1016/j.cbpa.2018.08.006 Chen H., Guo W., Shen J., Wang L., and Song J., 2018, Structural principles analysis of host-pathogen protein-protein interactions: a structural bioinformatics survey, IEEE Access, 6: 11760-11771. https://doi.org/10.1109/ACCESS.2018.2807881 Corrales‐Guerrero L.C., Prischi F., and Díaz-Moreno I., 2023, Editorial: weak interactions in molecular machinery volume II, Frontiers in Molecular Biosciences, 10: 1284353. https://doi.org/10.3389/fmolb.2023.1284353
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Computational Molecular Biology 2024, Vol.14, No.5, 191-201 http://bioscipublisher.com/index.php/cmb 191 Feature Review Open Access Protein-Protein Interaction Networks in Rice under Drought Stress: Insights from Proteomics and Bioinformatics Analysis Chunli Wang1, Nant Nyein Zar Ni Naing1,4, Cui Zhang1, JunjieLi 1, QianZhu1,2,3, Dongsun Lee 1,2,3, Lijuan Chen1,2,3 1 Rice Research Institute, Yunnan Agricultural University, Kunming, 650201, Yunnan, China 2 The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming, 650201, Yunnan, China 3 College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, 650201, Yunnan, China 4 Department of Plant Breeding, Physiology and Ecology, Yezin Agricultural University (YAU), Nay Pyi Taw, 15013, Myanmar Corresponding author: chenlijuan@hotmail.com Computational Molecular Biology, 2024, Vol.14, No.5 doi: 10.5376/cmb.2024.14.0022 Received: 27 Jul., 2024 Accepted: 06 Sep., 2024 Published: 20 Sep., 2024 Copyright © 2024 Wang et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang C.L., Ni Niang N.N.Z., Zhang C., Li J.J., Zhu Q., Lee D.S., and Chen L.J., 2024, Protein-protein interaction networks in rice under drought stress: insights from proteomics and bioinformatics analysis, Computational Molecular Biology, 14(5): 191-201 (doi: 10.5376/cmb.2024.14.0022) Abstract This review outlines the physiological and biochemical responses of plants to drought stress, explains the molecular mechanisms, and emphasizes the key role of proteomics in these responses. Drought stress causes dehydration and osmotic changes in plants, leading to cell membrane damage, accumulation of reactive oxygen species (ROS), and metabolic disorders. Plants respond to drought stress through a series of complex physiological and biochemical responses, including regulate of stomatal opening and closing, synthesis protective proteins and metabolites, activate antioxidant systems, and regulate gene expression. Through proteomic and bioinformatic analysis, we systematically synthesis findings that identified key response proteins in rice under drought stress, constructed and analyzed the PPI network, performed functional annotation and pathway enrichment analysis, and demonstrated specific PPI networks involving transcription factors and signaling proteins, interaction networks with osmoprotectants and stress-related proteins, and comparative analysis of PPI networks of different rice varieties under drought stress through case studies. By exploring the response mechanism of rice under drought stress, we propose to develop more effective drought resistance strategies to improve the stability and sustainability of rice production. Keywords Drought stress; Proteomics; Protein-protein interaction networks (PPI Networks); Rice; Bioinformatics analysis 1 Introduction Rice (Oryza sativa L.) is a staple food for more than half of the world's population, making it a critical crop for global food security. Increased rice production plays an extremely important role in ensuring food security and people's living standards. However, rice yields are highly susceptible to environmental stresses, particularly drought, which is one of the most severe limitations on rice productivity (Hamzelou et al., 2020). Drought stress affects approximately 50% of the world's rice production, leading to significant yield losses (Sircar and Parekh, 2018). Understanding the molecular mechanisms underlying drought tolerance in rice is essential for developing drought-resistant rice varieties that can withstand water-deficit conditions. Studying its response to drought stress can provide valuable insights into the adaptive mechanisms and potential targets for genetic improvement (Agrawal et al., 2016). Proteomics is the science of studying the protein composition of cells, tissues or organisms. With the development of science and technology, proteomics methods have changed to high-throughput methods such as tissue microarray (TMA), protein pathway array and mass spectrometry (Chandramouli et al., 2009). However, Protein-Protein Interaction Network Analysis (PPI Network Analysis) is one of the important research components of proteomics. PPI networks are crucial for understanding the complex biological processes that control cellular responses to environmental stresses. PPIs facilitate the coordination of various cellular functions by enabling proteins to interact and form functional complexes. For drought stress, PPI networks can reveal key regulatory proteins and pathways involved in stress response, signal transduction (Paul et al., 2015; Usman et al., 2020). Proteomic approaches, such as two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS), have advanced our ability to analyze these networks, providing a comprehensive view to
Computational Molecular Biology 2024, Vol.14, No.5, 191-201 http://bioscipublisher.com/index.php/cmb 192 understand the proteomic changes under drought conditions (Salekdeh et al., 2002). Nowadays by integrating bioinformatics tools, researchers can analyze PPI networks to identify critical proteins that interactions that contribute to drought tolerance. This review combines findings from proteomics and bioinformatics studies to comprehensively analyze the PPI network of rice under drought stress, summarizes the current knowledge on the effects of drought stress on rice at the proteomic level, clarifies the construction and analysis of the PPI network under drought stress conditions, identifies key proteins and interactions involved in the drought stress response of rice, and discusses the potential application of these findings to breeding programs and biotechnological interventions to enhance drought resistance. Through this review, we hope to bridge the gap between proteomics data and practical applications in rice improvement and provide directions for future research and development in this critical area. 2 Drought Stress Responses in Rice 2.1 Physiological and biochemical responses to drought stress Drought stress significantly impacts the physiological and biochemical processes in rice. One of the primary physiological responses is the reduction of leaf water content, which can lead to stomatal closure to minimize water loss through transpiration. This process, however, also limits CO2 uptake, thereby reducing photosynthesis and growth (Maksup et al., 2014). Additionally, drought stress induces the accumulation of osmo protectants such as proline and soluble sugars, which help maintain cell turgor and protect cellular structures (Hamzelou et al., 2020). In response to drought stress, plants accumulate organic and inorganic solute, achieve osmotic adaptations by accumulating osmoprotectants and increase antioxidant activity for scavenging Reactive Oxygen Species (ROS) to improve drought tolerance. 2.2 Molecular mechanisms responses to drought stress At the molecular level, drought stress triggers a complex network of gene expression changes. Key molecular mechanisms is the upregulated ABA (abscisic acid)-dependent signaling pathway with energy metabolic processes (Sircar and Parekh, 2019; Hsu et al., 2021). Various transcription factors (TFs) such as bHLH (basic helix-loop-helix) and bZIP (basic leucine zipper), MYB (myeloblastosis), are involved in the regulation of ABAdependent signaling pathways and play a major role in the stress response by regulating the expression of many downstream drought-responsive genes (Peleg et al., 2011; Soltanpour et al., 2022). Furthermore, drought stress induces the expression of heat shock proteins (HSPs), late embryogenesis abundant (LEAs), calmodulin-like protein (CML) and other stress-related proteins that help in protein folding, protection, and repair. Proteomic studies have identified actin depolymerizing factor and S-like RNase homologues proteins, that are differentially expressed under drought conditions, suggesting their roles in maintaining cellular structure and function during drought stress (Hong et al., 2016; Pant et al., 2022). 2.3 Importance of proteomics in understanding drought stress responses Proteomics provides a comprehensive approach to understanding the drought stress responses, which offering insights that are not apparent from transcriptomic or genomic studies alone. Proteomic analyses have revealed that drought stress leads to significant changes in the abundance of proteins involved in photosynthesis, carbohydrate metabolism, and protein synthesis and other metabolic pathways (Wu et al., 2016; Hamzelou et al., 2020). Wu et al. (2016) characterized a new ClpD1 protease, indicating a shift in metabolic priorities to cope with stress, which downregulated of photosynthetic proteins and upregulated of stress-related proteins in drought-tolerant rice varieties. Furthermore, the field of proteomics has been instrumental in uncovering previously unknown proteins that respond to drought stress. These proteins play vital roles in cell defense and energy metabolism, making them promising candidates for serving as biomarkers in the development of drought-tolerant crops (Maksup et al., 2014; Agrawal et al., 2016). The integration of proteomic data with transcriptomics and metabolomics can provide a system understanding for the complex regulatory networks involved in drought stress responses (Shu et al., 2011; Yun et al., 2022). By leveraging proteomic technologies, researchers can identify key proteins and pathways that contribute to drought tolerance, thereby facilitating the development of more resilient crop varieties through genetic engineering and breeding programs.
Computational Molecular Biology 2024, Vol.14, No.5, 191-201 http://bioscipublisher.com/index.php/cmb 193 3 Proteomics Approaches in Studying Drought Stress 3.1 Overview of proteomics techniques Proteomics techniques have become indispensable in studying plant responses to drought stress (Liu et al., 2019). High-performance liquid chromatography attached with tandem mass spectrometry (HPLC-MS/MS) and 2D-PAGE are the most commonly used methods. HPLC-MS/MS allows for the high-throughput identification and quantification of proteins, providing detailed insights into the proteome's composition and dynamics under stress conditions. similarly, 2D-PAGE method can separate proteins based on their isoelectric point and molecular weight, enable the detection of post-translational modifications and change in protein abundance. These techniques, combined with bioinformatics tools, facilitate the comprehensive analysis of protein expression and function in response to drought stress. 3.2 Quantitative proteomics in drought stress research Quantitative proteomics has been instrumental in understanding the molecular mechanisms underlying drought tolerance in rice. Techniques such as label-free quantification and tandem mass tag (TMT) multiplexing have been used to accurately measure changes in protein abundance. A study using label-free quantitative proteomics identified significant alterations in the rice roots proteome under drought conditions, highlighting the upregulation of stress-related proteins and the downregulation of photosynthetic machinery (Wu et al., 2016; Shi et al., 2018). Similarly, TMT-based approaches have revealed the differential expression of proteins (DEPs) involved in various metabolic pathways, providing insights into the adaptive responses of rice to water deficit (Mirzaei et al., 2012). 3.3 Identification of drought-responsive proteins in rice Several studies have identified proteins that respond to drought stress in rice. Paul et al. (2015) and Agrawal et al. (2016) performed proteomic analysis of rice leaves under drought conditions identified proteins involved in energy metabolism, cell defense, and signal transduction as being differentially expressed. Notably, proteins namely actin depolymerizing factor, chloroplastic glutathione-dependent dehydroascorbate reductase, and caffeoyl-CoAO-methyltransferase have been reported to increase in abundance during drought stress (Salekdeh et al., 2002; Ali and Komatsu, 2006). Additionally, dehydrogenase and pyruvate dehydrogenase proteins related to carbohydrate and energy metabolism, have been implicated in enhancing drought tolerance. These findings underscore the complex network of protein interactions and regulatory mechanisms that enable rice to adapt to drought stress. 4 PPI Networks 4.1 Definition and significance of PPI networks PPI networks are conceptualized as maps of the physical and functional interactions between proteins within a cell. These networks are of great importance for the comprehension of the intricate biological processes and pathways that regulate cellular functions. In the context of rice subjected to drought stress, PPI networks can elucidate the mechanisms through which proteins interact to mediate stress responses, thereby potentially identifying key proteins that contribute to drought tolerance. One of the study on Brachy podium distachyon roots and leaves under drought stress demonstrated the significance of PPI networks in generating synergistic responses to stress (Bian et al., 2017). Similarly, the hybrid protein interactome in rice has been showed to contribute to heterosis, with specific PPIs potentially enhancing stress resilience (Li et al., 2019). 4.2 Techniques for constructing PPI networks A variety of experimental techniques are employed in the construction of PPI networks. The yeast two-hybrid (Y2H) assay is a method used to detects physical interactions between two proteins. This is achieved by reconstituting a functional TF in yeast. It has been employed extensively for the mapping of large-scale PPI networks. Co-Immunoprecipitation (Co-IP) employs antibodies to precipitate a protein of interest along with its interacting partners from a cell lysate, thereby facilitating the identification of protein complexes. The label-free shotgun proteomics can be employed to identify and quantify proteins and their interactions under different conditions. For example, this approach has been used to examine the effects of drought stress in rice (Hamzelou et al., 2020; Bai et al., 2021).
Computational Molecular Biology 2024, Vol.14, No.5, 191-201 http://bioscipublisher.com/index.php/cmb 194 4.3 Bioinformatics tools and databases for PPI analysis The utilisation of bioinformatics tools and databases is a fundamental aspect of the analysis and visualisation of PPI networks. STRING is a database of known and predicted PPIs that integrates data from a variety of sources, including experimental data, computational prediction methods, and public text collections. It offers a comprehensive overview of protein interactions and is widely utilized tool in the field of PPI network analysis (Zainal-Abidin et al., 2022). CYTOSCAPE is an open-source software platform designed for the visualisation of complex networks and the integration of diverse attribute data. It is particularly useful for visualizing PPI networks and the subsequent analysis of their topological properties (Figure 1) (Zainal-Abidin et al., 2022). GO analysis is a method of categorizing proteins within PPI network based on their biological processes, cellular components, and molecular functions. This approach was employed to identify drought-responsive proteins in rice (Hamzelou et al., 2020). By integrating experimental data with bioinformatics tools, researchers can construct detailed PPI networks that provide insights into the molecular mechanisms underlying drought stress responses in rice. The networks can be employed to identify potential targets for genetic improvement and breeding programs, with the objective of enhancing drought tolerance in rice. Figure 1 Bioinformatics workflow for the construction of protein–protein interaction network (PPI) (Adopted from Zainal-Abidin et al., 2022) 5 Insights from Proteomics and Bioinformatics Analysis 5.1 Identification of key drought-responsive proteins in rice Proteomic studies have identified a number of key proteins that respond to drought stress in rice. For example, a study of diverse rice genotypes demonstrated that eight proteins were consistently induced across all genotypes under drought conditions, indicating their potential role in drought tolerance mechanisms (Hamzelou et al., 2020). Furthermore, the overexpression of specific proteins such as late embryogenesis abundant (LEA) proteins, has been demonstrated to enhance drought resistance in rice, thereby underscoring their pivotal role in stress response (Xiao et al., 2007). Another study identified 38 co-upregulated proteins related to drought tolerance in weedy rice, with six proteins exhibiting a significant association with drought tolerance (Han et al., 2020). These findings highlight the significance of these proteins in enhancing drought resilience in rice.
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