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 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 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)

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)

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 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 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.

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