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
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