Structural bioinformatics is a discipline that bridges the gap between biology and computational science. It focuses on determining, analyzing, and predicting the three-dimensional structures of biological macromolecules such as proteins, nucleic acids, and complex lipids.
The structure of a molecule is intimately linked to its function. By deciphering these intricate architectures, scientists gain invaluable insights into the molecular basis of life processes, serving this knowledge as the foundation for numerous advancements in various fields.
Image Credit: bluesroad/Shutterstock.com
Structural Bioinformatics: Foundations and Impact
To understand structural bioinformatics, we must first know the fundamental molecules of life:
- Proteins are linear chains of amino acids that fold into complex shapes (globular and fibrous conformations). Their structures determine their roles as enzymes, hormones, antibodies, and structural components.
- Nucleic acids can be defined as larger chains of nucleotides. These molecules store and transmit genetic information. DNA is a double helix formed by two chains of nucleotides, while RNA is a single chain essential for gene expression and protein synthesis.
- Lipids are biomolecules with a glycerol backbone that form distinct cellular components, such as cell membranes. Their structural properties influence cell signaling and energy storage.
In drug discovery, understanding the structure of target proteins is crucial for designing molecules that can bind specifically and inhibit cellular processes. Researchers can optimize drug candidates for efficacy and safety by identifying their molecular interactions with pathogenic particles.1
Structural bioinformatics also plays a pivotal role in unraveling complex diseases. Many diseases, such as cancer and neurodegenerative disorders, arise from protein misfolding, ultimately altering their function and structure.2 By studying these structural alterations, scientists can gain insights into their molecular mechanisms, identify potential biomarkers for early detection, and develop novel therapeutic strategies.
Unraveling the structure of enzymes and other biomolecules is also essential to engineer them for industrial applications such as, for instance, biofuel production.3
Learn more about genetics and genomics
Techniques in Structural Bioinformatics
Determining the structures of biomolecules has been a major challenge in biology.
Traditionally, experimental techniques like X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryo-electron microscopy (cryo-EM) have been the gold standard.
However, these methods are often time-consuming and resource-intensive. Fortunately, current structural bioinformatic techniques offer a versatile and cost-effective strategy to overcome this issue. Some core techniques used in structural bioinformatics include:
- Protein-Protein Interaction Prediction: Predicting the interaction between two proteins.
- Structure Alignment: Comparing protein structures to identify similarities and differences.
- Homology Modeling: Building a 3D model of a target protein based on the structure of a homologous protein with known structure.
- Ab Initio Protein Structure Prediction: Predicting protein structure without relying on experimental data or homology information.
Role of Bioinformatics in Simplifying Data
Applications of Structural Bioinformatics
One of the most impactful applications of structural bioinformatics is in drug discovery. By understanding the three-dimensional structure of a target protein involved in a disease, researchers can design molecules that precisely fit into the protein's binding site and inhibit its function.
Structural bioinformatics also plays a crucial role in protein engineering, where proteins are modified or designed to perform specific functions.
Researchers can identify key residues involved in catalysis by analyzing protein structures and modifying them to improve enzyme activity, stability, or substrate specificity. Understanding protein-protein interactions is also essential for designing molecules that can disrupt or enhance these interactions.4
In epidemiology, determining the structure of pathogenic structural components is essential for understanding viral replication and developing antiviral drugs.
For instance, structural characterization of the SARS-CoV-2 spike protein was crucial in the rapid development of COVID-19 vaccines5.
Challenges and Limitations
Structural bioinformatics faces significant challenges, including the difficulty of obtaining high-resolution structures for complex molecules, accurately modeling dynamic and flexible systems, and the computational demands of large-scale simulations.
Additionally, the immense amount of biological data often presents hurdles in data integration and analysis. To overcome these limitations, researchers are focusing on developing more sophisticated algorithms, and integrating multiple experimental and computational techniques.
By combining experimental data with advanced simulations and machine learning, scientists aim to understand the interplay between static structures and dynamic biological processes, ultimately leading to more accurate and predictive models.6
Future Directions in Structural Bioinformatics
Structural bioinformatics is undergoing rapid evolution, driven by advancements in technology and the growing volume of biological data. Machine learning is revolutionizing the field, enabling accurate structure prediction, virtual drug screening, and the identification of novel drug targets.7
Additionally, the ability to analyze vast datasets is unlocking new insights into protein function, interactions, and disease mechanisms. These emerging trends hold immense potential for accelerating drug discovery, understanding complex biological processes, and developing innovative biotechnologies.
Conclusion
Structural bioinformatics stands as a cornerstone of modern biological research, providing invaluable insights into complex biomolecules.
By elucidating the relationship between structure and function, this field has revolutionized drug discovery, protein engineering, and disease understanding.
However, challenges persist, requiring continuous innovation in computational methods, experimental techniques, and data integration.
As we discover more deeply the complexity of biomolecules, structural bioinformatics remains a vital tool for unlocking challenging life science questions and developing novel solutions to global challenges.
References
- Yoshino, R., Yasuo, N., & Sekijima, M. (2020). Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Scientific reports, 10(1), 12493. https://www.nature.com/articles/s41598-020-69337-9
- Sweeney, P., Park, H., Baumann, M., Dunlop, J., Frydman, J., Kopito, R., ... & Hodgson, R. (2017). Protein misfolding in neurodegenerative diseases: implications and strategies. Translational neurodegeneration, 6, 1-13. https://pubmed.ncbi.nlm.nih.gov/28293421/
- Binod, P., Gnansounou, E., Sindhu, R., & Pandey, A. (2019). Enzymes for second generation biofuels: recent developments and future perspectives. Bioresource Technology Reports, 5, 317-325. https://www.researchgate.net/publication/325818062_Enzymes_for_second_generation_biofuels_Recent_developments_and_future_perspectives
- Vaschetto, L. M. (2017). Understanding the role of protein interaction motifs in transcriptional regulators: implications for crop improvement. Briefings in Functional Genomics, 16(3), 152-155. https://pubmed.ncbi.nlm.nih.gov/27288433/
- Xia, X. (2021). Domains and functions of spike protein in Sars-Cov-2 in the context of vaccine design. Viruses, 13(1), 109. https://www.mdpi.com/1999-4915/13/1/109/review_report
- Calonaci, N., Jones, A., Cuturello, F., Sattler, M., & Bussi, G. (2020). Machine learning a model for RNA structure prediction. NAR genomics and bioinformatics, 2(4), lqaa090. https://academic.oup.com/nargab/article/2/4/lqaa090/5983421
- Wassan, J., Wang, H., & Zheng, H. (2018). Machine learning in bioinformatics. Encyclopedia of Bioinformatics and Computational Biology, 1, 300-308. https://www.researchgate.net/publication/326163536_Machine_Learning_in_Bioinformatics
Further Reading