In recent decades, remarkable advancements in the fields of biology and computer science have given rise to a dynamic and critically important discipline known as “Structural Bioinformatics.” Structural Bioinformatics was the first major effort to show the application of the principles and basic knowledge of the larger field of bioinformatics to questions focusing on macromolecular structure, such as the prediction of protein structure and how proteins carry out cellular functions, and how the application of bioinformatics to these life science issues can improve healthcare by accelerating drug discovery and development.
Structural bioinformatics focuses on the study and analysis of three-dimensional structures of biological macromolecules such as proteins, nucleic acids, and complexes. It combines principles from biology, computer science and mathematics to reveal the relationship between the structure, function and evolution of these molecules.
The history of Structural Bioinformatics as a research field traces back several decades. Initially, the primary focus was on experimental methods for solving biomolecular structures. With the advent of computational methods and breakthroughs in computer science, the possibility of more accurate and rapid modeling of biomolecular structures became available. Subsequent prominent advances in advanced algorithms, specialized software, and three-dimensional databases of biomolecular structures have transformed Structural Bioinformatics into a pivotal realm in biological research.
One of the key goals of structural bioinformatics is to understand how the structure of a macromolecule relates to its function. By studying the arrangement of atoms and residues in a molecule, researchers can gain insights into its biological activity, such as enzyme catalysis, protein-protein interactions, and molecular recognition processes.
Additionally, structural bioinformatics plays a crucial role in drug discovery and design. By analyzing the structure of target proteins and their binding sites, researchers can identify potential drug candidates and develop computational models to predict their binding affinity and efficacy.
Structural bioinformatics’ main goal is to create models and methods for studying and analyzing structural data. As well as solving various biological problems, these methods will help improve our understanding of biological systems. In summary, the purpose of these methods can be categorized into one or more of the following topics: developing databases to collect and store data, visualization and comparison of structures, protein classification, structural analysis, structure prediction, and simulation.
Some of the objectives and applications of Structural Bioinformatics include:
- Structure Prediction: Employing computational methods such as homology modeling, ab initio modeling, and molecular mechanics to predict the three-dimensional structures of biomolecules.
- Structural Analysis: Examining and scrutinizing the structural features of biomolecules to gain insights into their physical and chemical properties.
- Identification of Active Sites:Identifying and studying active sites on biomolecular structures, which are vital for drug design and understanding biological functions.
- Drug Design:Utilizing structural information to craft innovative and optimized drug compounds, thereby enhancing the efficacy and specificity of pharmaceutical interventions.
- Protein Classification: Categorizing proteins based on their structural characteristics, aiding in the systematic understanding of biomolecular diversity.
- Functional Protein Design: Designing proteins with specific functions, contributing to advancements in various fields.
Refrences:
- Gu, J., & Bourne, P. E. (2009). Structural Bioinformatics (2nd ed.). Wiley-Blackwell.
- BYJU’S. (n.d.). Bioinformatics. BYJU’S. https://byjus.com/biology/bioinformatics/
- Samish, I., Bourne, P. E., & Najmanovich, R. J. (2015). Achievements and challenges in structural bioinformatics and computational biophysics. Bioinformatics, 31(1), 146–150. https://doi.org/10.1093/bioinformatics/btu769
