Computational studies of structures, interactions and dynamics of biomolecules such as proteins, RNA and DNA

Since its initiation, marked essentially by the elucidation of the double helical structure of DNA by Watson and Crick, molecular biology has come a long way and has grown to become an integral part of modern post genomic biology. The central dogma of molecular biology, as initially formulated, described life in terms of the linear information flow:

DNA → RNA → Protein

A molecular level understanding of how structures of proteins, RNA and DNA are related to their sequences and functions respectively, in terms of physicochemical forces involved, has traditionally constituted a major thrust area of molecular biology research. Though recent discoveries, related to the multifarious roles of functional non (protein) coding RNAs, have transformed the linear central dogma into a network of information flow, the essential paradigm of, “sequence defines structure and structure defines function” has remained more or less unchanged.

With the advent of high throughput sequencing technology, and consequent development of bioinformatics as a discipline, it has now become increasingly clear that experimental determination of structures, interactions and dynamics of these numerous biomolecules constitute a serious limiting step towards our molecular level understanding of life processes. One way to address this yawning gap, between the number of known sequences on one hand and their structures and functions on the other, that has emerged with a fair amount of success in recent times is the application of computational methods to study the structures, interactions and dynamics of these biomolecules. The methodologies involve uses of both, pattern recognition approaches for the formulation of rules and correlation principles from sequences whose structures and/or functions are known; as well as more predictive computational approaches involving applications of Quantum and classical mechanics including those of statistical mechanics and thermodynamics.

Apart from their predictive potentials, in understanding the molecular basis of experimentally observable properties, these computational approaches underline a synergy between computational natural sciences and biology. They provide challenges to our understanding of physicochemical processes and contribute towards strengthening the foundations of physical sciences. In addition, computational approaches towards attaining molecular level understanding of biological processes have a wide range of biotechnological applications defining their scope. For example, a molecular level understanding of a process involved in diseased systems helps us in formulating strategies for intervention. On similar lines, understanding an enzyme function can help both in the design of biochemical reactors as well as in the design of customized enzymes.as these are nature optimized mediators of various useful catalytic processes. The list does not end here and the richness of the area is exemplified by the variety of sub areas of activities as detailed below.

Computational studies of structures, interactions and dynamics of biomolecules such as proteins, RNA and DNA

Computational Natural Sciences and Bioinformatics

Subareas