Research Interests | Research Interests |
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It is now well established that large-scale rearrangements in proteins are important for a variety of protein functions including catalysis and regulation of activity. The recent developments in experimental methods, especially cryo-electron microscopy (EM), have revealed that large-molecular assemblies are also highly dynamic. While experiment can provide a tremendous source of information on these dynamical properties, computational methods must be employed to complement experimental observations. The primary motivation behind our studies is to explore using computational approaches, at a near-atomic level, functionally important rearrangements in biological systems observed in experiments at high or low-resolution to obtain new insights into the mechanism of these transformations that are presently inaccessible to experiments. Elastic network normal mode analysis While the exploration of molecular motions of biological molecules and their assemblies by simulation approaches such as molecular dynamics has provided significant insights into structure-function relationships for small biological systems, studies of large-scale macromolecular assemblies by this technique are limited to relatively short timescales. To address longer time scale and larger systems, we are using normal mode analysis (NMA). With NMA the dynamics of the molecule is described as a collection of independent harmonic oscillators. Those oscillators with low frequency are collective and large amplitude motions and often correlate well with experimentally observed conformational changes. A simplified potential, an elastic network, which was first introduced by Monique Tirion (1), is used to represent the molecule as a set of pseudo-particles. Reduced representation of the molecule, whereby a single coordinate is used to represent several atoms provides a multi-scale description that can significantly reduce the computational expense and enable studies of very large macromolecular machines such as the ribosome, myosin, RNA polymerase, viruses
Computational approaches to structural proteomics Structural proteomics of biological complexes is emerging as a new endeavor at the confluence of several scientific areas. An ultimate goal of structural proteomics is the characterization and determination of macromolecular complexes and their functional states. The structures of these complexes are of great interest since many important cellular functions are accomplished by multi-component complexes of molecular machines. Indeed, these machines are involved in replication, transcription, protein synthesis, regulation of cellular transport and other core biological functions.Structural characterization of such complexes provides us information for understanding their functional roles and mechanisms, which will play an important role in lead discovery, and optimization of new drugs to treat human disease. However, the number of solved structures of such systems is still small compared to the number of possible complexes indicated from genomics studies. Structure determination of these macromolecules is an extremely difficult task. Therefore, it is critical to develop computational algorithm for macromolecular structure determination.
Unveiling molecular mechanisms of biological functions in large macromolecular assemblies We are interested in studying dynamics on the microsecond time scale of large macromolecules using different levels of coarse graining description, from atomically detailed atomic structure models to those employing only low-resolution structural information. Studies performed to study dynamical events related to virus maturation, protein synthesis or muscle contraction have been helpful to get a better understanding on how biological machines works.
Electrostatic properties/Free energy profile of conformational changes of biological moleculesOur previous work on the ribosome, myosin, viruses and dynamics of low-resolution data has provided a new perspective on how macromolecular assemblies work: they evolve to have a shape that is amenable for large scale, machine-like motions. While this principle would explain the motions that the molecules can undergo, in order to fully understand the mechanism of biological molecules, physical causes triggering these transitions need to be understood. For this, detailed studies on the energetics of the transition processes related to the triggering factors are necessary. Exploration of global dynamics and interpretation of low-resolution structural information The recent developments in electron microscopy (EM) have been producing a growing number of low-resolution structures of large assemblies. In particular, large conformational changes of macromolecular complexes have been characterized by cryo-EM. We are developing methods to assist the interpretation of the structure and dynamical properties of these experimental data. Normal mode analysis techniques have been applied to reduced representations of cryo-EM data and have shown to reproduce motions that have been observed experimentally. On the right one of the lowest frequency normal mode computed from a cryo-EM map of the ribosome reproduces remarkably well the well-known ratchet like motion.
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