Biology subjects

Murugan, R.

Publications and source records attributed to Murugan, R..

3 recordsLinked to original sources

bioRxiv2018

Theory on the looping mediated stochastic propulsion of transcription factors along DNA

We show that the looping mediated transcription activation by the combinatorial transcription factors (TFs) can be achieved via directional-dependent propulsion, tethered sliding and tethered binding-sliding-unbinding modes. In the propulsion mode, the first arrived TF at the cis-regulatory motifs (CRMs) further recruits other TFs via protein-protein interactions. Such TFs complex has two different types of DNA binding domains (DBDs) viz. DBD1 which forms tight site-specific complex with CRMs via hydrogen bonding network and the promoter specific DBD2s which form nonspecific interactions around CRMs. When the sum of these specific and cumulative nonspecific interactions is sufficient, then the flanking DNA of CRMs will be bent into a circle over the TFs complex. The number of TFs involved in the combinatorial regulation plays critical role here. When the site-specific interactions and the cumulative nonspecific interactions are strong enough to resist the dissociation, then the sliding of DBD2s well within the Onsager radius associated with the DBD2s-DNA interface towards the promoter is the only possible way to release the elastic stress of the bent DNA. The DBD2s form tight synaptosome complex upon finding the promoter via sliding. When the number of TFs is not enough to bend the DNA in to a circle, then tethered sliding or tethered binding-sliding-unbinding modes are the possibilities. In tethered sliding, the CRMs-TFs complex forms nonspecific contacts with DNA via dynamic loops and then slide along DNA towards promoter without dissociation. In tethered binding-sliding-unbinding, the CRMs-TFs performs several cycles of nonspecific binding-sliding-unbinding before finding the promoter. Elastic and entropic energy barriers associated with the looping of DNA shape up the distribution of distances between CRMs and promoters. The combinatorial regulation of TFs in eukaryotes has evolved to overcome the looping energy barrier.

biophysics↗

bioRxiv2017

Theory of site-specific DNA-protein interactions in the presence of nucleosome roadblocks

We show that nucleosomes can efficiently control the relative search times spent by transcription factors (TFs) on one- (1D) and three-dimensional (3D) diffusion routes towards locating their cognate sites on DNA. Our theoretical results suggest that the roadblock effects of nucleosomes are dependent on the relative position on DNA with respect to TFs and their cognate sites. Especially, nucleosomes exert maximum amount of hindrance to the 1D diffusion dynamics of TFs when they are positioned in between TFs and their cognate sites. The effective 1D diffusion coefficient ({chi}TF) associated with the dynamics of TFs in the presence of nucleosome decreases with the free energy barrier ({micro}) associated the sliding dynamics of nucleosomes as [Formula]. Subsequently the mean first passage time ({eta}L) that is required by TFs to scan L number of binding sites on DNA via 1D diffusion increases with as [Formula]. When TFs move close to nucleosomes then they exhibit a typical sub-diffusive dynamics. Nucleosomes can enhance the search dynamics of TFs when TFs present in between nucleosomes and transcription factor binding sites (TFBS). The level of enhancement effects of nucleosomes seems to be much lesser than the level of retardation effects when nucleosomes present in between TFs and their cognate sites. These results suggest that nucleosome depleted regions around the cognate sites of TFs is mandatory for an efficient site-specific interactions of TFs with DNA. Remarkably the genome wide positioning pattern of TFs shows maximum at their specific binding sites and the positioning pattern of nucleosome shows minimum at the specific binding sites of TFs under in vivo conditions. This seems to be a consequence of increasing level of breathing dynamics of nucleosome cores and decreasing levels of fluctuations in the DNA binding domains of TFs as they move across TFBS. Since the extent of breathing dynamics of nucleosomes and fluctuations in the DBDs of TFs are directly linked with their respective 1D diffusion coefficients, the dynamics of TFs becomes slow as they approach their cognate sites so that TFs form tight site-specific complex. Whereas the dynamics of nucleosomes becomes rapid so that they pass through the cognate sites of TFs. Several in vivo datasets on genome wide positioning pattern of nucleosomes as well as TFs seem to agree well with our arguments. We further show that the condensed conformational state of DNA can significantly decrease the retarding effects of nucleosome roadblocks. The retarding effects of nucleosomes on the 1D diffusion dynamics of TFs can be nullified when the degree of condensation of the genomic DNA is such that it can permit a jump size associated with the dynamics of TFs beyond k > 150 bps.

biophysics↗

bioRxiv2017

Theory On The Rate Equation Of Michaelis-Menten Type Single-Substrate Enzyme Catalyzed Reactions

Analytical solution to the Michaelis-Menten (MM) rate equations for single-substrate enzyme catalysed reaction is not known. Here we introduce an effective scaling scheme and identify the critical parameters which can completely characterize the entire dynamics of single substrate MM enzymes. Using this scaling framework, we reformulate the differential rate equations of MM enzymes over velocity-substrate, velocity-product, substrate-product and velocity-substrate-product spaces and obtain various approximations for both pre- and post-steady state dynamical regimes. Using this framework, under certain limiting conditions we successfully compute the timescales corresponding to steady state, pre- and post-steady states and also compute the approximate steady state values of velocity, substrate and product. We further define the dynamical efficiency of MM enzymes as the ratio between the reaction path length in the velocity-substrate-product space and the average reaction time required to convert the entire substrate into product. Here dynamical efficiency characterizes the phase-space dynamics and it would tell us how fast an enzyme can clear a harmful substrate from the environment. We finally perform a detailed error level analysis over various pre- and post-steady state approximations along with the already existing quasi steady state approximations and progress curve models and discuss the positive and negative points corresponding to various steady state and progress curve models.

biochemistry↗