01848nas a2200169   4500008004100000022001400041245012200055210006900177260007100246300001400317490000800331520115300339100001601492700002301508700002101531856012601552       2016                        eng d  a1520-610600aEffect of solvation on electron detachment and excitation energies of a green fluorescent protein chromophore variant0 aEffect of solvation on electron detachment and excitation energi  a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcMAY  a4410-44200 v1203 a<p>Hybrid quantum mechanics/molecular mechanics (QM/MM) is applied to the fluorinated green fluorescent protein (GFP) chromophore (DFHBDI) in its deprotonated form to understand the solvatochromic shifts in its vertical detachment energy (VDE) and vertical excitation energy (VEE). This variant of the GFP chromophore becomes fluorescent in an RNA environment and has a wide range of applications in biomedical and biochemical fields. From microsolvation studies, we benchmark (with respect to full QM) the accuracy of our QM/MM calculations with effective fragment potential (EFP) as the MM method of choice. We show that while the solvatochromic shift in the VEE is minimal (0.1 eV blue shift) and its polarization component is only 0.03 eV, the effect of the solvent on the VDE is quite large (3.85 eV). We also show by accurate calculations on the solvatochromic shift of the VDE that polarization accounts for similar to 0.23 eV and therefore cannot be neglected. The effect of the counterions on the VDE of the deprotonated chromophore in solvation is studied in detail, and a charge-smearing scheme is suggested for charged chromophores.</p>1 aBose, Samik1 aChakrabarty, Suman1 aGhosh, Debashree  uhttps://library.ncl.res.in/content/effect-solvation-electron-detachment-and-excitation-energies-green-fluorescent-protein02118nas a2200169   4500008004100000022001400041245008200055210006900137260000800206300001400214490000800228520154800236100001601784700002301800700002101823856010401844       2017                        eng d  a1520-610600aElectrostatic origin of the red solvatochromic shift of DFHBDI in RNA spinach0 aElectrostatic origin of the red solvatochromic shift of DFHBDI i  cMAY  a4790-47980 v1213 a<p>Interactions with the environment tune the spectral properties of biological chromophores, e.g., fluorescent proteins. Understanding the relative contribution of the various types of noncovalent interactions in the spectral shifts can provide rational design principles toward developing new fluorescent probes. In this work, we investigate the origin of the red shift in the absorption spectra of the difluoro hydroxybenzylidene dimethyl imidazolinone (DFHBDI) chromophore in RNA spinach as compared to the aqueous solution. We systematically decompose the effects of various components of interactions, namely, stacking, hydrogen bonding, and long-range electrostatics, in order to elucidate the relative role of these interactions in the observed spectral behavior. We find that the absorption peak of DFHBDI is red-shifted by similar to 0.35 eV in RNA relative to the aqueous solution. Earlier proposals from Huang and co-workers have implicated the stacking interactions between DFHBDI and nucleic acid bases to be the driving force behind the observed red shift. In contrast, our findings reveal that the long-range electrostatic interactions between DFHBDI and negatively charged RNA. make the most significant contribution. Moreover, we notice that the opposing electrostatic fields due to the RNA backbone and the polarized water molecules around the RNA give rise to the resultant red shift. Our results emphasize the effect of strong heterogeneity in the various environmental factors that might be competing with each other.</p>1 aBose, Samik1 aChakrabarty, Suman1 aGhosh, Debashree  uhttps://library.ncl.res.in/content/electrostatic-origin-red-solvatochromic-shift-dfhbdi-rna-spinach01565nas a2200145   4500008004100000245011300041210006900154260000800223300001400231490000700245520100700252100001601259700002101275856012301296       2017                        eng d00aInteraction energy driven biased sampling technique: a faster route to ionization spectra in condensed phase0 aInteraction energy driven biased sampling technique a faster rou  cOCT  a2248-22570 v383 aWe introduce a computationally efficient approach for calculating spectroscopic properties, such as ionization energies (IEs) in the condensed phase. Discrete quantum mechanical/molecular mechanical (QM/MM) approaches for spectroscopic properties in a dynamic system, such as aqueous solution, need a large sample space to obtain converged estimates, especially for the cases where particle (electron) number is not conserved, such as IEs or electron affinities (EAs). We devise a biased sampling technique based on an approximate estimate of interaction energy between the solute and solvent, that accelerates the convergence and therefore, reduces the computational cost significantly. The approximate interaction energy also provides a good measure of the spectral width of the chromophores in the condensed phase. This technique has been tested and benchmarked for (i) phenol, (ii) HBDI anion (hydroxybenzylidene dimethyl imidazolinone), and (iii) thymine in water. (c) 2017 Wiley Periodicals, Inc.1 aBose, Samik1 aGhosh, Debashree  uhttps://library.ncl.res.in/content/interaction-energy-driven-biased-sampling-technique-faster-route-ionization-spectra01825nas a2200181   4500008004100000245008000041210006900121260000800190300001600198490000700214520121800221100001601439700001901455700001801474700002101492700002101513856010901534       2018                        eng d00aMachine learning prediction of interaction energies in rigid water clusters0 aMachine learning prediction of interaction energies in rigid wat  cSEP  a22987-229960 v203 aClassical force fields form a computationally efficient avenue for calculating the energetics of large systems. However, due to the constraints of the underlying analytical form, it is sometimes not accurate enough. Quantum mechanical (QM) methods, although accurate, are computationally prohibitive for large systems. In order to circumvent the bottle-neck of interaction energy estimation of large systems, data driven approaches based on machine learning (ML) have been employed in recent years. In most of these studies, the method of choice is artificial neural networks (ANN). In this work, we have shown an alternative ML method, support vector regression (SVR), that provides comparable accuracy with better computational efficiency. We have further used many body expansion (MBE) along with SVR to predict interaction energies in water clusters (decamers). In the case of dimer and trimer interaction energies, the root mean square errors (RMSEs) of the SVR based scheme are 0.12 kcal mol(-1) and 0.34 kcal mol(-1), respectively. We show that the SVR and MBE based scheme has a RMSE of 2.78% in the estimation of decamer interaction energy against the parent QM method in a computationally efficient way.1 aBose, Samik1 aDhawan, Diksha1 aNandi, Sutanu1 aSarkar, Ram, Rup1 aGhosh, Debashree  uhttps://library.ncl.res.in/content/machine-learning-prediction-interaction-energies-rigid-water-clusters