WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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  • Article
    Mkate2-k67r/R197h-extra-bright Red Fluorescent Biomarker of New Generation. X-Ray Structure and Molecular Dynamic Properties
    (Maik Nauka/interperiodica/springer, 2024) Goryacheva, E. A.; Rossokhin, A. V.; Ruchkin, D. A.; Bogdanov, A. M.; Artemyev, I. V.; Pletneva, N. V.; Plenev, V. Z.
    Objective: Cell biology continuously shows the need for new fluorescent tags with advanced properties. The object of our current study is a new genetically encoded monomeric red fluorescent biomarker mKate2-K67R/R197H (lambda ex/lambda em 579/603 mn), designed from commercial biomarker mKate2 by two R197H/K67R mutations. The mKate2 precursor, a far-red fluorescent protein, is nearly 3-fold brighter than the previously designed mKate. Compared with commercial mKate2, the double mutant mKate2-K67R/R197H (alternative names FusionRed2 and Diogenes) exhibits an additional similar to 1.6-fold increase in fluorescence brightness and represents the next generation of extra-bright red fluorescent probes offering novel possibilities for fluorescent imaging of proteins in living cells and animals. Methods: The paper presents the results of X-ray and molecular dynamics study of new bright biomarker mKate2-K67R/R197H. Results and Discussion: The three dimensional structure of new advanced red fluorescent biomarker mKate2-K67R/R197H has been studied by X-ray method at 1.5 angstrom resolution supported by molecular dynamics (MD) study The principal structural fold of the protein is an 11-stranded beta-barrel. The nearest chromophore environment (<= 4 angstrom) comprises 18 tightly packed residues. Conclusions: The MD study showed that the brightness of mKate2-K67R/R197H and its mKate2 precursor correlates with the dipole moments of the amino acid environments of the chromophores. The higher the dipole moment, the higher the brightness of biomarker.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Understanding Neural Network Tuned Langevin Thermostat Effect on Predicting Thermal Conductivity of Graphene-Coated Copper Using Nonequilibrium Molecular Dynamics Simulations
    (Iop Publishing Ltd, 2024) Toprak, Kasim
    Copper has always been used in thermoelectric applications due to its extensive properties among metals. However, it requires further improving its heat transport performance at the nanosized applications by supporting another high thermal conductivity material. Herein, copper was coated with graphene, and the neural network fitting was employed for the nonequilibrium molecular dynamics simulations of graphene-coated copper nanomaterials to predict thermal conductivity. The Langevin thermostat that was tuned with a neural network fitting (NNF), which makes up the backbone of deep learning, generated the temperature difference between the two ends of the models. The NNF calibrated the Langevin thermostat damping constants that helped to control the temperatures precisely. The buffer and thermostat lengths were also analyzed, and they have considerable effects on the thermostat temperatures and a significant impact on the thermal conductivity of the graphene-coated copper. Regarding thermal conductivity, the four different shapes of vacancy defect concentrations and their locations in the graphene sheets were further investigated. The vacancy between the thermostats significantly decreases the thermal conductivity; however, the vacancy defect in thermostats does not have a similar effect. When the graphene is placed between two copper blocks, the thermal conductivity decreases drastically, and it continues to drop when the sine wave amplitude on the graphene sheet increases.