The physical adsorption of molecules (C(2)H(2), C(2)H(4), C(2)H(6), C(6)H(6), CH(4), H(2), H(2)O, N(2), NH(3), CO, CO(2), Ar) on a graphite substrate has been investigated at the DFT/CC level of theory. The calculated DFT/CC interaction energies were compared with the available experimental data at the zero coverage limit. The differences between the DFT/CC results and experiment are within a few tenths of kJ mol(-1) for the most accurate experimental estimates (Ar, H(2), N(2), CH(4)) and within 1-2 kJ mol(-1) for the other systems (C(2)H(2), C(2)H(4), C(2)H(6), C(6)H(6), CO, CO(2)). For water-graphite and ammonia-graphite complexes, DFT/CC predicts interaction energies of 13 kJ mol(-1) in good accord with the DF-DFT-SAPT and DFT-D calculations. The relevance of the results obtained with the coronene model for the description of the physisorption on graphite surface was also studied.
Accurate interaction energies of nonpolar (argon) and polar (water) adsorbates with graphene-based carbon allotropes were calculated by means of a combined density functional theory (DFT)-ab initio computational scheme. The calculated interaction energy of argon with graphite (-9.7 kJ mol(-1)) is in excellent agreement with the available experimental data. The calculated interaction energy of water with graphene and graphite is -12.8 and -14.6 kJ mol(-1), respectively. The accuracy of combined DFT-ab initio methods is discussed in detail based on a comparison with the highly precise interaction energies of argon and water with coronene obtained at the coupled-cluster CCSD(T) level extrapolated to the complete basis set (CBS) limit. A new strategy for a reliable estimate of the CBS limit is proposed for systems where numerical instabilities occur owing to basis-set near-linear dependence. The most accurate estimate of the argon and water interaction with coronene (-8.1 and -14.0 kJ mol(-1), respectively) is compared with the results of other methods used for the accurate description of weak intermolecular interactions.
Infrared attenuated total reflection spectroscopy was used for in situ observation of the deposition of collagen I on poly(2-hydroxyethyl methacrylate-co-methacrylic acid, 2.9%) hydrogels and subsequent attachment of laminin or fibronectin on the collagen surface. While there was no adsorption of collagen dissolved in an acid solution on the hydrogel surface, it deposited on the surface at pH 6.5. The collagen layers with attached laminin or fibronectin were stable on hydrogel surface in physiological solution. The modification with collagen and particularly with collagen and laminin or fibronectin allowed the adhesion and growth of mesenchymal stromal cells and astrocytes on the hydrogel surface.
In order to predict interaction interface for proteins, it is crucial to identify their characteristic features controlling the interaction process. We present analysis of 69 crystal structures of dimer protein complexes that provides a basis for reasonable description of the phenomenon. Interaction interfaces of two proteins at amino acids level were localized and described in terms of their chemical composition, binding preferences, and residue interaction energies utilizing Amber empirical force field. The characteristic properties of the interaction interface were compared against set of corresponding intramolecular binding parameters for amino acids in proteins. It has been found that geometrically distinct clusters of large hydrophobic amino acids (leucine, valine, isoleucine, and phenylalanine) as well as polar tyrosines and charged arginines are signatures of the protein-protein interaction interface. At some extent, we can generalize that protein-protein interaction (seen through interaction between amino acids) is very similar to the intramolecular arrangement of amino acids, although intermolecular pairs have generally lower interaction energies with their neighbors. Interfaces, therefore, possess high degree of complementarity suggesting also high selectivity of the process. The utilization of our results can improve interface prediction algorithms and improve our understanding of protein-protein recognition.
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