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The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS) allows to perform molecular simulations in an open boundary framework. It allows to change on the fly the resolution of specific subset of molecules (usually the solvent), which are free to diffuse between the atomistic region and the coarse-grained reservoir. So far, the method has been successfully applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a promising approach for rigorous calculations of ligand binding free energies in low-resolution protein models. Towards this goal, here we apply for the first time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its ability to reproduce structural and dynamic properties of both the proteins and the solvent, as obtained from atomistic simulations.
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S-nitrosylation, the covalent addition of NO to the thiol side chain of cysteine, is an important post-transitional modification that can alter the function of various proteins. The structural dynamics and vibrational spectroscopy of S-nitrosylation in the condensed phase is investigated for the methyl-capped cysteine model system and for myoglobin. Using conventional point charge and physically more realistic multipolar force fields for the -SNO group it is found that the SN- and NO-stretch and the SNO-bend vibrations can be located and distinguished from the other protein modes for simulations of MbSNO at 50 K. The finding of stable cis- and trans-MbSNO is consistent with experiments on other proteins as is the observation of buried -SNO. For MbSNO the observed relocation of the EF loop in the simulations by $sim 3$ AA/ is consistent with the available X-ray structure and the conformations adopted by the -SNO label are in good overall agreement with the X-ray structure. Despite the larger size of the -SNO group, MbSNO is found to recruit more water molecules within 10 AA/ of the modification site than WT Mb due to the stronger electrostatics. Similarly, when comparing the hydration between the A- and H-helices they differ by up to 30 % between WT and MbSNO. This suggests that local hydration can also be significantly modulated through nitrosylation.
Recent literatures reported blue-green emission from amyloid fibril as exclusive signature of fibril formation. This unusual visible luminescence is regularly used to monitor fibril growth. Blue-green emission has also been observed in crystalline protein and in solution. However, the origin of this emission is not known exactly. Our spectroscopic study of serum proteins reveals that the blue-green emission is a property of protein monomer. Evidences suggest that semiconductor-like band structure of proteins with the optical band-gap in the visible region is possibly the origin of this phenomenon. We show here that the band structure of proteins is primarily the result of electron delocalization through the peptide chain, rather than through the hydrogen bond network in secondary structure.
The interactions of a protein, its phase behavior, and ultimately, its ability to function, are all influenced by the interactions between the protein and its hydration waters. Here we study proteins with a variety of sizes, shapes, chemistries, and biological functions, and characterize their interactions with their hydration waters using molecular simulation and enhanced sampling techniques. We find that akin to extended hydrophobic surfaces, proteins situate their hydration waters at the edge of a dewetting transition, making them susceptible to unfavorable perturbations. We also find that the strength of the unfavorable potential needed to trigger dewetting is roughly the same, regardless of the protein being studied, and depends only the width of the hydration shell being perturbed. Our findings establish a framework for systematically classifying protein patches according to how favorably they interact with water.
Computational prediction of enzyme mechanism and protein function requires accurate physics-based models and suitable sampling. We discuss recent advances in large-scale quantum mechanical (QM) modeling of biochemical systems that have reduced the cost of high-accuracy models. Trade-offs between sampling and accuracy have motivated modeling with molecular mechanics (MM) in a multi-scale QM/MM or iterative approach. Limitations to both conventional density functional theory (DFT) and classical MM force fields remain for describing non-covalent interactions in comparison to experiment or wavefunction theory. Because predictions of enzyme action (i.e., electrostatics), free energy barriers, and mechanisms are sensitive to the protocol and embedding method in QM/MM, convergence tests and systematic methods for quantifying QM-level interactions are a needed, active area of development.
The poor photostability and low brightness of protein autofluorescence have been major limitations preventing the detection of label-free proteins at the single molecule level. Overcoming these issues, we report here a strategy to promote the photostability of proteins and use their natural tryptophan autofluorescence in the ultraviolet (UV) for fluorescence correlation spectroscopy (FCS). Combining enzymatic oxygen scavengers with antioxidants and triplet state quenchers greatly promotes the protein photostability, reduces the photobleaching probability and improves the net autofluorescence detection rate. Our results show that the underlying photochemical concepts initially derived for organic visible fluorescent dyes are quite general. Using this approach, we achieved UV fluorescence correlation spectroscopy on label-free streptavidin proteins containing only 24 tryptophan residues, 6.5x less than the current state-of-the-art. This strategy greatly extends the possibility to detect single label-free proteins with the versatility of single molecule fluorescence without requiring the presence of a potentially disturbing external fluorescent marker. It also opens new perspectives to improve the UV durability of organic devices.
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