The intensive concern over the biosafety of nanomaterials demands the systematic study of the mechanisms underlying their biological effects. Many of the effects of nanomaterials can be attributed to their interaction...The intensive concern over the biosafety of nanomaterials demands the systematic study of the mechanisms underlying their biological effects. Many of the effects of nanomaterials can be attributed to their interactions with proteins and their impacts on protein function. On the other hand, nanomaterials show potential for a variety of biomedical applications,many of which also involve direct interactions with proteins. In this paper, we review some recent computational studies on this subject, especially those investigating the interactions of carbon and gold nanomaterials. Beside hydrophobic andπ-stacking interactions, the mode of interaction of carbon nanomaterials can also be regulated by their functional groups.The coatings of gold nanomaterials similarly adjust their mode of interaction, in addition to coordination interactions with the sulfur groups of cysteine residues and the imidazole groups of histidine residues. Nanomaterials can interact with multiple proteins and their impacts on protein activity are attributed to a wide spectrum of mechanisms. These findings on the mechanisms of nanomaterial–protein interactions can further guide the design and development of nanomaterials to realize their application in disease diagnosis and treatment.展开更多
The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly ...The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.21273240,11204267,and 11474013)
文摘The intensive concern over the biosafety of nanomaterials demands the systematic study of the mechanisms underlying their biological effects. Many of the effects of nanomaterials can be attributed to their interactions with proteins and their impacts on protein function. On the other hand, nanomaterials show potential for a variety of biomedical applications,many of which also involve direct interactions with proteins. In this paper, we review some recent computational studies on this subject, especially those investigating the interactions of carbon and gold nanomaterials. Beside hydrophobic andπ-stacking interactions, the mode of interaction of carbon nanomaterials can also be regulated by their functional groups.The coatings of gold nanomaterials similarly adjust their mode of interaction, in addition to coordination interactions with the sulfur groups of cysteine residues and the imidazole groups of histidine residues. Nanomaterials can interact with multiple proteins and their impacts on protein activity are attributed to a wide spectrum of mechanisms. These findings on the mechanisms of nanomaterial–protein interactions can further guide the design and development of nanomaterials to realize their application in disease diagnosis and treatment.
文摘The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.
