No abstract

Gold nanoclusters (AuNCs) can be primed for biomedical applications through functionalization with peptide coatings. Often anchored by thiol groups, such peptide coronae not only serve as passivators but can also endow AuNCs with additional bioactive properties. In this work, we use molecular dynamics simulations to study the structure of a tridecapeptide-coated Au25 cluster and its subsequent interactions with the enzyme thioredoxin reductase 1, TrxR1. We find that, in isolation, both the distribution and conformation of the coating peptides fluctuate considerably. When the coated AuNC is placed around TrxR1, however, the motion of the highly charged peptide coating (+5e/peptide) is quickly biased by electrostatic attraction to the protein; the asymmetric coating acts to guide the nanocluster's diffusion toward the enzyme's negatively charged active site. After the AuNC comes into contact with TrxR1, its peptide corona spreads over the protein surface to facilitate stable binding with protein. Though individual salt bridge interactions between the tridecapeptides and TrxR1 are transient in nature, the cooperative binding of the peptide-coated AuNC is very stable, overall. Interestingly, the biased corona peptide motion, the spreading and the cooperation between peptide extensions observed in AuNC binding are reminiscent of bacterial stimulus-driven approaching and adhesion mechanisms mediated by cilia. The prevailing AuNC binding mode we characterize also satisfies a notable hydrophobic interaction seen in the association of thioredoxin to TrxR1, providing a possible explanation for the AuNC binding specificity observed in experiments. Our simulations thus suggest this peptide-coated AuNC serves as an adept thioredoxin mimic that extends an array of auxiliary structural components capable of enhancing interactions with the target protein in question.

Cation-pi interactions have proved to be important in proteins and protein-ligand complexes. Here, cation-pi interactions are analyzed for 282 non-redundant protein-RNA interfaces. The statistical results show that this kind of interactions exists in 65% of the interfaces. The four RNA bases are ranked as Gua>Ade>Ura>Cyt according to their propensity to participate in cation-pi interactions. The corresponding ranking for the involved amino acid residues is: Arg>Lys>Asn>Gln. The same trends are obtained based on the empirical energy calculation. The Arg-Gua pairs have the greatest stability and are also most frequently observed. The number of cation-pi pairs involving unpaired bases is 2.5 times as many as those involving paired bases. Hence, cation-pi interactions show sequence and structural specificities. For the bicyclic bases, Gua and Ade, their 5-atom rings participate in cation-pi interactions somewhat more than the 6-atom rings, with percentages of 54 and 46%, respectively, which is due to the higher cation-pi participation proportion (63%) of 5-atom rings in the paired bases. These results give a general view of cation-pi interactions at protein-RNA interfaces and are helpful in understanding the specific recognition between protein and RNA.

The binding interactions of small nuclear RNAs (snRNA) and the associated protein factors are critical to the function of spliceosomes in alternatively splicing primary RNA transcripts. Although molecular dynamics simulations are a powerful tool to interpret the mechanism of biological processes, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems, such as snRNA-protein complexes. We extend the coarse-grained Gaussian network model, which models the RNA-protein complexes as a harmonic chain of Ca, P, and O4 0 atoms, to investigating the impact of the snRNA-binding interaction on the dynamic stability of the human U1A protein, which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle. The results reveal that the first and third loops and the C-terminal helix regions of the U1A domain undergo a significant loss of flexibility upon the RNA binding due to the forming of mostly electrostatic and hydrogen bond interactions with RNA 5 0 stem and loop. By examining the residues whose mutations significantly change the binding free energy between U1A and snRNA, the Gaussian network model-based calculations show that not only the residues at the binding sites that are traditionally considered to play a major role in U1A-RNA association but also those residues that are far away from the RNA-binding interface can participate in the long-range allosteric signal transmission; these calculations are quantitatively consistent with the data observed in the recent snRNA binding experiments. The study demonstrates a useful avenue to utilize the simplified elastic network model to investigate the dynamics characteristics of the biologically important macromolecular interactions.

Amylosucrase (AS) is a kind of glucosyltransferases (E.C. 2.4.1.4) belonging to the Glycoside Hydrolase (GH) Family 13. In the presence of an activator polymer, in vitro, AS is able to catalyze the synthesis of an amylose-like polysaccharide composed of only α-1,4-linkages using sucrose as the only energy source. Unlike AS, other enzymes responsible for the synthesis of such amylose-like polymers require the addition of expensive nucleotide-activated sugars. These properties make AS an interesting enzyme for industrial applications. In this work, the structures and topology of the two AS were thoroughly investigated for the sake of explaining the reason why Deinococcus geothermalis amylosucrase (DgAS) is more stable than Neisseria polysaccharea amylosucrase (NpAS). Based on our results, there are two main factors that contribute to the superior thermostability of DgAS. On the one hand, DgAS holds some good structural features that may make positive contributions to the thermostability. On the other hand, the contacts among residues of DgAS are thought to be topologically more compact than those of NpAS. Furthermore, the dynamics and unfolding properties of the two AS were also explored by the gauss network model (GNM) and the anisotropic network model (ANM). According to the results of GNM and ANM, we have found that the two AS could exhibit a shear-like motion, which is probably associated with their functions. What is more, with the discovery of the unfolding pathway of the two AS, we can focus on the weak regions, and hence designing more appropriate mutations for the sake of thermostability engineering. Taking the results on structure, dynamics and unfolding properties of the two AS into consideration, we have predicted some novel mutants whose thermostability is possibly elevated, and hopefully these discoveries can be used as guides for our future work on rational design.