Protein folding is an important and challenging problem in molecular biology. During the last two decades, molecular dynamics (MD) simulation has proved to be a paramount tool and was widely used to study protein structures, folding kinetics and thermodynamics, and structure–stability–function relationship. It was also used to help engineering and designing new proteins, and to answer even more general questions such as the minimal number of amino acid or the evolution principle of protein families. Nowadays, the MD simulation is still undergoing rapid developments. The first trend is to toward developing new coarse‐grained models and studying larger and more complex molecular systems such as protein–protein complex and their assembling process, amyloid related aggregations, and structure and motion of chaperons, motors, channels and virus capsides; the second trend is toward building high resolution models and explore more detailed and accurate pictures of protein folding and the associated processes, such as the coordination bond or disulfide bond involved folding, the polarization, charge transfer and protonate/deprotonate process involved in metal coupled folding, and the ion permeation and its coupling with the kinetics of channels. On these new territories, MD simulations have given many promising results and will continue to offer exciting views. Here, we review several new subjects investigated by using MD simulations as well as the corresponding developments of appropriate protein models. These include but are not limited to the attempt to go beyond the topology based Gō‐like model and characterize the energetic factors in protein structures and dynamics, the study of the thermodynamics and kinetics of disulfide bond involved protein folding, the modeling of the interactions between chaperonin and the encapsulated protein and the protein folding under this circumstance, the effort to clarify the important yet still elusive folding mechanism of protein BBL, the development of discrete MD and its application in studying the α–β conformational conversion and oligomer assembling process, and the modeling of metal ion involved protein folding. © 2009 IUBMB IUBMB Life, 61(6): 627–643, 2009

Nucleic acids have been widely recognized as potential targets in drug discovery and aptamer selection. Quantifying the interactions between small molecules and nucleic acids is critical to discover lead compounds and design novel aptamers. Scoring function is normally employed to quantify the interactions in structure-based virtual screening. However, the predictive power of nucleic acid–ligand scoring functions is still a challenge compared to other types of biomolecular recognition. With the rapid growth of experimentally determined nucleic acid–ligand complex structures, in this work, we develop a knowledge-based scoring function of nucleic acid–ligand interactions, namely SPA-LN. SPA-LN is optimized by maximizing both the affinity and specificity of native complex structures. The development strategy is different from those of previous nucleic acid–ligand scoring functions which focus on the affinity only in the optimization. The native conformation is stabilized while non-native conformations are destabilized by our optimization, making the funnel-like binding energy landscape more biased toward the native state. The performance of SPA-LN validates the development strategy and provides a relatively more accurate way to score the nucleic acid–ligand interactions.

Motivation: Most biological processes are mediated by the protein–protein interactions. Determination of the protein–protein structures and insight into their interactions are vital to understand the mechanisms of protein functions. Currently, compared with the isolated protein structures, only a small fraction of protein–protein structures are experimentally solved. Therefore, the computational docking methods play an increasing role in predicting the structures and interactions of protein–protein complexes. The scoring function of protein–protein interactions is the key responsible for the accuracy of the computational docking. Previous scoring functions were mostly developed by optimizing the binding affinity which determines the stability of the protein–protein complex, but they are often lack of the consideration of specificity which determines the discrimination of native protein–protein complex against competitive ones. Results: We developed a scoring function (named as SPA-PP, specificity and affinity of the protein–protein interactions) by incorporating both the specificity and affinity into the optimization strategy. The testing results and comparisons with other scoring functions show that SPA-PP performs remarkably on both predictions of binding pose and binding affinity. Thus, SPA-PP is a promising quantification of protein–protein interactions, which can be implemented into the protein docking tools and applied for the predictions of protein–protein structure and affinity. Availability: The algorithm is implemented in C language, and the code can be downloaded from http://dl.dropbox.com/u/1865642/Optimization.cpp. Contact:jin.wang.1@stonybrook.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Staphylococcus aureus (S. aureus) is one of the most serious human pathogens. α-Hemolysin (Hla) secreted by S. aureus is a key toxin for various infections. We herein report that Honokiol, a natural plant polyphenol, inhibits the secretion and hemolytic activity of staphylococcal Hla with concomitant growth inhibition of S. aureus and protection of S. aureus-mediated cell injury within subinhibitory concentrations. In parallel, Honokiol attenuates the staphylococcal Hla-induced inflammatory response by inhibiting NLRP3 inflammasome activation in vitro and in vivo. Consequently, the biologically active forms of the inflammatory cytokines IL-1β and IL-18 are reduced significantly in response to Honokiol in mice infected with S. aureus. Experimentally, we confirm that Honokiol binds to monomeric Hla with a modest affinity without impairing its oligomerization. Based on molecular docking analyses in silico, we make a theoretical discovery that Honokiol is located outside of the triangular region of monomeric Hla. The binding model restricts the function of the residues related to membrane channel formation, which leads to the functional disruption of the assembled membrane channel. This research creates a new paradigm for developing therapeutic agents against staphylococcal Hla-mediated infections.