Proposed Mechanism for Emodin as Agent for Methicillin Resistant Staphylococcus Aureus: In Vitro Testing and In Silico Study.

In the search for a new anti-MRSA lead compound, emodin was identified as a good lead against methicillin-resistant Staphylococcus aureus (MRSA). Emodin serves as a new scaffold to design novel and effective anti-MRSA agents. Because rational drug discovery is limited by the knowledge of the drug target, α-hemolysin of Staphylococcus aureus was used in this study because it has an essential role in Staphylococcus infections and because emodin shares structural features with compounds that target this enzyme. In order to explore emodin's interactions with α-hemolysin, all possible ligand binding pockets were identified and investigated. Two ligand pockets were detected based on bound ligands and other reports. The third pocket was identified as a cryptic site after molecular dynamics (MD) simulations. MD simulations were conducted for emodin in each pocket to identify the most plausible ligand site and to aid in the design of potent anti-MRSA agents. Binding of emodin to site 1 was most stable (RMSD changes within 1 Å), while in site 2, the binding pose of emodin fluctuated, and it left after 20 ns. In site 3, it was stable during the first 50 ns, and then it started to move out of the binding site. Site 1 is a possible ligand binding pocket, and this study sheds more light on interaction types, binding mode, and key amino acids involved in ligand binding essential for better lead design. Emodin showed an IC50 value of 6.3 µg/mL, while 1, 6, and 8 triacetyl emodin showed no activity against MRSA. A molecular modeling study was pursued to better understand effective binding requirements for a lead.

SLLISWD sequence in the 10FNIII domain initiates fibronectin fibrillogenesis.

Fibronectin (FN) assembly into extracellular matrix is tightly regulated and essential to embryogenesis and wound healing. FN fibrillogenesis is initiated by cytoskeleton-derived tensional forces transmitted across transmembrane integrins onto RGD binding sequences within the tenth FN type III (10FNIII) domains. These forces unfold 10FNIII to expose cryptic FN assembly sites; however, a specific sequence has not been identified in 10FNIII. Our past steered molecular dynamics simulations modeling 10FNIII unfolding by force at its RGD loop predicted a mechanical intermediate with a solvent-exposed N terminus spanning the A and B ß-strands. Here, we experimentally confirm that the predicted 23-residue cryptic peptide 1 (CP1) initiates FN multimerization, which is mediated by interactions with 10FNIII that expose hydrophobic surfaces that support 8-anilino-1-napthalenesulfonic acid binding. Localization of multimerization activity to the C terminus led to the discovery of a minimal 7-amino acid "multimerization sequence" (SLLISWD), which induces polymerization of FN and the clotting protein fibrinogen in addition to enhancing FN fibrillogenesis in fibroblasts. A point mutation at Trp-6 that reduces exposure of hydrophobic sites for 8-anilino-1-napthalenesulfonic acid binding and ß-structure formation inhibits FN multimerization and prevents physiological cell-based FN assembly in culture. We propose a model for cell-mediated fibrillogenesis whereby cell traction force initiates a cascade of intermolecular exchange starting with the unfolding of 10FNIII to expose the multimerization sequence, which interacts with strand B of another 10FNIII domain via a Trp-mediated ß-strand exchange to stabilize a partially unfolded intermediate that propagates FN self-assembly.

Computational strategies for protein conformational ensemble detection.

Protein function is constrained by the three-dimensional structure but is delineated by its dynamics. This framework must satisfy specificity of function along with adaptability to changing environments and evolvability under external constraints. The accessibility of the available regions of the energy landscape for a set of conditions and shifts in the populations upon their modulation have effects propagating across scales, from biomolecular interactions, to organisms, to populations. Developing the ability to detect and juggle protein conformations supplemented by a physics-based understanding has implications for not only in vivo problems but also for resistance impeding drug discovery and bionano-sensor design.

Mapping the binding sites of challenging drug targets.

An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.