The potential of antimicrobial peptides (AMPs) as an alternative to conventional therapies is well recognized. Insights into the biological and biophysical properties of AMPs are thus key to understanding their mode of action. In this study, the mechanisms adopted by two AMPs in disrupting the Gram-negative Escherichia coli bacterial envelope were explored. BP100 is a short cecropin A-melittin hybrid peptide known to inhibit the growth of phytopathogenic Gram-negative bacteria. pepR, on the other hand, is a novel AMP derived from the dengue virus capsid protein. Both BP100 and pepR were found to inhibit the growth of E. coli at micromolar concentrations. Zeta potential measurements of E. coli incubated with increasing peptide concentrations allowed for the establishment of a correlation between the minimal inhibitory concentration (MIC) of each AMP and membrane surface charge neutralization. While a neutralization-mediated killing mechanism adopted by either AMP is not necessarily implied, the hypothesis that surface neutralization occurs close to MIC values was confirmed. Atomic force microscopy (AFM) was then employed to visualize the structural effect of the interaction of each AMP with the E. coli cell envelope. At their MICs, BP100 and pepR progressively destroyed the bacterial envelope, with extensive damage already occurring 2 h after peptide addition to the bacteria. A similar effect was observed for each AMP in the concentration-dependent studies. At peptide concentrations below MIC values, only minor disruptions of the bacterial surface occurred.
The acquisition of drug-resistance mutations by infectious pathogens remains a pressing health concern, and the development of strategies to combat this threat is a priority. Here we have applied a general strategy, inverse design using the substrate envelope, to develop inhibitors of HIV-1 protease. Structure-based computation was used to design inhibitors predicted to stay within a consensus substrate volume in the binding site. Two rounds of design, synthesis, experimental testing, and structural analysis were carried out, resulting in a total of 51 compounds. Improvements in design methodology led to a roughly 1000-fold affinity enhancement to a wildtype protease for the best binders, from K i of 30-50 nM in round one to below 100 pM in round two. Crystal structures of a subset of complexes revealed a binding mode similar to each design that respected the substrate envelope in nearly all cases. All four best binders from round one exhibited broad specificity against a clinically relevant panel of drug-resistant HIV-1 protease variants, losing no more than 6-13 fold affinity relative to wild type. Testing a subset of secondround compounds against the panel of resistant variants revealed three classes of inhibitorsrobust binders (maximum affinity loss of 14-16 fold), moderate binders (35-80 fold), and susceptible binders (greater than 100 fold). Although for especially high-affinity inhibitors additional factors may also be important, overall, these results suggest that designing inhibitors using the substrate envelope may be a useful strategy in the development of therapeutics with low susceptibility to resistance.
We have developed a Brownian dynamics simulation algorithm to generate Brownian trajectories of an isolated, rigid particle of arbitrary shape in the presence of electric fields or any other external agents. Starting from the generalized diffusion tensor, which can be calculated with the existing HYDRO software, the new program BROWNRIG (including a case-specific subprogram for the external agent) carries out a simulation that is analyzed later to extract the observable dynamic properties. We provide a variety of examples of utilization of this method, which serve as tests of its performance, and also illustrate its applicability. Examples include free diffusion, transport in an electric field, and diffusion in a restricting environment.
In the absence of an experimentally solved structure, a homology model of a protein target can be used instead for virtual screening of drug candidates by docking and scoring. This approach poses a number of questions regarding the choice of the template to use in constructing the model, the accuracy of the screening results, and the importance of allowing for protein flexibility. The present study addresses such questions with compound screening calculations for multiple homology models of five drug targets. A central result is that docking to homology models frequently yields enrichments of known ligands as good as that obtained by docking to a crystal structure of the actual target protein. Interestingly, however, standard measures of the similarity of the template used to build the homology model to the targeted protein show little correlation with the effectiveness of the screening calculations, and docking to the template itself often is as successful as docking to the corresponding homology model. Treating key side chains as mobile produces a modest improvement in the results. The reasons for these sometimes unexpected results, and their implications for future methodologic development, are discussed.
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