Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs

<div><p>Resistance to therapy limits the effectiveness of drug treatment in many diseases. Drug resistance can be considered as a successful outcome of the bacterial struggle to survive in the hostile environment of a drug-exposed cell. An important mechanism by which bacteria acquire drug resistance is through mutations in the drug target. Drug resistant strains (multi-drug resistant and extensively drug resistant) of <i>Mycobacterium tuberculosis</i> are being identified at alarming rates, increasing the global burden of tuberculosis. An understanding of the nature of mutations in different drug targets and how they achieve resistance is therefore important. An objective of this study is to first decipher sequence as well as structural bases for the observed resistance in known drug resistant mutants and then to predict positions in each target that are more prone to acquiring drug resistant mutations. A curated database containing hundreds of mutations in the 38 drug targets of nine major clinical drugs, associated with resistance is studied here. Mutations have been classified into those that occur in the binding site itself, those that occur in residues interacting with the binding site and those that occur in outer zones. Structural models of the wild type and mutant forms of the target proteins have been analysed to seek explanations for reduction in drug binding. Stability analysis of an entire array of 19 mutations at each of the residues for each target has been computed using structural models. Conservation indices of individual residues, binding sites and whole proteins are computed based on sequence conservation analysis of the target proteins. The analyses lead to insights about which positions in the polypeptide chain have a higher propensity to acquire drug resistant mutations. Thus critical insights can be obtained about the effect of mutations on drug binding, in terms of which amino acid positions and therefore which interactions should not be heavily relied upon, which in turn can be translated into guidelines for modifying the existing drugs as well as for designing new drugs. The methodology can serve as a general framework to study drug resistant mutants in other micro-organisms as well.</p> </div

Beyond the “Coffee Ring”: Re-entrant Ordering in an Evaporation-Driven Self-Assembly in a Colloidal Suspension on a Substrate

We study the phenomenon of evaporation-driven self-assembly of a colloid suspension of silica microspheres in the interior region and away from the rim of the droplet on a glass plate. In view of the importance of achieving a large-area, monolayer assembly, we first realize a suitable choice of experimental conditions, minimizing the influence of many other competing phenomena that usually complicate the understanding of fundamental concepts of such self-assembly processes in the interior region of a drying droplet. Under these simplifying conditions to bring out essential aspects, our experiments unveil an interesting competition between ordering and compaction in such drying systems in analogy to an impending glass transition. We establish a re-entrant behavior in the order–disorder phase diagram as a function of the particle density, such that there is an optimal range of the particle density to realize the long-range ordering. The results are explained with the help of simulations and phenomenological theory

Correlation coefficients <i>r</i>(<i>θ</i>, <i>τ</i>) between summary statistics of the epidemic and <i>SPV</i> characteristics for the synthetic data set.

<p>Correlation coefficients <i>r</i>(<i>θ</i>, <i>τ</i>) between summary statistics of the epidemic and <i>SPV</i> characteristics for the synthetic data set.</p

Case study 3—Studying Australian aboriginal ethnicities.

<p>Case study 3—Studying Australian aboriginal ethnicities.</p

<i>R</i>₀ as a function of <i>SPV</i> characteristics.

<p>The dependence of the basic reproduction number <i>R</i>₀ on several characteristics of the susceptibility profile vector of each (<i>E</i>, <i>V</i>) pair considering H1N1 strains isolated in 2009: (a) skewness of the <i>SPV</i>; (b) standard deviation of the <i>SPV</i>; (c) coefficient of variation of the <i>SPV</i>; (d) number of susceptibility sub-populations, <i>m</i>. Only epidemic pairs (<i>E</i>, <i>V</i>) with 1 < <i>R</i>₀ < 7 and <i>m</i> > 1 are plotted.</p

Variations in <i>SPV</i> characteristics, 2009 strains.

<p>Histograms for the values of the different susceptibility profile vector characteristics for the 4, 941 epidemic pairs involving H1N1 strains isolated in 2009: (a) <i>σ</i>(<i>SPV</i>); (b) <i>Sk</i>(<i>SPV</i>); (c) <i>CV</i>(<i>SPV</i>); (d) <i>m</i>; and (e) <i>β</i>.</p

Case study 1—Studying the predictive power of <i>σ</i>(<i>SPV</i>).

<p>Case study 1—Studying the predictive power of <i>σ</i>(<i>SPV</i>).</p

Workflow.

<p>Summary of the steps carried out in this work. Inputs from external sources are shown in dotted parallelograms.</p

<i>FI</i>_∞ as a function of <i>SPV</i> characteristics—Synthetic data set.

<p>Dependence of epidemic size on several characteristics of the <i>SPV</i> is analysed for the synthetic data set described in the text. (a) <i>β</i>; (b) <i>Sk</i>(<i>SPV</i>); (c) <i>σ</i>(<i>SPV</i>); (d) <i>CV</i>(<i>SPV</i>); (e) <i>m</i>.</p

Select case studies to study the observed behaviour in Fig 10.

<p>Select case studies to study the observed behaviour in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006069#pcbi.1006069.g010" target="_blank">Fig 10</a>.</p