Computational binding mechanism of Mycobacterium tuberculosis UDP-NAG enolpyruvyl transferase (MurA) with inhibitors fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic aci

Worldwide, tuberculosis (TB) remains the most frequent and important infectious disease causing morbidity and death. One-third of the world's population is infected with Mycobacterium tuberculosis (Mtb), the etiologic agent of TB. In this context, TB is in the top three, with malaria and HIV being the leading causes of death from a single infectious agent, and about two million deaths are attributable to TB annually. The bacterial enzyme MurA catalyzes the transfer of enolpyruvate from phosphoenolpyruvate (PEP) to uridine diphospho-N-acetylglucosamine (UNAG), which is the first committed step of bacterial cell wall biosynthesis. In this work, 3D structural model of Mtb-MurA enzyme has been developed, for the first time, by homology modeling and molecular dynamics simulation techniques. The model provided clear insight in its structure features, i.e. substrate binding pocket, and common docking site. Multiple sequence alignment and 3D structure model provided the putative substrate binding pocket of Mtb-MurA with respect to E.coli MurA. This analysis was helpful in identifying the binding sites and molecular function of the MurA homologue. Molecular docking study was performed on this 3D structural model, using different classes of inhibitors like fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic acid derivatives T6361, T6362 and the results showed that the 5-sulfonoxyanthranilic acid derivatives is showed best interaction compared with other inhibitor, taking in to this we also design a new efficient analogs of T6361 and T6362 which are showed even better interaction with Mtb-MurA than the parental5-sulfonoxy-anthranilic acid derivatives. Further the comparative molecular electrostatic potential and cavity depth analysis of Mtb-MurA suggested several important differences in its substrate and inhibitor binding pocket. Such differences could be exploited in the future for designing of a more specific inhibitor for Mtb-MurA enzym

Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies

Many real-world processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy time-series, a multi-input and multi-output recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from any given starting condition. It is further shown that this capability can be achieved by the Recurrent Neural Network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of Hénon Map as a representative of some real-world processes