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CitationKumar V, Chawla M, Cavallo L, Basit Wani A, Manhas A, et al. (2017) Complexation of trichlorosalicylic acids by alkaline and first row transition metals as a switch for their antibacterial activity. Inorganica Chimica Acta. Available: http:// dx.NirajUpadhyay, Complexation of trichlorosalicylic acids by alkaline and first row transition metals as a switch for their antibacterial activity, Inorganica Chimica Acta (2017), doi: http://dx. Abstract:3,5,6-trichlorosalicylic acid (TCSA) does not show a good antibacterial activity. In contrast, here metal complexes with TCSA have shown better antibacterial activity for selected bacterial strains with a good degree of selectivity. Amongst the eight synthesized essential metal complexes complexed with TCSA, Mn(II)-TCSA and Ni(II)-TCSA have been found to be more effective with MIC range 20-50 µg/L as compared to control (chloramphenicol). The activity of an individual complex against different microbes was not found to be identical, indicating the usage of an individual metal chelate against a targeted bacterial strain. Further, the protein (BSA) binding constant of TCSA and its metal complexes were determined and ordered as Ca(II)-TCSA > Cu(II)-TCSA > Mg(II)-TCSA >> Mn(II)-TCSA >> Zn(II)-TCSA >>> Ni(II)-TCSA >>> Co(II)-TCSA > Fe(II)-TCSA > TCSA. The present study has confirmed enhanced antibacterial activities and binding constants for metal chelates of TCSA as compared to free TCSA, which seems directly related with the antioxidant activities of these complexes. Further, bearing the ambiguity related to the structural characterization of the metal complexed with TCSA ligands, DFT calculations have been used as the tool to unravel the right environment around the metals, studying basically the relative stability of square planar and octahedral metal complexes with TCSA.

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In the present work, multiple pharmacophore-based virtual screening of the SPECS natural product database was carried out to identify novel inhibitors of the validated biological target, InhA. The pharmacophore models were built from the five different groups of the co-crystallized ligands present within the active site. The generated models with the same features from each group were pooled and subjected to the test set validation, receiver-operator characteristic analysis and Güner-Henry studies. A set of five hypotheses with sensitivity > 0.5, specificity > 0.5, area under curve (AUC) > 0.7, and goodness of hit score > 0.7 were retrieved and exploited for the virtual screening. The common hits (87 molecules) obtained from these hypotheses were processed via drug-likeness filters. The filtered molecules (27 molecules) were compared for the binding modes and the top scored molecules (12 molecules) along with the reference (triclosan (TCL), docking score = -11.65 kcal/mol) were rescored and reprioritized via molecular mechanics-generalized Born surface area approach. Eventually, the stability of reprioritized (10 molecules) docked complexes was scrutinized via molecular dynamics simulations. Moreover, the quantum chemical studies of the dynamically stable compounds (9 molecules) were performed to understand structural features essential for the activity. Overall, the protocol resulted in the recognition of nine lead compounds that can be targeted against InhA.

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