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Mutations in P53 cause a loop unfolding, resulting in loss of activity and finally leading to cancer. One strategically reported way to arrest such oncogenesis is the restoration of tertiary structure as well as the function of mutant P53. In this attempt, we have designed a benzo-pyrazole-based novel ligand starting from a carbazole compound (EYB or PK9324) reported earlier to reinstate such function in mutant P53 (Y220C mutant, PDB: 6GGD). Assuming PK9324 as the template scaffold, de novo technique (Genetic algorithm, eLEA3D) was adopted within the binding pocket of 6GGD and our ligand DLIG1 was designed after several rounds of mutations. Docking and molecular dynamics (MD) simulation revealed significant interactions with key amino acid residues such as Cys220, Asp228, Leu145, Trp146, Val147, Thr150, Pro151, Pro152, Pro222, Pro223, Asp228, and Thr230. Along with sufficient binding stability, the MMGBSA analysis revealed its comparable binding free energy with other reported reference ligands (i.e. PK9324 and PK9318). Similar to these reference ligands, DLIG1 exhibited specificity in binding towards the Y220C mutant rather than towards wild-type P53. Finally, DLIG1 displayed a reorientation of a hydrophobic cavity in Y220C that hinted restoration of electrostatic interactions within the key loops of P53 favoring regain of its function.

Funding Information: This work was supported by UIDB/50006/2020 with funding from FCT/MCTES through national funds. Publisher Copyright: © 2022 by the authors. Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including ‘mixtures-out’- and ‘compounds-out’-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures. publishersversion published

Additional file 1. File containing the QSAR-Co-X generated ROC plots (Figures S1–3) and additional information related to the several case studies (Tables S1–5).

Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700 032, India Indian Institute of Chemical Biology, 4, Raja S. C. Mullick Road, Jadavpur, Kolkata-700 032, India E-mail: tjupharm@yahoo.com Manuscript received online 03 July 2020, accepted 30 July 2020 Thirty 5-N-substituted-2-N-(arylsulphonyl)-L(+)glutamines were synthesized and evaluated biologically for their anticancer activities. The best active compound of this series showed 92.92% inhibition of tumor weight against Ehrlich Ascites Carcinoma cells. The most active compound was proved to be a competitive inhibitor of glutaminase in the enzyme assay. The best active compound may be a starting point to generate ‘lead’ for further exploration.