02045nas a2200241 4500008004100000022001400041245015200055210006900207260011100276300001200387490000700399520109900406653002701505653000901532653000901541653002101550653002401571653001701595100001801612700002201630700002101652856013001673 2016 eng d a1570-180800aStructural insight of NICD-MAML interactions: virtual screening, docking and molecular dynamics study for the identification of potential inhibitor0 aStructural insight of NICDMAML interactions virtual screening do aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a301-3130 v133 a

Activation of Notch signalling pathway is triggered by binding of NICD to transcription factor CSL and transcriptional co-activator MAML, which involves in various biological functions as well as progression of diseases. Recent prediction shows suppression of cancer causing genes of this pathway through inhibition of NICD-MAML interaction. Through virtual screening against ``NCI Diversity 3'' of Zinc database, we identified a potential inhibitor ``ZINC01690699'' (1-N,4-N-bis[3-(1H-benzimidazol-2-yl)phenyl]benzene-1,4-dicarboxamide; 1-N, 4-dicarboxamide) possessing highest binding affinity to block the two distinct Binding Sites of NICD to inhibit NICD-MAML interaction and also found the most imperative and essential Binding Site (Site I). Inhibition of this interaction caused by binding of ZINC01690699 is validated by protein-protein docking and the prolonged binding as well as stability of NICD-Inhibitor complex is supported by molecular dynamics simulation. The study not only identifies the best inhibitor but also proposes a potential drug for the treatment of cancers.

10afree energy of binding10aMAML10aNICD10aNotch signalling10apotential inhibitor10aZINC016906991 aSinha, Noopur1 aChowdhury, Saikat1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/structural-insight-nicd-maml-interactions-virtual-screening-docking-and-molecular-dynamics02536nas a2200205 4500008004100000022001400041245012100055210006900176260000800245520178100253653002102034653003402055653001302089653002402102653001502126100001802141700002202159700002102181856012802202 2019 eng d a0887-358500aMolecular basis of drug resistance in smoothened receptor: an in silico study of protein resistivity and specificity0 aMolecular basis of drug resistance in smoothened receptor an in  cOCT3 a

Smoothened (SMO) antagonist Vismodegib effectively inhibits the Hedgehog pathway in proliferating cancer cells. In early stage of treatment, Vismodegib exhibited promising outcomes to regress the tumors cells, but ultimately relapsed due to the drug resistive mutations in SMO mostly occurring before (primary mutations G497W) or after (acquired mutations D473H/Y) anti-SMO therapy. This study investigates the unprecedented insights of structural and functional mechanism hindering the binding of Vismodegib with sensitive and resistant mutant variants of SMO (SMOMut). Along with the basic dynamic understanding of Vismodegib-SMO complexes, network propagation theory based on heat diffusion principles is first time applied here to identify the modules of residues influenced by the individual mutations. The allosteric modulation by GLY497 residue in Vismodegib bound SMO wild-type (SMOWT) conformation depicts the interconnections of intermediate residues of SMO with the atom of Vismodegib and identify two important motifs (E-X-P-L) and (Q-A-N-V-T-I-G) mediating this allosteric regulation. In this study a novel computational framework based on the heat diffusion principle is also developed, which identify significant residues of allosteric site causing drug resistivity in SMOMut. This framework could also be useful for assessing the potential allosteric sites of different other proteins. Moreover, previously reported novel inhibitor ``ZINC12368305,'' which is proven to make an energetically favorable complex with SMOWT is chosen as a control sample to assess the impact of receptor mutation on its binding and subsequently identify the important factors that govern binding disparity between Vismodegib and ZINC12368305 bound SMOWT/Mut conformations.

10aallosteric sites10amolecular dynamics simulation10aMutation10asmoothened receptor10aVismodegib1 aSinha, Noopur1 aChowdhury, Saikat1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/molecular-basis-drug-resistance-smoothened-receptor-silico-study-protein-resistivity-and02331nas a2200265 4500008004100000022001400041245012500055210006900180260000800249300001400257490000700271520136700278653004601645653001201691653003401703653003001737653005201767653002101819100001901840700001801859700001801877700001901895700002101914856013001935 2020 eng d a1747-027700aPrediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: an AI-based approach0 aPrediction of survival rate and effect of drugs on cancer patien cSEP a1005-10190 v963 a

The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction of tumor grades and patients' overall survival are important for prognosis, risk factors identification and betterment of the treatment strategy, especially for highly lethal tumors, like gliomas. Here, with the help of more accurate and widely used machine learning-based approaches, we propose an integrative computational pipeline that incorporates somatic mutations and gene expression profile for survival and grade prediction of glioma patients and simultaneously relates it to the drugs to be administered. This study gives us a clear understanding that the same drug is not effective for the treatment of same grade of cancer if the gene mutations are different. The alteration in a specific gene plays a very important role in tumor progression and should also be considered for the selection of appropriate drugs. This proposed framework includes all the necessary factors required for enhancement of therapeutic designs and could be useful for clinicians in determining an accurate and personalized treatment strategy for individual patients suffering from different life threatening diseases.

10agene expression and copy number variation10agliomas10agrade and survival prediction10amachine learning strategy10asignificant gene prediction and effect of drugs10asomatic mutation1 aSaurabh, Rochi1 aNandi, Sutanu1 aSinha, Noopur1 aShukla, Mudita1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/prediction-survival-rate-and-effect-drugs-cancer-patients-somatic-mutations-genes-ai-based