Literature DB >> 18771818

3D-QSAR studies of boron-containing dipeptides as proteasome inhibitors with CoMFA and CoMSIA methods.

Yong-Qiang Zhu1, Meng Lei, Ai-Jun Lu, Xin Zhao, Xiao-Jin Yin, Qing-Zhi Gao.   

Abstract

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed for a series of dipeptide boronate proteasome inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 46 molecules served to establish the models. The optimum CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients (q(2)) of 0.676 and 0.630 and conventional coefficients (r(2)) of 0.989 and 0.956, respectively. The predictive capacities of both models were successfully validated by calculating a test set of 13 molecules that were not included in the training set. The predicted correlation coefficients (r(2)(pred)) of CoMFA and CoMSIA are 0.963 and 0.919, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of beta5 subunit of 20S proteasome, which suggests that the 3D-QSAR models constructed in this paper can be used to guide the development of novel dipeptide boronate inhibitors of 20S proteasome.

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Year:  2008        PMID: 18771818     DOI: 10.1016/j.ejmech.2008.07.019

Source DB:  PubMed          Journal:  Eur J Med Chem        ISSN: 0223-5234            Impact factor:   6.514


  7 in total

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Journal:  J Mol Model       Date:  2011-09-27       Impact factor: 1.810

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Authors:  Jianling Liu; Hong Zhang; Zhengtao Xiao; Fangfang Wang; Xia Wang; Yonghua Wang
Journal:  Int J Mol Sci       Date:  2011-03-09       Impact factor: 5.923

4.  Prediction of PKCθ inhibitory activity using the Random Forest Algorithm.

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Journal:  Int J Mol Sci       Date:  2010-09-20       Impact factor: 5.923

5.  A specific QSAR model for proteasome inhibitors from Oleaeuropaea and Ficuscarica.

Authors:  Lahmadi Ayoub; El-Aliani Aissam; Kasmi Yassine; Elantri Said; El Mzibri Mohammed; Aboudkhil Souad
Journal:  Bioinformation       Date:  2018-07-31

6.  Mechanism Exploration of Arylpiperazine Derivatives Targeting the 5-HT2A Receptor by In Silico Methods.

Authors:  Feng Lin; Feng Li; Chao Wang; Jinghui Wang; Yinfeng Yang; Ling Yang; Yan Li
Journal:  Molecules       Date:  2017-06-26       Impact factor: 4.411

7.  Profiling the Structural Determinants of Aryl Benzamide Derivatives as Negative Allosteric Modulators of mGluR5 by In Silico Study.

Authors:  Yujing Zhao; Jiabin Chen; Qilei Liu; Yan Li
Journal:  Molecules       Date:  2020-01-18       Impact factor: 4.411

  7 in total

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