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Thangapandian, Sundarapandian,John, Shalini,Lee, Keun Woo Adenine Press 2012 Journal of biomolecular structure & dynamics Vol.29 No.4
<P>Histone deacetylases (HDACs) are key regulators of gene expression and thereby compelling targets in the treatment of various cancers. Class- and isoform-selective HDAC inhibitors targeting the particular isoform to treat cancers without affecting the normal expression of other isoforms are highly desirable. Molecular dynamics simulations were performed with the set of selective inhibitors and HDAC isoforms of three different classes. The results were compared both within and across the isoforms. The hydrogen bonds between protein and inhibitors are directly correlated with the selective experimental activity. The calculated distances between important amino acids and the metal binding part of inhibitors have disclosed the optimal distance to be maintained by a selective inhibitor. In addition, the calculated non-bonded interaction energies between inhibitor and catalytic residues revealed that the subtle difference in the amino acids at the highly conserved active sites of HDAC isoforms effectively scripts the selectivity story observed experimentally. The results of this study provide valuable information in designing highly selective HDAC inhibitors.</P>
Thangapandian, Sundarapandian,John, Shalini,Son, Minky,Arulalapperumal, Venkatesh,Lee, Keun Woo Future Science 2013 Future medicinal chemistry Vol.5 No.1
<P>Human LTA4H catalyzes the conversion of LTA4 to LTB4 and plays a key role in innate immune responses. Inhibition of this enzyme can be a valid method in the treatment of inflammatory response exhibited through LTB4.</P>
Thangapandian, Sundarapandian,John, Shalini,Sakkiah, Sugunadevi,Lee, Keun Woo Blackwell Publishing Ltd 2011 Chemical biology & drug design Vol.78 No.2
<P>Very late antigen‐4 (VLA‐4) is an integrin protein, and its antagonists are useful as anti‐inflammatory drugs. The aim of this study is to discover novel virtual lead compounds to use them in designing potent VLA‐4 antagonists. A best pharmacophore model was generated with correlation coefficient of 0.935, large cost difference of 114.078, comprising two hydrogen bond acceptors and three hydrophobic features. It was further validated and used in database screening for potential VLA‐4 antagonists. A homology model of VLA‐4 was built and employed in molecular docking of screened hit compounds. Finally, two compounds were identified as potential virtual leads to be deployed in the designing of novel potent VLA‐4 antagonists.</P>
Genetic Function Approximation and Bayesian Models for the Discovery of Future HDAC8 Inhibitors
Thangapandian, Sundarapandian,John, Shalini,Lee, Keun-Woo Korean Society for Bioinformatics 2011 Interdisciplinary Bio Central (IBC) Vol.3 No.4
Background: Histone deacetylase (HDAC) 8 is one of its family members catalyzes the removal of acetyl groups from N-terminal lysine residues of histone proteins thereby restricts transcription factors from being expressed. Inhibition of HDAC8 has become an emerging and effective anti-cancer therapy for various cancers. Application computational methodologies may result in identifying the key components that can be used in developing future potent HDAC8 inhibitors. Results: Facilitating the discovery of novel and potential chemical scaffolds as starting points in the future HDAC8 inhibitor design, quantitative structure-activity relationship models were generated with 30 training set compounds using genetic function approximation (GFA) and Bayesian algorithms. Six GFA models were selected based on the significant statistical parameters calculated during model development. A Bayesian model using fingerprints was developed with a receiver operating characteristic curve cross-validation value of 0.902. An external test set of 54 diverse compounds was used in validating the models. Conclusions: Finally two out of six models based on their predictive ability over the test set compounds were selected as final GFA models. The Bayesian model has displayed a high classifying ability with the same test set compounds and the positively and negatively contributing molecular fingerprints were also unveiled by the model. The effectively contributing physicochemical properties and molecular fingerprints from a set of known HDAC8 inhibitors were identified and can be used in designing future HDAC8 inhibitors.
Thangapandian, Sundarapandian,Krishnamoorthy, Navaneethakrishnan,John, Shalini,Sakkiah, Sugunadevi,Lazar, Prettina,Lee, Yu-No,Lee, Keun-Woo Korean Chemical Society 2010 Bulletin of the Korean Chemical Society Vol.31 No.1
Human histamine H1 receptor (HHR1) is a G protein-coupled receptor and a primary target for antiallergic therapy. Here, the ligand-based three-dimensional pharmacophore models were built from a set of known HHR1 inverse agonists using HypoGen module of CATALYST software. All ten generated pharmacophore models consist of five essential features: hydrogen bond acceptor, ring aromatic, positive ionizable and two hydrophobic functions. Best model had a correlation coefficient of 0.854 for training set compounds and it was validated with an external test set with a high correlation value of 0.925. Using this model Maybridge database containing 60,000 compounds was screened for potential leads. A rigorous screening for drug-like compounds unveiled RH01692 and SPB00834, two novel molecules for HHR1 with good CATALYST fit and estimated activity values. The new lead molecules were docked into the active site of constructed HHR1 homology model based on recently crystallized squid rhodopsin as template. Both the hit compounds were found to have critical interactions with Glu177, Phe432 and other important amino acids. The interpretations of this study may effectively be deployed in designing of novel HHR1 inverse agonists.
Thangapandian, Sundarapandian,John, Shalini,Lee, Yuno,Kim, Songmi,Lee, Keun Woo Molecular Diversity Preservation International (MD 2011 INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES Vol.12 No.12
<P>Histone deacetylase 8 (HDAC8) is an enzyme involved in deacetylating the amino groups of terminal lysine residues, thereby repressing the transcription of various genes including tumor suppressor gene. The over expression of HDAC8 was observed in many cancers and thus inhibition of this enzyme has emerged as an efficient cancer therapeutic strategy. In an effort to facilitate the future discovery of HDAC8 inhibitors, we developed two pharmacophore models containing six and five pharmacophoric features, respectively, using the representative structures from two molecular dynamic (MD) simulations performed in Gromacs 4.0.5 package. Various analyses of trajectories obtained from MD simulations have displayed the changes upon inhibitor binding. Thus utilization of the dynamically-responded protein structures in pharmacophore development has the added advantage of considering the conformational flexibility of protein. The MD trajectories were clustered based on single-linkage method and representative structures were taken to be used in the pharmacophore model development. Active site complimenting structure-based pharmacophore models were developed using Discovery Studio 2.5 program and validated using a dataset of known HDAC8 inhibitors. Virtual screening of chemical database coupled with drug-like filter has identified drug-like hit compounds that match the pharmacophore models. Molecular docking of these hits reduced the false positives and identified two potential compounds to be used in future HDAC8 inhibitor design.</P>
Sundarapandian Thangapandian,Navaneethakrishnan Krishnamoorthy,Shalini John,Sugunadevi Sakkiah,Prettina Lazar,이윤호,이근우 대한화학회 2010 Bulletin of the Korean Chemical Society Vol.31 No.1
Human histamine H1 receptor (HHR1) is a G protein-coupled receptor and a primary target for antiallergic therapy. Here, the ligand-based three-dimensional pharmacophore models were built from a set of known HHR1 inverse agonists using HypoGen module of CATALYST software. All ten generated pharmacophore models consist of five essential features: hydrogen bond acceptor, ring aromatic, positive ionizable and two hydrophobic functions. Best model had a correlation coefficient of 0.854 for training set compounds and it was validated with an external test set with a high correlation value of 0.925. Using this model Maybridge database containing 60,000 compounds was screened for potential leads. A rigorous screening for drug-like compounds unveiled RH01692 and SPB00834, two novel molecules for HHR1 with good CATALYST fit and estimated activity values. The new lead molecules were docked into the active site of constructed HHR1 homology model based on recently crystallized squid rhodopsin as template. Both the hit compounds were found to have critical interactions with Glu177, Phe432 and other important amino acids. The interpretations of this study may effectively be deployed in designing of novel HHR1 inverse agonists
John, Shalini,Thangapandian, Sundarapandian,Arooj, Mahreen,Hong, Jong Chan,Kim, Kwang Dong,Lee, Keun Woo BioMed Central 2011 BMC bioinformatics Vol.12 No.14
<P><B>Background</B></P><P>Renin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.</P><P><B>Methods</B></P><P>Ligand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.</P><P><B>Results</B></P><P>The best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.</P><P><B>Conclusion</B></P><P>A quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.</P>