Recent advances in pharmacophore modeling and its application to anti-influenza drug discovery

Shin, Woo-Jin,Seong, Baik Lin Informa UK, Ltd. 2013 Expert opinion on drug discovery Vol.8 No.4

<P><B><I>Introduction:</I></B> The emergence of the highly pathogenic avian influenza (HPAI) H5N1 virus and the recent global circulation of H1N1 swine-origin influenza virus in 2009 have highlighted the need for new anti-influenza therapies. This has been made all the more important with the emergence of antiviral-resistant strains. Recent progress in achieving three-dimensional (3D) crystal structures of influenza viral proteins and efficient tools available for pharmacophore-based virtual screening are aiding us in the discovery and design of new antiviral compounds.</P><P><B><I>Areas covered:</I></B> This review discusses pharmacophore modeling as a potential cost-effective and time-saving technology for new drug discovery as an alternative to high-throughput screening. Based on this technical platform, the authors discuss current progress and future prospects for developing novel influenza antivirals against pre-existing or emerging novel targets.</P><P><B><I>Expert opinion:</I></B> Although it might be at an infant stage of development, the availability of the 3D crystal structures of influenza viral proteins is expected to accelerate the application of structure-based drug design (SBDD) and pharmacophore modeling. Furthermore, the neuraminidase inhibitor, one of the most successful examples of a SBDD, still receives great attention because of its superb antiviral activities and the resistance of influenza strains to oseltamivir. However, despite much success, pharmacophore-based virtual screening exhibits limited predictive power in hit identification. Further improvements in pharmacophore detection algorithms, proper combinations of <I>in silico</I> methods as well as judicious choosing of compounds are expected to improve the hit rate. With the help of these technologies, the discovery of anti-influenza agents will be accelerated.</P>

Natural compounds as potential Hsp90 inhibitors for breast cancer-Pharmacophore guided molecular modelling studies

Rampogu, Shailima,Parate, Shraddha,Parameswaran, Saravanan,Park, Chanin,Baek, Ayoung,Son, Minky,Park, Yohan,Park, Seok Ju,Lee, Keun Woo Elsevier 2019 Computational biology and chemistry Vol.83 No.-

<P><B>Abstract</B></P> <P>Breast cancer is one of the major impediments affecting women globally. The ATP-dependant heat shock protein 90 (Hsp90) forms the central component of molecular chaperone machinery that predominantly governs the folding of newly synthesized peptides and their conformational maturation. It regulates the stability and function of numerous client proteins that are frequently upregulated and/or mutated in cancer cells, therefore, making Hsp90 inhibition a promising therapeutic strategy for the development of new efficacious drugs to treat breast cancer. In the present <I>in silico</I> investigation, a structure-based pharmacophore model was generated with hydrogen bond donor, hydrogen bond acceptor and hydrophobic features complementary to crucial residues Ala55, Lys58, Asp93, Ile96, Met98 and Thr184 directed at inhibiting the ATP-binding activity of Hsp90. Subsequently, the phytochemical dataset of 3210 natural compounds was screened to retrieve the prospective inhibitors after rigorous validation of the model pharmacophore. The retrieved 135 phytocompounds were further filtered by drug-likeness parameters including Lipinski’s rule of five and ADMET properties, then investigated via molecular docking-based scoring. Molecular interactions were assessed using Genetic Optimisation for Ligand Docking program for 95 drug-like natural compounds against Hsp90 along with two clinical drugs as reference compounds – Geldanamycin and Radicicol. Docking studies revealed three phytochemicals are better than the investigated clinical drugs. The reference and hit compounds with dock scores of 48.27 (Geldanamycin), 40.90 (Radicicol), 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3) were further validated for their binding stability through molecular dynamics simulations. We propose that the non-macrocyclic scaffolds of three identified phytochemicals might aid in the development of novel therapeutic candidates against Hsp90-driven cancers.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Natural compounds are identified as Hsp90 inhibitors. </LI> <LI> These compounds show stable interactions with key residues. </LI> <LI> Non-quinone containing compounds were discovered by structure based pharmacophore modelling. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Pharmacophore Identification for Peroxisome Proliferator-Activated Receptor Gamma Agonists

손영식,이윤호,Chanin Park,Swan Hwang,Songmi Kim,Ayoung Baek,Minky Son,서정근,Hyong-Ha Kim,이근우 대한화학회 2011 Bulletin of the Korean Chemical Society Vol.32 No.1

Peroxisome proliferator-activated receptors (PPARs) are members of nuclear receptors and their activation induces regulation of fatty acid storage and glucose metabolism. Therefore, the PPARγ is a major target for the treatment of type 2 diabetes mellitus. In order to generate pharmacophore model, 1080 known agonists database was constructed and a training set was selected. The Hypo7, selected from 10 hypotheses, contains four features: three hydrogen-bond acceptors (HBA) and one general hydrophobic (HY). This pharmacophore model was validated by using 862 test set compounds with a correlation coefficient of 0.903 between actual and estimated activity. Secondly, CatScramble method was used to verify the model. Hence, the validated Hypo7 was utilized for searching new lead compounds over 238,819 and 54,620 chemical structures in NCI and Maybridge database, respectively. Then the leads were selected by screening based on the pharmacophore model, predictive activity, and Lipinski’s rules. Candidates were obtained and subsequently the binding affinities to PPARγ were investigated by the molecular docking simulations. Finally the best two compounds were presented and would be useful to treat type 2 diabetes.

Pharmacophore Identification for Peroxisome Proliferator-Activated Receptor Gamma Agonists

Sohn, Young-Sik,Lee, Yu-No,Park, Chan-In,Hwang, S-Wan,Kim, Song-Mi,Baek, A-Young,Son, Min-Ky,Suh, Jung-Keun,Kim, Hyong-Ha,Lee, Keun-Woo Korean Chemical Society 2011 Bulletin of the Korean Chemical Society Vol.32 No.1

Peroxisome proliferator-activated receptors (PPARs) are members of nuclear receptors and their activation induces regulation of fatty acid storage and glucose metabolism. Therefore, the $PPAR\gamma$ is a major target for the treatment of type 2 diabetes mellitus. In order to generate pharmacophore model, 1080 known agonists database was constructed and a training set was selected. The Hypo7, selected from 10 hypotheses, contains four features: three hydrogen-bond acceptors (HBA) and one general hydrophobic (HY). This pharmacophore model was validated by using 862 test set compounds with a correlation coefficient of 0.903 between actual and estimated activity. Secondly, CatScramble method was used to verify the model. Hence, the validated Hypo7 was utilized for searching new lead compounds over 238,819 and 54,620 chemical structures in NCI and Maybridge database, respectively. Then the leads were selected by screening based on the pharmacophore model, predictive activity, and Lipinski's rules. Candidates were obtained and subsequently the binding affinities to $PPAR\gamma$ were investigated by the molecular docking simulations. Finally the best two compounds were presented and would be useful to treat type 2 diabetes.

A Combined Pharmacophore-Based Virtual Screening, Docking Study and Molecular Dynamics (MD) Simulation Approach to Identify Inhibitors with Novel Scaffolds for Myeloid cell leukemia (Mcl-1)

Bao, Guang-Kai,Zhou, Lu,Wang, Tai-Jin,He, Lu-Fen,Liu, Tao Korean Chemical Society 2014 Bulletin of the Korean Chemical Society Vol.35 No.7

Chemical feature based quantitative pharmacophore models were generated using the HypoGen module implemented in DS2.5. The best hypothesis, Hypo1, which was characterized by the highest correlation coefficient (0.96), the highest cost difference (61.60) and the lowest RMSD (0.74), consisted of one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic and one ring aromatic. The reliability of Hypo1 was validated on the basis of cost analysis, test set, Fischer's randomization method and GH test method. The validated Hypo1 was used as a 3D search query to identify novel inhibitors. The screened molecules were further refined by employing ADMET, docking studies and visual inspection. Three compounds with novel scaffolds were selected as the most promising candidates for the designing of Mcl-1 antagonists. Finally, a 10 ns molecular dynamics simulation was carried out on the complex of receptor and the retrieved ligand to demonstrate that the binding mode was stable during the MD simulation.

A Combined Pharmacophore-Based Virtual Screening, Docking Study and Molecular Dynamics (MD) Simulation Approach to Identify Inhibitors with Novel Scaffolds for Myeloid cell leukemia (Mcl-1)

Guang-kai Bao,Lu Zhou,Tai-jin Wang,Lu-fen He,Tao Liu 대한화학회 2014 Bulletin of the Korean Chemical Society Vol.35 No.7

Chemical feature based quantitative pharmacophore models were generated using the HypoGen module implemented in DS2.5. The best hypothesis, Hypo1, which was characterized by the highest correlation coefficient (0.96), the highest cost difference (61.60) and the lowest RMSD (0.74), consisted of one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic and one ring aromatic. The reliability of Hypo1 was validated on the basis of cost analysis, test set, Fischer’s randomization method and GH test method. The validated Hypo1 was used as a 3D search query to identify novel inhibitors. The screened molecules were further refined by employing ADMET, docking studies and visual inspection. Three compounds with novel scaffolds were selected as the most promising candidates for the designing of Mcl-1 antagonists. Finally, a 10 ns molecular dynamics simulation was carried out on the complex of receptor and the retrieved ligand to demonstrate that the binding mode was stable during the MD simulation.

Identification of New Potential APE1 Inhibitors by Pharmacophore Modeling and Molecular Docking

이인원,윤종환,이건희,이민호 한국유전체학회 2017 Genomics & informatics Vol.15 No.4

Apurinic/apyrimidinic endonuclease 1 (APE1) is an enzyme responsible for the initial step in the base excision repair pathway and is known to be a potential drug target for treating cancers, because its expression is associated with resistance to DNA-damaging anticancer agents. Although several inhibitors already have been identified, the identification of novel kinds of potential inhibitors of APE1 could provide a seed for the development of improved anticancer drugs. For this purpose, we first classified known inhibitors of APE1. According to the classification, we constructed two distinct pharmacophore models. We screened more than 3 million lead-like compounds using the pharmacophores. Hits that fulfilled the features of the pharmacophore models were identified. In addition to the pharmacophore screen, we carried out molecular docking to prioritize hits. Based on these processes, we ultimately identified 1,338 potential inhibitors of APE1 with predicted binding affinities to the enzyme.

Identification of New Potential APE1 Inhibitors by Pharmacophore Modeling and Molecular Docking

Lee, In Won,Yoon, Jonghwan,Lee, Gunhee,Lee, Minho Korea Genome Organization 2017 Genomics & informatics Vol.15 No.4

Apurinic/apyrimidinic endonuclease 1 (APE1) is an enzyme responsible for the initial step in the base excision repair pathway and is known to be a potential drug target for treating cancers, because its expression is associated with resistance to DNA-damaging anticancer agents. Although several inhibitors already have been identified, the identification of novel kinds of potential inhibitors of APE1 could provide a seed for the development of improved anticancer drugs. For this purpose, we first classified known inhibitors of APE1. According to the classification, we constructed two distinct pharmacophore models. We screened more than 3 million lead-like compounds using the pharmacophores. Hits that fulfilled the features of the pharmacophore models were identified. In addition to the pharmacophore screen, we carried out molecular docking to prioritize hits. Based on these processes, we ultimately identified 1,338 potential inhibitors of APE1 with predicted binding affinities to the enzyme.

Pharmacophore Design for Anti-inflammatory Agent Targeting Interleukin-2 Inducible Tyrosine Kinase (Itk)

Chandrasekaran, Meganathan,Sakkiah, Sugunadevi,Thangapandian, Sundarapandian,Namadevan, Sundaraganesan,Kim, Hyong-Ha,Kim, Yong-Seong,Lee, Keun-Woo Korean Chemical Society 2010 Bulletin of the Korean Chemical Society Vol.31 No.11

A three dimensional pharmacophore model was generated for the molecules which are responsible for anti-inflammatory activities targeting Interleukin-2 inducible tyrosine kinase (Itk). 16 structurally diverse molecules were selected as training set to generate the hypotheses using Discovery Studio v2.1. The best hypothesis, Hypo1, comprises two hydrogen bond acceptor (HBA), one hydrophobic aromatic (HA), one ring aromatic (RA) and shows high cost difference (63.71), high correlation coefficient (0.97) as well as low RMS deviation (0.81). Hypo1 has been further validated toward a test set, decoy set and Fischer's randomization method. Furthermore, Hypo1 was used to screen NCI and Maybridge databases. Finally, 2 hit molecules were identified as potential leads against Itk, which may be useful for future drug development.

Pharmacophore Modeling and Molecular Dynamics Simulation to Find the Potent Leads for Aurora Kinase B

Sakkiah, Sugunadevi,Thangapandian, Sundarapandian,Kim, Yong-Seong,Lee, Keun-Woo Korean Chemical Society 2012 Bulletin of the Korean Chemical Society Vol.33 No.3

Identification of the selective chemical features for Aurora-B inhibitors gained much attraction in drug discovery for the treatment of cancer. Hence to identify the Aurora-B critical features various techniques were utilized such as pharmacophore generation, virtual screening, homology modeling, molecular dynamics, and docking. Top ten hypotheses were generated for Aurora-B and Aurora-A. Among ten hypotheses, HypoB1 and HypoA1 were selected as a best hypothesis for Aurora-B and Aurora-A based on cluster analysis and ranking score, respectively. Test set result revealed that ring aromatic (RA) group in HypoB1 plays an essential role in differentiates Aurora-B from Aurora-A inhibitors. Hence, HypoB1 used as 3D query in virtual screening of databases and the hits were sorted out by applying drug-like properties and molecular docking. The molecular docking result revealed that 15 hits have shown strong hydrogen bond interactions with Ala157, Glu155, and Lys106. Hence, we proposed that HypoB1 might be a reasonable hypothesis to retrieve the structurally diverse and selective leads from various databases to inhibit Aurora-B.