We compared binding affinity evaluations for 10 FKBP ligands with such state-of-the-art computational methods as FMO, QM/MM, MM-PB/SA, and MP-CAFEE. For the FKBP ligands, we confirmed that each method could provide good correlations between the experimental and computational binding affinities. From the calculated results, we discussed the importance of solvation effect and structural sampling for these methods in detail. In addition, we argued the issue of computational time and present arguments on the future perspective of the computational binding affinity evaluations.
{"title":"Comparison of binding affinity evaluations for FKBP ligands with state-of-the-art computational methods: FMO, QM/MM, MM-PB/SA and MP-CAFEE approaches","authors":"博文 渡邉, 成典 田中, 憲明 沖本, 亜樹 長谷川, 泰地 真弘人, 義明 谷田, 崇志 三井, 勝山 マリコ, 秀章 藤谷","doi":"10.1273/CBIJ.10.32","DOIUrl":"https://doi.org/10.1273/CBIJ.10.32","url":null,"abstract":"We compared binding affinity evaluations for 10 FKBP ligands with such state-of-the-art computational methods as FMO, QM/MM, MM-PB/SA, and MP-CAFEE. For the FKBP ligands, we confirmed that each method could provide good correlations between the experimental and computational binding affinities. From the calculated results, we discussed the importance of solvation effect and structural sampling for these methods in detail. In addition, we argued the issue of computational time and present arguments on the future perspective of the computational binding affinity evaluations.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"13 1","pages":"32-45"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81907242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The non-uniformity of gene expression data is one of the factors that make gene expression analysis difficult. Gene expression data often do not follow a normal distribution but rather various distributions within each group. Thus, it is impossible to apply basic statistical techniques such as the t-test. In this study, we have developed an analysis method for gene expression data obtained by microarrays using a fuzzy logic algorithm with original membership functions. The method automatically evaluates the data from a histogram of gene expression information for a patient group. Using this method, we predicted the efficacy of an anti-TNF-α treatment for rheumatoid arthritis. We created a prediction model for the effects of 14 weeks of anti-TNF-α treatment based on the gene expression data from the peripheral blood of rheumatoid arthritis patients before the treatment. The model had a predictive success of 89% in the model-establishing data group, 94% in the training group, and 89% in the validation group. The results suggest that the method presented here could be an extremely effective tool for gene expression analysis.
{"title":"Gene expression informatics with an automatic histogram-type membership function for non-uniform data","authors":"Akito Daiba, S. Ito, Tsutomu Takeuchi, M. Yohda","doi":"10.1273/CBIJ.10.13","DOIUrl":"https://doi.org/10.1273/CBIJ.10.13","url":null,"abstract":"The non-uniformity of gene expression data is one of the factors that make gene expression analysis difficult. Gene expression data often do not follow a normal distribution but rather various distributions within each group. Thus, it is impossible to apply basic statistical techniques such as the t-test. In this study, we have developed an analysis method for gene expression data obtained by microarrays using a fuzzy logic algorithm with original membership functions. The method automatically evaluates the data from a histogram of gene expression information for a patient group. Using this method, we predicted the efficacy of an anti-TNF-α treatment for rheumatoid arthritis. We created a prediction model for the effects of 14 weeks of anti-TNF-α treatment based on the gene expression data from the peripheral blood of rheumatoid arthritis patients before the treatment. The model had a predictive success of 89% in the model-establishing data group, 94% in the training group, and 89% in the validation group. The results suggest that the method presented here could be an extremely effective tool for gene expression analysis.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"62 1","pages":"13-23"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84492988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Single-spanning membrane proteins (MP1) occupy the largest component of membrane proteins in total open reading frames of organisms, having essential functions such as signal transduction, immunological reaction and cell adhesion. We developed a novel software system comprised of two filtering layers for predicting MP1 with or without a signal peptide region. In the first filtering layer, we selected membrane proteins with one or two transmembrane (TM) regions by the membrane protein prediction system SOSUI, which is accurate in predicting transmembrane regions but cannot identify signal peptide regions. The second filtering layer was comprised of several modules for distinguishing signal peptide regions. On the assumption that a signal peptide has two kinds of sequences at the N-terminus by which the signal peptide is embedded into membrane and cleaved at its C-terminal end, we calculated two discrimination scores by the canonical discriminant analysis, using averages of several physical properties around the first N-terminal hydrophobic cluster. This prediction system SOSUImp1 comprised of two filtering layers could discriminate very accurately among five types of proteins: cytoplasmic soluble proteins and secretory proteins, MP1 with and without a signal peptide, and multi spanning membrane proteins. The performance for MP1 with a signal peptide that is important in the cell-cell communication was particularly high compared with previous prediction systems.The prediction system SOSUImp1 and the dataset of 5932 proteins used for developing the system are available at http://bp.nuap.nagoya-u.ac.jp/sosui/mp1/
{"title":"SOSUImp1: high performance prediction system for single-spanning membrane proteins","authors":"T. Tsuji, F. Akazawa, Ryusuke Sawada, S. Mitaku","doi":"10.1273/CBIJ.10.E_2","DOIUrl":"https://doi.org/10.1273/CBIJ.10.E_2","url":null,"abstract":"Single-spanning membrane proteins (MP1) occupy the largest component of membrane proteins in total open reading frames of organisms, having essential functions such as signal transduction, immunological reaction and cell adhesion. We developed a novel software system comprised of two filtering layers for predicting MP1 with or without a signal peptide region. In the first filtering layer, we selected membrane proteins with one or two transmembrane (TM) regions by the membrane protein prediction system SOSUI, which is accurate in predicting transmembrane regions but cannot identify signal peptide regions. The second filtering layer was comprised of several modules for distinguishing signal peptide regions. On the assumption that a signal peptide has two kinds of sequences at the N-terminus by which the signal peptide is embedded into membrane and cleaved at its C-terminal end, we calculated two discrimination scores by the canonical discriminant analysis, using averages of several physical properties around the first N-terminal hydrophobic cluster. This prediction system SOSUImp1 comprised of two filtering layers could discriminate very accurately among five types of proteins: cytoplasmic soluble proteins and secretory proteins, MP1 with and without a signal peptide, and multi spanning membrane proteins. The performance for MP1 with a signal peptide that is important in the cell-cell communication was particularly high compared with previous prediction systems.The prediction system SOSUImp1 and the dataset of 5932 proteins used for developing the system are available at http://bp.nuap.nagoya-u.ac.jp/sosui/mp1/","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"22 1","pages":"61-73"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73441956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. BabuAjay, Chitti Sashikanth, B. Rajesh, Prasanth Vishnu, Kishen Radha Jv, R. ValiKhadar
Automated docking was performed on a series of thiazolo[5,4-f]quinazolin-9-one derivatives as GSK-3β inhibitors. The docking technique was employed to dock a set of representative compounds within the active site region of 1UV5 using AutoDock 3.05. For these compounds, the correlation between binding free energy (kcal/mol) and IC50 (μM) values were examined. The docking simulation clearly predicted the binding mode that is nearly similar to the crystallographic binding mode within 1.0 A RMSD. Based on the validations and interactions made by R1 and R2 substituents, inhibitor design was initiated by considering simple combinations. For the designed compounds where the interactions and dock scores are being considered for evaluation, compound 17 exhibited large binding energy (-13.14 kcal/mol) against GSK-3β than the remaining. The results help to understand the type of interactions that occur between designed ligands with GSK-3β binding site region and explain the importance of R1 and R2 substitutions on thiazolo[5,4-f]quinazolin-9-one derivatives.
对一系列噻唑[5,4-f]喹唑啉-9- 1衍生物作为GSK-3β抑制剂进行了自动对接。采用对接技术，利用AutoDock 3.05将一组具有代表性的化合物在1UV5的活性位点区域进行对接。对这些化合物进行了结合自由能(kcal/mol)与IC50 (μM)值的相关性分析。对接模拟清楚地预测了在1.0 A RMSD范围内与晶体学结合模式接近的结合模式。基于R1和R2取代基的验证和相互作用，我们开始考虑简单的组合来设计抑制剂。化合物17对GSK-3β具有较大的结合能(-13.14 kcal/mol)。这些结果有助于理解设计配体与GSK-3β结合位点区域之间发生的相互作用类型，并解释R1和R2取代对噻唑[5,4-f]喹唑啉-9- 1衍生物的重要性。
{"title":"GSK-3β 阻害剤のイン・シリコドッキングスタディーおよびリガンドデザイン","authors":"P. BabuAjay, Chitti Sashikanth, B. Rajesh, Prasanth Vishnu, Kishen Radha Jv, R. ValiKhadar","doi":"10.1273/CBIJ.10.1","DOIUrl":"https://doi.org/10.1273/CBIJ.10.1","url":null,"abstract":"Automated docking was performed on a series of thiazolo[5,4-f]quinazolin-9-one derivatives as GSK-3β inhibitors. The docking technique was employed to dock a set of representative compounds within the active site region of 1UV5 using AutoDock 3.05. For these compounds, the correlation between binding free energy (kcal/mol) and IC50 (μM) values were examined. The docking simulation clearly predicted the binding mode that is nearly similar to the crystallographic binding mode within 1.0 A RMSD. Based on the validations and interactions made by R1 and R2 substituents, inhibitor design was initiated by considering simple combinations. For the designed compounds where the interactions and dock scores are being considered for evaluation, compound 17 exhibited large binding energy (-13.14 kcal/mol) against GSK-3β than the remaining. The results help to understand the type of interactions that occur between designed ligands with GSK-3β binding site region and explain the importance of R1 and R2 substitutions on thiazolo[5,4-f]quinazolin-9-one derivatives.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"148 1","pages":"1-12"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77938205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduced the dynamic update technique into the monomer self-consistent charge (SCC) process of the fragment molecular orbital (FMO) method to reduce its computational costs. This technique has already been used for solving linear equations in some quantum chemical calculations. After performing test calculations on three typical polyglycines (GLY20, GLY40, and GLY60), we further performed the FMO calculations on the human immunodeficiency virus type 1 protease complexed with lopinavir using the dynamic update technique. These calculations demonstrate that the computational time of the monomer SCC process can be reduced by about one-third. Furthermore, we examined the dependence of the iteration number of the monomer SCC process on parallelization schemes.
{"title":"Acceleration of monomer self-consistent charge process in fragment molecular orbital method","authors":"Takeshi Ishikawa, K. Kuwata","doi":"10.1273/CBIJ.10.24","DOIUrl":"https://doi.org/10.1273/CBIJ.10.24","url":null,"abstract":"We introduced the dynamic update technique into the monomer self-consistent charge (SCC) process of the fragment molecular orbital (FMO) method to reduce its computational costs. This technique has already been used for solving linear equations in some quantum chemical calculations. After performing test calculations on three typical polyglycines (GLY20, GLY40, and GLY60), we further performed the FMO calculations on the human immunodeficiency virus type 1 protease complexed with lopinavir using the dynamic update technique. These calculations demonstrate that the computational time of the monomer SCC process can be reduced by about one-third. Furthermore, we examined the dependence of the iteration number of the monomer SCC process on parallelization schemes.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"7 1","pages":"24-31"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82938714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CDK2 (Cyclin Dependent Kinase 2) acts as a potential therapeutic target in cancer and several efforts have been made to find more specific, potent and selective ATP competitive CDK2 inhibitors. In this paper, we report a virtual screening approach that resulted in 54,558 Lipinski compliant hits from ZINC database based on the features exhibited by four compounds from our previous study. Docking and scoring of all compounds using GOLD (Genetic Optimisation for Ligand Docking) software, to evaluate the affinity of binding towards CDK2 enzyme 2UZO resulted in dock scores between 41.71 - 82.33 kcal/mol. The resultant dataset of 392 hits were filtered based on the specificity between CDK2 and GSK-3β (Glycogen Synthase Kinase-3β) to obtain 17 compounds that are more specific towards CDK2. Further, re-scoring of 17 best docked poses followed by a consensus scoring approach tested with five different scoring functions such as GOLD score, CHEM score implemented in GOLD 3.1, eHiTS_score (electronic High Throughput Screening), MolDock score of Molegro software and X-Score retrieved top hits. Finally, the top ten compounds were examined for anti-proliferative effects against human lung adenocarcinoma epithelial cell line, A549 using MTT assay.
CDK2(细胞周期蛋白依赖性激酶2)作为癌症的潜在治疗靶点，已经做出了一些努力来寻找更特异性，有效和选择性的ATP竞争性CDK2抑制剂。在本文中，我们报告了一种虚拟筛选方法，基于我们先前研究中四种化合物所表现出的特征，从锌数据库中获得了54,558个Lipinski符合点。使用GOLD (Genetic optimization for Ligand Docking)软件对所有化合物进行对接和评分，以评估与CDK2酶2UZO的结合亲和力，结果对接得分在41.71 - 82.33 kcal/mol之间。根据CDK2和GSK-3β(糖原合成酶激酶-3β)之间的特异性对392个命中数据集进行筛选，获得17个对CDK2更具特异性的化合物。此外，对17个最佳停靠姿态进行重新评分，随后采用共识评分方法测试了五种不同的评分功能，如GOLD评分，GOLD 3.1中实现的CHEM评分，eHiTS_score(电子高通量筛选)，Molegro软件的MolDock评分和X-Score检索到的顶级命中。最后，采用MTT法检测前十位化合物对人肺腺癌上皮细胞株A549的抑制增殖作用。
{"title":"Discovery of novel anti-proliferative compounds against A549 cells by virtual screening","authors":"P. A. Babu, P. A. Babu, M. Narasu, SRINIVAS KOLLI","doi":"10.1273/CBIJ.10.46","DOIUrl":"https://doi.org/10.1273/CBIJ.10.46","url":null,"abstract":"CDK2 (Cyclin Dependent Kinase 2) acts as a potential therapeutic target in cancer and several efforts have been made to find more specific, potent and selective ATP competitive CDK2 inhibitors. In this paper, we report a virtual screening approach that resulted in 54,558 Lipinski compliant hits from ZINC database based on the features exhibited by four compounds from our previous study. Docking and scoring of all compounds using GOLD (Genetic Optimisation for Ligand Docking) software, to evaluate the affinity of binding towards CDK2 enzyme 2UZO resulted in dock scores between 41.71 - 82.33 kcal/mol. The resultant dataset of 392 hits were filtered based on the specificity between CDK2 and GSK-3β (Glycogen Synthase Kinase-3β) to obtain 17 compounds that are more specific towards CDK2. Further, re-scoring of 17 best docked poses followed by a consensus scoring approach tested with five different scoring functions such as GOLD score, CHEM score implemented in GOLD 3.1, eHiTS_score (electronic High Throughput Screening), MolDock score of Molegro software and X-Score retrieved top hits. Finally, the top ten compounds were examined for anti-proliferative effects against human lung adenocarcinoma epithelial cell line, A549 using MTT assay.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"86 1","pages":"46-60"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85067292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Avupati, P. N. Kurre, Santoshi Rupa Bagadi, Murali Krishna Kumar Muthyala, R. Yejella
Molecular docking was performed on a series of bisaryl substituted thiazoles and oxazoles as PPARδ agonists. The docking technique was applied to dock a set of representative compounds within the active site region of 3D5F using Molegro Virtual Docker v 4.0.0. For these compounds, the correlation between binding free energy (kcal/mol) and log (1/EC50) values produces a good correlation coefficient (r2 = 0.719). The docking simulation clearly predicted the binding mode that is nearly similar to the crystallographic binding mode within 0.91A RMSD. Based on the validations and interactions made by Ar1 and Ar2 substituents, ligand design was initiated considering simple combinations. For the designed compounds biopharmaceutical properties e.g. Lipophilicity (logP), Solubility (logS), Ionization constant (pKa), Distribution coefficient (logD) are predicted computationally using ACD/ChemSketch v 12.0. The hydrogen bond interactions are examined and bivariate statistical correlation between predicted biological activity (log (1/EC50) and biopharmaceutical properties are considered for evaluation. Ligand 11 (cC) thus, showed high binding energy (-206.73 kcal/mol) against PPARδ. The results avail to understand the type of interactions that occur between designed ligands with PPARδ binding site region and explain the importance of Ar1 and Ar2 substitutions on derivatives of bisaryl substituted thiazoles and oxazoles.
分子对接了一系列双芳基取代噻唑和恶唑作为PPARδ激动剂。采用对接技术，利用Molegro Virtual Docker v 4.0.0将一组具有代表性的化合物在3D5F的活性位点区域进行对接。对于这些化合物，结合自由能(kcal/mol)与log (1/EC50)之间的相关系数较好(r2 = 0.719)。对接模拟在0.91A RMSD范围内清晰地预测了与晶体学结合模式几乎相似的结合模式。基于Ar1和Ar2取代基的验证和相互作用，开始考虑简单组合的配体设计。使用ACD/ChemSketch v 12.0计算预测所设计化合物的生物制药性质，如亲脂性(logP)、溶解度(log)、电离常数(pKa)、分布系数(logD)。研究了氢键相互作用，并考虑了预测生物活性(log (1/EC50))与生物制药性能之间的二元统计相关性。配体11 (cC)对PPARδ具有较高的结合能(-206.73 kcal/mol)。这些结果有助于了解设计配体与PPARδ结合位点区域之间发生的相互作用类型，并解释Ar1和Ar2取代对双芳基取代噻唑和恶唑衍生物的重要性。
{"title":"De novo Based Ligand generation and Docking studies of PPARδ Agonists: Correlations between Predicted Biological activity vs. Biopharmaceutical Descriptors","authors":"V. Avupati, P. N. Kurre, Santoshi Rupa Bagadi, Murali Krishna Kumar Muthyala, R. Yejella","doi":"10.1273/CBIJ.10.74","DOIUrl":"https://doi.org/10.1273/CBIJ.10.74","url":null,"abstract":"Molecular docking was performed on a series of bisaryl substituted thiazoles and oxazoles as PPARδ agonists. The docking technique was applied to dock a set of representative compounds within the active site region of 3D5F using Molegro Virtual Docker v 4.0.0. For these compounds, the correlation between binding free energy (kcal/mol) and log (1/EC50) values produces a good correlation coefficient (r2 = 0.719). The docking simulation clearly predicted the binding mode that is nearly similar to the crystallographic binding mode within 0.91A RMSD. Based on the validations and interactions made by Ar1 and Ar2 substituents, ligand design was initiated considering simple combinations. For the designed compounds biopharmaceutical properties e.g. Lipophilicity (logP), Solubility (logS), Ionization constant (pKa), Distribution coefficient (logD) are predicted computationally using ACD/ChemSketch v 12.0. The hydrogen bond interactions are examined and bivariate statistical correlation between predicted biological activity (log (1/EC50) and biopharmaceutical properties are considered for evaluation. Ligand 11 (cC) thus, showed high binding energy (-206.73 kcal/mol) against PPARδ. The results avail to understand the type of interactions that occur between designed ligands with PPARδ binding site region and explain the importance of Ar1 and Ar2 substitutions on derivatives of bisaryl substituted thiazoles and oxazoles.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"79 1","pages":"74-86"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79764494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Structure-based virtual screening is gaining popularity in drug discovery. A number of molecular docking programs and scoring functions have been developed in the community, but they had not fulfilled the demands for the improved accuracy, yet. In order to improve the accuracy, the consensus scoring method has been developed. It combines docking scores from various scoring functions without considering characteristics of the docking scores. In this study, we adopted the concepts of the consensus scoring, and improved the docking score from each docking programs, DOCK, FRED or GOLD, for virtual screening. Instead using simple sum of score components in those docking scores, weight factors of the score components were introduced and adjusted for better predictions of active ligands during virtual screening. Several optimization processes were tested to find the best optimization methods of the docking scores using a wide variety of 113 target proteins with over 2000 diverse decoys. Finally, the optimizations improved the chance to discover the active ligands by up to 52.4% (e.g. from 36.8% to 56.1% using GOLD) for the test set. Additionally, the combination of the optimized scores using GOLD and FRED improved success rate in the test set by 77.2%, and approximately 70% of ligands for target proteins were predictable in the test set with 20 times enrichment.
{"title":"Universal Optimizations of Scoring Functions for Virtual Screening","authors":"K. Onodera, S. Kamijo","doi":"10.1273/CBIJ.10.85","DOIUrl":"https://doi.org/10.1273/CBIJ.10.85","url":null,"abstract":"Structure-based virtual screening is gaining popularity in drug discovery. A number of molecular docking programs and scoring functions have been developed in the community, but they had not fulfilled the demands for the improved accuracy, yet. In order to improve the accuracy, the consensus scoring method has been developed. It combines docking scores from various scoring functions without considering characteristics of the docking scores. In this study, we adopted the concepts of the consensus scoring, and improved the docking score from each docking programs, DOCK, FRED or GOLD, for virtual screening. Instead using simple sum of score components in those docking scores, weight factors of the score components were introduced and adjusted for better predictions of active ligands during virtual screening. Several optimization processes were tested to find the best optimization methods of the docking scores using a wide variety of 113 target proteins with over 2000 diverse decoys. Finally, the optimizations improved the chance to discover the active ligands by up to 52.4% (e.g. from 36.8% to 56.1% using GOLD) for the test set. Additionally, the combination of the optimized scores using GOLD and FRED improved success rate in the test set by 77.2%, and approximately 70% of ligands for target proteins were predictable in the test set with 20 times enrichment.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"22 2","pages":"85-99"},"PeriodicalIF":0.3,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72586206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aflatoxin B1 (AFB1) is a harmful and cancer-causing mycotoxin generated by Aspergillus flavus. Its mechanism of toxicity has not been fully clarified and further research is required. In this study, we attempted to further clarify aflatoxin B1 toxicity using the results of S. cerevisiae gene expression analysis. In a Ser/Thr phosphatase 2C disruptant (ptc1Δ) with weakened activity of anti-toxic components (cell wall and membrane), the addition of low concentrations of sodium dodecyl sulfate resulted in elevated susceptibility to AFB1. From the microarray results, expression changes in DNA synthesis or repair, sphingolipid metabolism, glucose metabolism, and cell wall-related genes were well detected. Our results indicate that AFB1 causes sphingolipid metabolism disorder, leading to dysfunction in signal secretion and inhibition of efficient glucose metabolism, which supplies the materials for cell wall proteins and cellular components, resulting in repression of the stress response to external toxicants.
{"title":"Gene expression profile of MAP kinase PTC1 mutant exposed to Aflatoxin B1: dysfunctions of gene expression in glucose utilization and sphingolipid metabolism","authors":"Tadahiro Suzuki, Y. Iwahashi","doi":"10.1273/CBIJ.9.94","DOIUrl":"https://doi.org/10.1273/CBIJ.9.94","url":null,"abstract":"Aflatoxin B1 (AFB1) is a harmful and cancer-causing mycotoxin generated by Aspergillus flavus. Its mechanism of toxicity has not been fully clarified and further research is required. In this study, we attempted to further clarify aflatoxin B1 toxicity using the results of S. cerevisiae gene expression analysis. In a Ser/Thr phosphatase 2C disruptant (ptc1Δ) with weakened activity of anti-toxic components (cell wall and membrane), the addition of low concentrations of sodium dodecyl sulfate resulted in elevated susceptibility to AFB1. From the microarray results, expression changes in DNA synthesis or repair, sphingolipid metabolism, glucose metabolism, and cell wall-related genes were well detected. Our results indicate that AFB1 causes sphingolipid metabolism disorder, leading to dysfunction in signal secretion and inhibition of efficient glucose metabolism, which supplies the materials for cell wall proteins and cellular components, resulting in repression of the stress response to external toxicants.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"19 8","pages":"94-107"},"PeriodicalIF":0.3,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1273/CBIJ.9.94","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72421047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oral absorption of a drug is modeled by the differential equations for dissolution, permeation and gastrointestinal transit processes. The purpose of the present study was to compare simple approximate analytical solutions with full numerical solutions for the calculation of the fraction of a dose absorbed (Fa). The GI compartment model for numerical integration consisted of 1 stomach, 7 intestine and 1 colon compartments, whereas for analytical solutions a simple one well-stirred compartment was used. Full numerical solutions were obtained by numerically integrating the dissolution, permeation and gastrointestinal transit differential equations. In the numerical integration calculation, the concentration change in the GI tract, particle size reduction, transit of drugs, etc., was dynamically simulated. Precipitation in the GI tract and regional differences of solubility and permeability were not considered. In total, 7056 numerical integrations were performed, sweeping practical drug parameter ranges of solubility (0.001 to 1 mg/mL), diffusion coefficient (0.1 – 10 x 10 -6 cm 2 /sec), dose (1 to 1000 mg), particle diameter (1 to 300 μm) and effective permeability (0.03 – 10 x 10 -4 cm/sec). The analytical solutions investigated were (I) a sequential first order approximation (Fa =1–Pn/(Pn – Dn)exp(–Dn) + Dn/(Pn – Dn)exp(–Pn), Dn: dissolution number, Do: dose number and Pn: permeation number. Dn, Do and Pn are the dimensionless parameters which represent the dissolution time/GI transit time ratio, the solubility/dose ratio, and the permeation time/GI transit time ratio, respectively), (II) a limiting step approximation (the minimum value of Fa = 1–exp(–Pn), Fa = Pn/Do and Fa = 1–exp(–Dn)) and (III) a steady state approximation for the dissolved drug concentration (Fa =1–exp(–1/(1/Dn + Do/Pn)), if Do < 1, Do = 1). Fa values by (I) and (II) were higher than those by numerical integration for low solubility compounds (r 2 = 0.80 and 0.98, root mean square error (RMSE) = 0.28 and 0.079, respectively). By applying the steady state approximation, the correlation was improved (r 2 = 0.99, RMSE = 0.047). The steady state approximation for the dissolved drug concentration was appropriate for Fa calculation.
{"title":"Calculation of fraction of dose absorbed: comparison between analytical solution based on one compartment steady state concentration approximation and dynamic seven compartment model","authors":"K. Sugano","doi":"10.1273/CBIJ.9.75","DOIUrl":"https://doi.org/10.1273/CBIJ.9.75","url":null,"abstract":"Oral absorption of a drug is modeled by the differential equations for dissolution, permeation and gastrointestinal transit processes. The purpose of the present study was to compare simple approximate analytical solutions with full numerical solutions for the calculation of the fraction of a dose absorbed (Fa). The GI compartment model for numerical integration consisted of 1 stomach, 7 intestine and 1 colon compartments, whereas for analytical solutions a simple one well-stirred compartment was used. Full numerical solutions were obtained by numerically integrating the dissolution, permeation and gastrointestinal transit differential equations. In the numerical integration calculation, the concentration change in the GI tract, particle size reduction, transit of drugs, etc., was dynamically simulated. Precipitation in the GI tract and regional differences of solubility and permeability were not considered. In total, 7056 numerical integrations were performed, sweeping practical drug parameter ranges of solubility (0.001 to 1 mg/mL), diffusion coefficient (0.1 – 10 x 10 -6 cm 2 /sec), dose (1 to 1000 mg), particle diameter (1 to 300 μm) and effective permeability (0.03 – 10 x 10 -4 cm/sec). The analytical solutions investigated were (I) a sequential first order approximation (Fa =1–Pn/(Pn – Dn)exp(–Dn) + Dn/(Pn – Dn)exp(–Pn), Dn: dissolution number, Do: dose number and Pn: permeation number. Dn, Do and Pn are the dimensionless parameters which represent the dissolution time/GI transit time ratio, the solubility/dose ratio, and the permeation time/GI transit time ratio, respectively), (II) a limiting step approximation (the minimum value of Fa = 1–exp(–Pn), Fa = Pn/Do and Fa = 1–exp(–Dn)) and (III) a steady state approximation for the dissolved drug concentration (Fa =1–exp(–1/(1/Dn + Do/Pn)), if Do < 1, Do = 1). Fa values by (I) and (II) were higher than those by numerical integration for low solubility compounds (r 2 = 0.80 and 0.98, root mean square error (RMSE) = 0.28 and 0.079, respectively). By applying the steady state approximation, the correlation was improved (r 2 = 0.99, RMSE = 0.047). The steady state approximation for the dissolved drug concentration was appropriate for Fa calculation.","PeriodicalId":40659,"journal":{"name":"Chem-Bio Informatics Journal","volume":"28 1","pages":"75-93"},"PeriodicalIF":0.3,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78834088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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