Dissertations / Theses on the topic 'PHARMACOPHORE MODELING'

Iridoides são metabólitos secundários provenientes de angiospermas eudicotiledôneas, presentes principalmente em espécies das ordens Gentianales e Lamiales. Os iridoides dividem-se em carbocíclicos e seco-iridoides, ocorrendo comumente na forma glicosilada. Estes compostos são marcadores taxonômicos em algumas famílias vegetais e apresentam diversas atividades biológicas tais como cardiovascular, neuroprotetora e anti-Leishmania. Diante da importância dos iridoides, este trabalho teve como finalidade a prospecção química destes metabólitos em espécies nativas do Rio Grande Grande dos Sul, bem como a semissíntese de análogos e a investigação da atividade anti-Leishmania através de ensaios in vitro e modelagem molecular. Os compostos isolados foram identificados através de métodos espectroscópicos e os resultados comparados aos descritos na literatura. A partir de Escallonia bifida e Escallonia megapotamica (Escalloniaceae) foram isolados asperulosídeo, desacetilasperulosídeo, geniposídeo, ácido geniposídico e dafilosídeo, sendo que o asperulosídeo foi convertido em asperulosídeo tetraacetilado por meio de semissíntese. De Angelonia integerrima (Scrophulariaceae) foram obtidos galiridosídeo e antirrídeo. Nos experimentos in vitro para atividade anti-Leishmania, asperulosídeo, galiridosídeo, geniposídeo, ipolamida e teveridosídeo, nas concentrações 5-100 μM, não demonstraram inibição frente às formas promastigotas de Leishmania amazonensis. O estudo de modelagem molecular destes iridoides e daqueles descritos na literatura com atividade anti-Leishmania propôs um modelo farmacofórico que demonstrou que as diferenças estruturais não são responsáveis pela inatividade das moléculas isoladas neste trabalho. A perspectiva é realizar ensaios enzimáticos de tripanotiona redutase, bem com docking molecular e estudos de dinâmica molecular para investigar as interações entre grupamentos farmacofóricos das moléculas isoladas e o sítio de ligação de tripanotiona redutase.
Iridoids are secondary metabolites of eudicotyledonous angiosperms, present mainly in species of the orders Gentianales and Lamiales. The iridoids are divided into carbocyclic and seco-iridoids, occurring commonly in the glycosylated form. These compounds are taxonomic markers in same families of plants and have shown cardiovascular, neuroprotective and anti-Leishmania activities. In view of the importance of iridoids, this work aimed to the chemical prospection of these metabolites of native species of Rio Grande do Sul, as well as semi-synthesis of analogues and to investigate the anti-Leishmania activity through in vitro assays and molecular modeling. The isolated compounds were identified by spectroscopic methods and the results compared to those described in literature. From Escallonia bifida and Escallonia megapotamica (Escalloniaceae) asperuloside, deacetylasperuloside, geniposide, geniposidic acid and daphyloside were isolated, being asperuloside developed in asperuloside tetraacetylated by means of semi- synthesis. From Angelonia integerrima (Scrophulariaceae) galiridoside and antirride were obtained. In the in vitro experiments for anti-Leishmania activity, asperuloside, galiridoside, geniposideo, ipolamiide and theveridoside in concentrations 5-100 μM, did not demonstrate inhibition in promastigote form of Leishmania amazonensis. The molecular modeling study of these iridoids and those described in the literature with anti-Leishmania activity proposed a pharmacophoric model that demonstrated that the structures are not responsible by the inactivity of the molecules isolated in this work. The prospect is to carry out enzymatic assays of trypanothione redutase as well as molecular docking and molecular dynamics studies to investigate the interactions between pharmacophoric grouping of the isolated molecules and the trypanothione reductase binding site.

Alzheimer’s disease is a progressive neurodegenerative disorder generally affecting people above the age of 65 years. Even though the pathophysiological hallmarks of AD were established more than a hundred years ago, there is yet to be a drug that can stop its characteristic neuronal damage. Of the five currently FDA-approved drugs, galantamine has a unique mechanism of action. Apart from being an AChE inhibitor, galantamine can effectively potentiate (positive allosteric modulator) the effect of agonists at nAChRs at concentrations lower than those required for its action as an AChE inhibitor. Perhaps the clinical benefits observed with galantamine are associated mainly with its nAChRs-PAM action and not its AChE inhibitory effect. Inhibiting AChE causes a delay in the degradation of ACh and a prolonged presence of ACh might act at either nAChRs or mAChRs. By indirectly targeting mAChRs as well, AChE inhibitors may lead to potential side effects. Hence there is a need for specific nAChR agents. The aim of this study was to identify the structural features of galantamine that contribute solely towards its a7 nAChR-PAM effect. In doing so, we wish to divorce the structural features that might be important for interacting with AChE. Using the deconstruction approach, we have synthesized structurally abbreviated analogs of galantamine. To study the probable interactions, we docked these molecules in human a7 nAChR homology models. Ultimately, it is of interest to determine which analogs retain the PAM activity of galantamine and to address that, a preliminary screening was performed with a select few analogs using the two-electrode voltage clamp technique

The affinity of a drug to its target protein is one of the key properties of a drug. Although there are experimental methods to measure the binding affinity, they are expensive and relatively slow. Hence, accurately predicting this property with software tools would be very beneficial to drug discovery. In this thesis, several applications have been developed to model and predict the binding mode of a ligand to a protein, to evaluate the feasibility of that prediction and to perform model interpretability in deep neural networks trained on protein-ligand complexes.
La afinidad de un fármaco a su proteína diana es una de las propiedades clave de un fármaco. Actualmente, existen métodos experimentales para medir la afinidad, pero son muy costosos y relativamente lentos. Así, predecir esta propiedad con precisión empleando herramientas de software sería muy beneficioso para el descubrimiento de fármacos. En esta tesis se han desarrollado aplicaciones de software para modelar y predecir el modo de unión de ligando a proteína, para evaluar cómo de factible es tal predicción y para interpretar redes neuronales profundas entrenadas en complejos proteína-ligando.

Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xvii, 271 p.; also includes graphics. Includes bibliographical references (p. 245-269). Available online via OhioLINK's ETD Center

Schizophrenia is a chronic psychotic illness affecting ~21 million people globally. Currently available antipsychotic agents act through a dopamine D2 receptor mechanism, and produce extrapyramidal or metabolic side effects. Hence, there is a need for novel targets and agents. The mGlu2/5-HT2A receptor heteromer has been implicated in the action of antipsychotic agents, and represents a novel and attractive therapeutic target for the treatment of schizophrenia. A long-term goal of this project is to synthesize bivalent ligands where a 5-HT2A receptor antagonist is tethered to an mGlu2 PAM via a linker. The goals of the investigation were to study the SAR of risperidone (an atypical antipsychotic agent) at 5-HT2A receptors using a “deconstruction-reconstruction-elaboration” approach to determine the minimal structural features of risperidone that contribute to its 5-HT2A receptor affinity and antagonism, and to determine where on the “minimized risperidone” structure an mGlu2 PAM can be introduced. Additional goals included studying the binding modes of various mGlu2 PAMs and identifying where on an mGlu2 PAM a risperidone “partial” structure could be introduced. Biological studies of deconstructed/elaborated analogs of risperidone suggest that the entire structure of risperidone is not necessary for 5-HT2A receptor affinity and antagonism, and that a fluoro group contributes to 5-HT2A binding. 6-Fluoro-3-(4-piperidinyl)-1,2-benz[d]isoxazole that has only half the structural features of risperidone retains 5-HT2A receptor affinity and antagonist activity, and represents the “minimized risperidone” structure with the piperidine nitrogen atom representing a potential linker site for eventual construction of bivalent ligands. Molecular modeling studies at 5-HT2A receptors suggest that risperidone and its analogs have more than one binding mode. Modeling studies to evaluate binding modes of various PAMs at mGlu2 receptors, coupled with known SAR information, were used to identify a PAM (JNJ-40411813), and the pyridone nitrogen atom of JNJ-40411813 as a potential linker site. Additionally, potential synthetic routes for JNJ-40411813 were explored that might be of value in the synthesis of bivalent ligands. Based on the structural features of 6-fluoro-3-(4-piperidinyl)-1,2-benz[d]isoxazole, a new pharmacophore for 5-HT2A receptor antagonists, consisting of one aromatic region, a basic protonated amine and hydrogen bond acceptors, has been proposed.

Novel and potent ligands to the serotonin7 (5-HT7) receptor have been synthesized. The synthesized compounds include a set of substituted pyrimidines which show high affinity to the 5-HT7 receptor, synthesized by previously described methods [1,2] in high yield. Comparing the affinities of substituted pyrimidines to previously calculated models [3,4] yielded new hypotheses about the nature of interaction between the pyrimidine ligands and the 5-HT7 binding site. Several new series of compounds were synthesized by various methods to validate these hypotheses, including a conjugate addition to vinylpyrimidines [5]. These compounds include benzofurans, oximes, hydrazones, as well as a group of substituted piperazines. All series of compounds show affinity to the 5-HT7 receptor comparable to previously synthesized 5-HT7 ligands. Several of the synthesized ligands show affinity which exceeds that of currently available ligands. The synthesized compounds were evaluated quantitatively by calculating a three-dimensional quantitative structure-affinity relationship (3D-QSAR) for the 5-HT7 receptor. Evaluation of the calculated model validated qualitative assumptions about the data set as well as described regions of interaction in greater detail than previously available. These observations give further insight on the nature of ligand-binding site interactions with highly potent ligands such as 4-(3-furyl)-2-(N-methylpiperazino)pyrimidine which will lead to more potent 5-HT7 receptor ligands. Additionally, a model was calculated for affinity to the 5-HT2a receptor. Comparing this model to that calculated for affinity to the 5-HT7 receptor identified two regions which may be exploited in future sets of ligands to increase selectivity to the 5HT7 receptor.

Zolpidem, an imidazopyridine hypnotic, which acts on GABA‐A receptors has been associated with the development of a number of disturbing neuropsychiatric adverse drug reactions (ADRs) including parasomnias, amnesia and hallucinations. Although other non-hypnotic medications are also implicated in the induction of such adverse events; the mechanism behind these ADRs remains elusive and have been postulated to arise from off-target receptor or pathway activation resulting in the disruption and dysregulation of keyneurotransmitters including GABA, acetylcholine, noradrenaline and dopamine. Using a novel multidisciplinary approach, we aim to investigate relationships between these rare idiosyncratic ADRs (parasomnia, movement-based parasomnia, non-movement based parasomnia, amnesia and hallucination) by identifying similarities in the chemical structure amongst drugs that share these reactions. These shared structural features or motifs may enable diverse drugs to participate in a pharmacological reaction with a mutual target receptor or pathway. By combining human population pharmacovigilance data with in silico computational techniques, insights into mechanisms underlying idiosyncratic reactions or toxicities can be elucidated.

O câncer cervical afeta milhões de mulheres em todo o mundo a cada ano. A maioria dos casos de câncer cervical é causada pelo vírus do papiloma humano (HPV) que é sexualmente transmissível. Cerca de 40 tipos de HPV infectam o colo do útero e estes são designados como sendo de alto ou de baixo risco com base no seu potencial para provocar lesões de alto grau e câncer. A oncoproteína E7 do HPV está diretamente envolvida no aparecimento de câncer de colo do útero. Esta se associada com a proteína pRb e outros alvos celulares que promovem a imortalização celular e carcinogênese. Apesar de muito progresso nos estudos sobre os HPVs de alto risco, ainda não existe uma terapêutica adequada para o tratamento das lesões e câncer causados por este vírus. Este trabalho teve como objetivo entender as diferenças estruturais entre E7 de alto e baixo risco e sugerir, através de análises de bioinformática, possíveis sítios de ligação e inibidores para a E7. Esta é a primeira descrição da modelagem e análise de dinâmica molecular de quatro estruturas tridimensionais completas da E7 dos tipos de alto risco (HPV tipos 16 e 18), de baixo risco (HPV tipo 11) e não relacionadas ao câncer cervical (HPV tipo 1A). Os modelos foram construídos por uma abordagem híbrida usando modelagem por homologia e ab initio. Os modelos foram usados em simulações de dinâmica molecular por 50 ns, sob condições normais de temperatura e pressão. A desordem intrínseca da sequência da proteína E7 foi avaliada com o uso de ferramentas in silico. Os domínios N-terminal de todas as E7 estudadas, mesmo as de alto risco, exibiram estruturas secundárias depois da modelagem. Nas análises da trajetória da dinâmica molecular, as E7s dos HPVs dos tipos 16 e 18 apresentaram maior instabilidade nos seus domínios N-terminais em relação aos do HPV dos tipos 11 e 01. No entanto, esta variação não afetou a conformação das estruturas secundárias durante a simulação. A análise com ANCHOR indicou que as regiões CR1 e CR2 regiões dos tipos de HPV 16 e 18 contêm possíveis alvos para a descoberta da droga. Já a região CR3 do domínio C-terminal indicou estabilidade nas análises in silico e, por isso, foi usada como alvo de busca de modelos farmacofóricos e docking macromolecular. A proteína usada como modelo foi a E7 do HPV tipo 45 resultante de análises de ressonância magnética nuclear (RMN) e depositada no banco de dados de proteína (ID: 2F8B). Foram selecionados por análises sequenciais de busca farmacofórica, docking e re-docking, 19 compostos (extraídos de amplas bibliotecas de pequenos ligantes) com potencial para candidatos a inibidores da E7. Eles foram avaliados quanto a sua função de pontuação, mapas de interação receptor-ligante e toxicidade e os melhores foram indicados para estudos futuros.
Cervical cancer affects millions of women around the world each year. Most cases of cervical cancer are caused by human papilloma virus (HPV) which is sexually transmitted. About 40 types of HPV infect the cervix and these are designated as being at high or low risk based on their potential to cause high-grade lesions and cancer. The E7 oncoprotein from HPV is directly involved in the onset of cervical cancer. It associates with the pRb protein and other cellular targets that promote cell immortalization and carcinogenesis. Although the progress in studies with high-risk HPVs there is still no adequate therapy for the treatment of lesions and cancers caused by this virus. This study aimed to understand the structural differences between E7 of high and low risk and suggest, with the aid of bioinformatics analyzes, possible binding sites and inhibitors for the E7. This is the first description of the modeling and molecular dynamics analysis of four complete three-dimensional structures of E7 from high-risk types (HPV types 16 and 18), low risk (HPV type 11) and that not related to cervical cancer (HPV 01). The models were constructed by a hybrid approach using homology modeling and ab initio. The models were used in molecular dynamics simulations for 50 ns, under normal temperature and pressure. The intrinsic disorder of the E7 protein sequence was assessed using in silico tools. The N-terminal domains of all E7s, even the high-risks, showed secondary structures after modeling. In the trajectory analyzes of molecular dynamics, the E7s of HPV types 16 and 18 showed high instability in their N-terminal domains than those of HPV types 11 and 01, however, this variation did not affect the conformation of secondary structures during the simulation. The analysis with ANCHOR indicated that regions CR1 and CR2 regions of types of HPV 16 and 18 contain possible targets for drug discovery. The CR3 region of the C-terminal domain indicated stability by in silico analyzes and was therefore used as target to search for pharmacophoric models and \"docking\". The protein used as a model was the E7, from HPV type 45, constructed by analysis of nuclear magnetic resonance (NMR) and deposited in the protein data bank (ID: 2F8B). It was selected 19 compounds as potential candidates for E7 inhibitors (extracted from large libraries of small ligands) using sequential pharmacophore search, docking and re-docking analyzes. They were evaluated for their scoring function, maps of receptor-ligand interactions and toxicity and the best suited were indicated for future studies.

Philosophiae Doctor - PhD
Given the significant disease burden caused by human coronaviruses, the discovery of an effective antiviral strategy is paramount, however there is still no effective therapy to combat infection. This thesis details the in silica exploration of ligand libraries to identify candidate lead compounds that, based on multiple criteria, have a high probability of inhibiting the 3 chymotrypsin-like protease (3CUro) of human coronaviruses. Atomistic models of the 3CUro were obtained from the Protein Data Bank or theoretical models were successfully generated by homology modelling. These structures served the basis of both structure- and ligand-based drug design studies. Consensus molecular docking and pharmacophore modelling protocols were adapted to explore the ZINC Drugs-Now dataset in a high throughput virtual screening strategy to identify ligands which computationally bound to the active site of the 3CUro . Molecular dynamics was further utilized to confirm the binding mode and interactions observed in the static structure- and ligand-based techniques were correct via analysis of various parameters in a IOns simulation. Molecular docking and pharmacophore models identified a total of 19 ligands which displayed the potential to computationally bind to all 3CUro included in the study. Strategies employed to identify these lead compounds also indicated that a known inhibitor of the SARS-Co V 3CUro also has potential as a broad spectrum lead compound. Further analysis by molecular dynamic simulations largely confirmed the binding mode and ligand orientations identified by the former techniques. The comprehensive approach used in this study improves the probability of identifying experimental actives and represents a cost effective pipeline for the often expensive and time consuming process of lead discovery. These identified lead compounds represent an ideal starting point for assays to confirm in vitro activity, where experimentally confirmed actives will be proceeded to subsequent studies on lead optimization.

碩士
國立臺北科技大學
化學工程與生物科技系生化與生醫工程碩士班
106
C-C chemokine receptor type 5 (CCR5), a member of G protein-coupled receptors (GPCRs), not only plays a significant role in inflammatory responses, but also correlates with HIV infection and cancer progression. Recently, blocking of CCR5 was considered as an effective strategy in HIV/cancers therapy. However, only Maraviroc has been approved by FDA in 2007, while the other CCR5 inhibitors were failed in their clinical trials. For searching novel, effective and safe CCR5 inhibitors, a series of computer-aided drug design was performed, including structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET prediction, molecular dynamic (MD) simulation and binding free energy calculation. The best pharmacophore model, Model_1, was evaluated by Güner-Henry (GH) scoring method and used for screening potential compounds from the National Cancer Institute (NCI) database. The screened compounds were further filtered by molecular docking, ADMET prediction, MD simulation and binding free energy calculation. At the end, one compound was considered as the most potential novel CCR5 inhibitor based on reasonable binding pose, good ADMET properties, better binding affinity in comparison to the only FDA-approved Maraviroc. In summary, we successfully discovered a novel CCR5 inhibitor candidate, which can be validated through in vitro/in vivo biological tests in the future.

碩士
國立彰化師範大學
生物技術研究所
98
Malaria, which is caused by infections of the human malaria parasites Plasmodium falciparum. It’s global infectious and Parasitic disease, approximately 40% of the world’s population is at risk of developing malaria. Each year, there are approximately 300–500 million cases of malaria (more in Africa), killing between one and three million people, the majority of whom are pregnant women and young children. The focus now is to reduce malaria parasite resistance and the toxicity of anti-malarial drugs. Plasomodium falciparum dihydroorotate dehydrogenase (PfDHODH) was validated as a new drug target through the identification of potent and block pyrimidine biosynthesis to inhibit malarial activity. The main objective of this study is to collect literature for 67 effective inhibitors of PfDHODH, by clustering methods to create pharmacophore. The best scoring pharmacophore hypothesis have an above 0.9 correlation coefficient. And test set analysis, a correction coefficient above 0.8. Then use docking software creation of 255 possible configurations and select two groups according to different fitness scores and creating pharmacophore. Comparison of the relevance and accuracy of three, provide a valuable tool in designing new leads with desired biological activity by virtual screening.The pharmacophore hypothesis generated by the inhibitor of 10, use 3D quantitative structure-activity relationship method to build CoMFA, CoMSIA models. Expect model can be expected for inhibitors of the functional groups modified to provide useful information.

碩士
國立臺北科技大學
化學工程研究所
100
Human African trypanosomiasis (HAT) or sleeping sickness is caused by subspecies of the parasitic hemoflagellate Trypanosoma brucei. The chemotherapy of HAT currently centers on only small numbers of drugs, most of these were discovered more than forty years, and are plagued by various side effects. In quest of possible ways to understand the structural requirement for anti-trypanosomal mechanism and design novel compounds, the Hypogen 3D-QSAR pharmacophore analysis, CoMFA, virtual screening, docking and molecular dynamics can be powerful tools, which had the ability to design novel chemical entities with enhanced inhibitory potencies against Trypanosome brucei. The best pharmacophore model Hypo1 shows the highest correlation coefficient (R=0.93), and also shows a high goodness of GH score (0.82). After that, Hypo1 is used as a 3D query for virtual screening to discover potential inhibitors from NCI database. We then used CDocker and MD simulation program to analyze virtual hits. Finally, only one compound was remained which will be possible to view as a persuasive Trypanosomal inhibitor. In CoMFA studies, a good values of R2=0.95 from training set and promising predictive power from cross validation (q2=0.57) were obtained. Based on the model suggested steric and electrostatic interactions, we designed 8 novel compounds and analyzed their interaction and binding poses by CDocker consensus scoring function. Molecular modeling and CoMFA analysis were performed to obtain useful information about the structural requirements for the HAT inhibitors which could be utilized in its future design.

Molecular docking is a computational tool commonly applied in drug discovery projects and fundamental biological studies of protein-ligand interactions. Traditionally, molecular docking is used to address one of three following questions: (i) given a ligand molecule and a protein receptor, predict the binding mode (pose) of the ligand within the context of a receptor, (ii) screen a collection of small-molecules against a receptor and rank ligands by their likelihood of being active, and (iii) given a ligand molecule and a target receptor, predict the binding affinity of the two. Here, we focus on the first two questions, namely ranking and pose prediction. Currently, state-of-the-art docking algorithms predict poses within 2A of the native pose in a rate lower than ∼60% and in many cases, below 40%. In ranking, their ability to identify active ligands is inconsistent and generally suffers from high false-positive rate. In this thesis we present novel algorithms to enhance the ability of molecular docking to address these two questions. These algorithms do not substitute traditional docking but rather being applied on top of them to provide synergistic effect. Our algorithms improve pose predictions by 0.5-1.0A and ranking order for 23% of the targets in gold-standard benchmarks. As importantly, the algorithms improve the consistence of the posing and ranking predictions over diverse sets of targets and screening libraries. In addition to the posing and ranking, we present the pharmacophore concept. A pharmacophore is an ensemble of physiochemical descriptors associated with a biological target that elucidates common interaction patterns of ligands with that target. We introduce a novel pharmacophore inference algorithm and demonstrate its utilization in molecular docking. This thesis is outlined as follow. First we introduce the molecular docking approach for pose prediction and ranking. Second, we discuss the pharmacophore concept and present algorithms for pharmacophore inference. Third, we demonstrate the utilization of pharmacophores for pose prediction by re-scoring candidate poses generated by docking algorithms. Finally, we present algorithms to improve ranking by reducing bias in scoring functions employed by docking algorithms.

碩士
國立清華大學
資訊工程學系
100
In this research, our objective is to build the pharmacophore models for selected target proteins that can identify inhibitors with high biological activities and to execute computer-aided drug design. Human dihydroorotate dehydrogenase (hDHODH) is an enzyme which is strongly correlated with certain cancers and autoimmune and inflammatory diseases. Thromboxane A2 receptor (TP) promotes platelet aggregation when activated by thromboxane A2, but over activation of TP may lead to thrombosis and other cardiovascular diseases. Due to the importance of these two proteins related to many human diseases, we use them as targets to build the hypotheses based on a set of known inhibitors, and then use cost function analysis, Fischer’s randomization and goodness of hit test to validate the quality and the confidence of statistical significance of our models. The results show that our models have excellent prediction ability. According to the crystal structures have been solved or not, we can construct different workflows for hDHODH and TP. Consequently, the pharmacophore model, Lipinski’s Rule-of-Five and CDOCKER docking program were integrated into a workflow for the discovery of potential inhibitor candidates from database. Through these workflows, 155 candidates for hDHODH and 5,621 candidates for TP are retrieved for further study.

碩士
長庚大學
資訊工程學系
101
DNA-damage is induced by ionizing radiation, genotoxic chemicals or collapsed replication forks. When DNA was damaged or the responses of cells were failure, the mutation associated with the breast or ovarian cancer of genes may occur. To prevent and repair the DNA-damage, mammalian cells will control and stabilize the genome by cell cycle checkpoint. Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion and virtual screening techniques. The PhModels can identify inhibitors with high biological activities, combinatorial fusion can facilitate feature selection and combination for improving predictive accuracy in protein structure classification, and virtual screening techniques were used to screen National Cancer Institute (NCI) database and retrieve the compounds that fit all the pharmacophoric features for potential inhibitors with high interaction energy. The training set were generated ten PhModels by the HypoGen Best, Fast, and Caesar algorithms, respectively, and the testing set were generated the best PhModel for each algorithm. Then we used combinatorial fusion to analyze the coefficient correlation of the PhModels that the algorithm combined with Besttrain-Besttest and Fasttrain-Fasttest would generate the best model. The potential inhibitors were selected from NCI database by screening and molecular docking with CDOCKER docking program. Finally, the selected compounds contain the identified pharmacophoric features and have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study.

碩士
國立臺北科技大學
生化與生醫工程研究所
101
Alzheimer''s disease (AD) is the most common progressive chronic neurodegenerative disorder characterized by loss of neurones particularly in those regions associated with cognitive functions and cortical atrophy. Neuropathological hallmarks include neurofibrillary tangles (NFTs) and amyloid-beta plaques. To date, no truly effective therapy drugs has been developed for AD. Previous studies show that tau protein and beta-secretase (BACE1) are two predominant targets for anti-AD drugs. In this study, we applied many computational approaches including pharmacophore modeling, 3D-QSAR modeling, molecular docking, molecular dynamics (MD) simulations and virtual screening to discover more potential anti-AD drugs. For tau protein, we constructed structure-based pharmacophore model that was developed using the representative docked conformations from the most effective peptide inhibitors. This model was subsequently used as a 3D-query in virtual screening to identify potential hits from Traditional Chinese Medicine (TCM) database. The binding stabilities of these hits were further validated using molecular dynamics simulations. Finally, only three compounds were identified as potential leads, which exhibited similar binding affinities in comparison to the most effective peptide inhibitor for tau protein. As to BACE1, we constructed multicomplex-based pharmacophore model by a collection of 9 crystal structures of BACE1-inhibitor complex. This model was validated by Guner-Henry (GH) scoring methods, applied to screen the TCM database and and to align the structurally diverse BACE1 inhibitors. Then, 3D-QSAR analysis and molecular docking were conducted to retrieve two potential lead compounds. In summary, the results of this study can be applied to the design of new and more potent anti-AD drugs for clinical purposes.

碩士
國立臺北科技大學
化學工程研究所
99
Influenza A virus, a major cause of human and animal loss, reproduces rapidly, mutates frequently and occasionally crosses species barriers. The recent emergence around the world of pig influenza related to highly pathogenic forms of the human virus has emphasized the urgent need for new effective therapies. Influenza endonuclease is an attractive target of antiviral therapy for influenza infections. In this study, molecular docking was used to dock 49 influenza endonuclease inhibitors into the active site of the influenza endonuclease with the purpose of designing a novel antiviral agent having enhanced biological activities against the enzyme. A novel consensus scoring function was constructed by combining 11 different scoring methods after partial least squares regression. This consensus scoring function was able to successfully estimate the pIC50 (-log IC50) value of a wide range of ligands with the correlation coefficients (R2) of 0.827 and 0.855 for the training and test sets, respectively. This function was further validated by the receiver-operating characteristic (ROC) curve. The results showed that the consensus scoring function developed here is applicable in virtual screening and for future in silico drug design. Our interaction energy analysis also suggested that Lys137 and Lys134 are significant for the inhibitory activity and can be used to modify the inhibitors for higher inhibiting capabilities. Furthermore, a ligand-based approach was performed to establish a pharmacophore model for virtual screening of novel influenza endonuclease inhibitors. Docking and consensus scoring were conducted to predict the estimated activity of these compounds. The results of this study can be applied to the design of new and more potent influenza endonuclease inhibitors for clinical purposes.

碩士
國立臺北科技大學
化學工程研究所
101
Benign prostatic hyperplasia (BPH) is caused by the augmented levels of androgen dihydrotestosterone (DHT) that is involved in the growth of prostate in human. 5a-Reductase type II (5aR2) is an intracellular enzyme that catalyzes the formation of DHT from testosterone; hence the inhibition of 5aR2 has emerged as one of the most promising strategies for the treatment of BPH. However, the steroidal structure of 5aR2 inhibitors may incur hormonal adverse effects. Until recently, an effective anti-BPH drug without side effects has not been discovered. Therefore, we applied many computational approaches that integrate ligand-based pharmacophore, 3D-QSAR, virtual screening, molecular docking and molecular dynamic (MD) simulations for searching more effective and less side effects 5aR2 inhibitors. The best pharmacophore model (Hypo1) was validated by Guner-Henry (GH) scoring method. This well validated Hypo1 was then used as a 3D-query in virtual screening to identify potential hits from Maybridge and National Cancer Institute (NCI) databases. Then these hits were subsequently filtered by molecular docking and MD simulations. After screening, one hit was identified as a potential lead based on high predicted inhibitory activity and binding affinity to 5aR2 in comparison to the most active inhibitor (Finasteride). In appendixes, pharmacophore and CoMFA model was performed on a set of 25 human nonsteroidal 5?R2 inhibitors and successfully identified 7 hit compounds with novel scaffolds were retrieved as leads. In summary, the results of this study can be applied to the design of new and more potent anti-BPH drugs for clinical purposes.

碩士
國立臺北科技大學
生物科技研究所
100
Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids and fibrous elements in the large arteries; moreover, it is the primary cause of cardiovascular diseases. In previous studies, a great number of anti-atherosclerotic drugs have been developed but several side effects were found in animal and human studies. Until recently, an effective anti-atherosclerotic drug without side effects has not been discovered. Therefore, we applied many computational approaches including pharmacophore modeling, virtual screening, molecular docking and molecular dynamic for searching more effective and less side effects anti-atherosclerotic drugs. Furthermore, it was found that there are two predominant targets for anti-atherosclerotic: Acyl-coenzyme A: cholesterol acyltransferase (ACAT) and Cholesteryl ester transfer protein (CETP). For ACAT, there are two similar types ACAT-1 and ACAT-2, and then we constructed ligand-base pharmacophore models: HipHopRefine and HypoRefine for ACAT-1 and ACAT-2 respectively. After Güner–Henry (GH) scoring methods validation, both of HipHopRefine and HypoRefine show good predictive ability. Subsequently, we utilized two pharmacophore models to screen ZINC database for obtaining more potential dual ACAT inhibitors. After virtual screening, 10 hits with high pharmacophore fitvalue and diverse scaffolds were identified as potential lead compounds. As to CETP, we also constructed HipHop pharmacophore model by a series of CETP inhibitors to search another potential drugs of atherosclerosis. The best model HipHop-1 was further validated by GH scoring methods and applied to screen the NCI and Maybridge databases. Then, molecular docking and molecular dynamic were conducted to retrieve 4 potential compounds. In summary, the results of this study can be applied to the design of new and more potent anti-atherosclerotic drugs for clinical purposes.

Alzheimer’s Disease is progressive disorder whose pathophysiology and therapeutic status still stands unclear. As of now, all the therapies are confined to symptomatic relief, disease modifying therapies are therefore the need of the hour. Modulation in Wnt cascade has already been linked to varied disorders which include AD and type 2 diabetes mellitus too. The interlink between insulin signaling pathway and Wnt cascade has been well acknowledged through a number of preclinical and clinical studies. This in silico-based study is focused upon investigating the link between curative effects of FDA approved anti- diabetic drug and Wnt signaling cascade. We prepared a library consisting of 143 FDA approved antidiabetic medicines with an aim of repurposing them as GSK 3 beta inhibitor, which is present in increased amounts in AD brain. Pharmacophore modelling was performed for these drugs and the lead hits were then subjected to ligand- protein based molecular docking followed by molecular dynamics simulation. ZINC04803471 emerged as a clear winner that can inhibit GSK 3 beta, which leads to beta catenin degeneration and thereby downregulates the canonical Wnt signaling cascade in the AD brain. Although this is just a tip of an iceberg, further in vitro investigation is necessary to validate the effectiveness of the compound.

博士
國立清華大學
資訊工程學系
99
Quantitative Structure Activity Relationships (QSAR) is an important technique in the rational drug design, which was used to build computational models to find a statistically significant correlation between the receptor and inhibitors. There are two mainstream of 3D-QSAR technologies, namely Comparative Molecular Field Analysis (CoMFA)/ Comparative Molecular Similarity Index Analysis (CoMSIA) and Pharmacophore. Most significant function of pharamcophore model is to use 3D screen to recognize the related target protein inhibitors. However, the number of pharmacophore features was restricted as five chemical features at maximum, and which could not describe the 3D space limitation of the binding site. This restriction induces incompletely describing the chemical features of inhibitors. Contrastingly, the other two models, CoMFA and CoMSIA were not suitable to search 3D databases, but can easily be used to modify the molecule structure optimization and describe the limit range of molecule weights. Additional, CoMFA and CoMSIA models use contours to describe the chemical features of inhibitor. The number of contours was not restricted, that could reflect the chemical features of inhibitor. Therefore, CoMFA and CoMSIA models could provide better predication ability to predict the bioactivity. According to above characters, we prefer to combine these two different technologies. We propose a 3D-QSAR combination modeling approach to solve two 3D-QSAR technical shortcomings of each other. Our combination approach could provide a valuable tool in the design of new leads with desired biological activity by virtual screening.

Signal transducer and activator of transcription 3 (STAT3) is a cancer-driving proto-oncoprotein that represents a novel target for the development of chemotherapeutics. In this study, the functional requirements to furnish a potent STAT3 inhibitor was investigated. First, a series of peptidomimetic inhibitors were rationally designed from lead parent peptides. Prepared peptidomimetics overcame the limitations normally associated with peptide agents and displayed improved activity in biophysical evaluations. Notably, lead peptidomimetic agents possessed micromolar cellular activity which was unobserved in both parent peptides. Peptidomimetic design relied on computational methods that were also employed in the design of purine based STAT3 inhibitory molecules. Docking studies with lead STAT3-SH2 domain inhibitory molecules identified key structural and chemical information required for the construction of a pharmacophore model. 2,6,9-heterotrisubstituted purines adequately fulfilled the pharmacophore model and a library of novel purine-based STAT3 inhibitory molecules was prepared utilizing Mitsunobu chemistry. Several agents from this new library displayed high affinity for the STAT3 protein and effectively disrupted the STAT3:STAT3-DNA complex. Furthermore, these agents displayed cancer-cell specific toxicity through a STAT3 dependant mechanism. While purine agents elicited cellular effects, the dose required for cellular efficacy was much higher than those observed for in vitro STAT3 dimer disruption. The diminished cellular activity could be attributed to the apparent poor cell permeability of the first generation purine library; thus, a second library of purine molecules was constructed to improve cell penetration. Unfortunately, iii 2nd generation purine inhibitors failed to disrupt phosphorylated STAT3 activity and suffered from poor cell permeability. However, a lead sulfamate agent was discovered that showed potent activity against multiple myeloma cancer cells. Investigations revealed potential kinase inhibitory activity as the source of the sulfamate purine’s biological effect. Explorations into the development of a potent STAT3 SH2 domain binder, including the creation of salicylic purine and constrained pyrimidine molecules, are ongoing. Finally, progress towards the creation of a macrocyclic purine combinatorial library has been pursued and is reported herein.