Relevant bibliographies by topics / In-Silico identification

Academic literature on the topic 'In-Silico identification'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "In-Silico identification"

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Chen, Ping, Jun Duan, Liang Jiang, Qiong Liu, Ping Zhao, Qingyou Xia, and Huibi Xu. "In silico identification of silkworm selenoproteomes." Chinese Science Bulletin 51, no. 23 (December 2006): 2860–67. http://dx.doi.org/10.1007/s11434-006-2206-x.

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Reddy, Bandi Deepa, and Ch M. Kumari Chitturi. "Screening and Identification of Microbial Derivatives for Inhibiting Legumain: An In silico Approach." Journal of Pure and Applied Microbiology 12, no. 3 (September 30, 2018): 1623–30. http://dx.doi.org/10.22207/jpam.12.3.69.

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Moss, Alan, Stephen Madden, Padraic Mac Mathuna, and Peter Doran. "In silico gene identification in colonic neoplasia." Gastroenterology 124, no. 4 (April 2003): A110. http://dx.doi.org/10.1016/s0016-5085(03)80540-1.

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Esposito, C., L. Wiedmer, and A. Caflisch. "In Silico Identification of JMJD3 Demethylase Inhibitors." Journal of Chemical Information and Modeling 58, no. 10 (September 18, 2018): 2151–63. http://dx.doi.org/10.1021/acs.jcim.8b00539.

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Duckworth, D. Malcolm, and Philippe Sanseau. "In silico identification of novel therapeutic targets." Drug Discovery Today 7, no. 11 (May 2002): S64—S69. http://dx.doi.org/10.1016/s1359-6446(02)02282-1.

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Kaiser, Markus, and Christian Ottmann. "In Silico Identification of an Interferon Inhibitor." ChemMedChem 7, no. 4 (January 20, 2012): 555–57. http://dx.doi.org/10.1002/cmdc.201100579.

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Sen, Madhab Kumar, Kateřina Hamouzová, Sunil Kanti Mondal, and Josef Soukup. "Identification of the optimal codons for acetolactate synthase from weeds: an in-silico study." Plant, Soil and Environment 67, No. 6 (May 21, 2021): 331–36. http://dx.doi.org/10.17221/562/2020-pse.

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Although various studies of codon usage bias have been reported in a broad spectrum of organisms, no studies to date have examined codon usage bias for herbicide target genes. In this study, we analysed codon usage patterns for the acetolactate synthase (ALS) gene in eight monocot weeds and one model monocot. The base composition at the third codon position follows C3 > G3 > T3 > A3. The values of the effective number of codons (ENC or Nc) indicate low bias, and ENC or Nc vs. GC3 plot suggests that this low bias is due to mutational pressure. Low codon adaptation index and codon bias index values further supported the phenomenon of low bias. Additionally, the optimal codons, along with over- and under-represented codons, were identified. Gene design using optimal codons rather than overall abundant codons produce improved protein expression results. Our results can be used for further studies, including eliciting the mechanisms of herbicide resistance (occurring due to elevation of gene expression levels) and the development of new compounds, their efficiency and risk assessment for herbicide resistance evolution.
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Sharanee Kumar, Ilakiya, Nadiah Zaharin, and Kalaivani Nadarajah. "In silico Identification of Resistance and Defense Related Genes for Bacterial Leaf Blight (BLB) in Rice." Journal of Pure and Applied Microbiology 12, no. 4 (December 30, 2018): 1867–76. http://dx.doi.org/10.22207/jpam.12.4.22.

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Nimrod, G., F. Glaser, D. Steinberg, N. Ben-Tal, and T. Pupko. "In silico identification of functional regions in proteins." Bioinformatics 21, Suppl 1 (June 1, 2005): i328—i337. http://dx.doi.org/10.1093/bioinformatics/bti1023.

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Sun, Pingping, Sijia Guo, Jiahang Sun, Liming Tan, Chang Lu, and Zhiqiang Ma. "Advances in In-silico B-cell Epitope Prediction." Current Topics in Medicinal Chemistry 19, no. 2 (March 28, 2019): 105–15. http://dx.doi.org/10.2174/1568026619666181130111827.

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Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.
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Dissertations / Theses on the topic "In-Silico identification"

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Tiwari, Vijay, Derek Stuffle, and Aruna Kilaru. "Identification and In-Silico Analysis of Fatty Acid Amide Hydrolases in Tomato." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/4797.

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N-acylethanolamines (NAEs) are a family of signaling lipids derived from a minor membrane lipid constituent N-acylphosphatidylethanolamine (NAPE). In Arabidopsis, NAE mediates physiological functions such as seedling growth, flowering, and response to stress via abscisic acid (ABA) –dependent and –independent signaling pathways. The function of NAEs is terminated by a highly conserved fatty acid amide hydrolase (FAAH). Studies in model plant Arabidopsis showed the significant role of NAEs that makes it relevant to elucidate the conserved metabolic pathway of NAEs in crop species such as tomato. It is hypothesized that there is a functional FAAH in tomato that hydrolyzes NAEs. To test this hypothesis, AtFAAH was used as a template to identify putative FAAH sequences in tomato, using BLASTX. Six SlFAAH sequences with the conserved amidase signature sequence and the catalytic triad, formed by Lys205, Ser281, and Ser305 in AtFAAH, were identified. Phylogenetic analysis of putative SlFAAH homologs and other FAAH family proteins (Arabidopsis, rice and moss), using CLUSTALW, revealed the two sequences that are closely related to the functionally characterized AtFAAH1. Using molecular visualization system (PyMOL), protein structures of putative SlFAAH1and 2 were predicted and compared with AtFAAH; both sequences showed similar domain structure to AtFAAH, with minor differences in spatial arrangement. For further biochemical characterization, full-length coding sequence of SlFAAH1 and SlFAAH2 were isolated and cloned into a heterologous expression system. The expressed protein will be characterized for its hydrolytic activity against radiolabelled NAE substrates. Furthermore, transcript levels for SlFAAH1 and SlFAAH2 will be quantified and correlated with the NAE levels in various tissues to predict their role in tissue-specific NAE hydrolysis. Together, these molecular and biochemical characterization studies in tomato are expected to further validate the conserved nature of NAE metabolic pathway in plants.
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Salentin, Sebastian. "In Silico Identification of Novel Cancer Drugs with 3D Interaction Profiling." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226435.

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Cancer is a leading cause of death worldwide. Development of new cancer drugs is increasingly costly and time-consuming. By exploiting massive amounts of biological data, computational repositioning proposes new uses for old drugs to reduce these development hurdles. A promising approach is the systematic analysis of structural data for identification of shared binding pockets and modes of action. In this thesis, I developed the Protein-Ligand Interaction Profiler (PLIP), which characterizes and indexes protein-ligand interactions to enable comparative analyses and searching in all available structures. Following, I applied PLIP to identify new treatment options in cancer: the heat shock protein Hsp27 confers resistance to drugs in cancer cells and is therefore an attractive target with a postulated drug binding site. Starting from Hsp27, I used PLIP to define an interaction profile to screen all structures from the Protein Data Bank (PDB). The top prediction was experimentally validated in vitro. It inhibits Hsp27 and significantly reduces resistance of multiple myeloma cells against the chemotherapeutic agent bortezomib. Besides computational repositioning, PLIP is used in docking, binding mode analysis, quantification of interactions and many other applications as evidenced by over 12,000 users so far. PLIP is provided to the community online and as open source.
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Gómez-Porras, Judith Lucia. "In silico identification of genes regulated by abscisic acid in Arabidopsis thaliana (L.) Heynh." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980562899.

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Szolkiewicz, Michal Jerzy. "Homology-based in silico identification of putative protein-ligand interactions in the malaria parasite." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/41019.

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Malaria is still one of the most proli c communicable diseases in the world with more than 200 million infections annually, its greatest e ect is felt in the poor nations with-in sub-saharan Africa and south-east Asia. It is especially fatal for women and children where out of the 660 000 fatalities in 2010, 86% were below the age of 5. In the past decade the global fatality rate due to malaria has been signi cantly reduced, primarily due to proliferation of vector control using treated nets and indoor residual spraying of DDT. There have, however, been few innovations in anti-malarial therapeutics and with the threat of the spread of drug resistant strains a need still exists to develop novel drugs to combat malaria infections. One of the major hinderances to drug development is the huge cost of the drug development process, where candidate failures late in development are extremely costly. This is where post-genomic information has the potential of adding great value. By using all available data pertaining to a disease, one gains higher discerning power to select good drug candidates and identify risks early in development before serious investments are made. This need provided the motivation for the development of Discovery; a tool to aid in the identi cation of protein targets and viable lead compounds for the treatment of malaria. Discovery was developed at the University of Pretoria to be a platform for a large spectrum of biological data focused on the malaria causing Plasmodium parasite. It conglomerates various data types into a web-based interface that allows searching using logical lters or by using protein or chemical start points. In 2010 it was decided to rebuild Discovery to improve it's functionality and optimize query times. Also, since its inception various new datasources became available speci cally related to bio-active molecules, these include the ChEMBL database and TCAMS dataset of bio-active molecules and the focus of this project was the integration of said datasets into Discovery. Large quantities of high quality bioactivity data have never been available in the public domain and this has opened up the opportunity to gain even greater insight into the activity of chemical compounds in malaria. Due to conserved structural/functional similarities of proteins between di erent species it is possible to derive predictions about a malaria protein or a chemicals activity in malaria due to experiments carried out on other organisms. These comparisons can be leveraged to highlight potential new compounds that were previously not considered or prevent wasting resources persuing potential compounds that pose threats of toxicity to humans. This project has resulted in a web based system that allows one to search through the chemical space of the malaria parasite. Allowing them to view sets of predicted protein-ligand interactions for a given protein based on that proteins similarity to those existing in the bio-active molecule databases.
Dissertation (MSc)--University of Pretoria, 2014.
gm2014
Biochemistry
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Musyoka, Thommas Mutemi. "Combined in silico approaches towards the identification of novel malarial cysteine protease inhibitors." Thesis, Rhodes University, 2017. http://hdl.handle.net/10962/4488.

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Malaria an infectious disease caused by a group of parasitic organisms of the Plasmodium genus remains a severe public health problem in Africa, South America and parts of Asia. The leading causes for the persistence of malaria are the emergence of drug resistance to common antimalarial drugs, lack of effective vaccines and the inadequate control of mosquito vectors. Worryingly, accumulating evidence shows that the parasite has developed resistant to the current first-line treatment based on artemisinin. Hence, the identification and characterization of novel drug targets and drugs with unique mode of action remains an urgent priority. The successful sequencing and assembly of genomes from several Plasmodium species has opened an opportune window for the identification of new drug targets. Cysteine proteases are one of the major drug targets to be identified so far. The use of cysteine protease inhibitors coupled with gene manipulation studies has defined specific and putative roles of cysteine proteases which include hemoglobin degradation, erythrocyte rupture, immune evasion and erythrocyte invasion, steps which are central for the completion of the Plasmodium parasite life cycle. In an aim to discover potential novel antimalarials, this thesis focussed on falcipains (FPs), a group of four papain-like cysteine proteases from Plasmodium falciparum. Two of these enzymes, FP-2 and FP-3 are the major hemoglobinases and have been validated as drug targets. For the successful elimination of malaria, drugs must be safe and target both human and wild Plasmodium infective forms. Thus, an incipient aim was to identify protein homologs of these two proteases from other Plasmodium species and the host (human). From BLASTP analysis, up to 16 FP-2 and FP-3 homologs were identified (13 plasmodial proteases and 3 human cathepsins). Using in silico characterization approaches, the intra and inter group sequence, structural, phylogenetic and physicochemical differences were determined. To extend previous work (MSc student) involving docking studies on the identified proteins using known FP-2 and FP-3 inhibitors, a South African natural compound and its ZINC analogs, molecular dynamics and binding free energy studies were performed to determine the stabilities and quantification of the strength of interactions between the different protein-ligand complexes. From the results, key structural elements that regulate the binding and selectivity of non-peptidic compounds onto the different proteins were deciphered. Interaction fingerprints and energy decomposition analysis identified key residues and energetic terms that are central for effective ligand binding. This research presents novel insight essential for the structure-based molecular drug design of more potent antimalarial drugs.
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Lee, Adam. "The in silico identification and analysis of ancient and recent endogenous retroviruses in mammalian genomes." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/39972.

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Recent advances in DNA sequencing technologies have led to a vast plethora of vertebrate genomes being made available for bioinformatic analysis and investigation. This has presented retrovirologists with many new opportunities to study endogenous retroviruses (ERVs) - selfish genetic element (SGEs) endogenised within the genomic DNA of their hosts. Many of these ERVs exist as molecular fossils of past germline infections by their exogenous counterparts, representing approximately 8-10% of mammalian genomes. While the majority are thought to be inactive today, one particular retroviral group - HERV-K(HML-2) - has been implicated in recent activity. In this thesis, efficient, synergistic in silico techniques have been implemented, with which intensive, genome-wide retroviral screens were performed. This has culminated in the identification of 11 novel, insertionally polymorphic human ERVs (HERVs), belonging to the HERV-K(HML-2) lineage, in two high- coverage archaic hominid genomes. This thesis also identifies the oldest ERV described to date - orthologous across all placental mammals - estimated to have endogenised in the germline of an ancestral mammal, 128-140 million years ago. Three SGEs, found to be endogenised within this ancient ERV, have also been described and assigned a minimum age of 104 million years, making these the oldest, definitively dated SGEs. This thesis also presents a computer program for renaming all identified ERVs in vertebrate genomes, according to a newly designed nomenclature standard to be implemented globally, that aims to unambiguously catalogue all the ERVs identified, to date.
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Tahir, Shifa. "A docking-based method for in silico epitope determination." Thesis, Tours, 2018. http://www.theses.fr/2018TOUR4008.

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Le développement des anticorps thérapeutiques s'est rapidement accéléré dans les 10 dernières années et concerne un nombre croissant de pathologies. La connaissance de l'épitope, à savoir la région de la cible à laquelle l'anticorps se fixe, est essentielle pour la compréhension des effets fonctionnels de ce dernier. Nous avons développé une méthode in silico, MAbTope, qui permet une prédiction précise de cet épitope, quand bien même aucune structure 3D de l'anticorps d’intérêt n'est résolue. Cette méthode se base sur une méthode d'amarrage protéine-protéine développée auparavant dans l’équipe BIOS. Le jeu d'apprentissage a été fortement enrichi en complexes anticorps-cibles, de nouvelles fonctions de score spécifiques ont été mises au point, et le plus important, l'objectif de l'apprentissage-machine a été modifié pour optimiser non plus la conformation de !'assemblage, mais la prédiction de l'épitope. Nous montrons que la méthode qui en résulte permet une prédiction précise et robuste de l'épitope, que la structure 3D de l'anticorps soit connue ou non. Nous montrons également comment les prédictions peuvent être facilement exploitées pour la validation expérimentale. Enfin, nous montrons comment la méthode peut être utilisée pour étudier à haut-débit le recouvrement d'épitopes par des anticorps ayant la même cible
The development of therapeutic antibodies has been rapidly increasing in the last 10 years, with application to an increasing number of pathologies. The knowledge of the epitope, the region of the antigen to which the antibody binds, is crucial for understanding its functional effects. We have developed an in silico method, MAbTope, which allows the accurate prediction of the epitope, regardless of the availability of the 3D structure of the antibody of interest. This method is based on a protein-protein docking method previously developed in the BIOS group. The learning dataset was enlarged in antibody-antigen complexes, new specific scoring functions have been designed, and very importantly, the objective of machine-learning was switched from the conformational perspective towards the epitope determination perspective. We show that the resulting method allows robust and accurate prediction, whether or not the 3D structure of the antibody is available. We also show how the predictions can be easily exploited for experimental validation. Finally, we show how this method can be used for high-throughput epitope binning
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Zhang, Jin. "In silico Identification of Thyroid Disrupting Chemicals : among industrial chemicals and household dust contaminants." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-125631.

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Thyroid disruptions by xenobiotics have been associated with a broad spectrum of severe adverse human health effects, such as impaired brain development and metabolic syndrome. Ingestion of indoor dust and contact with industrial chemicals are two significant human exposure routes of thyroid hormone disrupting chemicals (THDCs), raising serious concerns for human health. However, it is a laborious and costly process to identify THDCs using conventional experimental methods, due to the number of chemicals in commerce and the varieties of potential disruption mechanisms. In this thesis, we are aimed at in silico identification of novel THDCs targeting transthyretin (TTR) and thyroid hormone receptor (THR) among dust contaminants and commonly used industrial chemicals. In vitro assays were used to validate the in silico prediction results. Co-crystallization and molecular dynamics (MD) simulations were applied to reveal binding modes of THDCs at the studied biological targets and to explain their intermolecular recognition. The main findings presented in this thesis are: 1. Over 144 environmental pollutants have been confirmed as TTR-binders in vitro and these cover a wide range of environmental pollutants and show distinct chemical profiles including a large group of halogenated aromatic compounds and a second group of per- and polyfluoroalkyl substances. (Paper I) 2. In total 485 organic contaminants have been reported to be detected in household dust. The developed QSAR classification model predicted 7.6% of these dust contaminants and 53.1% of their metabolites as potential TTR-binders, which emphasizes the importance of metabolic bioactivation. After in vitro validation, four novel TTR binders with IC50 ≤ 10 µM were identified, i.e. perfluoroheptanesulfonic acid, 2,4,2',4'-tetrahydroxybenzophenone (BP2), 2,4,5-trichlorophenoxyacetic acid, and 3,5,6-trichloro-2-pyridinol. (Paper II) 3. The development of a robust structure-based virtual screening (VS) protocol resulted in the prediction of 31 dust contaminants as potential binders to THRβ1 including musk compounds, PFASs, and bisphenol A derivatives. The in vitro experiments confirmed four compounds as weak binders to THRβ1, i.e. 2,4,5-trichlorophenoxyacetic acid, bisphenol A (3-chloro-2-hydroxypropyl) (2,3-dihydroxypropyl) ether, 2,4,2',4'-tetrahydroxybenzophenone, and 2,4-dichlorophenoxyacetic acid. (Paper III) 4. We revealed the binding conformations of perfluorooctanesulfonic acid, perfluorooctanoic acid, and BP2 in the thyroxine binding sites (TBSs) of TTR by co-crystallizing TTR with the three compounds. A VS protocol was developed based on the TTR complex structures that predicted 192 industrial chemicals as potential binders to TTR. Seven novel TTR binders were confirmed by in vitro experiments including clonixin, 2,6-dinitro-p-cresol (DNPC), triclopyr, fluroxypyr, bisphenol S, picloram, and mesotrione. We further co-crystallized TTR with PBS, clonixin, DNPC, and triclopyr, and their complex structures showed that the compounds bind in the TBSs as proposed by the VS protocol. In summary, 13 indoor dust contaminants and industrial chemicals were identified as THDCs using a combination of in silico and in vitro approaches. To the best of our knowledge, none of these compounds has previously been reported to bind to TTR or THR. The identifications of these THDCs improve our understanding on the structure-activity relationships of THDCs. The crystal structures of TTR-THDC complexes and the information on THDC-Target intermolecular interactions provide a better understanding on the mechanism-of-actions behind thyroid disruption. The dataset compiled and in silico methods developed serve as a basis for identification of more diverse THDCs in the future and a tool for guiding de novo design of safer replacements.
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Ludaka, Namhla. "Identification of biomarkers associated with cervical cancer: a combined in silico and molecular approach." University of the Western Cape, 2014. http://hdl.handle.net/11394/4363.

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>Magister Scientiae - MSc
Cervical cancer is the leading cause of cancer mortality among black women in South Africa. It is estimated that this disease kills approximately 8 women in South Africa every day. Cervical cancer is caused by the human papillomavirus (HPV) with the most common screening method for cervical cancer being Papanicolaou (Pap) smear, test amongst others. However, less than 20% of South African women go for these tests. There are several reasons why women do not go for these tests but the invasiveness of the test is one of the major causes for the low rate of screening. Lateral flow devices offer medical diagnosis at the point- of-care, allowing for the quick initiation of the appropriate therapeutic response. These tests are more cost-effective for the healthcare delivery industry, and can potentially be used by patients to self-test in the privacy of their homes and allow them to make informed decisions about their health. Therefore, the aim of this study was to use computational methods to identify serum biomarkers for cervical cancer that can be used to develop a point-of-care diagnostic device for cervical cancer. An in silico approach was used to identify genes implicated in the initiation and development of cervical cancer. Several bioinformatics tools were employed to extract a list of genes from publicly available cancer repositories. Multiple gene enrichment analysis tools were employed to analyze the selected candidate genes. Through this pipeline, ~28190 genes were identified from the various databases and were further refined to only 10 genes. The 10 genes were identified as potential cervical cancer biomarkers. A subcellular compartmentalization analysis clustered the proteins encoded by these genes as cell surface, secretory granules and extracellular space/matrix proteins. The selected candidate genes were predicted to be specific for cervical cancer tissue in a cancer tissue specificity meta-analysis study. The expression levels of the candidate genes were compared relative to each other and a graph constructed using gene expression data generated by GeneHub-GEPIS and TiGER databases. Further gene enrichment analysis was performed such as protein-protein interactions, transcription factor analysis, pathway analysis and co-expression analysis, with 9 out of the10 of the candidate genes showing co-expression. A gene expression analysis done on cervical cancer cell lines, other cancer cell lines and normal fibroblast cell line revealed differential expression of the candidate genes. Three candidate genes were significantly expressed in cervical cancer, while the seven remaining genes showed over expression in other cancer types. The study serves as basis for future investigations to diagnosis of cervical cancer, as well as for cancers. Thus, they could also serve as potential drug targets for cancer therapeutics and diagnostics.
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Kellett, Kathryn Emily. "Development of chemical sensors for rapid identification of amphetamine-related new psychoactive substances." Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/17686.

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A molecular receptor for mephedrone, an amphetamine-like NPS, was developed using host-guest chemistry and pharmacophoric design. The in-field detection of new psychoactive substances (NPS) is an area that has garnered considerable attention in the last few years. With the continuously expanding number of NPS on the market, traditional detection mechanisms lack the selectivity needed. In this project a new methodology has been developed for the design of host molecules for use in in-field detection, based on biomimetic design. To understand what a sensory molecular needs to be selective against, GC-MS and HPLC analysis were employed to identify and quantify thirteen aminoindane internet samples. It was found that the composition of internet samples varies greatly in terms of concentration of active ingredient, with a range of 17-95 % w/w of active ingredient identified. It was also found that caffeine was the most common cutting agent with a range of 27.7-30.2 % w/w identified. This highlights the need for both selectivity and sensitivity in detection mechanisms. Using the principles of biomimetic design, a methodology for the treatment of protein-ligand interactions was developed. Protein-ligand binding data collected from the Protein Databank was analysed for mephedrone related structures and common cutting agents, identified through aminoindane internet sample analysis and literature sources. From this work a three-point pharmacophoric model was developed, upon which two host molecules were considered, macrocyclic calixarenes and acyclic anthraquinones. Both contained the three binding interactions deduced from the pharmacophore design; two p-stacking interactions and one hydrogen bond acceptor. The final host molecule taken forward for testing was 1,8-dibenzylthiourea anthracene (Probe 1). The binding affinity of Probe 1 to mephedrone was tested using 1H-NMR. An estimated association constant of 104 M-1 was calculated, with a 1:1 binding stoichiometry. Along with ESI-MS and DFT calculations, it was found that mephedrone binds to Probe 1 in a concerted fashion with a three-point binding geometry, with two hydrogen bonds and one p-stacking interaction. A modest optical response using fluorescence spectroscopy was also observed between mephedrone and Probe 1 at high molar concentrations. A more pronounced response was observed upon addition of high molar concentrations of flephedrone. 1H-NMR showed that Probe 1 selectively bound mephedrone over methamphetamine as well as the four most common cutting agents identified from literature: lidocaine, caffeine, paracetamol and benzocaine, which have been shown to cause false positives in previous studies. Probe 1 showed significant selectivity for the β-ketoamine arrangement. This is supported by the systematic analysis of mephedrone, methamphetamine, mephedrone precursor and flephedrone. This is the first time this has been achieved using host-guest chemistry. A protocol was developed to successfully detect mephedrone via Probe 1 using NMR spectroscopy in a simulated street sample containing two of the most common cutting agents, benzocaine and caffeine. To further aid future design of small host molecules a methodology for the in silico analysis of small molecule host-guest binding using metadynamics was explored. Solvent interactions with the host and guest molecules were observed, highlighting the importance of solvent choice in binding studies. Metadynamics shows potential to be used in further work for improving the approach in which host molecules are designed in future.
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Books on the topic "In-Silico identification"

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Darryl, León, and Markel Scott, eds. In silico technologies in drug target identification and validation. Boca Raton: CRC Press, 2006.

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Markel, Scott, and Darryl Leon. In Silico Technologies in Drug Target Identification and Validation. Taylor & Francis Group, 2006.

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In silico technologies in drug target identification and validation. Boca Raton, FL: CRC/Taylor & Francis, 2006.

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Markel, Scott, and Darryl Leon. In Silico Technologies in Drug Target Identification and Validation. Taylor & Francis Group, 2006.

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(Editor), Darryl Leon, and Scott Markel (Editor), eds. In Silico Technologies in Drug Target Identification and Validation (Drug Discoveries Series). CRC, 2006.

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Book chapters on the topic "In-Silico identification"

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Trosset, Jean-Yves, and Christian Cavé. "In Silico Drug–Target Profiling." In Target Identification and Validation in Drug Discovery, 89–103. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9145-7_6.

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Yella, Venkata Rajesh, and Manju Bansal. "In silico Identification of Eukaryotic Promoters." In Systems and Synthetic Biology, 63–75. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9514-2_4.

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Schuster, Andrew, Grant W. Hennig, Nicole Ortogero, Dickson Luong, and Wei Yan. "In Silico Identification of Novel Endo-siRNAs." In RNA Interference, 341–51. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1538-5_21.

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Flower, Darren R., Matthew N. Davies, and Irini A. Doytchinova. "Identification of Candidate Vaccine Antigens In Silico." In Immunomic Discovery of Adjuvants and Candidate Subunit Vaccines, 39–71. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5070-2_3.

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Nadarajah, Kalaivani K. "In Silico Identification of Plant-Derived Secondary Metabolites in Defense." In In Silico Approach for Sustainable Agriculture, 275–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0347-0_16.

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Arif, K. M. Taufiqul, Rachel K. Okolicsanyi, Larisa M. Haupt, and Lyn R. Griffiths. "MicroRNA–Target Identification: A Combinatorial In Silico Approach." In Methods in Molecular Biology, 215–30. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2982-6_14.

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de Jonge, Ronnie. "In Silico Identification and Characterization of Effector Catalogs." In Plant Fungal Pathogens, 415–25. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-501-5_25.

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Trosset, Jean-Yves, and Christian Cavé. "In Silico Target Druggability Assessment: From Structural to Systemic Approaches." In Target Identification and Validation in Drug Discovery, 63–88. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9145-7_5.

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Davies, Matthew N., and Darren R. Flower. "In Silico Identification of Novel G Protein Coupled Receptors." In Methods in Molecular Biology, 25–36. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-310-7_2.

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Davies, Matthew N., David E. Gloriam, and Darren R. Flower. "In Silico Identification of Novel G Protein-Coupled Receptors." In Neuromethods, 3–18. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-179-6_1.

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Conference papers on the topic "In-Silico identification"

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Beck, Dominik, Miriam Brandl, Tuan D. Pham, Chung-Che Chang, Xiaobo Zhou, Tuan D. Pham, Xiaobo Zhou, et al. "In-Silico Identification Of Micro-Loops In Myelodysplastic Syndromes." In 2011 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-11). AIP, 2011. http://dx.doi.org/10.1063/1.3596650.

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Ersoz, Nur Sebnem, Yasin Guzel, and Burcu Bakir-Gungor. "In-Silico Identification of Papillary Thyroid Carcinoma Molecular Mechanisms." In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE, 2019. http://dx.doi.org/10.1109/siu.2019.8806542.

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Barreira, Nina, Rodrigo Silva, Tatiana Tilli, and Patrícia Neves. "Identification of breast cancer neoantigens using in silico methodologies." In IV International Symposium on Immunobiologicals & VII Seminário Anual Científico e Tecnológico. Instituto de Tecnologia em Imunobiológicos, 2019. http://dx.doi.org/10.35259/isi.sact.2019_32690.

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Tyagi, Rashmi, Dhruv Kumar, and V. Samuel Raj. "In silico identification of potential inhibitors against Mycobacterial proteasome." In 2018 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2018. http://dx.doi.org/10.1109/bsb.2018.8770625.

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Soni, Abhishek, and Vikas Kaushik. "In Silico Identification of Inhibitors as Antagonist for HCV Treatment." In 2018 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2018. http://dx.doi.org/10.1109/bsb.2018.8770606.

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Kaur, Rajbir, and Vikas Kaushik. "In Silico Peptide based Vaccine Identification against Swine Influenza Virus." In 2018 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2018. http://dx.doi.org/10.1109/bsb.2018.8770636.

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Sinkoe, Andrew, A. Agung Julius, and Juergen Hahn. "In silico identification of potential transcriptional regulators associated with human MAPK signaling." In 2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC). IEEE, 2015. http://dx.doi.org/10.1109/nebec.2015.7117197.

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Zhang, Jintao, and Jun Huan. "Novel biological network features discovery for in silico identification of drug targets." In the ACM international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1882992.1883014.

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Vychyk, P. V., and Y. A. Nikolaichik. "Identification of bacterial virulence factors based on an integration of experimental and in silico transcription factor target discovery." In 2nd International Scientific Conference "Plants and Microbes: the Future of Biotechnology". PLAMIC2020 Organizing committee, 2020. http://dx.doi.org/10.28983/plamic2020.278.

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Chua, Huey-Eng, Sourav S. Bhowmick, Lisa Tucker-Kellogg, Qing Zhao, C. Forbes Dewey, and Hanry Yu. "In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network." In the 2nd ACM SIGHIT symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2110363.2110381.

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Reports on the topic "In-Silico identification"

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Rafaeli, Ada, and Russell Jurenka. Molecular Characterization of PBAN G-protein Coupled Receptors in Moth Pest Species: Design of Antagonists. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7593390.bard.

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The proposed research was directed at determining the activation/binding domains and gene regulation of the PBAN-R’s thereby providing information for the design and screening of potential PBAN-R-blockers and to indicate possible ways of preventing the process from proceeding to its completion. Our specific aims included: (1) The identification of the PBAN-R binding domain by a combination of: (a) in silico modeling studies for identifying specific amino-acid side chains that are likely to be involved in binding PBAN with the receptor and; (b) bioassays to verify the modeling studies using mutant receptors, cell lines and pheromone glands (at tissue and organism levels) against selected, designed compounds to confirm if compounds are agonists or antagonists. (2) The elucidation ofthemolecular regulationmechanisms of PBAN-R by:(a) age-dependence of gene expression; (b) the effect of hormones and; (c) PBAN-R characterization in male hair-pencil complexes. Background to the topic Insects have several closely related G protein-coupled receptors (GPCRs) belonging to the pyrokinin/PBAN family, one with the ligand pheromone biosynthesis activating neuropeptide or pyrokinin-2 and another with diapause hormone or pyrokinin-1 as a ligand. We were unable to identify the diapause hormone receptor from Helicoverpa zea despite considerable effort. A third, related receptor is activated by a product of the capa gene, periviscerokinins. The pyrokinin/PBAN family of GPCRs and their ligands has been identified in various insects, such as Drosophila, several moth species, mosquitoes, Triboliumcastaneum, Apis mellifera, Nasoniavitripennis, and Acyrthosiphon pisum. Physiological functions of pyrokinin peptides include muscle contraction, whereas PBAN regulates pheromone production in moths plus other functions indicating the pleiotropic nature of these ligands. Based on the alignment of annotated genomic sequences, the primary and secondary structures of the pyrokinin/PBAN family of receptors have similarity with the corresponding structures of the capa or periviscerokinin receptors of insects and the neuromedin U receptors found in vertebrates. Major conclusions, solutions, achievements Evolutionary trace analysisof receptor extracellular domains exhibited several class-specific amino acid residues, which could indicate putative domains for activation of these receptors by ligand recognition and binding. Through site-directed point mutations, the 3rd extracellular domain of PBAN-R was shown to be critical for ligand selection. We identified three receptors that belong to the PBAN family of GPCRs and a partial sequence for the periviscerokinin receptor from the European corn borer, Ostrinianubilalis. Functional expression studies confirmed that only the C-variant of the PBAN-R is active. We identified a non-peptide agonist that will activate the PBAN-receptor from H. zea. We determined that there is transcriptional control of the PBAN-R in two moth species during the development of the pupa to adult, and we demonstrated that this transcriptional regulation is independent of juvenile hormone biosynthesis. This transcriptional control also occurs in male hair-pencil gland complexes of both moth species indicating a regulatory role for PBAN in males. Ultimate confirmation for PBAN's function in the male tissue was revealed through knockdown of the PBAN-R using RNAi-mediated gene-silencing. Implications, both scientific and agricultural The identification of a non-peptide agonist can be exploited in the future for the design of additional compounds that will activate the receptor and to elucidate the binding properties of this receptor. The increase in expression levels of the PBAN-R transcript was delineated to occur at a critical period of 5 hours post-eclosion and its regulation can now be studied. The mysterious role of PBAN in the males was elucidated by using a combination of physiological, biochemical and molecular genetics techniques.
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Rafaeli, Ada, Russell Jurenka, and Chris Sander. Molecular characterisation of PBAN-receptors: a basis for the development and screening of antagonists against Pheromone biosynthesis in moth pest species. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7695862.bard.

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The original objectives of the approved proposal included: (a) The determination of species- and tissue-specificity of the PBAN-R; (b) the elucidation of the role of juvenile hormone in gene regulation of the PBAN-R; (c) the identificationof the ligand binding domains in the PBAN-R and (d) the development of efficient screening assays in order to screen potential antagonists that will block the PBAN-R. Background to the topic: Moths constitute one of the major groups of pest insects in agriculture and their reproductive behavior is dependent on chemical communication. Sex-pheromone blends are utilised by a variety of moth species to attract conspecific mates. In most of the moth species sex-pheromone biosynthesis is under circadian control by the neurohormone, PBAN (pheromone-biosynthesis-activating neuropeptide). In order to devise ideal strategies for mating disruption/prevention, we proposed to study the interactions between PBAN and its membrane-bound receptor in order to devise potential antagonists. Major conclusions: Within the framework of the planned objectives we have confirmed the similarities between the two Helicoverpa species: armigera and zea. Receptor sequences of the two Helicoverpa spp. are 98% identical with most changes taking place in the C-terminal. Our findings indicate that PBAN or PBAN-like receptors are also present in the neural tissues and may represent a neurotransmitter-like function for PBAN-like peptides. Surprisingly the gene encoding the PBAN-receptor was also present in the male homologous tissue, but it is absent at the protein level. The presence of the receptor (at the gene- and protein-levels), and the subsequent pheromonotropic activity are age-dependent and up-regulated by Juvenile Hormone in pharate females but down-regulated by Juvenile Hormone in adult females. Lower levels of pheromonotropic activity were observed when challenged with pyrokinin-like peptides than with HezPBAN as ligand. A model of the 3D structure of the receptor was created using the X-ray structure of rhodopsin as a template after sequence alignment of the HezPBAN-R with several other GPCRs and computer simulated docking with the model predicted putative binding sites. Using in silico mutagenesis the predicted docking model was validated with experimental data obtained from expressed chimera receptors in Sf9 cells created by exchanging between the three extracellular loops of the HezPBAN-R and the Drosophila Pyrokinin-R (CG9918). The chimera receptors also indicated that the 3ʳᵈ extracellular loop is important for recognition of PBAN or Diapause hormone ligands. Implications: The project has successfully completed all the objectives and we are now in a position to be able to design and screen potential antagonists for pheromone production. The successful docking simulation-experiments encourage the use of in silico experiments for initial (high-throughput) screening of potential antagonists. However, the differential responses between the expressed receptor (Sf9 cells) and the endogenous receptor (pheromone glands) emphasize the importance of assaying lead compounds using several alternative bioassays (at the cellular, tissue and organism levels). The surprising discovery of the presence of the gene encoding the PBAN-R in the male homologous tissue, but its absence at the protein level, launches opportunities for studying molecular regulation pathways and the evolution of these GPCRs. Overall this research will advance research towards the goal of finding antagonists for this important class of receptors that might encompass a variety of essential insect functions.
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