Дисертації з теми "Prediction of binding affinity"
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Jovanovic, Srdan. "Rapid, precise and reproducible binding affinity prediction : applications in drug discovery." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10053853/.
Повний текст джерелаUslan, Volkan. "Support vector machine-based fuzzy systems for quantitative prediction of peptide binding affinity." Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/11170.
Повний текст джерелаBodnarchuk, Michael. "Predicting the location and binding affinity of small molecules in protein binding sites." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/348170/.
Повний текст джерелаErdas, Ozlem. "Modelling And Predicting Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608792/index.pdf.
Повний текст джерелаGuedes, Isabella Alvim. "Development of empirical scoring funcions forn predicting proteinligand binding affinity." Laboratório Nacional de Computação Científica, 2016. https://tede.lncc.br/handle/tede/247.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes)
Molecular docking is a methodology that aims to predict the binding modes and affinity of a small molecule within the binding site of the receptor target of interest. It is an approach widely used by the pharmaceutical industry and the academic community for identification and optimization of lead compounds, contributing to the reduction of cost, time and failures in the development of new drugs. Current docking methods and the associated scoring functions exhibit good performances in identifying experimental binding modes. However, the detection of active compounds among a decoy set of ligands and the accurate prediction of binding affinity remain challenging tasks. The DockThor program developed in our group has obtained promising results in comparative studies with other well established and widely used protein-ligand docking programs for predicting experimental binding modes. Despite useful for pose prediction, the current scoring function implemented in DockThor is not suitable for predicting binding affinities of protein-ligand complexes, obtaining no correlation with measured affinity data. In this work, we develop several scoring functions with physically-based features for predicting binding affinities of protein-ligand complexes trained with diverse machine learning techniques. The final scoring functions consist of force-field based terms related to the intermolecular interactions (electrostatic and van der Waals potentials), an original term for the ligand entropy (number of frozen rotatable bonds), ligand and protein desolvation and the hydrophobic effect. Then, we developed general and target-classes scoring functions, the last to account for binding characteristics associated with a target class of interest, focusing on proteases, kinases and protein-protein interactions complexes (PPIs). The scoring functions were derived using linear regression (MLR) and seven more advanced machine learning techniques for nonlinear problems. The training and testing were carried out using high-quality datasets composed of experimental structures of diverse protein-ligand complexes with binding affinities data available (Kd or Ki). Additionally, we also derived general scoring functions trained with redocking results from the DockThor program. The scoring functions trained with docking results obtained promising performances when evaluated in both experimental and docking structures, indicating that they are reliable to be used in real virtual screening experiments. The scoring functions developed in this work have demonstrated to be competitive with the best-evaluated linear and nonlinear scoring functions in benchmarking studies described in the literature. The scoring functions derived for specific classes of targets also exhibited promising performances, achieving great improvements when using nonlinear approaches compared to the linear models. Moreover, the consensus scoring strategy investigated in this work exhibited impressive results, ranking among the top-three models with the best predictive performances on all cases. The development of the scoring functions implemented in this thesis is a crucial step to make the DockThor an even more competitive program, enabling the development of the high-throughput virtual screening program and portal DockThor-VS.
Atracamento molecular é uma metodologia que tem por objetivo prever a conformação e a afinidade de uma pequena molécula no sítio de ligação do receptor alvo de interesse. É uma abordagem amplamente utilizada pela indústria farmacêutica e pela comunidade acadêmica para identificação e otimização de compostos líderes, contribuindo para a redução de custo, tempo e falhas no desenvolvimento de novos fármacos. As metodologias atuais de atracamento molecular e as funções de avaliação associadas possuem bom desempenho em identificar modos de ligação. Entretanto, a detecção de compostos ativos dentre inativos e a predição acurada da afinidade de ligação ainda são grandes desafios. O programa DockThor, desenvolvido pelo nosso grupo de pesquisa, tem obtido resultados promissores em estudos comparativos com outros programas de atracamento molecular bem estabelecidos e amplamente utilizados pela comunidade científica para a predição de modos de ligação. Apesar de ser bastante útil para predição de poses, a função de avaliação atualmente implementada no DockThor não é adequada para prever afinidade de complexos proteína-ligante, não obtendo correlação com dados experimentais. Neste trabalho, nós desenvolvemos diversas funções de avaliação com características baseadas na física para prever afinidade de ligação de complexos proteína-ligante, treinadas com diversas técnicas de aprendizagem de máquina. As funções de avaliação finais consistem de termos baseados em campo de força relacionados com as interações intermoleculares (potenciais eletrostático e de van der Waals), um termo original para a entropia do ligante (número de ligações rotacionáveis congeladas), dessolvatação do ligante e da proteína e o efeito hidrofóbico. Desenvolvemos então funções de avaliação gerais e específicas para classes de alvos, esta para considerar características específicas associadas com a classe de alvo de interesse, focando em proteases, cinases e complexos de interações proteína-proteína (PPIs). As funções de avaliação foram derivadas utilizando regressão linear (MLR) e sete outras técnicas mais avançadas de aprendizagem de máquina para problemas não lineares. O processo de treinamento e teste foi realizado utilizando conjuntos de dados de alta qualidade compostos de estruturas experimentais de diversos complexos proteína-ligante com dados de afinidade de ligação disponíveis (Kd ou Ki). Adicionalmente, também derivamos funções de avaliação gerais treinadas com resultados do atracamento molecular com o programa DockThor. As funções treinadas com resultados de atracamento obtiveram desempenho promissor quando avaliadas tanto em estruturas experimentais quanto provenientes de atracamento molecular, indicando que elas são confiáveis para serem usadas em experimentos reais de triagem virtual. As funções desenvolvidas neste trabalho demonstraram ser competitivas com as melhores funções de avaliação lineares e não lineares em estudos comparativos descritas na literatura. As funções específicas para classes de alvos também exibiram desempenhos promissores, alcançando significativa melhoria quando utilizando abordagens não lineares comparadas com os modelos lineares. Além disso, a estratégia de avaliação consenso investigada neste trabalho exibiu resultados impressionantes, ficando entre os três melhores modelos com melhores desempenhos preditivos em todos os casos. O desenvolvimento das funções de avaliação implementadas nesta tese é um passo crucial para tornar o programa DockThor ainda mais competitivo, possibilitando o desenvolvimento do programa e do portal de triagem virtual em larga escala DockThor-VS.
Matereke, Lavious Tapiwa. "Analysis of predictive power of binding affinity of PBM-derived sequences." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018666.
Повний текст джерелаYoldas, Mine. "Predicting The Effect Of Hydrophobicity Surface On Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613215/index.pdf.
Повний текст джерелаShoemake, Claire. "The use of static and dynamic models for the prediction of ligand binding affinity using oestrogen and androgen nuclear receptors as case studies." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.478985.
Повний текст джерелаAtkovska, Kalina, Sergey A. Samsonov, Maciej Paszkowski-Rogacz, and M. Teresa Pisabarro. "Multipose Binding in Molecular Docking." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-147177.
Повний текст джерелаNordesjö, Olle. "Searching for novel protein-protein specificities using a combined approach of sequence co-evolution and local structural equilibration." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-275040.
Повний текст джерелаPraig, Vera Gertraud. "Immobilised glutathione for affinity binding." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620379.
Повний текст джерелаKuzmich, Oleksandra. "Metal Labeling for Low Affinity Binding Biomolecules." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/18862.
Повний текст джерелаCapture compound mass spectrometry (CCMS) is a chemical proteomics technique that has the advantage of addressing low abundant target proteins in lysates as well as in living cells. The CCMS is based on small molecule probes (capture compounds) that consist of three functionalities: a small molecule (quite often it is a drug), which interacts with the target protein; the moiety that allows covalent attachment of the molecular probe to the protein; the one that allows detection. The detection moiety utilized for CCMS can offer high sensitivity; however, the challenge of absolute quantification is still a bottleneck of this technique. Metal Coded Affinity Tagging (MeCAT) is a quantitative approach based on the chemical labeling with lanthanide; it allows obtaining both the structural and quantitative information. In this work for the first time the successful utilization of chemoproteomic probes functionalized with a metal tag for the detection and absolute quantification of target proteins was established. With the experiments both on isolated enzymes and living cells it was determined that MeCAT does not negatively influence other functional parts of the probes; therefore, capture compounds functionalized with lanthanide chelates demonstrate similar affinity to the target as the reference probes. Moreover, metal tags utilized for this type of molecular probes can offer a promising elemental imaging technique. However, to achieve the sufficient resolution multiple metal tags per molecular probe are needed. The striking advantage of the approach of utilization metal functionalized capture compound combined with ICP-MS detection is that it allows absolute quantification of crosslink yield, what cannot be performed with other detection methods applied for this technology.
Mayes, Andrew Geoffrey. "Quantitative aspects of affinity adsorption." Thesis, University of Bath, 1992. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303403.
Повний текст джерелаLang, Birthe Agnetha. "Nanofibrous affinity membranes containing non-antibody binding proteins." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/15326/.
Повний текст джерелаReiersen, Herald. "Development of methods for modulating binding protein affinity." Thesis, University of Bath, 2000. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323721.
Повний текст джерелаEletr, Ziad Moustafa Kuhlman Brian A. "Determining and modulating E2-HECT binding affinity and specificity." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1365.
Повний текст джерелаTitle from electronic title page (viewed Apr. 25, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biochemistry and Biophysics Program in Molecular and Cellular Biophysics." Discipline: Biochemistry and Biophysics; Department/School: Medicine.
Donoghue, Gavin. "Structure and binding affinity in DNA minor groove binders." Thesis, University of Strathclyde, 2010. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=12768.
Повний текст джерелаLawden, Kim Hilary. "The design and synthesis of endotoxin-binding affinity ligands." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627354.
Повний текст джерелаLee, Hui-Chih 1963. "Theoretical and experimental studies of the plasma protein binding of high affinity binding drugs." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277955.
Повний текст джерелаPokhrel, Pujan. "Prediction of DNA-Binding Proteins and their Binding Sites." ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/honors_theses/114.
Повний текст джерелаSundaramoorthy, Meena. "MODULATION OF HIGH AFFINITY HORMONE BINDING TO LH/CG RECEPTOR." UKnowledge, 2002. http://uknowledge.uky.edu/gradschool_theses/209.
Повний текст джерелаAdes, Sarah Ellen. "The engrailed homeodomain : determinants of DNA-binding affinity and specificity." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/32174.
Повний текст джерелаEk, Moira. "Bacterial Display of a Tau-Binding Affibody Construct:Towards Affinity Maturation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278580.
Повний текст джерелаAggregering av mikrotubuli-associerat protein tau är involverad i patologin av flera neurodegenerativa sjukdomar, däribland Alzheimers sjukdom. Affibodymolekylen TP4 har visat sig inhibera denna aggregeringsprocess, och överföring av dess målbindande positioner till ett dimeriskt affibodyprotein har tidigare gett upphov till seqTP4, en så kallad sequestrin. Detta projekt utgör ett led i processen att affinitetsmaturera seqTP4, med hjälp av två olika metoder för presentation av proteiner på ytan av bakterieceller. Ett error-prone PCR-bibliotek uttrycktes först på ytan av Staphylococcus carnosus-celler för selektion av varianter med ökad affinitet för tau, vilket resulterade i ett bibliotek av 1.4×107 transformanter. Flödescytometriska tester tydde på svårigheter i detta upplägg på grund av ospecifika interaktioner, och emedan flera olika angreppssätt för att förmildra dessa problem undersöktes, misslyckades slutligen två cellsorteringsförsök. Omkloning av seqTP4 och biblioteket till en vektor för ytpresentation på Escherichia coli resulterade i funktionellt ytuttryck av seqTP4 på E. coli JK321- och BL21-celler, och ett BL21-bibliotek bestående av 1.6×109 transformanter. Ett första flödescytometriskt test av detta bibliotek tyder på närvaron av varianter med förbättrad förmåga att binda tau, och vägen ligger nu relativt öppen för cellsortering.
Jiang, Tian. "Drug affinity and binding site signatures in extrasynaptic GABAA receptors." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27104.
Повний текст джерелаNewell, John Glen. "Identification of a determinant of high affinity agonist binding of ã-aminobutyric acid type A receptors and the role of high affinity agonist binding in desensitization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0010/NQ59643.pdf.
Повний текст джерелаNilvebrant, Johan. "An albumin-binding domain as a scaffold for bispecific affinity proteins." Doctoral thesis, KTH, Proteomik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105425.
Повний текст джерелаQC 20121122
Tang, Wen. "Bioactive Surface-Targeting Modular Peptide-Dendrons: Synthesis, Binding Affinity and Applicaiton." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1406049913.
Повний текст джерелаPuckett, Nathan. "Effects of Binding Affinity between Bovine Serum Albumin and Platinum Drugs." TopSCHOLAR®, 2017. http://digitalcommons.wku.edu/theses/1977.
Повний текст джерелаChmura, A. J. "Rational engineering of antibodies with irreversible binding : antibodies with infinite affinity /." Connect to Digital dissertations. Restricted to UC campuses. Access is free to UC campus dissertations, 2001. http://uclibs.org/PID/11984.
Повний текст джерелаDegree granted in Chemistry. Dissertation completed in 2001; degree granted in 2002. Also available via the World Wide Web. (Restricted to UC campuses).
Lindgren, Joel. "Chemical Engineering of Small Affinity Proteins." Doctoral thesis, KTH, Proteinteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141014.
Повний текст джерелаQC 20140207
Brooks, Anthony J. "Computational prediction of HLA-DR binding peptides." Thesis, University of Aberdeen, 1999. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU118106.
Повний текст джерелаHuang, Bingding. "Improving protein docking with binding site prediction." Doctoral thesis, [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1216305428189-09951.
Повний текст джерелаMyers, Terence Anthony. "Assessment of chitosans as support matrices for dye-ligand affinity chromatography." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295399.
Повний текст джерела陳磊碩 and Lui-sek Chan. "Chemical modification of immunoglobulins and the effects on antigen binding site affinity." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B29913378.
Повний текст джерелаHoang, Vi K. B. "Binding Affinity and Antifungal Activity of Immune-Fusion Proteins against Candida albicans." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10825595.
Повний текст джерелаCandida albicans is a yeast-like fungal pathogen that can cause infections ranging from superficial to life-threatening systemic candidiasis. Current treatments for systemic candidiasis are available but often ineffective and toxic. Consequently, it is necessary to develop new therapeutic approaches. The purpose of this study was to construct antibody-based fusion proteins that can bind to C. albicans cells and eliminate them. Two such fusion proteins were constructed. Each one is composed of M1 Fab as the antibody component that binds to C. albicans mannan and the antifungal peptide HPRP-A1. HPRP-A1 was attached via a 15-amino acid linker to either the C-terminus of the constant light chain of M1 Fab (M1 Fab-HPRP-CL) or the N-terminus of the variable light chain of M1 Fab (M1 Fab-HPRP-VL). Binding of the fusion proteins to purified C. albicans mannan was assessed with enzyme-linked immunosorbent assay and the half maximal effective concentration (EC50) for each fusion protein was estimated. EC50 for M1 Fab-HPRP-CL was 273.6 compared to 74.1 for the original M1 Fab (p < 0.05), whereas M1 Fab-HPRP-VL did not show any binding activity, indicating a negative impact on the antibody binding by the linked peptide. Similarly, M1 Fab-HPRP-CL also showed reduced binding for C. albicans cells when compared to M1 Fab as determined with immunofluorescence microscopy and flow cytometry. The effect of M1 Fab-HPRP-CL on the growth of C. albicans cells was analysed using microdilution and absorbance. At 16 µM, the growth of yeast cells treated with M1 Fab-HPRP-CL was 47.1 % of the growth control, compared to 43.5 % for M1 Fab (p > 0.05) and to 1.9 % for HPRP-A1 by itself (p < 0.001). Moreover, HPRP-A1 killed C. albicans at 32 µM and 64 µM, while M1 Fab and M1 Fab-HPRP-CL did not, indicating a loss of the antifungal activity of HPRP-A1 when attached to the antibody. These data together provide valuable insights into the development of novel antibody-based therapeutics as an alternative treatment for candidiasis.
Chan, Lui-sek. "Chemical modification of immunoglobulins and the effects on antigen binding site affinity /." [Hong Kong] : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13731506.
Повний текст джерелаGuba, Nina Marie. "THE ANALYSIS OF EMDOGAIN BINDING AFFINITY FOR DIFFERENT PARTICULATE BONE GRAFT MATERIALS." Master's thesis, Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/506081.
Повний текст джерелаM.S.
Objectives: Traditional guided tissue regeneration procedures use particulate bone graft materials and occlusive membranes with the primary aim of reconstitution of the supporting periodontal tissues. Currently, the Food and Drug Administration has cleared only four treatment modalities for true periodontal regeneration. These materials are autogenous bone, demineralized freeze dried bone allograft, LANAP (Millennium Dental Technologies INC, Cerritos, CA) and Emdogain (Institut Straumann AG, Basel, Switzerland). The biologically inactive nature of many commercially available bone graft materials provides an opportunity for the addition of certain biologic materials to enhance the healing response. The development of an adequate carrier for biologic agents is a crucial step in the creation of a bioactive graft material. This experiment uses Emdogain (Institut Straumann AG, Basel, Switzerland) to study the specific characteristics of protein binding and release on three different commonl
Temple University--Theses
Poosapati, Anusha. "Disorder Levels of c-Myb Transactivation Domain Regulate its Binding Affinity to the KIX Domain of CREB Binding Protein." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7436.
Повний текст джерелаKarlsson, Mikael. "Determination of antibody affinity and kinetic binding constants in Gyrolab Bioaffy microfluidic CD." Thesis, Linköping University, The Department of Physics, Chemistry and Biology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11616.
Повний текст джерелаStudies of binding reactions are of highest importance in a vast number of areas of biomedicine and biotechnology. A demand for fast and accurate small-volume measurements grows stronger, partly due to the development of therapeutic antibodies. In this report, a novel method for studies of binding reactions of antibodies is described. The use of a microfluidic platform shows promising results in determination of affinity binding constants.
Affinities between 1E-09 and 1E-11 M have been determined for four TSH antibodies. Reproducibility tests give a CV below 10%, using different Gyrolab instruments and microfluidic CD:s. The method carries the advantages of using solution-based measurements of unmodified molecules. Also an initial proof-of-concept for measurement of binding reaction rate constants shows further usage of the method. The kinetic association rate constant has been determined to 2E+06 M-1s-1 for one antibody. The possibility of using this method for screening of antibody libraries is also discussed.
Naples, Mark. "Determinants of high affinity ligand binding to the group III metabotropic glutamate receptors." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63174.pdf.
Повний текст джерелаChattopadhyay, Madhuri. "Copper binding in the prion protein : coordination, affinity and effects on amyloid formation /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2006. http://uclibs.org/PID/11984.
Повний текст джерелаCook, Ian Haston. "Affinity isolation and characterisation of PtdIns(3,4,5)P3 binding proteins from brain tissue." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597915.
Повний текст джерелаChunsrivirot, Surasak. "Binding affinity of a small molecule to an amorphous polymer in a solvent." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65771.
Повний текст джерелаPage 169 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 165-168).
Crystallization is a commonly used purification process in industrial practice. It usually begins with heterogeneous nucleation on a foreign surface. The complicated mechanism of heterogeneous nucleation is not well understood, but we hypothesize a possible correlation between binding affinity to a surface and nucleation enhancement. Amorphous polymers have been used in controlling crystallization. However, to our knowledge no attempt has been made to investigate the possibility of using binding affinity to help guide the selection of polymers promoting heterogeneous nucleation. This study investigated the possibility of using binding affinity of one molecule and many molecules to help guide the selection of these polymers. To measure the binding affinity of one molecule, we developed a two-step approach to compute the free energy of binding to a binding site, using a system of ethylene glycol, polyvinyl alcohol (PVA), and heavy water (D20). The first step of our approach uses Adsorption Locator to identify probable binding sites and molecular dynamics to screen for the best binding sites. The second step employs the Blue-Moon Ensemble method to compute the free energy of binding. We then applied our procedure to the systems of aspirin binding on the surfaces of four nonporous crosslinked polymers in ethanol-water 38 v%. These polymers are poly(4- acryloylmorpholine) (PAM), poly(2-carboxyethyl acrylate) (PCEA), poly(4-hydroxylbutyl acrylate) (PHBA), and polystyrene (PS), and they all are crosslinked with divinylbenzene (DVB). We developed an approach to construct these crosslinked polymers and built three independent surfaces for each polymer. We found the similarity between the trend of heterogeneous nucleation activity and that of the average free energies of binding to the best site of each polymer surface. To measure the binding affinity of many molecules, preferential interaction coefficient and the number of aspirin molecules associated with the area of the binding site was calculated. We found that there is also a similarity between the trend of heterogeneous nucleation activity and that of number of aspirin molecules associated with the area of the binding site (taken into account the effects of polar/apolar atom interactions between an aspirin and a polymer). These results suggest the possibility of using binding affinity, especially the free energy of binding to the best site and the number of nucleating molecule, to help guide the selection of polymers promoting heterogeneous nucleation.
by Surasak Chunsrivirot.
Ph.D.
Hoffman, Crystal Joyce. "Glucocorticoid Receptor Density and Binding Affinity in Horses with Systemic Inflammatory Response Syndrome." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/48423.
Повний текст джерелаMaster of Science
Linhult, Martin. "Protein engineering to explore and improve affinity ligands." Doctoral thesis, KTH, Biotechnology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3632.
Повний текст джерелаIn order to produce predictable and robust systems forprotein purification and detection, well characterized, small,folded domains descending from bacterial receptors have beenused. These bacterial receptors, staphylococcal protein A (SPA)and streptococcal protein G (SPG), possess high affinity to IgGand / or HSA. They are composed of repetitive units in whicheach one binds the ligand independently. The domains foldindependently and are very stable. Since the domains also havewellknown three-dimensional structures and do not containcysteine residues, they are very suitable as frameworks forfurther protein engineering.
Streptococcal protein G (SPG) is a multidomain proteinpresent on the cell surface ofStreptococcus. X-ray crystallography has been used todetermine the binding site of the Ig-binding domain. In thisthesis the region responsible for the HSA affinity of ABD3 hasbeen determined by directed mutagenesis followed by functionaland structural analysis. The analysis shows that the HSAbindinginvolves residues mainly in the second α-helix.
Most protein-based affinity chromatography media are verysensitive towards alkaline treatment, which is the preferredmethod for regeneration and removal of contaminants from thepurification devices in industrial applications. Here, aprotein engineering strategy has been used to improve thetolerance to alkaline conditions of different domains fromprotein G, ABD3 and C2. Amino acids known to be susceptibletowards high pH were substituted for less alkali susceptibleresidues. The new, engineered variants of C2 and ABD shownhigher stability towards alkaline pH. Also, very important forthe potential use as affinity ligands, these mutated variantsretained the secondary structure and the affinity to HSA andIgG, respectively. Moreover, dimerization was performed toinvestigate whether a higher binding capacity could be obtainedby multivalency. For ABD, binding studies showed that divalentligands coupled using non-directed chemistry demonstrated anincreased molar binding capacity compared to monovalentligands. In contrast, equal molar binding capacities wereobserved for both types of ligands when using a directed ligandcoupling chemistry involving the introduction and recruitmentof a unique C-terminal cysteine residue.
The staphylococcal protein A-derived domain Z is also a wellknown and thoroughly characterized fusion partner widely usedin affinity chromatography systems. This domain is consideredto be relatively tolerant towards alkaline conditions.Nevertheless, it is desirable to further improve the stabilityin order to enable an SPA-based affinity medium to withstandeven longer exposure to the harsh conditions associated withcleaning in place (CIP) procedures. For this purpose adifferent protein engineering strategy was employed. Smallchanges in stability due to the mutations would be difficult toassess. Hence, in order to enable detection of improvementsregarding the alkaline resistance of the Z domain, a by-passmutagenesis strategy was utilized, where a mutated structurallydestabilized variant, Z(F30A) was used as a surrogateframework. All eight asparagines in the domain were exchangedone-by-one. The residues were all shown to have differentimpact on the alkaline tolerance of the domain. By exchangingasparagine 23 for a threonine we were able to remarkablyincrease the stability of the Z(F30A)-domain towards alkalineconditions. Also, when grafting the N23T mutation to the Zscaffold we were able to detect an increased tolerance towardsalkaline treatment compared to the native Z molecule. In allcases, the most sensitive asparagines were found to be locatedin the loops region.
In summary, the work presented in this thesis shows theusefulness of protein engineering strategies, both to explorethe importance of different amino acids regarding stability andfunctionality and to improve the characteristics of aprotein.
Keywords:binding, affinity, human serum albumin (HSA),albumin-binding domain (ABD), affinity chromatography,deamidation, protein A, stabilization, Z-domain, capacity,protein G, cleaning-in-place (CIP), protein engineering, C2receptor.
Zhao, Huiying. "Protein function prediction by integrating sequence, structure and binding affinity information." Thesis, 2014. http://hdl.handle.net/1805/3913.
Повний текст джерелаProteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.
Hou, Tien-Yi, and 侯天儀. "Prediction of Estrogen Receptor Alpha Binding Affinity by Pharmacophore Ensemble/Support Vector Machine." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/20144702474560764707.
Повний текст джерела國立東華大學
化學系
103
The estrogen receptor alpha (ERα) is one of estrogen receptor subtype which can be activated by the hormone estrogen to affect a variety of physiological functions such as growth of mammary glands, pubertal development, and reproductive behavior. Moreover, ERα is the main therapeutic target for treating ER positive breast cancer. The endocrine disrupting chemicals (EDCs) can disturb the endocrine system via receptors especially ERα. An in silico model was developed to predict the binding affinity of ERα using the pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from literatures. The prediction by the PhE/SVM model are in good agreement with the experimental observations for those molecules in the training set (n = 31, r2 = 0.80, q2 = 0.77, RMSE = 0.57, s = 0.58), the test set (n = 179, r2 = 0.95, RMSE = 0.33, s = 0.26), and the outlier set (n = 15, r2 = 0.82, RMSE = 0.56, s = 0.49). When subjected to a variety of statistical validations, the developed PhE/SVM model consistently met those stringent criteria. A mock test by marketed drug also asserted its predictivity. Thus, this PhE/SVM models is an accurate predictive tool to promote drug discovery for the treatment of ER positive breast cancer and to identify the potential EDCs of ERα.
Chang, Tien-Chu, and 張添嵀. "Improving Scoring Function Model for Predicting Protein-Ligand Binding Affinity." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/39955217161511685223.
Повний текст джерела國立臺灣大學
工程科學及海洋工程學研究所
99
Our study proposes a novel MIXScore, a scoring function which improves the prediction of protein-ligand binding affinities. The prediction is an important issue in structure-based drug discovery and design. Typically, scoring functions can be classified into three groups: force-field, knowledge-based, and empirical. Traditional validation methods such as 5-fold cross validation and Leave-One-Out cross validation (LOO) do not encounter over-fitting problem, but the assessments may be too optimistic because the complexes in the same protein families may be distributed in training set and testing set at the same time. Therefore, Kramer and Gedeck provided a special method called Leave-Cluster-Out cross validation (LCO) and recommended that LCO could avoid an overoptimistic bias. We combine hybridized orbital atom type pair descriptors and X-CSCORE descriptors which in the knowledge-based and empirical fields into a feature vector, totally 210 descriptors. Random forest regression is applied to build the predict model. The performance of MIXScore is evaluated by adopting PDBbind07 and PDBbind09 as benchmarks and compared with several existing scoring functions. PDBbind07 is used for independent test and PDBbind09 is used for LCO cross validation. The independent test shows that MIXScore is better than RF-Score published in 2010 (RMSE = 1.98kcal/mol and R2 = 0.691). In LCO cross validation, although the similarities between training and testing sets are excluded, MIXScore still provides stable predicting ability such that MIXScore outperforms RF-Score and the work proposed by Kramer and Gedeck. These results show that MIXScore is a competitive scoring function. MIXScore may also have good external predictability as the modified R2 (Rm2) is greater than 0.5 (0.530) in the independent test. This study not only improves the performance of predicting binding affinities but discovers the homogenous of proteins in PDBbind dataset will cause overoptimistic bias. The strongest outlier in PDBbind09 and the importance of each X-CSCORE descriptors are shown as well.
Chiang, Ming-Keng, and 江銘耿. "Predicting ABCG2 Inhibitor Binding Affinity Using Pharmacophore Ensemble/Support Vector Machine." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/23232490754898853810.
Повний текст джерела國立東華大學
化學系
99
ABCG2 (BCRP) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Because of this phenomenon which results in ABCG2 repels a variety of drugs and hence the resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. Finally, the effect of cure is hard to attain. Accordingly, using inhibitor revers function and expression of ABCG2 on a cancer cell is the method of solving ABCG2 extrusion drugs An in silico model was derived to predict the inhibition of ABCG2 the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model are in good agreement with the experimental observed values for those molecules in the training set (n = 28, r2 = 0.87, q2 = 0.83, RMSE = 0.52, s = 0.25), test set (n = 31, r2 = 0.87, RMSE = 0.34, s = 0.23) and outlier set (n = 9, r2 = 0.84, RMSE = 0.47, s = 0.27). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. Thus, it can be asserted that this PhE/SVM model is an accurate, fast and robust model and can be employed to predict ABCG2 inhibitor binding affinity to facilitate drug discovery and drug development.
Chao, Chien-Ho, and 趙健合. "Predicting Binding Affinity of Protein-DNA Interactions Using Machine Learning-based Scoring Functions." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/82595131989538619851.
Повний текст джерела國立臺灣大學
生物產業機電工程學研究所
99
Proteins and DNA play important roles to maintaining life in living cells. The binding of protein to specific DNA sequences is the beginning of lots of bio-activities. For instance, the binding of regulatory sites of DNA by transcription factors, which are a kind of proteins that trigger transcription of a particular gene, initiates the transcription process. Research on this issue could facilitate the studies of gene regulation and regulatory networks. For these reasons, the study of interactions between protein and DNA has attracted much attention for a long time. Recently, with the advances of computer technology and algorithm development, developing computational methods to predict binding affinity of protein-protein, protein-ligand and even protein-DNA interactions has been largely considered recently. Some of the scoring functions for predicting protein-ligand are shown to perform well on this challenge. In this thesis, a machine learning-based scoring function was developed to predict the binding affinity of protein-DNA interactions. For this purpose, a high-quality dataset containing the information of binding affinity associated with a protein-DNA complex was collected from PDBbind. The performance of the proposed method was compared with existing scoring functions, and it is concluded that the proposed machine learning-based scoring function perfrom well in predicting the binding affinities of protein-DNA complexes and can benefit future studies on this problem.