Dissertations / Theses: '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/.

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As we move towards an era of personalised medicine, the identification of lead compounds requires years of research and considerable financial backing, in the development of targeted therapies for cancer. We use molecular modelling and simulation to screen a library of active compounds, and understand the ligand-protein interaction at the molecular level in appropriate protein targets, in a bid to identify the most active lead drug candidates. In recent times, good progress has been made in accurately predicting binding affinities for drug candidates. Advances in high-performance computation (HPC), mean it is now possible to run a larger number of calculations in parallel, paving the way for multiple replica simulations from which binding affinities are obtained. This, then, allows for a tighter control of errors and in turn, a higher confidence in the binding affinity predictions. Here, we present ESMACS (Enhanced Sampling of Molecular dynamics with Approximation of Continuum Solvent) and TIES (Thermodynamic Integration with Enhanced Sampling); a new framework from which binding affinities are calculated. ESMACS performs 25 replica simulations of the same ligand-receptor system with the only difference being the initial momentum of each atom. From this ensemble of trajectories, an extended MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) free energy method is employed. The TIES protocol constitutes 5 replicas simulations per lambda state followed by the integration of the potential derivatives of each lambda state, generating a relative binding affinity. This is all tied together using the BAC (Binding Affinity Calculator) which automates the ESMACS and TIES workflow. ESMACS and TIES, given suitable access to HPC resources, can compute binding affinities in a matter of hours on a supercomputer; the size of such machines therefore means that we can reach the industrial scale of demand necessary to impact drug discovery programmes.

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Uslan, 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.

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Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. First, the support-vector based regression is used to identify initial parameter values of the consequent part of type-1 and interval type-2 fuzzy systems. Second, an overlapping clustering concept is used to derive interval valued parameters of the premise part of the type-2 fuzzy system. Publicly available peptide binding affinity data sets obtained from the literature are used in the experimental studies of this thesis. First, the proposed models are blind validated using the peptide binding affinity data sets obtained from a modelling competition. In that competition, almost an equal number of peptide sequences in the training and testing data sets (89, 76, 133 and 133 peptides for the training and 88, 76, 133 and 47 peptides for the testing) are provided to the participants. Each peptide in the data sets was represented by 643 bio-chemical descriptors assigned to each amino acid. Second, the proposed models are cross validated using mouse class I MHC alleles (H2-Db, H2-Kb and H2-Kk). H2-Db, H2-Kb, and H2-Kk consist of 65 nona-peptides, 62 octa-peptides, and 154 octa-peptides, respectively. Compared to the previously published results in the literature, the support vector-based type-1 and support vector-based interval type-2 fuzzy models yield an improvement in the prediction accuracy. The quantitative predictive performances have been improved as much as 33.6\% for the first group of data sets and 1.32\% for the second group of data sets. The proposed models not only improved the performance of the fuzzy system (which used support vector-based regression), but the support vector-based regression benefited from the fuzzy concept also. The results obtained here sets the platform for the presented models to be considered for other application domains in computational and/or systems biology. Apart from improving the prediction accuracy, this research study has also identified specific features which play a key role(s) in making reliable peptide binding affinity predictions. The amino acid features "Polarity", "Positive charge", "Hydrophobicity coefficient", and "Zimm-Bragg parameter" are considered as highly discriminating features in the peptide binding affinity data sets. This information can be valuable in the design of peptides with strong binding affinity to a MHC I molecule(s). This information may also be useful when designing drugs and vaccines.

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Bodnarchuk, 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/.

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In this thesis, various methods for locating and scoring the binding affinity of water molecules and molecular fragments in protein binding sites are described. The primary aim of this work is to understand how different methodologies compare to one another and how, by carefully choosing the correct method, they can be used to extract information on how small molecules interact with proteins. Three different methods are used to predict the location and affinity of water molecules; Just Add Water Molecules (JAWS), Grand Canonical Monte Carlo (GCMC) and double-decoupling. By applying the methods to the N9-Neuraminidase system, it can be shown that all of the methods predict the same binding free energy of the water molecules to within error. The JAWS method was shown to be advantageous for the rapid prediction of the binding free energy of water molecules, whilst GCMC was preferred for the prediction of hydration sites. The combination of the methods were used on a variety of novel test cases, including hydrophobic cavities and protein kinases. These test cases highlight how the methods can be used to accurately predict hydration patterns as a function of the binding free energy in GCMC simulations, and how these patterns can be used to dictate ligand design in a drug discovery context. The approaches described are likely to be of interest to the pharmaceutical industry. A JAWS based fragment based drug discovery methodology is also described, which takes into account key features commonly neglected by existing computational approaches such as fragment-solvent competition and fragment desolvation. This method is used upon the Kinesin Spindle Protein and factor Xa, and demonstrates that the method is able to accurately locate the position of molecular fragments and water molecules compared to crystallographic ligands.

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Erdas, 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.

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Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values of electrostatic potential. These values also provide features for prediction system. Support vector machines and partial least squares method are used for predicting binding affinity of compounds, and results are compared.

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Guedes, 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.

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Matereke, Lavious Tapiwa. "Analysis of predictive power of binding affinity of PBM-derived sequences." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018666.

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A transcription factor (TF) is a protein that binds to specific DNA sequences as part of the initiation stage of transcription. Various methods of finding these transcription factor binding sites (TFBS) have been developed. In vivo technologies analyze DNA binding regions known to have bound to a TF in a living cell. Most widely used in vivo methods at the moment are chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) and DNase I hypersensitive sites sequencing. In vitro methods derive TFBS based on experiments with TFs and DNA usually in artificial settings or computationally. An example is the Protein Binding Microarray which uses artificially constructed DNA sequences to determine the short sequences that are most likely to bind to a TF. The major drawback of this approach is that binding of TFs in vivo is also dependent on other factors such as chromatin accessibility and the presence of cofactors. Therefore TFBS derived from the PBM technique might not resemble the true DNA binding sequences. In this work, we use PBM data from the UniPROBE motif database, ChIP-seq data and DNase I hypersensitive sites data. Using the Spearman’s rank correlation and area under receiver operating characteristic curve, we compare the enrichment scores which the PBM approach assigns to its identified sequences and the frequency of these sequences in likely binding regions and the human genome as a whole. We also use central motif enrichment analysis (CentriMo) to compare the enrichment of UniPROBE motifs with in vivo derived motifs (from the JASPAR CORE database) in their respective TF ChIP-seq peak region. CentriMo is applied to 14 TF ChIP-seq peak regions from different cell lines. We aim to establish if there is a relationship between the occurrences of UniPROBE 8-mer patterns in likely binding regions and their enrichment score and how well the in vitro derived motifs match in vivo binding specificity. We did not come out with a particular trend showing failure of the PBM approach to predict in vivo binding specificity. Our results show Ets1, Hnf4a and Tcf3 show prediction failure by the PBM technique in terms of our Spearman’s rank correlation for ChIP-seq data and central motif enrichment analysis. However, the PBM technique also matched the in vivo binding specificities of FoxA2, Pou2f2 and Mafk. Failure of the PBM approach was found to be a result of variability in the TF’s binding specificity, the presence of cofactors, narrow binding specificity and the presence ubiquitous binding patterns.

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Yoldas, 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.

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This study aims to predict the binding affinity of the PCP-like compounds by means of molecular hydrophobicity. Molecular hydrophobicity is an important property which affects the binding affinity of molecules. The values of molecular hydrophobicity of molecules are obtained on three-dimensional coordinate system. Our aim is to reduce the number of points on the hydrophobicity surface of the molecules. This is modeled by using self organizing maps (SOM) and k-means clustering. The feature sets obtained from SOM and k-means clustering are used in order to predict binding affinity of molecules individually. Support vector regression and partial least squares regression are used for prediction.

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Shoemake, 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.

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Atkovska, 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.

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Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.

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Nordesjö, 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.

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Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional mutants and demonstrate a ~30 fold improvement in initial positive predictive value compared with choosing randomly from a sequence space of 160 000 variants in the top 20 mutants. We also demonstrate qualitative differences in the predictions of the two scores, primarily a tendency for the coevolutionary score to miss out on one class of functional mutants with enriched frequency of the amino acid threonine in one position.

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Praig, Vera Gertraud. "Immobilised glutathione for affinity binding." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620379.

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Kuzmich, Oleksandra. "Metal Labeling for Low Affinity Binding Biomolecules." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/18862.

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Unter den Techniken der chemischen Proteomik hat Capture Compound – Massenspektrometrie (CCMS) den Vorteil, Interaktionen von Molekülen mit geringer Affinität zueinander effektiv untersuchen zu können. CCMS beruht auf kleinen molekularen Sonden (Capture Compounds, CCs), die aus drei funktionalen Bestandteilen bestehen: die Selektivitätsfunktion, ist ein kleines Molekül, das mit einem Zielprotein eine schwache Wechselwirkung eingeht. Die zweite Funktionalität erlaubt kovalente Anhaftung der molekularen Sonde an Proteine. Der dritte Anteil erlaubt Detektion mit sehr guten Sensitivität; allerdings ist die Quantifizierung weiterhin ein Schwachpunkt dieser Technik. Ziel dieses Projektes ist, eine in CCMS verwendbare Quantifizierungsmethode zu entwickeln. Heutzutage gibt es zahlreiche MS-basierte Quantifizierungsstrategien; unsere beruht auf der Einführung von Lanthanoid-haltigen Labels – Metal Coded Affinity Tagging (MeCAT). In dieser Arbeit wurde erstmalig die erfolgreiche Verwendung mit Metall- Markern chemoproteomischer Sonden (CCs) zur Detektion und absoluten Quantifizierung von Zielproteinen mit schwacher Wechselwirkung etabliert. Mit den Experimenten an isolierten Enzymen und an lebenden Zellen wurde nachgewiesen, dass Metall-Marker keinen negativen Einfluss auf andere funktionelle Teile chemoproteomischer Sonden haben. CCs, die mit Lanthanoid-Chelaten funktionalisiert sind, zeigen ähnliche Affinität zu ihren Zielproteinen wie die Referenz-Sonden. Zudem erlauben Metall-Marker, die für diese Art molekularer Sonden verwendet werden, die Entwicklung einer element-basierten Technik zur Bilderzeugung. Der herausragende Vorteil der Metall-funktionalisierten CCs kombiniert mit ICP-MS ist, dass diese eine absolute Quantifizierung der Ausbeute der Quervernetzungen ermöglichen.
Capture 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.

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Mayes, Andrew Geoffrey. "Quantitative aspects of affinity adsorption." Thesis, University of Bath, 1992. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303403.

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Lang, Birthe Agnetha. "Nanofibrous affinity membranes containing non-antibody binding proteins." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/15326/.

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The specific removal of molecules from various media is an area receiving increasing attention. Affnity membranes, i.e. membranes containing ligands, which can specifically capture target molecules, can meet this demand. One important area, in which the use of affinity membranes will be beneficial, is blood filtration, specifically haemodialysis treatments. The specific removal of toxins can reduce treatment time and/or frequency and therefore increase patients' quality of life as well as reduce costs for the health care sector. The presented research investigates the feasibility of combining a new class of nonantibody binding proteins (AdhironTM binders), which can be specifically designed to capture target molecules, with electrospun nanofibrous polysulphone (PSu) membranes to create an affinity membrane for the specific removal of target molecules. Adhiron binders against a model target protein (modified green fluorescent protein (mGFP)) were successfully produced and characterised. Suitable parameters for the electrospinning of PSu into smooth bead-free fibres were identified. Two different approaches for the functionalisation of PSu fibres were evaluated: incorporation of the Adhiron binders within the fibre and attachment of the binders to the functionalised PSu fibre surfaces. With the latter approach functionalisation was achieved by means of attaching Adhiron binders to surface functionalised fibres, on which the Adhiron binders were immobilised via a biotin-streptavidin bridge. The functionalised membrane specifically removed target molecules out of simple and complex solutions.

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Reiersen, 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.

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Eletr, 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.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.
Title 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.

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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.

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Lawden, 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.

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Lee, 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.

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A disadvantage of traditional equilibrium dialysis for highly protein bound drugs is the analytically low drug concentration found on the buffer side. We propose to replace a certain percentage of buffer with plasma containing drug in order to increase the total drug concentration on the buffer side. Computer simulations were performed to examine the effects of the percentage of plasma replacement of the buffer upon the increase of the total drug concentration on the buffer side after equilibrium dialysis. Further simulation results indicated that the development of a concise equation estimating the drug's equilibrium association binding constant (Ka) was feasible. Two high binding drugs, diazepam and nortriptyline, were examined to verify the advantages offered by the new proposed method and their Ka values were computed using the experimental results and the mathematical equation developed. The resulting data agreed well with theoretical predictions.

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Pokhrel, Pujan. "Prediction of DNA-Binding Proteins and their Binding Sites." ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/honors_theses/114.

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DNA-binding proteins play an important role in various essential biological processes such as DNA replication, recombination, repair, gene transcription, and expression. The identification of DNA-binding proteins and the residues involved in the contacts is important for understanding the DNA-binding mechanism in proteins. Moreover, it has been reported in the literature that the mutations of some DNA-binding residues on proteins are associated with some diseases. The identification of these proteins and their binding mechanism generally require experimental techniques, which makes large scale study extremely difficult. Thus, the prediction of DNA-binding proteins and their binding sites from sequences alone is one of the most challenging problems in the field of genome annotation. Since the start of the human genome project, many attempts have been made to solve the problem with different approaches, but the accuracy of these methods is still not suitable to do large scale annotation of proteins. Rather than relying solely on the existing machine learning techniques, I sought to combine those using novel “stacking technique” and used the problem-specific architectures to solve the problem with better accuracy than the existing methods. This thesis presents a possible solution to the DNA-binding proteins prediction problem which performs better than the state-of-the-art approaches.

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Sundaramoorthy, Meena. "MODULATION OF HIGH AFFINITY HORMONE BINDING TO LH/CG RECEPTOR." UKnowledge, 2002. http://uknowledge.uky.edu/gradschool_theses/209.

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Precise control of physiological phenomena is performed by various kinds of receptormediated signaling. The vast majority of receptors belong to the superfamily of G proteincoupled receptors (GPCRs), which form one of the largest protein families. In theclassical model of GPCR signaling, stimulation of seven transmembrane spanning GPCRleads to the activation of heterotrimeric G proteins, which dissociate into a and bgsubunits. The subunits activate effector molecules, which include second messengergenerating systems, giving rise to various kinds of cellular responses. The LH/CGreceptor is a member of the glycoprotein hormone receptor family along with the FSHand TSH receptors, which belongs to the GPCR superfamily. Human chorionicgonadotropin (hCG) binds to the exodomain of LH/CG receptor and the resulting hCGexodomaincomplex is thought to interact with the endodomain of the receptor to bringabout hormone signal.Unfortunately, little evidence is available for the precise hormonecontact points in the exo domain and endo domain of the receptor. The affinity ofhormone binding to the exodomain was enhanced when the endodomain was truncated.This suggests that the endodomain modulates the hormone binding to the exodomain ofthe receptor. To understand this, the role of exoloop 2 on the modulation of high affinityhormone binding to the exodomain was studied using photoaffinity labeling technique.

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22

Ades, 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.

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23

Ek, 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.

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Aggregation of microtubule-associated protein tau is involved in the pathology of several neurodegenerative diseases, including Alzheimer’s disease. The affibody TP4 has been shown to inhibit this aggregation process, and its target-binding positions were previously grafted onto a dimeric affibody scaffold, creating the sequestrin seqTP4. This project constitutes a part of the affinity maturation process of seqTP4, using two different bacterial display methods. An error-prone PCR library was first expressed on Staphylococcus carnosus cells for selection of variants with improved tau-binding properties, resulting in a library of 1.4×107 transformants. Flow cytometric tests indicated difficulties in the setup due to nonspecific interactions, and whereas several different approaches to alleviate the problems were investigated, two cell sorting attempts were ultimately unsuccessful. Subcloning of seqTP4 and the library to an Escherichia coli surface display vector resulted in functional surface expression of seqTP4 on E. coli JK321 and BL21 cells, and a BL21 library size of 1.6×109 transformants. An initial flow cytometric test of this library indicates the presence of improved tau-binding variants, paving the way for future cell sorting.
Aggregering 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.

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24

Jiang, Tian. "Drug affinity and binding site signatures in extrasynaptic GABAA receptors." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27104.

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GABAA receptors are ligand-gated ion channels that play vital roles in the central nervous system due to their widespread distribution and involvement in vital biochemical process. GABAA receptors that express extrasynaptically are suggested as important targets for treating disorders such as epilepsy, sleep disturbances, stress, and mood disorders. Various pharmaceutical campaigns have succeeded in developing pharmacologically and clinically important drugs for the active sites of GABAA receptors. However, as the drugs do not exclusively bind to the targeted subtypes, they are usually associated with severe side effects. Therefore, it is of great importance to explore binding pockets on extrasynaptic GABAA receptors that have the potential to be targets for subtype-selective drugs that exclusively work on extrasynaptic receptors. GABAA receptors are assembled from five subunits of four different types, with multiple promising subunit compositions. This results in many different subunit interfaces and binding sites in between subunits. In the present study, possible drug binding pockets on GABAA receptors were mapped and compared, both sequentially and structurally. The neurosteroid and general anesthetic pockets on the α4β3δ GABAA receptor were identified to be more likely targets than other pockets for the development of extrasynaptic-selective drugs. The binding sites on the homology models of GABAA receptors in the active conformation were used in the analysis throughout the thesis, as the determined sites are for positive modulators. A novel targeted molecular dynamic method was co-developed here for simulating the activation pathway of the α4β3δ GABAA receptor following the path of α1 glycine receptors, as no active conformation has been released for GABAA receptors. During protein activation an increase in the druggability of the receptor was observed, as well as movement correlations in the two determined binding sites. To stabilise the structure of α4β3δ GABAA receptor in the open conformation before investigating the binding sites in the simulation, two equilibration methods were revised and compared here. One of the methods was then chosen to equilibrate the receptor in the active state in the molecular dynamics simulation, as it performed better to keep the protein channel ion-permeable and keep the two determined binding sites stable within the simulation. Significantly, a tilting caused by H-bonds between the β3 and δ subunit was observed during the simulations after equilibration by both methods, which could affect the drug binding to δ-containing GABAA receptors. Finally, promising sites and binding modes for THDOC and DS2 on α4β3δ GABAA receptor were investigated using molecular docking and molecular dynamics simulations. Residues that form stable contacts with ligands were identified, which offer mutation targets for further confirmation of binding sites. The δN318 residue is suggested here as the key residue that contributes to the favorable binding energy of THDOC on δ+β3– interface, which agrees with the experimental result that THDOC shows a dramatically higher modulation effect on δ-containing receptors than those without the δ subunit. This offers the opportunity to develop δ-selective drugs based on the molecular structure of THDOC. Together these results provide important new information about drug binding sites in GABAA receptors for developing extrasynaptic-selective drugs for treating epilepsy, sleep disturbances, stress, and mood disorders with minimum side effects. The contributions including information about the binding pockets and unique residues that are potential targets for designing extrasynaptic-selective drugs was identified. The promising binding pockets and binding modes for two drug molecules – THDOC and DS2 was also investigated. Furthermore, novel methodologies have been provided in this thesis for investigating drug binding sites in different conformations and for exploring the activation pathway of GABAA receptors. These methodologies could further be used as tools for understanding the selectivity and druggability of binding sites, simulating the conformational transition pathway, and exploring the subtype-selective drugs on other proteins.

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25

Newell, 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.

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26

Nilvebrant, 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.

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Protein engineering and in vitro selection systems are powerful methods to generate binding proteins. In nature, antibodies are the primary affinity proteins and their usefulness has led to a widespread use both in basic and applied research. By means of combinatorial protein engineering and protein library technology, smaller antibody fragments or alternative non-immunoglobulin protein scaffolds can be engineered for various functions based on molecular recognition. In this thesis, a 46 amino acid small albumin-binding domain derived from streptococcal protein G was evaluated as a scaffold for the generation of affinity proteins. Using protein engineering, the albumin binding has been complemented with a new binding interface localized to the opposite surface of this three-helical bundle domain. By using in vitro selection from a combinatorial library, bispecific protein domains with ability to recognize several different target proteins were generated. In paper I, a bispecific albumin-binding domain was selected by phage display and utilized as a purification tag for highly efficient affinity purification of fusion proteins. The results in paper II show how protein engineering, in vitro display and multi-parameter fluorescence-activated cell sorting can be used to accomplish the challenging task of incorporating two high affinity binding-sites, for albumin and tumor necrosis factor-alpha, into this new bispecific protein scaffold. Moreover, the native ability of this domain to bind serum albumin provides a useful characteristic that can be used to extend the plasma half-lives of proteins fused to it or potentially of the domain itself. When combined with a second targeting ability, a new molecular format with potential use in therapeutic applications is provided. The engineered binding proteins generated against the epidermal growth factor receptors 2 and 3 in papers III and IV are aimed in this direction. Over-expression of these receptors is associated with the development and progression of various cancers, and both are well-validated targets for therapy. Small bispecific binding proteins based on the albumin-binding domain could potentially contribute to this field. The new alternative protein scaffold described in this thesis is one of the smallest structured affinity proteins reported. The bispecific nature, with an inherent ability of the same domain to bind to serum albumin, is unique for this scaffold. These non-immunoglobulin binding proteins may provide several advantages as compared to antibodies in several applications, particularly when a small size and an extended half-life are of key importance.

QC 20121122

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27

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.

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28

Puckett, Nathan. "Effects of Binding Affinity between Bovine Serum Albumin and Platinum Drugs." TopSCHOLAR®, 2017. http://digitalcommons.wku.edu/theses/1977.

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Platinum complex drugs such as cisplatin have been used as highly successful chemotherapy drugs since the 1970s. We are interested in how the ligands attached to cisplatin analogs influences their reactivity with biologically relevant targets along with time and amount. For this study, reactions were conducted to determine the reactivity between different platinum compounds and the protein bovine serum albumin. Various platinum compounds with different ligands were reacted in varying amounts with albumin in ammonium acetate buffer for either 1 hour, 4 hours, or 24 hours. Each reaction was quenched after the designated reaction time by dialysis and the platinum bound to the protein was determined by use of ICP. LC-MS was used to find exact peptide residues platinum complexes prefer to bind with but was found to be ineffective. Results show that time has a more significant affect on binding over amount of platinum present. In respect to changing the leaving or carrier ligands on the platinum complex, these changes on the complex did not affect binding significantly with bovine serum albumin. Triamine platinum complexes also seem to bind significantly more than diamine platinum complexes along with anionic form platinum complexes binding significantly better than the cationic form platinum complexes.

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29

Chmura, 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.

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Thesis (Ph. D.)--University of California, Davis, 2002.
Degree granted in Chemistry. Dissertation completed in 2001; degree granted in 2002. Also available via the World Wide Web. (Restricted to UC campuses).

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30

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.

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Small robust affinity proteins have shown great potential for use in therapy, in vivo diagnostics, and various biotechnological applications. However, the affinity proteins often need to be modified or functionalized to be successful in many of these applications. The use of chemical synthesis for the production of the proteins can allow for site-directed functionalization not achievable by recombinant routes, including incorporation of unnatural building blocks. This thesis focuses on chemical engineering of Affibody molecules and an albumin binding domain (ABD), which both are three-helix bundle proteins of 58 and 46 amino acids, respectively, possible to synthesize using solid phase peptide synthesis (SPPS). In the first project, an alternative synthetic route for Affibody molecules using a fragment condensation approach was investigated. This was achieved by using native chemical ligation (NCL) for the condensation reaction, yielding a native peptide bond at the site of ligation. The constant third helix of Affibody molecules enables a combinatorial approach for the preparation of a panel of different Affibody molecules, demonstrated by the synthesis of three different Affibody molecules using the same helix 3 (paper I). In the next two projects, an Affibody molecule targeting the amyloid-beta peptide, involved in Alzheimer’s disease, was engineered. Initially the N-terminus of the Affibody molecule was shortened resulting in a considerably higher synthetic yield and higher binding affinity to the target peptide (paper II). This improved variant of the Affibody molecule was then further engineered in the next project, where a fluorescently silent variant was developed and successfully used as a tool to lock the amyloid-beta peptide in a β-hairpin conformation during studies of copper binding using fluorescence spectroscopy (paper III). In the last two projects, synthetic variants of ABD, interesting for use as in vivo half-life extending partners to therapeutic proteins, were engineered. In the first project the possibility to covalently link a bioactive peptide, GLP-1, to the domain was investigated. This was achieved by site-specific thioether bridge-mediated cross-linking of the molecules via a polyethylene glycol (PEG)-based spacer. The conjugate showed retained high binding affinity to human serum albumin (HSA) and a biological activity comparable to a reference GLP-1 peptide (paper IV). In the last project, the possibility to increase the proteolytic stability of ABD through intramolecular cross-linking, to facilitate its use in e.g. oral drug delivery applications, was investigated. A tethered variant of ABD showed increased thermal stability and a considerably higher proteolytic stability towards pepsin, trypsin and chymotrypsin, three important proteases found in the gastrointestinal (GI) tract (paper V). Taken together, the work presented in this thesis illustrates the potential of using chemical synthesis approaches in protein engineering.

QC 20140207

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31

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.

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The experimental determination of peptide binding affinity to Major Histocompatibility Complex (MHC) molecules yields information which is often important to our understanding of the mechanics of autoimmunity, a widely occurring phenomenon giving rise to extensive pathological changes in many disease conditions. However, the most common biochemical technique by which this information is obtained (Competitive binding assaying) is a time consuming and costly procedure. Consequently, many groups have been using computational methods to try and predict which peptides are likely to bind to MHC molecules, and so replace the experimental approach with the cheaper and faster computational alternative. However, whilst some of the systems produced over the last decade have attained reasonably high levels of accuracy, they have all suffered from debilitating limitations which have hampered their widespread use in one way or another. Most commonly, the systems are not only restricted to predicting peptide binding in the context of a single MHC allele, but they also require large volumes of experimentally determined peptide binding data for use during their calibration, or 'training'. Presented here is a system which focuses on predicting peptides which are likely to bind to members of HLA-DR, a large and commonly occurring subset of (class II) MHC molecules which are expressed in humans. The system is based upon a unique three dimensional structural modelling technique which (a) is able to produce peptide binding predictions with comparable accuracy to the current 'state-of-the-art', yet (b) only requires a fraction of the training data, and (c) when trained, is not specific to any single allele, but applicable to any member of the HLA-DR family.

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32

Huang, 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.

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33

Myers, 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.

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34

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.

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35

Hoang, 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.

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Candida 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.

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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.

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37

Guba, 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.

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Oral Biology
M.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

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38

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.

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Intrinsically disordered proteins (IDPs) do not form stable tertiary structures like their ordered partners. They exist as heterogeneous ensembles that fluctuate over a time scale. Intrinsically disordered regions and proteins are found across different phyla and exert crucial biological functions. They exhibit transient secondary structures in their free state and become folded upon binding to their protein partners via a mechanism called coupled folding and binding. Some IDPs form alpha helices when bound to their protein partners. We observed a set of cancer associated IDPs where the helical binding segments of IDPs are flanked by prolines on both the sides. Helix-breaking prolines are frequently found in IDPs flanking the binding segment and are evolutionarily conserved across phyla. Two studies have shown that helix flanking prolines modulate the function of IDPs by regulating the levels of disorder in their free state and in turn regulating the binding affinities to their partners. We aimed to study if this is a common phenomenon in IDPs that exhibit similar pattern in the conservation of helix flanking prolines. We chose to test the hypothesis in c-Myb-KIX : IDP-target system in which the disordered protein exhibits high residual helicity levels in its free state. c-Myb is a hematopoietic regulator that plays a crucial role in cancer by binding to the KIX domain of CBP. Studying the functional regulation of c-Myb by modulating the disorder levels in c-Myb and in IDPs in general provides a better understanding of the way IDPs function and can be used in therapeutic strategies as IDPs are known to be involved in regulating various cellular processes and diseases. To study the effect of conserved helix flanking prolines on the residual helicity levels of c-Myb and its binding affinity to the KIX domain of CBP, we mutated the prolines to alanines. Mutating prolines to alanines increased the helicity levels of c-Myb in its free state. This small increase in the helicity levels of a highly helical c-Myb showed almost no effect on the binding affinity between cMyb and KIX. We hypothesized that there is a helical threshold for coupled folding and binding beyond which helicity levels of the free state IDP have no effect on its binding to their ordered protein partner. To test this hypothesis, we mutated solvent exposed amino acid residues in c-Myb that reduce its overall helicity and studied its effect on the binding affinity between c-Myb and KIX. Over a broad range of reduction in helicity levels of the free state did not show an effect on the binding affinity but beyond a certain level, decrease in helicity levels showed pronounced effects on the binding affinity between c-Myb and KIX.

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39

Karlsson, 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.

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Studies 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.

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40

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.

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41

Chattopadhyay, 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.

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42

Cook, 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.

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The aim of this project was to identify proteins capable of specifically binding PtdIns(3,4,5)P₃ in the brain and then to establish the cellular significance of their ability to respond to P13K signalling. Potential phosphoinositide binding proteins from mouse brain cytosolic fractions, were captured using matrices carrying the tethered homologs of PtdIns(3,4,5)P₃. Individual PtdIns(3,4,5)P₃ bead binding assays enabled mass spectrometric identification of over 100 proteins. They included several proteins with established phosphoinositide binding domains and many proteins with no reported phosphoinositide binding properties. Ten proteins or domains were selected for further analysis using recombinant protein based in vitro lipid binding assays. Four were proven to bind a range of phosphoinositides. These were the isolated PH domain from PLC-12 which preferentially bound PtdIns(4,5)P₂over PtdIns(3,4,5)P₃and PtdIns(4,5)P₂ equally, the ENTH domain of Epsin2 which preferentially bound PtdIns(3,4)P₂ over PtdIns(3,4,5)P₃over PtdIns(4,5)P₂ and the Cullin homology domain from Cullin 4b which preferentially bound PtdIns(3,4,5)P₃over PtdIns(4,5)P₂. As Cullin 4b had a high specificity for PtdIns(3,4,5)P₃, a novel observation, the Cullin family was studied in greater detail. Cullin proteins are core components of E3 ubiquitin ligases. All of the Cullins, both full length and isolated Cullin homology domains bound to PtdIns(3,4,5)P₃beads but different family members showed different phosphoinositide specificity. Precedent has shown that a large subset, but not all, PI3K-effectors translocate in response to activation of class I PI3K. Hence we sought to find evidence for a physiological role for PI3K activity in the regulation of Cullins by studying their cellular distribution. This work provides evidence that some Cullins translocate only weakly in response to PI3K activation.

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43

Chunsrivirot, 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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2011.
Page 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.

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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.

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There were three objectives of this study. The first was to determine if commercially available fluorochromes could be used to determine the glucocorticoid receptor (GR) density and binding affinity (BA) in equine peripheral blood mononuclear cells. The second was to determine if there was a correlation between elevated plasma cortisol and GR density or binding affinity in healthy adult horses. The third objective was to evaluate the HPA axis in adult horses presenting with systemic inflammatory response syndrome (SIRS), and to determine where any alterations in HPA axis function occur in these patients compared to healthy adults. For the first part of the study, peripheral venous blood was collected from 3 healthy research horses on 3 days. Peripheral blood mononuclear cells were isolated using Ficoll gradient centrifugation. Phycoerythrin (PE)-CD44 was then used to extracellularly label leukocytes, and then an intracellular GR antibody was used to determine a baseline measurement of GR density and fluorescein isothiocyanate (FITC)-dexamethasone was used to determine binding affinity via flow cytometric analysis. Comparison of control samples to those for CD44, GR density, and GR binding affinity showed a statistically significant difference for all samples (P<0.0001, P<0.0001, and P<0.0001 respectively). This showed that the CD44, GR antibody, and FITC-dexamethasone could successfully be used to analyze equine peripheral blood mononuclear cells for GR activity. For the second part of the study, an ACTH stimulation test was performed on 8 healthy horses in order to induce an increase in endogenous cortisol production. Plasma cortisol levels, GR density, and GR binding affinity were measured at baseline, 4, 8, and 24 hours after treatment. Median basal cortisol concentration was 4.9, range 3.2-6.1 μg/dl. This initially increased following ACTH stimulation to 5.6, range 4.8-7.4 μg/dl, then showed a significant decrease by 8 hours post ACTH administration to 1.4, range 1.1-2.7 μg/dl (P=0.0221). No correlation was observed between plasma cortisol concentration in healthy horses and GR density or binding affinity (r=-0.145, P=0.428 and r=0.046, P=0.802, respectively). For the third phase of the study, horses (N=10) with systemic inflammatory response syndrome (SIRS) were compared to healthy, age and sex matched controls (N=10) presenting for lameness evaluation or ophthalmologic examination. Blood was collected from SIRS cases and controls on presentation to the Equine Medical Center. A CBC, serum biochemistry, and serum ACTH and cortisol measurements were performed. GR density and binding affinity were also determined. Nonsurvivors had a significantly decreased GR binding affinity (P=0.008) and demonstrated a trend towards an increase in the ACTH:cortisol ratio. ROC analysis was performed for serum ACTH and cortisol concentrations, the ACTH:cortisol ratio, GR density and GR binding affinity, and triglycerides to determine cut-off values associated with nonsurvival. These were then used to analyze this population using Fischer's exact test to determine the odds ratio (OR) associated with nonsurvival for each variable. This revealed that a serum triglyceride concentration greater than 28.5 mg/dl was associated with nonsurvival (OR=117, 95% CI, 1.94-7060). The other variables were not found to be significantly associated with nonsurvival, although a Delta BA% of less than 35.79% was found to be closely associated with nonsurvival (OR=30.33, 95% CI, 0.96-960.5). Additionally, a significant negative correlation was detected between the plasma ACTH concentration and Delta BA% (r=-0.685, P=0.029) and the ACTH:cortisol ratio and the Delta BA% (r=-0.697, P=0.025). This study showed that nonsurviving horses with SIRS had a significantly decreased GR binding affinity compared to survivors, and a tendency toward an increase in their ACTH:cortisol ratios. This confirms that HPA axis dysfunction occurs in adult horses with SIRS as tissue resistance to glucocorticoids, and potentially relative adrenal insufficiency as well. These results suggest that there are horses with SIRS that might benefit from "physiologic" doses of synthetic glucocorticoids to complement their relative adrenal insufficiency in addition to their poor tissue sensitivity. Further research should focus on methods to more rapidly determine which horses might benefit from treatment with glucocorticoids on presentation, as well as to more accurately determine prognosis for survival.
Master of Science

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45

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.

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In 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.

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46

Zhao, Huiying. "Protein function prediction by integrating sequence, structure and binding affinity information." Thesis, 2014. http://hdl.handle.net/1805/3913.

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Indiana University-Purdue University Indianapolis (IUPUI)
Proteins 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.

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47

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.

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碩士
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.

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48

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.

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碩士
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.

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49

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.

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碩士
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.

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50

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.

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碩士
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.

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