Relevant bibliographies by topics / Protein binding – Mathematical models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Protein binding – Mathematical models'

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Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Protein binding – Mathematical models"

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Palacio-Castañeda, Valentina, Simon Dumas, Philipp Albrecht, Thijmen J. Wijgers, Stéphanie Descroix, and Wouter P. R. Verdurmen. "A Hybrid In Silico and Tumor-on-a-Chip Approach to Model Targeted Protein Behavior in 3D Microenvironments." Cancers 13, no. 10 (May 18, 2021): 2461. http://dx.doi.org/10.3390/cancers13102461.

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To rationally improve targeted drug delivery to tumor cells, new methods combining in silico and physiologically relevant in vitro models are needed. This study combines mathematical modeling with 3D in vitro co-culture models to study the delivery of engineered proteins, called designed ankyrin repeat proteins (DARPins), in biomimetic tumor microenvironments containing fibroblasts and tumor cells overexpressing epithelial cell adhesion molecule (EpCAM) or human epithelial growth factor receptor (HER2). In multicellular tumor spheroids, we observed strong binding-site barriers in combination with low apparent diffusion coefficients of 1 µm2·s−1 and 2 µm2 ·s−1 for EpCAM- and HER2-binding DARPin, respectively. Contrasting this, in a tumor-on-a-chip model for investigating delivery in real-time, transport was characterized by hindered diffusion as a consequence of the lower local tumor cell density. Finally, simulations of the diffusion of an EpCAM-targeting DARPin fused to a fragment of Pseudomonas aeruginosa exotoxin A, which specifically kills tumor cells while leaving fibroblasts untouched, correctly predicted the need for concentrations of 10 nM or higher for extensive tumor cell killing on-chip, whereas in 2D models picomolar concentrations were sufficient. These results illustrate the power of combining in vitro models with mathematical modeling to study and predict the protein activity in complex 3D models.
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Middendorf, Thomas R., and Richard W. Aldrich. "Structural identifiability of equilibrium ligand-binding parameters." Journal of General Physiology 149, no. 1 (December 19, 2016): 105–19. http://dx.doi.org/10.1085/jgp.201611702.

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Understanding the interactions of proteins with their ligands requires knowledge of molecular properties, such as binding site affinities and the effects that binding at one site exerts on binding at other sites (cooperativity). These properties cannot be measured directly and are usually estimated by fitting binding data with models that contain these quantities as parameters. In this study, we present a general method for answering the critical question of whether these parameters are identifiable (i.e., whether their estimates are accurate and unique). In cases in which parameter estimates are not unique, our analysis provides insight into the fundamental causes of nonidentifiability. This approach can thus serve as a guide for the proper design and analysis of protein–ligand binding experiments. We show that the equilibrium total binding relation can be reduced to a conserved mathematical form for all models composed solely of bimolecular association reactions and to a related, conserved form for all models composed of arbitrary combinations of binding and conformational equilibria. This canonical mathematical structure implies a universal parameterization of the binding relation that is consistent with virtually any physically reasonable binding model, for proteins with any number of binding sites. Matrix algebraic methods are used to prove that these universal parameter sets are structurally identifiable (SI; i.e., identifiable under conditions of noiseless data). A general approach for assessing and understanding the factors governing practical identifiability (i.e., the identifiability under conditions of real, noisy data) of these SI parameter sets is presented in the companion paper by Middendorf and Aldrich (2017. J. Gen. Physiol. https://doi.org/10.1085/jgp.201611703).
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Premarathna, Galkande Iresha, and Leif Ellingson. "A mathematical representation of protein binding sites using structural dispersion of atoms from principal axes for classification of binding ligands." PLOS ONE 16, no. 4 (April 8, 2021): e0244905. http://dx.doi.org/10.1371/journal.pone.0244905.

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Many researchers have studied the relationship between the biological functions of proteins and the structures of both their overall backbones of amino acids and their binding sites. A large amount of the work has focused on summarizing structural features of binding sites as scalar quantities, which can result in a great deal of information loss since the structures are three-dimensional. Additionally, a common way of comparing binding sites is via aligning their atoms, which is a computationally intensive procedure that substantially limits the types of analysis and modeling that can be done. In this work, we develop a novel encoding of binding sites as covariance matrices of the distances of atoms to the principal axes of the structures. This representation is invariant to the chosen coordinate system for the atoms in the binding sites, which removes the need to align the sites to a common coordinate system, is computationally efficient, and permits the development of probability models. These can then be used to both better understand groups of binding sites that bind to the same ligand and perform classification for these ligand groups. We demonstrate the utility of our method for discrimination of binding ligand through classification studies with two benchmark datasets using nearest mean and polytomous logistic regression classifiers.
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Ruan, Shuxiang, and Gary D. Stormo. "Inherent limitations of probabilistic models for protein-DNA binding specificity." PLOS Computational Biology 13, no. 7 (July 7, 2017): e1005638. http://dx.doi.org/10.1371/journal.pcbi.1005638.

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Sedaghat, Ahmad R., Arthur Sherman, and Michael J. Quon. "A mathematical model of metabolic insulin signaling pathways." American Journal of Physiology-Endocrinology and Metabolism 283, no. 5 (November 1, 2002): E1084—E1101. http://dx.doi.org/10.1152/ajpendo.00571.2001.

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We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-ζ is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling.
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Kimchi, Ofer, Carl P. Goodrich, Alexis Courbet, Agnese I. Curatolo, Nicholas B. Woodall, David Baker, and Michael P. Brenner. "Self-assembly–based posttranslational protein oscillators." Science Advances 6, no. 51 (December 2020): eabc1939. http://dx.doi.org/10.1126/sciadv.abc1939.

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Recent advances in synthetic posttranslational protein circuits are substantially impacting the landscape of cellular engineering and offer several advantages compared to traditional gene circuits. However, engineering dynamic phenomena such as oscillations in protein-level circuits remains an outstanding challenge. Few examples of biological posttranslational oscillators are known, necessitating theoretical progress to determine realizable oscillators. We construct mathematical models for two posttranslational oscillators, using few components that interact only through reversible binding and phosphorylation/dephosphorylation reactions. Our designed oscillators rely on the self-assembly of two protein species into multimeric functional enzymes that respectively inhibit and enhance this self-assembly. We limit our analysis to within experimental constraints, finding (i) significant portions of the restricted parameter space yielding oscillations and (ii) that oscillation periods can be tuned by several orders of magnitude using recent advances in computational protein design. Our work paves the way for the rational design and realization of protein-based dynamic systems.
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Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (February 27, 2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were validated on the PDBbind v2019 core set and CSAR-HiQ sets. The PrtCmm IFP Score outperformed several other models in predicting protein–ligand binding affinities. Besides, conventional ECFPs were simplified to generate new IFPs, which provided consistent but faster predictions. The relationship between the base atom properties of ECFPs and the accuracy of predictions was also investigated. Availability PrtCmm IFP has been implemented in the IFP Score Toolkit on github (https://github.com/debbydanwang/IFPscore). Supplementary information Supplementary data are available at Bioinformatics online.
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Conradi Smith, Gregory Douglas. "Allostery in oligomeric receptor models." Mathematical Medicine and Biology: A Journal of the IMA 37, no. 3 (December 10, 2019): 313–33. http://dx.doi.org/10.1093/imammb/dqz016.

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Abstract We show how equilibrium binding curves of receptor homodimers can be expressed as rational polynomial functions of the equilibrium binding curves of the constituent monomers, without approximation and without assuming independence of receptor monomers. Using a distinguished spanning tree construction for reduced graph powers, the method properly accounts for thermodynamic constraints and allosteric interactions between receptor monomers (i.e. conformational coupling). The method is completely general; it begins with an arbitrary undirected graph representing the topology of a monomer state-transition diagram and ends with an algebraic expression for the equilibrium binding curve of a receptor oligomer composed of two or more identical and indistinguishable monomers. Several specific examples are analysed, including guanine nucleotide-binding protein-coupled receptor dimers and tetramers composed of multiple ‘ternary complex’ monomers.
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Jiang, Yao, Hui-Fang Liu, and Rong Liu. "Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions." PLOS Computational Biology 17, no. 4 (April 19, 2021): e1008951. http://dx.doi.org/10.1371/journal.pcbi.1008951.

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The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.
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Sohrabi-Jahromi, Salma, and Johannes Söding. "Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins." Bioinformatics 37, Supplement_1 (July 1, 2021): i308—i316. http://dx.doi.org/10.1093/bioinformatics/btab300.

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Abstract Motivation Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do not take multivalent binding into account. Results We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two cooperatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions. Availability and implementation BMF source code is available at https://github.com/soedinglab/bipartite_motif_finder under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Dissertations / Theses on the topic "Protein binding – Mathematical models"

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Sidiqi, Mahjooba. "The structure and RNA-binding of poly (C) protein 1." University of Western Australia. School of Biomedical, Biomolecular and Chemical Sciences, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0077.

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[Truncated abstract] Regulation of mRNA stability is an important posttranscriptional mechanism involved in the control of gene expression. The rate of mRNA decay can differ greatly from one mRNA to another and may be regulated by RNA-protein interactions. A key determinant of mRNA decay are sequence instability (cis) elements often located in the 3' untranslated region (UTR) of many mRNAs. For example, the AU rich elements (AREs), are such well characterized elements, and most commonly involved in promoting mRNA degradation, and specific binding of proteins to these elements leading to the stabilization of some mRNAs. Other cis-elements have been described for mRNA in which mRNA stability is a critical component of gene regulation. This includes the androgen receptor (AR) UC-rich cis element in its 3'UTR. The AR is a key target for therapeutics in human prostate cancer and thus understanding the mechanism involved in regulating its expression is an important goal. The [alpha]CP1 protein, a KH-domain containing RNA-binding protein has been found to bind this UC-rich region of the AR and is thought to play an important role in regulating AR mRNA expression. [alpha]CP1 protein is a triple KH (hnRNP K homology) domain protein with specificity for Crich tracts of RNA and ssDNA (single stranded DNA). Relatively little is known about the structural interaction of [alpha]CP1 with target RNA cis elements, thus the present study aimed to better understand the nature of interaction between 30 nt 3'UTR UC-rich AR mRNA and [alpha]CP1 protein using various biophysical techniques, in an attempt to determine which [alpha]CP1 domain or combination of domains is involved in RNA-binding. These studies could ultimately provide novel targets for drugs aimed to regulate AR mRNA expression in prostate cancer cells. At the commencement of this study little was known about the structure of the [alpha]CP1- KH domains and their basis for poly (C) binding specificity. ... Additional studies addressed the significance of the four core recognition nucleotides (TCCC) using a series of cytosine to thymine mutants. The findings verified some of the results predicted from structural studies, especially the need for maximum KH binding to a core tetranucleotide recognition sequence. Our mutational studies of the four core bases confirmed the importance of cytosine in positions two and three as no binding was observed, while some binding was observed when the fourth base was mutated. In summary, the work presented in this thesis provides new detailed insight into the molecular interactions between the [alpha]CP1-KH domain and AR mRNA. Furthermore, these studies shed light on the nature of protein/mRNA interactions in general, as well as the specific complex that forms on AR mRNA. These studies have provided new understanding into the mode of [alpha]CP1 binding at a target oligonucleotide binding site and, provide a foundation for future studies to define structure of multiprotein/oligonucleotide complexes involved in AR mRNA gene regulation. Understanding the detailed interaction between the AR mRNA and [alpha]CP1 could provide possible targets for drug development at reducing AR expression in prostate cancer cells by interfering with the interaction of [alpha]CP1 and AR-mRNA.
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Geli, Rolfhamre Patricia. "From penicillin binding proteins to community interventions : mathematical and statistical models related to antibiotic resistance /." Stockholm : Department of Mathematics, Stockholm University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8477.

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Roussel, Céline. "Etude du rôle des chélateurs calciques sur les oscillations du potentiel membranaire neuronal: approche expérimentale et théorique." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210854.

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Les neurones sont des cellules excitables capables de coder et transmettre l’information sous forme d’oscillations du potentiel membranaire. Cette activité électrique est produite par une modification des flux ioniques transmembranaires. Les neurones constituent un exemple d’oscillateur cellulaire dont la dynamique non linéaire permet l’apparition d’une activité électrique complexe. Dans ce système, les ions calciques sont des messagers intracellulaires importants. Ils servent de médiateur entre un signal électrique et un signal chimique, par une modulation de l’activité enzymatique de certaines protéines. Ils interviennent dans de nombreuses fonctions neuronales, dont l’excitabilité électrique. Un des mécanismes mis en place par les neurones pour contrôler l’homéostasie du calcium intracellulaire provient de protéines cytoplasmiques capables de lier les ions calciques. Ces protéines jouent un rôle de « tampon » du calcium. Cependant, toutes leurs fonctions n’ont pas encore été mises en évidence. C’est l’objectif de notre travail. Nous avons voulu comprendre le rôle joué par une protéine « tampon » particulière, la calrétinine, sur le mode de décharge électrique d’un neurone où elle est exprimée en abondance, le grain cérébelleux. Pour cela, nous avons utilisé une approche théorique et expérimentale.

Au niveau théorique, nous avons élaboré un modèle mathématique de l’activité électrique du grain cérébelleux, prenant en compte la chélation du calcium intracellulaire. Il permet de clarifier le rôle de la chélation du calcium intracellulaire sur les oscillations du potentiel membranaire. La modélisation de l’activité électrique du grain cérébelleux repose sur le formalisme développé par Hodgkin et Huxley pour l’axone géant de calmar. Dans ce contexte, l’application de la conservation de la charge au circuit équivalent de la membrane cellulaire fournit un système d’équations différentielles ordinaires, non linéaires. Dès lors, notre modèle nous a permis d’étudier l’impact des variations de la concentration de chélateur calcique sur les oscillations du potentiel membranaire. Nous avons ainsi pu constater qu’une diminution de la concentration en chélateur calcique induisait une augmentation de l’excitabilité électrique du grain cérébelleux, sans altérer le régime d’oscillations. Par contre, en augmentant fortement la concentration en chélateur calcique, nous avons montré que le grain cérébelleux changeait de dynamique oscillatoire, montrant des transitions d’un mode de décharge périodique régulier vers des oscillations en salve du potentiel membranaire.

Au niveau expérimental, nous avons vérifié les résultats prévus par le modèle théorique. Nous avons ainsi montré que des grains de souris transgéniques déficientes en calrétinine présentaient une excitabilité électrique accrue par rapport aux grains contrôles.

Puis, en restaurant un niveau de chélation calcique normal dans ces grains, par perfusion intracellulaire de chélateur calcique, nous montrons qu’ils retrouvent un niveau d’excitabilité normal. Ensuite, nous avons introduit dans des grains cérébelleux de souris sauvages, une forte concentration en chélateur calcique exogène. Conformément aux résultats théoriques, nous avons pu observer des transitions vers des oscillations en salve du potentiel membranaire. Enfin, nous avons montré que l’absence de calrétinine affecte les paramètres morphologiques du grain cérébelleux des souris transgéniques déficientes en calrétinine.

En conclusion, ces résultats suggèrent que le mode de décharge des cellules excitables peut être modulé d’une façon importante par les protéines liant le calcium. De ce fait, des changements dans le niveau d’expression et/ou dans la localisation subcellulaire des protéines liant le calcium pourraient aussi jouer un rôle critique dans la régulation de processus physiologiques contrôlés par l’excitabilité membranaire. De plus, les mécanismes que nous avons mis en évidence pourraient être à l’origine d’un nouveau principe de régulation de la signalisation dans les circuits neuronaux et pourraient jouer un rôle fonctionnel dans le contrôle du codage de l’information et de son stockage dans le système nerveux central.
Doctorat en sciences, Spécialisation physique
info:eu-repo/semantics/nonPublished

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Hinkle, Adam R. "Tight-binding calculation of electronic properties of oligophenyl and oligoacene nanoribbons." Virtual Press, 2008. http://liblink.bsu.edu/uhtbin/catkey/1398716.

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Within recent years, allotropic structures of carbon have been produced in the forms of tubes and ribbons which offer the promise of extraordinary electronic and thermal properties. Here we present analyses of oligophenyl and oligoacene systems–infinite, one-dimensional chains of benzene rings linked along the armchair and zigzag directions. These one-dimensional structures, which are amenable to calculation by analytical means, exhibit features very similar to carbon nanotubes and nanoribbons. Using a tight-binding Hamiltonian we analytically determine the density of states, local density of states, and energy-band structure for the phenyl and the acene. We also examine the effect of disorder on the energies and the corresponding states.
Department of Physics and Astronomy
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Chiang, T. "Mathematical and statistical models for the analysis of protein." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597600.

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Protein interactions, both amongst themselves and with other molecules, are responsible for much of the work within the cellular machine. As the number of protein interaction data sets grow in number and in size, from experiments such as Yeast 2-Hybrid or Affinity Purification followed by Mass Spectrometry, there is a need to analyse the data both quantitatively and qualitatively. One area of research is determining how reliable a report of a protein interaction is – whether it could be reproduced if the experiment were repeated, or if it were tested using an independent assay. One might aim to score each reported interaction using a quantitative measure of reliability. Ultimately, protein interactions need to be addressed at the systems level where both the dynamic and functional nature of protein complexes and other types of interactions is ascertained. In this dissertation, I present two methodological developments that are useful towards elucidating the nature of protein interaction graphs in the model organism Saccharomyces cerevisiae. The first one aims to estimate the sensitivity and specificity of a protein interaction data set, and does that, as much as possible, by looking at the data set’s internal consistency and reproducibility. The second method aims to estimate the node degree distribution, using a multinomial model which is fit by maximum likelihood. In the development of the methods for the analysis of the protein interactions, computational tools were built in the statistical environment R. Such tools are necessary for the implementation of each analytic step, for rendering visualisations of intermediate and conclusive results, and for the construction of optimal work-flows so as to make our research reproducible and extensible. We have also included such a work-flow in this dissertation as well as the software engineering component of the research.
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Gregor, Craig Robert. "Epitopes, aggregation and membrane binding : investigating the protein structure-function relationship." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5833.

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The three-dimensional structure of a protein, formed as a result of amino-acid sequences folding into compact domains, is regarded as a key factor in its biological function. How and why proteins fold into specific topologies, remain the key focus of scientific research in the field of biophysics. By stripping down complex reactions down to the most basic elements, biophysicists aim to develop simplified models for biological phenomena such as antibody discrimination, viral fusion or self-assembly. Focusing on small model peptide systems, rather than the full proteins from which they were derived, was hoped to result in accurate structural measurements and provide a more transparent comparison between simulation and experiment. The aim of this research was therefore to investigate how accurate these models were when compared against experiment. Furthermore, while breaking down the complex biological phenomena into simple models, there was also a conscious effort to ensure that the models were representative of real biological systems, and a major focus was therefore aimed at determining whether any meaningful biomedical insight may be extrapolated from such models. Peptides found in hormones (human chorionic gonadotropin, luteinizing hormone), viruses (HIV) and amyloid diseases (transthyretin) were selected in order to probe a variety of questions in relation to the aforementioned biological phenomena. Namely, how the primary sequence influenced the three-dimensional structure (and thus its biological function), how its environment could influence such a confirmation, and how these systems aggregated. This doctoral study has made use of a combination of computer simulations and experimental techniques to investigate a selection of biologically relevant peptides; utilising classical atomistic molecular dynamics (MD) simulations to characterise the free-energy landscapes of the chosen peptides, and compare these findings with the secondary structure content predicted by spectroscopic methods such as circular dichroism and infrared spectroscopy. The peptide systems studied within, were found to be characterised by rugged free-energy landscapes unlike their protein counterparts (defined by singular, deep minima). Furthermore, these landscapes were found to be highly plastic and sensitive to changes in the local environment.
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Allison, Jerry Dewell. "An implementation of the competitive Gaussian model for metal-humic binding in a general speciation model." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/25965.

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Nordling, Erik. "Biocomputational studies on protein structures /." Stockholm, 2002.

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Smoler, Eliezer. "Mathematical models to predict milk protein concentration from dietary components fed to dairy cows." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308060.

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Chu, Vano. "Molecular recognition in the streptavidin-biotin system /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8106.

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Books on the topic "Protein binding – Mathematical models"

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Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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Rice, Stuart Alan, I. Prigogine, and Richard A. Friesner. Computational methods for protein folding. Chichester: Wiley, 2002.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Dordrecht: Springer-Science+Business Media, B.V., 2003.

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Koliński, Andrzej. Lattice models of protein folding, dynamics, and thermodynamics. Austin, Tex: R.G. Landes, 1996.

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Koliński, Andrzej. Multiscale approaches to protein modeling. New York: Springer Science + Business Media, 2011.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.

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Rangwala, Huzefa, G. Karypis, and G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.

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Chandru, Vijay. Protein folding on lattices: An integer programming approach. Bangalore: Indian Institute of Management, 2002.

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Calcium signalling in cancer. Boca Raton: CRC, 2001.

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Book chapters on the topic "Protein binding – Mathematical models"

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Sharp, Kim A. "Statistical Thermodynamics of Binding and Molecular Recognition Models." In Protein-Ligand Interactions, 1–22. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645947.ch1.

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Vacca, Marcella, Floriana Della Ragione, Kumar Parijat Tripathi, Francesco Scalabrì, and Maurizio D’Esposito. "MECP2: A Multifunctional Protein Supporting Brain Complexity." In Mathematical Models in Biology, 109–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_8.

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D’Agostino, Daniele, Andrea Clematis, Emanuele Danovaro, and Ivan Merelli. "Modelling of Protein Surface Using Parallel Heterogeneous Architectures." In Mathematical Models in Biology, 189–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_14.

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Zhao, Hongyu, Baolin Wu, and Ning Sun. "DNA-protein binding and gene expression patterns." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 259–74. Beachwood, OH: Institute of Mathematical Statistics, 2003. http://dx.doi.org/10.1214/lnms/1215091147.

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Tapia-Rojo, Rafael, Juan José Mazo, Andrés González, M. Luisa Peleato, Maria F. Fillat, and Fernando Falo. "Free Energy Landscape Analysis of Mesoscopic Model for Finding DNA-Protein Binding Sites." In Trends in Mathematics, 81–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08138-0_15.

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Xia, Xuhua. "Protein and Codon Substitution Models and Their Evolutionary Distances." In A Mathematical Primer of Molecular Phylogenetics, 141–70. Includes bibliographical references and index.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429425875-4.

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Sun, Hongzhe, Mark C. Cox, Hongyan Li, and Peter J. Sadler. "Rationalisation of metal binding to transferrin: Prediction of metal-protein stability constants." In Metal Sites in Proteins and Models, 71–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62870-3_3.

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Lapedes, Alan S., Bertrand Giraud, LonChang Liu, and Gary D. Stormo. "Correlated mutations in models of protein sequences: phylogenetic and structural effects." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 236–56. Hayward, CA: Institute of Mathematical Statistics, 1999. http://dx.doi.org/10.1214/lnms/1215455556.

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Schuster, Stefan, Marko Marhl, Milan Brumen, and Reinhart Heinrich. "Influence of Calcium Binding to Proteins on Calcium Oscillations and ER Membrane Potential Oscillations. A Mathematical Model." In Information Processing in Cells and Tissues, 137–50. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5345-8_15.

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Krepets, Vladimir V., and Natalya V. Belkina. "Prediction of Binding Affinities for Protein-Ligand Complexes with Neural Network Models." In Discovery Science, 240–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44418-1_19.

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Conference papers on the topic "Protein binding – Mathematical models"

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Kauffman, Chris, Huzefa Rangwala, and George Karypis. "IMPROVING HOMOLOGY MODELS FOR PROTEIN-LIGAND BINDING SITES." In Proceedings of the CSB 2008 Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2008. http://dx.doi.org/10.1142/9781848162648_0019.

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Rui Gao, Juanyi Yu, Mingjun Zhang, and Tzyh-Jong Tarn. "Mathematical models of protein secondary structures and gene mutation." In 2009 International Conference on Mechatronics and Automation (ICMA). IEEE, 2009. http://dx.doi.org/10.1109/icma.2009.5246578.

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RAIMONDO, DOMENICO, ALEJANDRO GIORGETTI, DOMENICO COZZETTO, and ANNA TRAMONTANO. "QUALITY AND EFFECTIVENESS OF PROTEIN STRUCTURE COMPARATIVE MODELS." In Proceedings of the International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773685_0017.

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Yang, Wenyi, and Lei Deng. "PNAB: Prediction of protein-nucleic acid binding affinity using heterogeneous ensemble models." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8982930.

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Koh, Sung, G. K. Ananthasuresh, and Christopher Croke. "Design of Reduced Protein Models by Energy Minimization Using Mathematical Programming." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4382.

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Zhang, Linda Yu, Emilio Gallicchio, and Ronald M. Levy. "Implicit solvent models for protein-ligand binding: Insights based on explicit solvent simulations." In SIMULATION AND THEORY OF ELECTROSTATIC INTERACTIONS IN SOLUTION. ASCE, 1999. http://dx.doi.org/10.1063/1.1301542.

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Pap, Gergely, Krisztian Adam, Zoltan Gyorgypal, Laszlo Toth, and Zoltan Hegedus. "Training models employing physico-chemical properties of DNA for protein binding site detection." In 2021 International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2021. http://dx.doi.org/10.1109/icapai49758.2021.9462057.

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WÜST, T., D. P. LANDAU, C. GERVAIS, and YING XU. "MONTE CARLO SIMULATIONS OF PROTEIN MODELS: AT THE INTERFACE BETWEEN STATISTICAL PHYSICS AND BIOLOGY." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304900_0006.

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Matveev, Konstantin I., Thomas T. Goodman, Jingyang Chen, and Suzie H. Pun. "Parametric Modeling Study of Nanoparticle Penetration Into Spherical Cell Clusters." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41153.

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Abstract:
Nanoparticle-based drug delivery is a promising cancer treatment method due to the ability to target tumor sites by preferential extravasation and to deliver higher loads of therapeutics. Although nanoparticle penetration in tumor tissue is limited due to diffusional restrictions, delivery can be improved by enzymatic degradation of extracellular matrix proteins at the tumor site. Here, a mathematical model describing transport of nanoparticles in non-uniformly porous spheroids is developed, accounting for binding of particles with cells and endocytosis. Results of parametric simulations for nanoparticle concentration inside spheroids highlight the influence of various system parameters. Preliminary experimental data show qualitative agreement with the theory. These results are useful for understanding nanoparticle delivery and for designing drug delivery strategies.
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Gaivoronskaya, Irina, and Valenitna Kolpakova. "MATHEMATICAL MODELS FOR THE SYNTHESIS OF PLANT-BASED COMPOSITIONS WITH IMPROVED AMINO ACID COMPOSITION." In GEOLINKS Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/geolinks2021/b1/v3/12.

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Abstract:
The aim of the work was to optimize the process of obtaining multicomponent protein compositions with high biological value and higher functional properties than the original vegetable protein products. Was realized studies to obtain biocomposites on the base of pea protein-oat protein and pea protein-rice protein. Developed composites were enriched with all limited amino acids. For each of the essential amino acids, the amino acid score was 100% and higher. Protein products used in these compositions are not in major allergen list, which allows to use these compositions in allergen-free products and specialized nutrition. To determine biosynthesis parameters for compositions from pea protein and various protein concentrates with the use of transglutaminase enzyme, was studied effect of concentration and exposition time on the amount of amino nitrogen released during the reaction. Decreasing of amino nitrogen in the medium indicated the occurrence of a protein synthesis reaction with the formation of new covalent bonds. Were determined optimal parameters of reaction: the hydromodule, the exposure time, the concentration of EP of the preparation, were obtained mathematical models. Studies on the functional properties of composites, the physicochemical properties of the proteins that make up their composition, and structural features will make it possible to determine the uses in the manufacture of food products based on their ability to bind fat, water, form foam, gels, and etc.
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