The diagnostic and pharmaceutical industries rely on tools for characterizing, discovering, and developing bio-molecular interactions. Diagnostic assays require high affinity capture probes and binding specificity for accurate detection of biomarkers. Selection of drug candidates depends on the drug residency time and duration of drug action. Further, biologic drugs can induce anti-drug antibodies, which require characterization to determine the impact on the drug safety and efficacy. Label-free biosensors are an attractive solution for analyzing these and other bio-molecular interactions because they provide information based on the characteristics of the molecules themselves, without disturbing the native biological systems by labeling. While label-free biosensors can analyze a broad range of analytes, small molecular weight analytes (molecular weight < 1kDa) are the most challenging. Affinity measurements for small molecular weight targets require high sensitivity and long-term signal stability. Additional difficulties occur with different liquid refractive indices that result from to temperature, composition, or matrix effects of sensor surfaces. Some solutions utitlize strong solvents to increase the solubility of small molecules, which also alter the refractive index. Moreover, diagnostics require affinity measurements in relevant solutions, of various refractive indices. When a refractive index difference exists between the analyte solution and the wash buffer, a background signal is generated, referred to as the bulk effect, obscuring the small signal due to surface binding in the presence of large fluctuations due to variations of the optical refractive index of the solutions. The signal generated by low molecular weight analytes is small, and conventional wisdom tends toward signal amplification or resonance for detection of these small signals. With this approach, Surface Plasmon Resonance (SPR) has become the gold standard in affinity measurement technologies. SPR is an expensive and complex technology that is highly susceptible to the bulk effect. SPR uses a reference channel to correct for the bulk effect in post-processing, which requires high precision and sophisticated temperature control, further increasing the cost and complexity. Additionally, multiplexing is desirable as it allows for simultaneous measurements of multiple ligands; however, multiplexing is only possible in the imaging modality of SPR, which has lower sensitivity and difficulty with referencing. The Interferometric Reflectance Imaging Sensor (IRIS) is a low-cost, optical label-free bio-molecular interaction analysis technology capable of providing precise binding affinity measurements; however, limitations in sensitivity and usability have previously prevented its widespread adaptation. Overcoming these limitations requires the implementation of automation, compact and easy-to-use instrumentation, and increased sensitivity. Here, we explore methods for improved sensitivity and usability. We achieve noise reduction and elimination of solution artifacts (bulk effect) through engineered illumination uniformity and temporal and spatial image processing. To validate these methods, we experimentally analyze small molecule molecular interactions to demonstrate highly sensitive kinetic binding measurements, independent of solution refractive index. / 2023-09-24T00:00:00Z

To induce the production of antibodies, exogenous antigens are taken up and degraded in antigen presenting cells in vivo. Since this process inevitably lead to distort antigen¡¦s structure, it is likely that some arising antibodies following immunization may not react appropriately with native protein. In the present study, comparative studies on the reactivity of cobrotoxin and recombinant cobrotoxin toward anti-cobrotoxin antibodies were carried out. CD spectra and acrylamide quenching of Trp fluorescence showed that global structure of recombinant cobrotoxin was different from that of native toxin. Results of ELISA and dot blotting assay revealed that recombinant cobrotoxin had a superior reactivity toward anti-cobrotoxin antibodies than native toxin did. Reactivity with antibody fractions specifically against N-terminal region or C-terminal region of cobrotoxin also showed the same results. The binding of recombinant cobrotoxin with antibodies was stronger than that of cobrotoxin as revealed by ammonium thiocyanate elution assay. Recombinant protein was susceptible to reduce its antigenicity after tryptic digestion compared to cobrotoxin. Distorting disulfide linkages at C-terminus caused a marked decrease in immunoreactivity of recombinant cobrotoxin, indicating that anti-cobrotoxin antibodies mostly recognized conformation-dependent epitopes. Moreover, cobrotoxin and recombinant cobrotoxin showed a similar immunoreactivity under denaturing condition. Taken together, these results suggest that native conformation with cobrotoxin may unfavorably impede the interaction of some epitope(s) with anti-cobrotoxin antibodies.

Successful tissue engineering strategies in the nervous system must be carefully crafted to interact favorably with the complex biochemical signals of the native environment. To date, all chronic implants incorporating electrical conductivity degrade in performance over time as the foreign body reaction and subsequent fibrous encapsulation isolate them from the host tissue. Our goal is to develop a peptide-based interfacial biomaterial that will non-covalently coat the surface of the conducting polymer polypyrrole, allowing the implant to interact with the nervous system through both electrical and chemical cues. Starting with a candidate peptide sequence discovered through phage display, we used computational simulations of the peptide on polypyrrole to describe the bound peptide structure, explore the mechanism of binding, and suggest new, better binding peptide sequences. After experimentally characterizing the polymer, we created a molecular mechanics model of polypyrrole using quantum mechanics calculations and compared its in silico properties to experimental observables such as density and chain packing. Using replica exchange molecular dynamics, we then modeled the behavior of affinity binding peptides on the surface of polypyrrole in explicit water and saline environments. Relative measurements of the contributions of each amino acid were made using distance measurements and computational alanine scanning. / text

Cette thèse étudie trois sujets relevant de la biologie structurale, de lagénétique et de l'immunologie.Premièrement, nous développons de nouveaux prédicteurs de l'affinité deliaison de complexes protéiques, produisant des résultats de niveau ``état del'art''. Nous calculons d'abord 12 variables modélisant diverses propriétésstructurales des complexes. Nous générons et évaluons des estimateursutilisant des sous ensembles de ces variables, de façon à identifier les plusperformants. Le logiciel associé est distribué dans la Structural BioinformaticsLibrary.Deuxièmement, nous proposons de nouvelles analyses de complexes Ig-Ag.D'une part nous concevons un classificateur distinguant les types de ligand desIg. D'autre part, nous montrons que le modèle précédent prédit fidèlementl'affinité de complexes Ig-Ag. Enfin, nous quantifions la contribution des CDR3de la chaine lourde à l'affinité de liaison, et montrons qu'il contribuesignificativement plus que les autres CDR.Enfin, nous nous intéressons à la modélisation de la diversité des répertoiresde chaîne lourde des Igs, à partir de données de séquençage de CDR3, dans unmodèle de vaccin chez le poisson. Nous analysons les répertoires dans troisconditions: naifs, vaccinés et vaccinés + infectés. Nous comparons lesrépertoires de deux individus en utilisant la « earth-mover distance », laquelleexploite la correspondance entre clonotypes de deux répertoires, révélant ainsides informations inaccessibles aux méthodes basées sur les indices dediversité.Dépôt de thèseDonnées complémentairesPour caractériser la notion de réponse immunitaire publique / privée, nousquantifions le chevauchement des clonotypes exprimés entre individus de lamême ou de différentes conditions / This thesis investigates three topics at the cross-roads of structural biology,genetics and immunology.First, we develop a pipeline to design and select binding affinity predictors forprotein complexes, yielding state-of-the art results. The first step is the designand computation of 12 different variables accounting for geometric andphysico-chemical properties of the complexes. The second step is thegeneration and evaluation of models using subsets of these variables, followedby the selection of the best performing ones. The corresponding software isdistributed within the Structural Bioinformatics Library.Second, we provide an analysis of the interface properties of Ig-Ag complexes.In particular, we design a classifier using two descriptors, which is able todistinguish ligand types. We also apply the previous binding affinity predictionmodel to Ig-Ag complexes and obtain accurate predictions. We then develop aquantitative model for the contribution of VH CDR3 to the binding affinity andinteraction specificity, and show that it contributes significantly more thanother CDRs.Third, we model the diversity of VH CDR3 repertoires from Ig RNA sequencingdata in a fish vaccination model. We analyze repertoires from three conditions:naive, vaccinated and vaccinated + infected fish. Comparison of the repertoiresof two individuals uses the earth-mover distance (EMD). By exploiting amapping between the clonotypes of the repertoires, we show that EMD revealsinformation beyond classical methods based on diversity indexes. Tocharacterize the notion of public / private immune response, we quantify theoverlap of clonotypes between individuals of the same or different conditions

This thesis focuses mainly on the consequences that single mutations have on structural, functional and energetic aspects of the protein cyanovirin-N. In order to estimate the free energy of single mutations, we have applied thermodynamics integration and Bennett acceptance ratio techniques. Replica exchange molecular dynamics has been applied to accelerate simulations for complicated scenarios. Our studies suggest that certain single mutations may be promising to improve binding affinity to Manα1→2Manα but we also learned that the simplistic view of a strong hydrogen bond correlating to a high binding affinity may not always be correct. Finally, we explored in detail the widely used mutation P51G for its impact on protein rigidity at the very important hinge region as well as for its possible effect on glycan binding.

Due to the pandemic, research concerning SARS-CoV-2 became of the utmost importance. In this research, we aimed to find and synthesize a library of peptide epitopes that carry functional properties of the ACE-2 receptor binding to the virus protein for the purpose of creating a therapeutic treatment (i.e. viral inhibition). In order to do this, we used MST to determine binding affinity. After that, we validated the binding properties of our peptide epitopes and applied them as SARSCoV-2 antibody indicators using ELISA. We, then, functionalized gold nanoparticles with the peptide epitopes to assess its utility as a potential SARS-CoV-2 competitive inhibitor. From the set of peptides in the library, P25 showed the most functional properties in both MST and serological ELISA, while P1 successfully conjugated to the gold nanoparticles in different forms (PEG-P1, linker-P1, and mutated P1). Finally, P1 was validated to have antibody binding through sandwich ELISA. In the future, these findings can be applied to inhibit viral activity through drug delivery.

No description available.

Emergence of avian origin and equine origin canine influenza viruses (CIVs) in Asia and the United States brings important concerns. Humans are in closer and more frequent contact with dogs than other common hosts of influenza. Thus, CIV is a potential threat to human health. However, little is known about the determinants of CIV host tropism or the transmissibility of CIVs to humans. An amino acid change (W222L) was implicated in modifying hemagglutinin receptor binding by CIV. This was tested using reverse genetics, glycan microarray and virus histochemistry. Glycan microarray demonstrated that avian-origin CIV (H3N2-222W) bind predominantly to alpha-2, 3 linked glycans. Virus histochemistry indicated that rH3N2-222L had higher binding affinity with epithelial cilia of canine tracheal tissue and weaker binding with avian tracheal tissue. Ferret infection demonstrated that the avian-origin H3N2 CIV could cause infection and limited to rhinitis, suggesting that CIV could infect humans.

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.

The outbreak of pandemic influenza and its ability to spread rapidly makes it a severe threat to public health. Antiviral drugs such as oseltamivir (Roche’s Tamiflu™) and zanamivir (GlaxoSmithKline’s Relenza™) are neuraminidase (NA) inhibitors (NI), which bind more tightly to NA than its natural substrate, sialic acid. However, the virus can acquire resistance to antiviral drugs by developing single point mutations (such as H274Y) in the target protein. Thus in some cases the drugs may not be as effective as expected. The high level of inconsistency exhibited by fluorometric assays and the short half-life of the chemiluminescent assay for monitoring drug resistance lead to the need for a simple, label-free, reliable assay. To address this problem, this work focused on three main objectives: 1) to determine the binding affinities of two common anti-viral drugs (oseltamivir and zanamivir) against the influenza NA wild type and drug resistant mutants using bioinformatics software Schrodinger Suite™ 2010. 2) To develop a reliable label-free, real-time, surface plasmon resonance (SPR) assay to measure the binding affinity between influenza viral coat protein neuraminidase (wild type and mutant) and anti-viral drugs. 3) To develop an SPR inhibition assay to quantitatively compare the interactions of sialic acid, zanamivir and oseltamivir with the viral coat protein neuraminidase (wild type and mutant). The entire docking process was carried out using Schrödinger Suite™ 2010. The 2009 pandemic H1N1 neuraminidase (PDB: 3NSS) was used throughout the docking studies as the wild type structure. Five mutants (H274Y, N294S, H274N, A346N and I222V) and three ligands (sialic acid, oseltamivir and zanamivir) were built using the maestro module. The grid-based ligand docking with energetics (GLIDE) module and induced fit docking (IFD) module were used for docking studies. The binding affinities, Gibbs free energy change (∆G) and molecular mechanics-generalized born energy/ solvent accessible area (MM-GB/SA) values for wild-type NA interactions show that both the antiviral drugs studied interact strongly with the wild-type protein. The ∆G values for all antiviral interactions with mutant NA forms were reduced in magnitude, thereby indicating that they are less favourable than interactions with the wild-type protein. A similar trend was observed with MM-GB/SA results. Amongst all of the computed values, MM-GB/SA was the closest to the experimental data. In several cases, the interactions between the anti-viral drugs and NA mutants were markedly less favourable than those between sialic acid and the same mutants, indicating that these mutations could confer anti-viral resistance. Influenza NA wild-type and H274Y mutant were expressed in baculovirus expression system (BVES) in insect cells. The expressed proteins were partially purified using the standard purification techniques of anion exchange and size exclusion chromatography (SEC). A fluorometric activity assay was performed on the recombinant proteins. Both the wild type and the mutant showed similar level of activities. In addition, the recombinant NAs were used in an inhibition assay. Oseltamivir was found to be sensitive to wild type protein (IC50 = 0.59 nM) and resistant to the H274Y mutant protein (IC50 = 349.43 nM). On the other hand, zanamivir was sensitive to both wild type (IC50 = 0.26 nM) and the H274Y mutant (IC50 = 0.44 nM). This indicated that zanamivir was a more potent inhibitor than oseltamivir. These findings were in good agreement with the literature. An SPR assay for accurate monitoring of influenza antiviral drug resistance was developed. A spacer molecule (1, 6- hexanediamine) was site-specifically tethered to the inert 7-hydroxyl group of zanamivir. The tethered zanamivir was immobilized onto an SPR GLC chip to obtain a final immobilization response of 431 response units (RU). The reference subtracted binding responses obtained for NA wild-type and H274Y mutant were analysed using the ProteOn Manager™ Software tools. The SPR curves were fitted to a simple Langmuir 1:1 model with drift to obtain association rate constant (ka) and dissociation rate constants (kd). The relative binding values obtained from literature and the current SPR assay (1.9 and 1.7 respectively) suggested that the current SPR assay yielded similar results to the existing labelled enzymatic assay. In addition, an SPR inhibition assay was developed. The calculated IC50-spr values were compared and it was observed that oseltamivir was sensitive to wild type protein (IC50-spr = 7.7 nM) and resistant to the H274Y mutant protein (IC50-spr = 256 nM). On the other hand, zanamivir was sensitive to both wild type (IC50-spr = 2.16 nM) and the H274Y mutant (IC50-spr = 2.4 nM). Sialic acid was also found to be sensitive to both wild type (IC50-spr = 5.5 nM) and H274Y mutant (IC50-spr = 3.25 nM). In the cases studied, the viral proteins remained sensitive to sialic acid, consistent with retention of virulence of these mutant strains. It was concluded that zanamivir is a more potent inhibitor than oseltamivir for treating the H274Y mutant. Comparison of the SPR inhibition results with the docking results revealed a similar trend. The wild-type NA and H27Y mutant retained binding affinity for sialic acid and zanamivir. Oseltamivir showed a significant decrease in binding affinity for the H274Y mutant compared with the wild-type. This was because of the disruption of the salt bridge formation within NA that was vital for oseltamivir activity. To my knowledge, this is the first SPR biosensor assay developed to monitor influenza antiviral drug resistance. There is a tremendous scope to extend this study to more mutants and new antiviral drugs. This could pave the way for a reliable SPR biosensor assay to replace low consistency labelled enzymatic assays.