Linear Precision Glycomacromolecules with Varying Interligand Spacing and Linker Functionalities Binding to Concanavalin A and the Bacterial Lectin FimH

Igde, Sinaida; Röblitz, Susanna; Müller, Anne; Kolbe, Katharina; Boden, Sophia; Fessele, Claudia; Weber, Marcus; Hartmann, Laura

doi:10.1002/mabi.201700198

Cited by 34 publications

(60 citation statements)

References 69 publications

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“…Clearly, kinITC‐ETC greatly increases the value of ITC data, allowing the direct determination of all relevant biophysical constants describing a binding event within one experiment. Although bearing a great potential to expand the positive impact of kinetic investigations in drug discovery, kinITC has only hesitantly found its way into SKR studies yet . To reduce the initial reservation for the application of kinITC, kinetic parameters derived from ITC experiments are independently validated for the first time in the present study.…”

Section: Introductionmentioning

confidence: 99%

“…Althoughb earing ag reat potentialt oe xpand the positive impact of kinetic investigations in drug discovery,k inITC has only hesitantly found its way into SKR studies yet. [26] To reduce the initial reservation for the application of kinITC, kinetic parameters derived from ITC experiments are independently validated for the first time in the present study.I na ddition, we demonstrate that kinITC is ap owerful tool for "data mining". Therefore, the binding kinetics of as eries of congeneric mannosides bindingt ot he lectin domain of FimH (FimH LD ) were determined.F imH is av irulence factor located at the tip of type 1p ili of uropathogenic E. coli (UPEC) strains.…”

Section: Introductionmentioning

confidence: 99%

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KinITC—One Method Supports both Thermodynamic and Kinetic SARs as Exemplified on FimH Antagonists

Zihlmann

Silbermann

Sharpe

et al. 2018

Chemistry A European J

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Affinity data, such as dissociation constants (K ) or inhibitory concentrations (IC ), are widely used in drug discovery. However, these parameters describe an equilibrium state, which is often not established in vivo due to pharmacokinetic effects and they are therefore not necessarily sufficient for evaluating drug efficacy. More accurate indicators for pharmacological activity are the kinetics of binding processes, as they shed light on the rate of formation of protein-ligand complexes and their half-life. Nonetheless, although highly desirable for medicinal chemistry programs, studies on structure-kinetic relationships (SKR) are still rare. With the recently introduced analytical tool kinITC this situation may change, since not only thermodynamic but also kinetic information of the binding process can be deduced from isothermal titration calorimetry (ITC) experiments. Using kinITC, ITC data of 29 mannosides binding to the bacterial adhesin FimH were re-analyzed to make their binding kinetics accessible. To validate these kinetic data, surface plasmon resonance (SPR) experiments were conducted. The kinetic analysis by kinITC revealed that the nanomolar affinities of the FimH antagonists arise from both (i) an optimized interaction between protein and ligand in the bound state (reduced off-rate constant k ) and (ii) a stabilization of the transition state or a destabilization of the unbound state (increased on-rate constant k ). Based on congeneric ligand modifications and structural input from co-crystal structures, a strong relationship between the formed hydrogen-bond network and k could be concluded, whereas electrostatic interactions and conformational restrictions upon binding were found to have mainly an impact on k .

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

KinITC—One Method Supports both Thermodynamic and Kinetic SARs as Exemplified on FimH Antagonists

Zihlmann

Silbermann

Sharpe

et al. 2018

Chemistry A European J

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“…Using the measured ITC experimental data, the binding rate k 12 and the dissociation rate k 21 can be estimated for each step of the titration [41]. Usually, these rates are assumed to be constants.…”

Section: Kinetic Itcmentioning

confidence: 99%

“…From the ligand concentration and from the electric power which is used to keep the temperature constant, the binding rate of the process can be calculated (mM −1 s −1 ). The blue stars represent experimental results taken from [41]. If the binding would be a one-step molecular process, then the rate should be constant number.…”

Section: Kinetic Itcmentioning

confidence: 99%

Implications of PCCA+ in Molecular Simulation

Weber

2018

Computation

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Upon ligand binding or during chemical reactions the state of a molecular system changes in time. Usually we consider a finite set of (macro-) states of the system (e.g., 'bound' vs. 'unbound'), although the process itself takes place in a continuous space. In this context, the formula χ = XA connects the micro-dynamics of the molecular system to its macro-dynamics. χ can be understood as a clustering of micro-states of a molecular system into a few macro-states. X is a basis of an invariant subspace of a transfer operator describing the micro-dynamics of the system. The formula claims that there is an unknown linear relation A between these two objects. With the aid of this formula we can understand rebinding effects, the electron flux in pericyclic reactions, and systematic changes of binding rates in kinetic ITC experiments. We can also analyze sequential spectroscopy experiments and rare event systems more easily. This article provides an explanation of the formula and an overview of some of its consequences.Keywords: Robust Perron Cluster Analysis; membership function; invariant subspace. MSC: 60J22 The Foundation of PCCA+Molecular simulation is used as a tool to estimate thermodynamical properties. Moreover, it could be used for investigation of molecular processes and their time scales. In computational drug design, e.g., binding affinities of ligand-receptor systems are estimated. In this case, trajectories in a 3N-dimensional space Ω of molecular conformations (N is the number of atoms) are generated. They will be denoted as micro-states x ∈ Ω in the following. After simulation the micro-states are classified as "bound" or "unbound". This classification can be achieved by projecting the state space Ω onto a low number n of macro-states of the system. Spectral clustering algorithms are one possible method in this context. Robust Perron Cluster Analysis (PCCA+) turned out to be a commonly used practical tool for this algorithmic step [1].Conformation Dynamics has been established in the late 1990s. From molecular simulations [2] transition matrices P ∈ R m×m were derived. The entries of P represent conditional transition probabilities between pre-defined m subsets of the state space Ω of a molecular process. Today P would be denoted as Markov State Model. According to the transfer operator theory and PCCA [3] the eigenvectors of these matrices were studied in the early 2000s leading to an empirical observation [4] and Figure 1: Given a matrix X ∈ R m×n of the n m leading eigenvectors of the m × m-transition matrix P, the m rows of X -plotted as n-dimensional points -seem to always form an (n − 1)-simplex. Some of those m points are located at the vertices of the simplices, other points are located on the edges. The points which are located at the n vertices of these simplices represent the thermodynamically stable conformations of the molecular systems. Whereas, the points which are located on the edges of the simplices represent transition regions. In order to transform the (n − 1)-simplex σ 1 ...

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“…While the binding mechanisms of one ligand to one receptor through one binding site have been studied widely, theoretical models for multivalent ligands and receptors are still lacking. In order to conduct numerical modeling and compare them to actual experimental results, ITC and kinITC data previously presented by Igde et al [14] were used. So far, the average k on value of all titration steps has been used to obtain the binding rate k on in the reaction equation above.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Multivalent Ligand-Receptor Binding Measured by kinITC

et al. 2019

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In addition to the conventional Isothermal Titration Calorimetry (ITC), kinetic ITC (kinITC) not only gains thermodynamic information, but also kinetic data from a biochemical binding process. Moreover, kinITC gives insights into reactions consisting of two separate kinetic steps, such as protein folding or sequential binding processes. The ITC method alone cannot deliver kinetic parameters, especially not for multivalent bindings. This paper describes how to solve the problem using kinITC and an invariant subspace projection. The algorithm is tested for multivalent systems with different valencies.

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Linear Precision Glycomacromolecules with Varying Interligand Spacing and Linker Functionalities Binding to Concanavalin A and the Bacterial Lectin FimH

Cited by 34 publications

References 69 publications

KinITC—One Method Supports both Thermodynamic and Kinetic SARs as Exemplified on FimH Antagonists

KinITC—One Method Supports both Thermodynamic and Kinetic SARs as Exemplified on FimH Antagonists

Implications of PCCA+ in Molecular Simulation

Modeling of Multivalent Ligand-Receptor Binding Measured by kinITC

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