Protein aggregation kinetics, mechanism, and curve-fitting: A review of the literature

Morris, Aimee M.; Watzky, Murielle A.; Finke, Richard G.

doi:10.1016/j.bbapap.2008.10.016

Cited by 629 publications

(695 citation statements)

References 147 publications

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“…This allows indirect spectrophotometric observation of in vitro fibrillogenesisturbidimetry ( Figure 1A). 19,29 The two sequential phases of fibrillogenesis, nucleation and fiber growth, feature independent rates, which can be determined graphically ( Figure 1B). The effects of six macromolecular crowders (PVP 40 kDa and 360 kDa, dextran 70 kDa and 200 kDa, and Ficoll 70 kDa and 400 kDa) on the kinetics of fibrillogenesis were assessed by turbidimetry.…”

Section: ■ Introductionmentioning

confidence: 99%

Synergistic Rate Boosting of Collagen Fibrillogenesis in Heterogeneous Mixtures of Crowding Agents

et al. 2015

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ABSTRACT:The competition for access to space that arises between macromolecules is the basis of the macromolecular crowding phenomenon, known to modulate biochemical reactions in subtle ways. Crowding is a highly conserved physiological condition in and around cells in metazoans, and originates from a mixture of heterogeneous biomolecules. Here, using collagen fibrillogenesis as an experimental test platform and ideas from the theory of nonideal solutions, we show that an entropy-based synergy is created by a mixture of two different populations of artificial crowders, providing small crowders with extra volume occupancy when in the vicinity of bigger crowders. We present the physiological mechanism by which synergistic effects maximize volume exclusion with the minimum amount of heterogeneous crowders, demonstrating how the evolutionarily optimized crowded conditions found in vivo can be reproduced effectively in vitro.

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Section: ■ Introductionmentioning

confidence: 99%

Synergistic Rate Boosting of Collagen Fibrillogenesis in Heterogeneous Mixtures of Crowding Agents

et al. 2015

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“…Kinetic studies and data curve fitting are keys to rigorous mechanistic studies [4]. When combined with experimental kinetic and thermodynamic data, models of aggregation kinetics can provide a unique and noninvasive way to gain qualitative and quantitative details about the aggregation mechanism and also help in the design of experiments and additives to more accurately predict or control aggregation rates [5].…”

Section: Introductionmentioning

confidence: 99%

Aggregation Kinetics for Monoclonal Antibody Products

Arora¹,

Bansal²,

Joshi³

et al. 2014

IJCEA

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Abstract-Monoclonal antibodies have emerged as a valuable class of therapeutic products. However, the industry still faces pertinent challenges with respect to product stability, particularly product aggregation that leads to toxicity and immunogenicity. The present project is an attempt to propose a kinetic model for the aggregation of monoclonal antibodies using the analysis of experiments that were performed for several different combinations of buffer type, temperature, pH and salt concentration pertaining to three types of chromatography -protein A chromatography, cation exchange chromatography and anion exchange chromatography. Modified Lumry Eyring model has been employed by taking into account the reversibility of each step in the aggregation process. MATLAB R2011b has been used to find the optimum values of the kinetic rate constants for the different cases. The model can go a long way in reducing the extent of aggregation by helping to accurately choose the experimental conditions with the minimum number of experiments.

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“…Here we introduce a theoretical model that combined with light-scattering experiments allows us to quantify these rates and the reversible binding energy as a function of T. We apply this method to the reversible aggregation of thermoresponsive polystyrene/poly(N-isopropylacrylamide) core-shell nanoparticles, as a model system for biomolecules. We find that the binding energy changes sharply with T, and relate this remarkable switchable behavior to the hydrophobic-hydrophilic transition of the thermosensitive nanoparticles.The association and self-organization of biomolecules [1] and nanoparticles [2] play a fundamental role in many physiological processes [3], such as protein amyloid formation which is responsible for neuro-degenerative diseases [4], as well as in technological processes. In particular the assembly of nanoparticles has become a key step in the synthesis of nanomaterials with new optical and mechanical properties [5].…”

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confidence: 99%

Quantifying the Reversible Association of Thermosensitive Nanoparticles

et al. 2011

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Under many conditions, biomolecules and nanoparticles associate by means of attractive bonds, due to hydrophobic attraction. Extracting the microscopic association or dissociation rates from experimental data is complicated by the dissociation events and by the sensitivity of the binding force to temperature (T). Here we introduce a theoretical model that combined with light-scattering experiments allows us to quantify these rates and the reversible binding energy as a function of T. We apply this method to the reversible aggregation of thermoresponsive polystyrene/poly(N-isopropylacrylamide) core-shell nanoparticles, as a model system for biomolecules. We find that the binding energy changes sharply with T, and relate this remarkable switchable behavior to the hydrophobic-hydrophilic transition of the thermosensitive nanoparticles.The association and self-organization of biomolecules [1] and nanoparticles [2] play a fundamental role in many physiological processes [3], such as protein amyloid formation which is responsible for neuro-degenerative diseases [4], as well as in technological processes. In particular the assembly of nanoparticles has become a key step in the synthesis of nanomaterials with new optical and mechanical properties [5]. In order to understand and control all these association processes, it is essential to understand and control the association kinetics [3,4]. This is challenging in the case of biomolecules and nanoparticles where often the microscopic binding energy, which directly determines the rate of dimer formation, is comparable to the thermal energy k B T. This poses two major difficulties: (i) the experimental detection of the binding energy has to be accurate down to the k B T scale, and (ii) the association kinetics is affected by dissociation events [6,7] since the binding energy is comparable with the average kinetic energy of the particles/molecules. It has been recently shown that a clear understanding of protein aggregation under physiological conditions cannot be achieved without the understanding of aggregate dissociation [7]. A further complication arises from the fact that the binding energy can be very sensitive to changes of the solution parameters, such as T and pH [3].In this Letter we propose a detection strategy that can overcome many of the difficulties to date in extracting quantitative information about the intrinsic rates of association and dissociation processes. The essence of the strategy is to combine a kinetic model with the analysis of dynamic light scattering (DLS) data of aggregation kinetics. Dynamic light scattering has proven to be a reliable tool for analyzing irreversible aggregation [8].Here we show that it can be used for measuring reversible processes as well, but the data analysis requires a new model fully accounting for dissociation processes. Here we introduce such a model and demonstrate its applicability in light scattering experiments of thermosensitive nanoparticles in aqueous suspension. We also show how to quantify the T-dependent int...

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Protein aggregation kinetics, mechanism, and curve-fitting: A review of the literature

Cited by 629 publications

References 147 publications

Synergistic Rate Boosting of Collagen Fibrillogenesis in Heterogeneous Mixtures of Crowding Agents

Synergistic Rate Boosting of Collagen Fibrillogenesis in Heterogeneous Mixtures of Crowding Agents

Aggregation Kinetics for Monoclonal Antibody Products

Quantifying the Reversible Association of Thermosensitive Nanoparticles

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