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A minimal model of peptide binding predicts ensemble properties of serum antibodies.

ABSTRACT: BACKGROUND: The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings. RESULTS: Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones. CONCLUSIONS: Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.

SUBMITTER: Greiff V

PROVIDER: S-EPMC3311590 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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A minimal model of peptide binding predicts ensemble properties of serum antibodies.

Greiff Victor V Redestig Henning H Lück Juliane J Bruni Nicole N Valai Atijeh A Hartmann Susanne S Rausch Sebastian S Schuchhardt Johannes J Or-Guil Michal M

BMC genomics 20120221

<h4>Background</h4>The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy an ...[more]

PMID: 22353141

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Project description:Minimal physiologically-based pharmacokinetic (mPBPK) models provide a sensible modeling approach when fitting only plasma (or blood) data yielding physiologically-relevant PK parameters that may provide more practical value than parameters of mammillary models. We propose a second-generation mPBPK model specifically for monoclonal antibodies (mAb) by including (lumping) several essential components of mAb PK used in full PBPK models. These components include convection as the primary mechanism of antibody movement from plasma into tissues and return to plasma with interstitial fluid as the major extravascular distribution space. The model divides tissue spaces into two groups according to their vascular endothelial structure, leaky and tight, which consequently allows discernment of two types and general sites of distribution. This mPBPK model was applied to two mAbs in mice and ten mAbs with linear kinetics in humans. The model captured their plasma PK profiles well with predictions of concentrations in interstitial fluid for two types of tissues. Predictions of tissue concentrations for mAb 7E3 and 8C2 were consistent with actual measurements in mice, indicating the feasibility of this model in assessing extravascular distribution in the two categories of tissues. The vascular reflection coefficients (σ₁) of tight tissues (V(tight)) ranged 0.883-0.987 and coefficients (σ₂) for leaky tissues (V(leaky)) ranged 0.311 to 0.837. The plasma clearance (CL(p)) varied among the mAbs in humans from 0.0054 to 0.03 L/h. In addition, applying this model generates parameters for mAb transcapillary escape rates and assesses major sites of elimination. Four of ten mAbs exhibited better fitting statistics premised on elimination from interstitial fluid than from plasma. This approach allows comparisons of mAb PK when only plasma data are available, provides more realistic parameters and predictions than mammillary models, and may provide an intermediate step towards utilizing full PBPK models for mAbs.

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A minimal model of peptide binding predicts ensemble properties of serum antibodies.

Publications

A minimal model of peptide binding predicts ensemble properties of serum antibodies.

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