Dataset Information

Multiplex measurement of protein-peptide dissociation constants using dialysis and mass spectrometry.

ABSTRACT: We propose a high-throughput method for quantitatively measuring hundreds of protein-peptide binding affinities in parallel. In this assay, a solution of protein is dialyzed into a buffer containing a pool of potential binding peptides, such that upon equilibration the relative abundance of a peptide species is mathematically related to that peptide's dissociation constant, K_d . We use isobaric multiplexed quantitative proteomics to simultaneously determine the relative abundance, and hence the K_d and its associated error, for an entire peptide library. We apply this technique, which we call PEDAL (parallel equilibrium dialysis for affinity learning), to determine accurate K_d 's between a PDZ domain and hundreds of peptides, spanning an affinity range of multiple orders of magnitude in a single experiment. PEDAL is a convenient, fast, and low-cost method for measuring large numbers of protein-peptide affinities in parallel, providing a rare combination of true in-solution binding equilibria with the ability to multiplex.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC10031237 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Multiplex measurement of protein-peptide dissociation constants using dialysis and mass spectrometry.

Zhao Yu Y Grigoryan Gevorg G

Protein science : a publication of the Protein Society 20230401 4

We propose a high-throughput method for quantitatively measuring hundreds of protein-peptide binding affinities in parallel. In this assay, a solution of protein is dialyzed into a buffer containing a pool of potential binding peptides, such that upon equilibration the relative abundance of a peptide species is mathematically related to that peptide's dissociation constant, K<sub>d</sub> . We use isobaric multiplexed quantitative proteomics to simultaneously determine the relative abundance, and ...[more]

PMID: 36823715

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Project description:Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.

Dataset Information

Multiplex measurement of protein-peptide dissociation constants using dialysis and mass spectrometry.

Publications

Multiplex measurement of protein-peptide dissociation constants using dialysis and mass spectrometry.

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