Accounting for Solvent Signal Offsets in the Analysis of Interferometric Sedimentation Velocity Data

Abstract

Sedimentation velocity (SV) analytical ultracentrifugation has re-emerged as an important tool in the characterization of biological macromolecules and nanoparticles. The computational analysis of the evolution of the macromolecular concentration profile allows the characterization of many hydrodynamic and thermodynamic properties of the macromolecules and their interactions. The Rayleigh interference optical system is often the detection method of choice, for its usually superior data quality and the wide applicability of refractive index sensitive detection. However, the interference optical system is also sensitive to the redistribution of co-solvent molecules, which are not of primary experimental interest. In principle, their contribution can be eliminated by an exact geometric and compositional match of the sample solution and the reference solution, achieving the complete optical subtraction of unwanted buffer signals. Unfortunately, in practice, this can often not be perfectly achieved for various reasons, leading to signal offsets arising from unmatched sedimentation of solvent components. If unrecognized, this can lead to significant misfit, accompanied by significant errors in the macromolecular sedimentation parameters. In the present work, we describe an approach of computationally accounting for signals from sedimenting buffer components through explicitly modeling their redistribution with Lamm equation solutions, implemented in the software SEDFIT. We demonstrate how this can restore the SV analysis to yield a high quality fit of the data and to provide correct macromolecular sedimentation parameters.

Hydrodynamic and Functional Analysis of HIV-1 Vif Oligomerization

“…MBP used in this study contained the vector-derived sequence NAAAEFPAGN, which could promote non-specific dimerization. In support of this, when MBP was cloned and expressed without these additional amino acids, AUC revealed that the protein is monomeric at high concentrations (64). The species observed around s 20,w = 3.8 S (44 kDa) have estimated molecular masses in close agreement with the calculated molecular masses of the monomeric MBP-Vif(101–176) (50 kDa) and MBP-Vif(101–192) (52 kDa).…”

Extended Fujita approach to the molecular weight distribution of polysaccharides and other polymeric systems

“…Experimentally, care has to be expressed in filling the cells – menisci in solvent and solution channels need to be carefully matched, otherwise necessitating extended models accounting for co-solvent re-distribution [45]. The scans to be included in the analysis should comprise representatively the entire sedimentation process, from the beginning of depletion near the meniscus from the sedimentation of the fastest species at early times up to the virtual complete sedimentation of the smallest species from the radial observation window.…”

The analysis of macromolecular interactions by sedimentation equilibrium

“…100 scans were acquired at 7 minute intervals and a rotor speed of 50 krpm; absorbance data were collected as single absorbance measurements at 280 nm using a radial spacing of 0.003 cm. Data were analyzed in SEDFIT 12.1b [5] in terms of a continuous c( s ) distribution; when required the use of a buffer mismatch model was implemented in order to account for slight meniscus mismatches [65]. Solution densities ρ and viscosities η were calculated using the program SEDNTERP 1.09 [40].…”