Global Multi-Method Analysis of Affinities and Cooperativity in Complex Systems of Macromolecular Interactions

Cooperativity, multisite, and multicomponent interactions are hallmarks of biological systems of interacting macromolecules. Their thermodynamic characterization is often very challenging due to the notoriously low information content of binding isotherms. We introduce a strategy for the global multimethod analysis of data from multiple techniques (GMMA) that exploits enhanced information content emerging from the mutual constraints of the simultaneous modeling of orthogonal observables from calorimetric, spectroscopic, hydrodynamic, biosensing, or other thermodynamic binding experiments. We describe new approaches to address statistical problems that arise in the analysis of dissimilar data sets. The GMMA approach can significantly increase the complexity of interacting systems that can be accurately thermodynamically characterized

List of Additional Output Parameters.

Output will be written in an xml formatted file in the designated output folder. Output parameters include the same parameters regarding data, model, and solution conditions as the input parameters, but also include the additional parameters in this table.</p

Comparison of <i>c</i>(<i>s</i>) distributions computed with the command line initialization of SEDFIT and with manual operation.

The distribution from command line operation (Fig 2), and exhibits a monomer peak at 6.477 S with 29.20% of signal, a trace degradation product at 4.199 S with 0.95% of signal, a dimer peak at 9.473 S with 12.51% of signal, and higher aggregates with collective sw 16.799 S and 51.77% of signal. The analogous manually operated analysis producing a monomer peak at 6.481 S with 29.27% of signal, a degradation product of 4.178S with 0.92% of the signal, a dimer peak at 9.488 S with 12.51% of signal, and higher aggregates with collective sw of 16.81 S with 51.73% of signal. Integration and plot were made using the software GUSSI [59], which can be spawned from the script mlSEDFIT.</p

Example input xml file.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an indispensable tool for the study of particle size distributions in biopharmaceutical industry, for example, to characterize protein therapeutics and vaccine products. In particular, the diffusion-deconvoluted sedimentation coefficient distribution analysis, in the software SEDFIT, has found widespread applications due to its relatively high resolution and sensitivity. However, a lack of suitable software compatible with Good Manufacturing Practices (GMP) has hampered the use of SV-AUC in this regulatory environment. To address this, we have created an interface for SEDFIT so that it can serve as an automatically spawned module with controlled data input through command line parameters and output of key results in files. The interface can be integrated in custom GMP compatible software, and in scripts that provide documentation and meta-analyses for replicate or related samples, for example, to streamline analysis of large families of experimental data, such as binding isotherm analyses in the study of protein interactions. To test and demonstrate this approach we provide a MATLAB script mlSEDFIT.</div

Example for postprocessing of results from SEDFIT analysis in mlSEDFIT.

The output generated through the command line interface can be read in the mlSEDFIT script. For example, integration of distribution peaks can be carried out in this script after mouse clicks on the peaks in the distribution plot, as shown.</p

Sedimentation analysis of a stressed NISTmAb sample at 50,000 rpm and 20°C using the command line operation of SEDFIT.

Top: Scan files and best fit (for clarity, showing black dots only for every 2nd data point of every 2nd scan) with a c(s) model automatically converged to a final rmsd of 0.006743 OD (colored lines). Progression of scan time is indicated by color from purple to red. Middle and Bottom: Residuals bitmap and residuals overlay. Plot was made using the software GUSSI [59], which is spawned from the script mlSEDFIT.</p

List of Input Parameters.

Input parameters will be read from a file named as third command line parameter. It is in xml format and parameters are case sensitive. An example can be found in S1 File.</p

Flowchart for the use of SEDFIT in command line operation with a secondary software.

The secondary software organizes access control and preprocesses data. After SEDFIT is spawned by the secondary program, it reads a specifically formatted input file, and provides a graphical user interface with options controlled by the secondary program. Upon termination of SEDFIT analysis, its output files are read, and quality control, postprocessing and documentation by the secondary program can take place. This flow allows a single or multiple copies of SEDFIT to be utilized solely as a computational module within a framework of the secondary program, which may enforce GMP compatibility, incorporate results into meta-analyses, and/or provide an expert system or AI for automated analysis and quality control.</p

Analysis of Protein Interactions with Picomolar Binding Affinity by Fluorescence-Detected Sedimentation Velocity

The study of high-affinity protein interactions with equilibrium dissociation constants (<i>K</i>_D) in the picomolar range is of significant interest in many fields, but the characterization of stoichiometry and free energy of such high-affinity binding can be far from trivial. Analytical ultracentrifugation has long been considered a gold standard in the study of protein interactions but is typically applied to systems with micromolar <i>K</i>_D. Here we present a new approach for the study of high-affinity interactions using fluorescence detected sedimentation velocity analytical ultracentrifugation (FDS-SV). Taking full advantage of the large data sets in FDS-SV by direct boundary modeling with sedimentation coefficient distributions <i>c</i>(s), we demonstrate detection and hydrodynamic resolution of protein complexes at low picomolar concentrations. We show how this permits the characterization of the antibody–antigen interactions with low picomolar binding constants, 2 orders of magnitude lower than previously achieved. The strongly size-dependent separation and quantitation by concentration, size, and shape of free and complex species in free solution by FDS-SV has significant potential for studying high-affinity multistep and multicomponent protein assemblies

Global density variation SV analysis of the IgG sample recorded with the absorbance data at 280 nm.

<p>The sets of panels present the data in (A) H₂O, (B) 50% H₂¹⁸O, and (C) 90% H₂¹⁸O based buffer. The presentation is analogous to that in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026221#pone-0026221-g001" target="_blank">Figure 1</a>. Rmsd of the fit was 0.00462 OD (A), 0.00632 OD (B), and 0.00465 OD (C). The best-fit <i>c</i>(<i>s</i>) distribution from this analysis is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026221#pone-0026221-g005" target="_blank">Figure 5</a>, and the projections of the error surface in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026221#pone-0026221-g009" target="_blank">Figure 9</a>.</p