An automated interface for sedimentation velocity analysis in SEDFIT

Abstract

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.

Fig 1. 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.

Fig 2. 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 2^nd data point of every 2^nd 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.

Fig 3. Comparison of c(s) 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 s_w 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 s_w 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.

Fig 4. 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.