Multivariate Secondary Structure Estimation (SSE) with High Throughput CD

Download PDF May 26, 2017

Introduction

High Throughput CD System for Automated Protein Secondary Structure Estimation

This application note evaluates the use of the J-1500 high throughput CD (HTCD) system for automated protein secondary structure estimation.  Measurements reveal information regarding the secondary structure or backbone conformation of a protein. CD measurements can be used to determination the secondary structure content of a biomolecule to elucidate the relationship between structure and function or verify protein stability. The high throughput CD system with JASCO’s  multivariate Secondary Structure Estimation (SSE) program can be used to quickly analyze CD data for routine quantitative research and quality control. A large number of samples can be automatically measured and efficiently analyzed using the High-Throughput CD (HTCD) system with Spectra Manager and multivariate SSE program.

The secondary structure of eight proteins are evaluated using the Multivariate SSE program.

High throughput CD
J-1500 CD Spectrometer

Experimental

Measurement conditions   
Data acquisition interval0.1 nmResponse time2 seconds
Spectral bandwidth1 nmScan speed100 nm/min
Accumulations2 timesPath length1 mm

Keywords

Protein secondary structure estimation, protein SSE, circular dichroism, high throughput circular dichrosim, high throughput CD, HTCD, Multivariate, Biochemistry, Pharmaceuticals, multiplate, 96 well plate

Results

Prior to obtaining the sequence measurement, the pathlength and mean residue molar concentrations have to be specified so that the optical constants can be automatically calculated for the secondary structure estimation (Figure 1).

Figure 1. Multivariate parameter setup specifying path length and mean residue molar concentration

CD measurements in the far-UV region were obtained for eight 0.1 mg/mL protein samples with varying secondary structure compositions and are shown in Figure 2.

Figure 2. CD spectra of protein samples

The secondary structure estimated using PLS multivariate analysis of the protein spectra are shown in Figure 1 . Table 1 compares these results with X-ray crystallography data. Figure 3 shows the secondary structure results from the JWMVS program.

Table 1. Comparison of Multivariate SSE results with X-ray crystallography data

   Helix (%) Sheet (%)Turn (%)  Other (%)
 Lysozyme (Lyz) PLS 42.8 0.4 24.4 32.4
 X-ray 41 4 19 35
 Cytochrome C (CytC) PLS 42.6 3.1 18.1 36.2
 X-ray 42 8 9 42
 Concanavalin A (ConA) PLS 5.1 44.6 13.9 36.4
 X-ray 2 36 12 49
 β-Lactoglobulin PLS 17.8 35.5 12.3 34.4
 X-ray 13 34 13 41
 Trypsin Inhibitor PLS 13.9 25.3 17.3 43.5
 X-ray 2 33 10 55
 Ribonuclease A (RNaseA) PLS 21.5 14.7 22.4 41.4
 X-ray 22 19 11 48
 Human Serum Albumin (HSA) PLS 66.8 1.3 8.2 23.7
 X-ray 72 0 8 20
 Hemoglobin (Hb) PLS 61.1 0 18 20.9
 X-ray 75 0 10 15

1 W. C. Johnson, Proteins: Structure, Function, and Genetics (1999), 35, 307-312.
2 PDB: Trypsin inhibitor: 1ba7 (DSSP), HAS: 1bm0 (DSSP)

Figure 3. Results  using the Multivariate SSE program

Conclusion

This application note illustrates the use of the Multivariate SSE program to estimate secondary structure following CD measurement. The PLS method shows good agreement with secondary structure results obtained by X-ray crystallography.

About the Author

Leah Pandiscia received her PhD from Drexel University where she studied Biophysical Chemistry. She is a Spectroscopy Applications Scientist at JASCO.