Machine learning enabled protein secondary structure characterization using drop-coating deposition Raman spectroscopy.

Protein structure characterization is critical for therapeutic protein drug development and production. Drop-coating deposition Raman (DCDR) spectroscopy offers rapid and cost-effective acquisition of vibrational spectral data characteristic of protein secondary structures. Amide I region (1600 -1700 cm) and amide II region (1500-1600 cm) of DCRD Raman spectra measured for model proteins of varying molecular size and structural distribution were first analyzed by peak fitting for their proportions of six secondary structure motifs: α-helices, 3-helices, β-sheets, turns (β-turns and γ-turns), bends, and random coil. The high spectral resolution and superior signal-to-noise of DCDR spectra made it possible to estimate all six structural motifs at accuracy comparable to X-ray crystallographic measurement. The ease of DCDR measurement was further explored by introducing machine learning algorithm to spectroscopic data analysis. Partial Least Squares (PLS) regression modeling was used as a machine learning tool to predict the protein secondary structural composition from the amide I band of model proteins. Once developed on a training sample set, the PLS model was tested by applying to a sample set that was not used previously for model development. Low prediction errors were achieved at 1.36 %, 0.78 %, 0.42 % 0.41 %, 0.81 %, and 0.52 %, respectively for the six structural component, α-Helix, β-Sheet, 3-helices, random, turns, and bends. The PLS model was further tested on an independent sample set that contains three IgG proteins. The proportion ofα-Helix, β-Sheet, 3-Helix were estimated with an error of 3.1 %, 2.3 % and 2.8 %, respectively.