Machine learning enabled protein secondary structure characterization using drop-coating deposition Raman spectroscopy
Abstract
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-1) and amide II region (1500-1600 cm-1) 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, 310-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, 310-helices, random, turns, and bends. The PLS model was further tested…
Read the paper
Related studies
- Synthesis, In Vitro and In Silico Molecular Docking Studies of Novel Phthalimide–Pyrimidine Hybrid Analogues to Thalidomide as Potent Antitubercular Agents
- Exploring the inhibitory performance of expired moxifloxacin and norfloxacin on copper corrosion in saline environment
- Molecular investigation of antimicrobial peptides against Helicobacter pylori proteins using a peptide-protein docking approach
- Screening of novel narrow-spectrum benzofuroxan derivatives for the treatment of multidrug-resistant tuberculosis through in silico, in vitro, and in vivo approaches
- Synthesis, Characterization, Anticancer, Antioxidant, and In Silico Evaluation of Semicarbazides and Thiosemicarbazides
- Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.