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Development of a simple, label-free screening technique capable of precisely and directly sensing interaction-in-solution over a size range from small molecules to large proteins such as antibodies could offer an important tool for researchers and pharmaceutical companies in the field of drug development. In this work, we present a thermostable Raman interaction profiling (TRIP) technique that facilitates low-concentration and low-dose screening of binding between protein and ligand in physiologically relevant conditions. TRIP was applied to eight protein–ligand systems, and produced reproducible high-resolution Raman measurements, which were analyzed by principal component analysis. TRIP was able to resolve time-depending binding between 2,4-dinitrophenol and transthyretin, and analyze biologically relevant SARS-CoV-2 spike-antibody interactions. Mixtures of the spike receptor–binding domain with neutralizing, nonbinding, or binding but nonneutralizing antibodies revealed distinct and reproducible Raman signals. TRIP holds promise for the future developments of high-throughput drug screening and real-time binding measurements between protein and drug. 

Abstract Amino‐acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.