Discovery of Amino Acid fingerprints transducing their amphoteric signatures by field-effect transistors

Protein sequencing is key to many biological fields to advance science and prospect medical applications based on proteomics. However, even with advanced techniques such as mass spectrometry it is hard to detect the fluctuations in AA sequences and optical/geometrical isomers, and cannot provide wide access to ex-novo sequencing of low quantities of proteins. We are presenting a new and disruptive method based on FET sensors for AA/polypeptide detection. We show unique signals (fingerprints) of every single AA and polypeptide mutations by solving the site-binding model self-consistently with the Gouy-Chapman-Stern model. The surface potential (Ψo), 2nd gradient of the surface potential (δΨo2/δpH2) and total surface capacitance (CT) are used as fingerprint signals to differentiate between the amino acids including the drain current variation as the signal transduction. These fingerprints are based on orthogonal properties of the AAs, which are the different proton affinities of each of the radicals, their dielectric constant and effective length, which is sensitive to the relative positions of the radicals to distinguish between isomers. We studied the generation of signals and proposed a novel noise-filtering technique based on the Fast-Fourier Transform (FFT) and the significantly improved analytical model which is solved iteratively to reduce data loss. The minimum combined fingerprint resolution is 0.1 units of pH for the separation of singularities found in δΨo2/δpH2, linked to the minimum capacitance of the AAs with a needed resolution of 0.01mF/m2 for surface densities of 1014cm-2, and which can be normalized to lower densities. The effect of noise (>SNR=10dB) and silanol sites can be negated by correlating the AAs signatures from δΨo2/δpH2 and capacitance. Thus, the designed methodology and approach can help immensely in designing a new and efficient tool for protein sequencing while solving the problems related to the signal transduction of sensors.