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

Protein sequencing is a key to many of the biological fields to advance science at the sub-atomic level. However, even with advanced techniques such as mass spectrometry, the fluctuations in the amino acids/genes are hard to comprehend. We are presenting something new and disruptive related to FET-based sensors for single amino acid/polypeptide detection with the effect of redundant reactive sites and reduced noise. We have used a modified site-binding model in self-consistency with the Gouy-Chapman-Stern model to express the fingerprints of a single amino acid or polypeptide mutation. Surface potential, 2nd gradient of the surface potential (δΨ2/δpH2) and total surface capacitance are used as fingerprint signals to differentiate between the amino acids including the drain current variation as the signal transduction. A novel noise-filtering technique is proposed using the Fast-Fourier Transform and derived analytical model solved iteratively to smoothen the experimental data while reducing the possible data loss. The minimum pH resolution of δΨ2/δpH2 is 0.1 and the minimum capacitance resolution is 0.01mF/m2 for differentiating the AAs or polypeptides. The effect of noise (>SNR=10dB) and silanol sites can be negated by correlating the AAs signatures from δΨ2/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.