Amino-acids recognition and fast, cheap and reliable methods for proteins and peptides sequencing is a serious bottleneck for the rising of proteomics, i.e. the study of the proteins content and their functionality within a cell. The current experimental techniques preclude the reliable sequencing of the huge protein population within a single cell that is two orders of magnitude larger than the human genome.
Recently many applications of nano-devices and nano-structured materials have allowed the development of new nano-technology strategies to attain DNA sequencing using, for instance, the ion blockade current signals flowing through a nanopore during DNA translocation. However such strategy is of little aid in the case of protein sequencing for many reasons related, basically, to the more complex variety of objects to be detected.
As an alternative approach, transverse current measurements through nano-gaps have been considered for protein sequencing using mainly Au based nano-device.
The present project is oriented to study new graphene based nano-devices for amino-acids recognition and protein sequencing through quantum tunneling current measurements.
The nano-structured physical system and phenomena of interest involve several aspects, such as the local vibrational modes and phonon assisted tunneling events, the steady state approach versus the typical ground state one, Coulomb Blockade phenomena etc. that are of interest also for fundamental processes involved in the physics of transport in mesoscopic media.
Fast, Reliable and cheap AAs sequencing of proteins and peptides is a key factor that could push proteomics ahead thus helping diagnosis and therapeutics. Presently peptides and protein sequencing is slow, expensive, computationally demanding and affected by errors and limits. Therefore nowadays new methods are being studied and considered benefitting of the exceptional rise of nanotechnology and nano-science, particularly concerning hybrid systems involving inorganic surfaces and nano-structures and organic o biological molecules such as proteins.
Among the methods under study, nano-gap sequencing, also in conjunction with more usual blockade ionic current, seems very promising and currently some articles have reported the successful identification of AAs sequences using, for instance, STM assisted recognition or mechanically controllable break junctions (MCNJs). In these cases, hydrogen bonds are typically formed through recognition molecules and the AAs but different sizes nanogaps were necessary and up to five AAs and post-translational modified AAs were identified with a confidence of 95%.
However these techniques must be combined with machine learning techniques for large dataset analysis and are still limited to few cases of recognition.
In other cases, graphene based nanopore/nanoribbon geometries were employed to measure simultaneously both the blockade and the transverse currents exploiting the electrophoretic translocation.
The usage of the GNRs gaps, as proposed in the present study, would represent an important improvement in the recognition of AAs because the suggested protocol is basically independent on the AAs size and does not involve necessarily the usage of specific recognition molecules that may limit the of validity of MCNJs nano-gaps detection to specific AAs.
Moreover, GNRs allow the exploitation (at least partially due to slightly different properties of nano-ribbons with respect to graphene foils) of the exceptional conductance properties of graphene and, contextually, guarantee the single molecule detection due to the atomic size resolution of the device; these aspect would induce improved signals with reduced role of post-processing data seta analyses and improved recognition confidence.
Lastly, but more importantly, the array geometry of the GNRs device would exploit simultaneous transverse current signals coming from PBs and the AAs side chains that could improve the recognition confidence. The extreme control in nano-device manufacturing, that is nowadays possible down to the atomistic scale, is a good basis to a successful and practical outcome of the present proof of concept study.