Lead identification and optimization in crude samples using label free resonant acoustic profiling


  • This article is published in Journal of Molecular Recognition as a special issue on Affinity 2009, edited by Gideon Fleminger, Tel-Aviv University, Tel-Aviv, Israel and George Ehrlich, Hoffmann-La Roche, Nutley, NJ, USA.


Detecting the molecular basis of protein–protein recognition is an essential element in understanding protein function because their ability to form specific complexes with other proteins underlies most cellular processes. The use of labels has limitations, such as changes to the binding kinetics due to the alterations in structure and function that occur with label addition, difficulty in detecting biochemical activities and the need for additional steps in assay development. These issues have driven the development of label-free formats for identifying the full range of biochemical activities.

Although optical-based systems dominate the label-free biosensor market, electrochemical, piezoelectric and acoustic devices represent similar but significantly less expensive alternatives. Acoustic biosensors have been employed in the label-free detection of an incredibly broad range of analytes, from interfacial chemistries and lipid membranes, to small molecules and whole cells.

Resonant acoustic profiling (RAP) technology offers label-free, real-time analysis of biomolecular interactions and offers an efficient way to optimize the development and production process of recombinant proteins. RAP measures only the physical binding events and is insensitive to refractive index and colour changes. This enables direct measurement in undiluted crude and complex samples, such as cell culture media or periplasmic extracts, without intensive assay calibration. This advantage simplifies experimental design and eliminates expensive time-consuming purification of often limited material, while delivering high content information. In this respect RAP technology reduces costs and increases the throughput and the density of information to optimize and control the processes more effectively. Copyright © 2010 John Wiley & Sons, Ltd.