The proteomes of cells, tissues, and organisms reflect active cellular processes and change continuously in response to intracellular and extracellular cues. Deep, quantitative profiling of the proteome, especially if combined with mRNA and metabolite measurements, should provide an unprecedented view of cell state, better revealing functions and interactions of cell components. Molecular diagnostics and biomarker discovery should benefit particularly from the accurate quantification of proteomes, since complex diseases like cancer change protein abundances and modifications. Currently, shotgun mass spectrometry is the primary technology for high-throughput protein identification and quantification; while powerful, it lacks high sensitivity and coverage. We draw parallels with next-generation DNA sequencing and propose a strategy, termed fluorosequencing, for sequencing peptides in a complex protein sample at the level of single molecules. In the proposed approach, millions of individual fluorescently labeled peptides are visualized in parallel, monitoring changing patterns of fluorescence intensity as N-terminal amino acids are sequentially removed, and using the resulting fluorescence signatures (fluorosequences) to uniquely identify individual peptides. We introduce a theoretical foundation for fluorosequencing and, by using Monte Carlo computer simulations, we explore its feasibility, anticipate the most likely experimental errors, quantify their potential impact, and discuss the broad potential utility offered by a high-throughput peptide sequencing technology.
The development of next-generation DNA and RNA sequencing methods has transformed biology, with current platforms generating >1 billion sequencing reads per run. Unfortunately, no method of similar scale and throughput exists to identify and quantify specific proteins in complex mixtures, representing a critical bottleneck in many biochemical and molecular diagnostic assays. What is urgently needed is a massively parallel method, akin to next-gen DNA sequencing, for identifying and quantifying peptides or proteins in a sample. In principle, single-molecule peptide sequencing could achieve this goal, allowing billions of distinct peptides to be sequenced in parallel and thereby identifying proteins composing the sample and digitally quantifying them by direct counting of peptides. Here, we discuss theoretical considerations of single molecule peptide sequencing, suggest one possible experimental strategy, and, using computer simulations, characterize the potential utility and unusual properties of this future proteomics technology.