The development of next-generation DNA sequencing methods for quickly acquiring genome and gene expression information has transformed biology. The basis of """"""""""""""""next-gen"""""""""""""""" DNA sequencing is the acquisition of large numbers of short reads (typically 35-500 nucleotides) in parallel. Currently available single-molecule next-gen sequencing platforms monitor the sequencing of single DNA molecules using fluorescence microscopy, allowing for approx. a 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, molecular diagnostic, and biomarker discovery assays. What is urgently needed is a massively parallel method, akin to next-gen DNA sequencing, for identifying and quantifying individual peptides or proteins in a sample. I propose a single-molecule peptide sequencing strategy that will achieve exactly this goal. This will in principle allow billions of distinct peptides to be sequenced in parallel (or at least sequenced sufficiently to provide informative sequence patterns), thereby identifying proteins composing the sample and digitally quantifying them by direct counting of peptides. This transformative approach should enable the quantitative, massively parallel sequencing of proteins. Success of the proposed research wil create a technology suficiently ready for real-world protein sequencing problems. Such an approach would have broad applications across biology and medicine, and could be as fundamental for proteins as, for example, PCR is for nucleic acid research. Potential applications include, for example, profiling of protein expression in normal body niches or in disease, metaproteomics, profiling the circulating serum antibodies, the search for and quantification of protein post-translational modifications, and, of particular interest, identifyin biomarkers relevant to cancer and infectious diseas
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