Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier

We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates. Author summaryScientists often wish to know which proteins, and at what quantities, are present in a sample. The field of proteomics offers a number of technologies that aid in this, such as tandem mass spectrometry and immunoassays, that provide different tradeoffs between sensitivity, throughput, and generality. One new technology, known as fluorosequencing, detects and provides partial sequences for individual peptide or protein molecules from a sample in a highly parallelized fashion. However, as only partial sequences are measured, the resulting sequencing reads must be matched to a reference database of possible proteins, such as might be obtained from the human genome. We describe a suitable computer algorithm for performing this matching of fluorosequencing reads to a reference database while accounting for the most prevalent types of sequencing errors. We detail its performance and implementation, and describe a number of uncommon algorithmic improvements and approximations which allow this approach to scale to classification against the whole human proteome. The resulting software, known as whatprot, allows researchers to interpret fluorosequencing reads and better apply this emergent single molecule protein sequencing technology.