The Spectralis model aims to improve a given set of de novo predictions through an auxiliary task of "bin classification." Operating on a mass axis discretized into 1 Da bins, the task involves predicting whether a given bin contains a singly charged b-or y-ion. An accurate solution to this task will, in principle, allow for a direct read-out of the corresponding peptide sequence. This prediction task is performed using a CNN that employs a novel type of convolutional layer, an "amino-acid gapped" layer, consisting of convolutional filters with gaps corresponding to the masses of the amino acids. The Spectralis model leverages predictions made by existing de novo prediction methods (Casanovo and Novor) to transform them into more accurate predictions. The paper also proposes a method, Spectralis-score, for recalibrating scores from Novor and Casanovo using a machine learning post-processor. Finally, the paper describes a genetic algorithm method, Spectralis-EA, that takes a candidate peptide predicted by Casanovo, produces a population of peptide variants, and then employs a genetic algorithm to optimize the Spectralis-score.