2018 YPIC Challenge: A case study in characterizing an unknown protein sample

Abstract

For the 2018 YPIC Challenge contestants were invited to try to decipher two unknown English questions encoded by a synthetic protein expressed in Escherichia coli. In addition to deciphering the sentence, contestants were asked to determine the 3D structure and detect any post-translation modifications left by the host organism. We present our experimental and computational strategy to characterize this sample by identifying the unknown protein sequence and detecting the presence of post-translational modifications. The sample was acquired with dynamic exclusion disabled to increase the signal-to-noise ratio of the measured molecules, after which spectral clustering was used to generate high-quality consensus spectra. De novo spectrum identification was used to determine the synthetic protein sequence, and any post-translational modifications introduced by E. coli on the synthetic protein were analyzed via spectral networking. This workflow resulted in a de novo sequence coverage of 70%, on par with sequence database searching performance. Additionally, the spectral networking analysis indicated that no systematic modifications were introduced on the synthetic protein by E. coli. The strategy presented here can be directly used to analyze samples for which no protein sequence information is available or when the identity of the sample is unknown. All software and code to perform the bioinformatics analysis is available as open source, and self-contained Jupyter notebooks are provided to fully recreate the analysis.

Current state, existing challenges, and promising progress for de novo sequencing and assembly of monoclonal antibodies

“…BICEPS follows a similar approach by using Bayesian statistical criteria to identify the optimal trade-off between the database and spectral information to identify proteins (70). Hence, homology search can be combined together with de novo sequencing to improve the discovery of protein sequence information and overcome problems caused by mass segment errors (22,71,72). Commercial software packages like PEAKS AB (23) and Supernovo (9) use antibody germline sequences as a starting point together with de novo sequencing results to identify monoclonal antibodies.…”

Unified and standardized mass spectrometry data processing in Python using spectrum_utils

“…Since its introduction, spectrum_utils has been used in tools that perform spectral library searching 10 and spectrum clustering, 11 to preprocess MS/MS spectra prior to deep learning applications, 12,13 to plot MS/MS spectra from online data repositories, 14 and to assist in MS/MS processing and visualization efforts for dozens of other projects. 15–22…”

“…Since its introduction, spectrum_utils has been used in tools that perform spectral library searching 10 and spectrum clustering, 11 to preprocess MS/MS spectra prior to deep learning applications, 12,13 to plot MS/MS spectra from online data repositories, 14 and to assist in MS/MS processing and visualization efforts for dozens of other projects. [15][16][17][18][19][20][21][22] Here we present recent updates to spectrum_utils. It has now been extended with functionality for common and versatile data manipulation and visualization tasks when working with MS data in Python, including support for community data standards, updated visualization functionalities, and performance improvements.…”