Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review.

Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.

Data storage using peptide sequences.

Humankind is generating digital data at an exponential rate. These data are typically stored using electronic, magnetic or optical devices, which require large physical spaces and cannot last for a very long time. Here we report the use of peptide sequences for data storage, which can be durable and of high storage density. With the selection of suitable constitutive amino acids, designs of address codes and error-correction schemes to protect the order and integrity of the stored data, optimization of the analytical protocol and development of a software to effectively recover peptide sequences from the tandem mass spectra, we demonstrated the feasibility of this method by successfully storing and retrieving a text file and the music file Silent Night with 40 and 511 18-mer peptides respectively. This method for the first time links data storage with the peptide synthesis industry and proteomics techniques, and is expected to stimulate the development of relevant fields.

Establishment of a spectral database for classification of edible oils using matrix-assisted laser desorption/ionization mass spectrometry.

In this study, we aim to establish a comprehensive spectral database for analysis of edible oils using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). More than 900 edible oil samples, including 30 types of edible oils, were analyzed and compared, and the characteristic peaks and spectral features of each edible oil were obtained. Edible oils were divided into eight groups based on their characteristic spectral patterns and principal component analysis results. An overall correct rate of 97.2% (98.1% for testing set) was obtained for classification of 435 edible oil products using partial least square-discriminant analysis, with nearly 100% correct rate for commonly used edible oils. Differentiation of counterfeit edible oils, repeatedly cooked edible oils and gutter oils from normal edible oils could also be achieved based on the MALDI-MS spectra. The establishment of this spectral database provides reference spectra for spectral comparison and allows rapid classification of edible oils by MALDI-MS.