Supplementary Dataset 15 - Human proteome-derived peptide libraries generated with trypsin, chymotrypsin, and GluC
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Exploris 480
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Amy Weeks
PROVIDER: MSV000091261 | MassIVE | Sat Feb 11 17:47:00 GMT 2023
SECONDARY ACCESSION(S): PXD040058
REPOSITORIES: MassIVE
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Project description:High specificity and ease of use make trypsin the most used enzyme in proteomics. Proteases with complementary cleavage specificity to trypsin have been applied to obtain additional data. However, use of proteases with broad specificity proved especially challenging. In this work, we analyzed the characteristics of five protease alternatives to trypsin for protein identification and sequence coverage when applied to S. pombe whole cell lysates. The specificity of the protease heavily impacted on the number of proteins identified. Proteases with higher specificity let to the identification of more proteins than proteases with lower specificity. However, AspN, GluC, chymotrypsin and proteinase K largely benefited from being paired with trypsin in sequential digestion, as had been shown by us for elastase before. In the most extreme case, the addition of trypsin to a proteinase K digest increased the number of identified proteins by 524 %. Also, AspN (82 %) and GluC (74 %) protein identifications largely improved following the additional digestion with trypsin. In general, protein identifications improved most over the use of the single protease when the enzymes followed on an initial digestion with trypsin. In the most extreme case, the sequential digest with trypsin and AspN yielded even higher number of protein identifications than digesting with trypsin alone.
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Project description:Mesotrypsin is an unusual human trypsin isoform with inhibitor resistance and the ability to degrade trypsin inhibitors. Degradation of the protective serine protease inhibitor Kazal type 1 (SPINK1) by mesotrypsin in the pancreas may contribute to the pathogenesis of pancreatitis. Here we tested the hypothesis that the regulatory digestive protease chymotrypsin C (CTRC) mitigates the harmful effects of mesotrypsin by cleaving the autolysis loop. As human trypsins are post-translationally sulfated in the autolysis loop, we also assessed the effect of this modification. We found that mesotrypsin cleaved in the autolysis loop by CTRC exhibited catalytic impairment on short peptides due to a 10-fold increase in Km , it digested β-casein poorly and bound soybean trypsin inhibitor with 10-fold decreased affinity. Importantly, CTRC-cleaved mesotrypsin degraded SPINK1 with markedly reduced efficiency. Sulfation increased mesotrypsin activity but accelerated CTRC-mediated cleavage of the autolysis loop and did not protect against the detrimental effect of CTRC cleavage. The observations indicate that CTRC-mediated cleavage of the autolysis loop in mesotrypsin decreases protease activity and thereby protects the pancreas against unwanted SPINK1 degradation. The findings expand the role of CTRC as a key defense mechanism against pancreatitis through regulation of intrapancreatic trypsin activity.
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Project description:In this study, we present a hybrid deep neural network DeepPhospho which conceptually differs from all previous deep learning models for unmodified or modified peptide predictions in regard to peptide representation learning. Our approach utilizes a multi-module network and self attention mechanism to learn a highly expressive peptide representation, yielding more accurate predictions. When evaluated with multiple phosphoproteomics datasets acquired by DIA or DDA methods, DeepPhospho surpasses existing benchmarks and tools in the prediction of fragmentation patterns for phosphopeptides. In certain cases, the large variance between a DeepPhospho predicted MSMS spectrum and an experimentally assigned spectrum revealed the latter was a false identification while the predicted spectrum closely mimics the bona fide spectrum. Moreover, accurate prediction of chromatographic retention time for any phosphopeptide sequence is integrated into DeepPhospho, which allows for convenient construction of in silico spectral libraries to enhance DIA phosphoproteomics data mining.
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