[An enrichment strategy based on hydrophobic tagging and reversed-phase chromatographic separation for the analysis of lysine-containing peptides].

Lysine (K) is widely used in the design of lysine-targeted crosslinkers, structural elucidation of protein complexes, and analysis of protein-protein interactions. In "shotgun" proteomics, which is based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), proteins from complex samples are enzymatically digested, generating thousands of peptides and presenting significant challenges for the direct analysis of K-containing peptides. In view of the lack of effective methods for the enrichment of K-containing peptides, this work developed a method which based on a hydrophobic-tag-labeling reagent C10-S-S-NHS and reversed-phase chromatography (termed as HYTARP) to achieve the efficient enrichment and identification of K-containing peptides from complex samples. The C10-S-S-NHS synthesized in this work successfully labeled standard peptides containing various numbers of K and the labeling efficiency achieved up to 96% for HeLa cell protein tryptic digests. By investigating the retention behavior of these labeled peptides in C18 RP column, we found that most K-labeled peptides were eluted once when acetonitrile percentage reached 57.6% (v/v). Further optimization of the elution gradient enabled the efficient separation and enrichment of the K-labeled peptides in HeLa digests via a stepwise elution gradient. The K-labeled peptides accounted for 90% in the enriched peptides, representing an improvement of 35% compared with the number of peptides without the enrichment. The dynamic range of proteins quantified from the enriched K-containing peptides spans 5-6 orders of magnitude, and realized the detection of low-abundance proteins in the complex sample. In summary, the HYTARP strategy offers a straightforward and effective approach for reducing sample complexity and improving the identification coverage of K-containing peptides and low-abundance proteins.

The Null-Test for peptide identification algorithm in Shotgun proteomics.

The present research proposed general evaluation strategy named Null-Test for peptide identification algorithm in Shotgun proteomics. The Null-Test method based on random matching can be utilized to check whether the algorithm has a tendency to make a mistake or has potential bugs, faultiness, errors etc., and to validate the reliability of the identification algorithm. Unfortunately, none of the five famous identification software could pass the most stringent Null-Test. PatternLab had good performance in both Null-Test and routine search by making a good control on the overfitting with sound design. The fuzzy logics based method presented as another candidate strategy could pass the Null-Test and has competitive efficiency in peptide identification. Filtering the results by appropriate FDR would increase the number of discoveries in an experiment, at the cost of losing control of Type I errors. Thus, it is necessary to utilize some more stringent criteria when someone wants to design or analyze an algorithm/software. The more stringent criteria will facilitate the discovery of latent bugs, faultiness, errors etc. in the algorithm/software. It would be recommended to utilize independent search combining random database with statistics theorem to estimate the accurate FDR of the identified results. BIOLOGICAL SIGNIFICANCE: In the past decades, considerable effort has been devoted to developing a sensitive algorithm for peptide identification in Shotgun proteomics. However, little attention has been paid to controlling the reliability of the identification algorithm at the design stage. The Null-Test based on random matching can be utilized to check whether the algorithm has a tendency to make a mistake or has potential bugs, faultiness, errors etc. However, it turns out that none of the five famous identification software could pass the most stringent Null-Test in the present study, which should be taken into account seriously. Accordingly, a candidate strategy based on fuzzy logics has been demonstrated the possibility that an identification algorithm can pass the Null-Test. PatternLab shows that earlier control on overfitting is valuable for designing an efficient algorithm.

[Fast optimization of stepwise gradient conditions for ternary mobile phase in reversed-phase high performance liquid chromatography].

In high performance liquid chromatography, it is necessary to apply multi-composition gradient elution for the separation of complex samples such as environmental and biological samples. Multivariate stepwise gradient elution is one of the most efficient elution modes, because it combines the high selectivity of multi-composition mobile phase and shorter analysis time of gradient elution. In practical separations, the separation selectivity of samples can be effectively adjusted by using ternary mobile phase. For the optimization of these parameters, the retention equation of samples must be obtained at first. Traditionally, several isocratic experiments are used to get the retention equation of solute. However, it is time consuming especially for the separation of complex samples with a wide range of polarity. A new method for the fast optimization of ternary stepwise gradient elution was proposed based on the migration rule of solute in column. First, the coefficients of retention equation of solute are obtained by running several linear gradient experiments, then the optimal separation conditions are searched according to the hierarchical chromatography response function which acts as the optimization criterion. For each kind of organic modifier, two initial linear gradient experiments are used to obtain the primary coefficients of retention equation of each solute. For ternary mobile phase, only four linear gradient runs are needed to get the coefficients of retention equation. Then the retention times of solutes under arbitrary mobile phase composition can be predicted. The initial optimal mobile phase composition is obtained by resolution mapping for all of the solutes. A hierarchical chromatography response function is used to evaluate the separation efficiencies and search the optimal elution conditions. In subsequent optimization, the migrating distance of solute in the column is considered to decide the mobile phase composition and sustaining time of the latter steps until all the solutes are eluted out. Thus the first stepwise gradient elution conditions are predicted. If the resolution of samples under the predicted optimal separation conditions is satisfactory, the optimization procedure is stopped; otherwise, the coefficients of retention equation are adjusted according to the experimental results under the previously predicted elution conditions. Then the new stepwise gradient elution conditions are predicted repeatedly until satisfactory resolution is obtained. Normally, the satisfactory separation conditions can be found only after six experiments by using the proposed method. In comparison with the traditional optimization method, the time needed to finish the optimization procedure can be greatly reduced. The method has been validated by its application to the separation of several samples such as amino acid derivatives, aromatic amines, in which satisfactory separations were obtained with predicted resolution.