Modeling and prediction of peptide drift times in ion mobility spectrometry using sequence-based and structure-based approaches

The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella)

It is usually held that good-quality models for the biological activity of peptides must take into account their 3D architecture and descriptors of quantum mechanics. However, the present study shows that it is possible to build up models without these complex calculations. The structure of tripeptides represented by sequences of one-symbol abbreviations of the corresponding amino acids serves to build up quantitative structure-activity relationships for the antioxidant activity of tripeptides from frog skin. The statistical quality of the best model for the validation set is n = 27, r² = 0.93, RMSE = 0.15.

Protein-protein interactions among signaling pathways may become new therapeutic targets in liver cancer (Review)

Numerous signaling pathways have been shown to be dysregulated in liver cancer. In addition, some protein-protein interactions are prerequisite for the uncontrolled activation or inhibition of these signaling pathways. For instance, in the PI3K/AKT signaling pathway, protein AKT binds with a number of proteins such as mTOR, FOXO1 and MDM2 to play an oncogenic role in liver cancer. The aim of the present review was to focus on a series of important protein-protein interactions that can serve as potential therapeutic targets in liver cancer among certain important pro-carcinogenic signaling pathways. The strategies of how to investigate and analyze the protein-protein interactions are also included in this review. A survey of these protein interactions may provide alternative therapeutic targets in liver cancer.

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

Background

Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.

Results

In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.

Conclusions

Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques.