Signal-3L 2.0: A Hierarchical Mixture Model for Enhancing Protein Signal Peptide Prediction by Incorporating Residue-Domain Cross-Level Features.

Signal peptides play key roles in targeting and translocation of integral membrane proteins and secretory proteins. However, signal peptides present several challenges for automatic prediction methods. One challenge is that it is difficult to discriminate signal peptides from transmembrane helices, as both the H-region of the peptides and the transmembrane helices are hydrophobic. Another is that it is difficult to identify the cleavage site between signal peptides and mature proteins, as cleavage motifs or patterns are still unclear for most proteins. To solve these problems and further enhance automatic signal peptide recognition, we report a new Signal-3L 2.0 predictor. Our new model is constructed with a hierarchical protocol, where it first determines the existence of a signal peptide. For this, we propose a new residue-domain cross-level feature-driven approach, and we demonstrate that protein functional domain information is particularly useful for discriminating between the transmembrane helices and signal peptides as they perform different functions. Next, in order to accurately identify the unique signal peptide cleavage sites along the sequence, we designed a top-down approach where a subset of potential cleavage sites are screened using statistical learning rules, and then a final unique site is selected according to its evolution conservation score. Because this mixed approach utilizes both statistical learning and evolution analysis, it shows a strong capacity for recognizing cleavage sites. Signal-3L 2.0 has been benchmarked on multiple data sets, and the experimental results have demonstrated its accuracy. The online server is available at www.csbio.sjtu.edu.cn/bioinf/Signal-3L/ .

Compound Library Screening Identified Cardiac Glycoside Digitoxin as an Effective Growth Inhibitor of Gefitinib-Resistant Non-Small Cell Lung Cancer via Downregulation of α-Tubulin and Inhibition of Microtubule Formation.

Non-small-cell lung cancer (NSCLC) dominates over 85% of all lung cancer cases. Epidermal growth factor receptor (EGFR) activating mutation is a common situation in NSCLC. In the clinic, molecular-targeting with Gefitinib as a tyrosine kinase inhibitor (TKI) for EGFR downstream signaling is initially effective. However, drug resistance frequently happens due to additional mutation on EGFR, such as substitution from threonine to methionine at amino acid position 790 (T790M). In this study, we screened a traditional Chinese medicine (TCM) compound library consisting of 800 single compounds in TKI-resistance NSCLC H1975 cells, which contains substitutions from leucine to arginine at amino acid 858 (L858R) and T790M mutation on EGFR. Attractively, among these compounds there are 24 compounds CC50 of which was less than 2.5 µM were identified. We have further investigated the mechanism of the most effective one, Digitoxin. It showed a significantly cytotoxic effect in H1975 cells by causing G2 phase arrest, also remarkably activated 5' adenosine monophosphate-activated protein kinase (AMPK). Moreover, we first proved that Digitoxin suppressed microtubule formation through decreasing α-tubulin. Therefore, it confirmed that Digitoxin effectively depressed the growth of TKI-resistance NSCLC H1975 cells by inhibiting microtubule polymerization and inducing cell cycle arrest.