Biology subjects

Lin, T.-T.

Publications and source records attributed to Lin, T.-T..

5 recordsLinked to original sources

bioRxiv2025

Characterization of cytokine treatment on human pancreatic islets by top-down proteomics

Type 1 diabetes (T1D) results from autoimmune-mediated destruction of insulin-producing {beta} cells in the pancreatic islet. This process is modulated by pro-inflammatory cytokine signaling, which have been previously shown to alter protein expression in ex vivo islets. Herein, we applied top-down proteomics to globally evaluate proteoforms from human islets treated with proinflammatory cytokines (interferon-{gamma} and interleukin-1{beta}). We measured 1636 unique proteoforms across 6 donors and two time points (control and 24-hours post-treatment) and observed consistent changes in abundance across the glicentin-related pancreatic polypeptide (GRPP) and major proglucagon fragment regions of glucagon, as well as the LF-19/catestatin and vasostatin-1/2 region of chromogranin-A. We also observe several proteoforms that increase after cytokine-treatment or are exclusively observed after cytokine-treatment including forms of beta-2 Microglobulin (B2M), high-mobility group N2 protein (HMGN2), and chemokine (C-X-C motif) ligands (CXCL). Together, our quantitative results provide a baseline proteoform profile for human islets and identify several proteoforms that may serve as interesting candidate markers for T1D progression or therapeutic intervention. Statement of significance of the studyThis work applies a top-down proteomics workflow for the characterization and label-free quantification of proteoforms from human islets in the context of inflammation. The workflow is optimized for challenges unique to the islet proteome including high disulfide-linkage content and frequent truncation events, resulting in many proteoforms <5 kDa. There are limited examples of top-down proteomics characterization of human islets, thus this study provides a baseline characterization of the proteoforms of major hormones including insulin, glucagon, chromogranin-A and -B, and somatostatin. The quantitative results of proteoform abundances before and after cytokine treatment, which mimics the proinflammatory environment during T1D progression, enables the discovery of interesting proteoform candidates as potential markers of T1D progression.

biochemistry↗

bioRxiv2022

Peptide-based drug predictions for cancer therapy using deep learning

BackgroundTherapeutic drugs used in cancer treatment have ineffectiveness and resistance to drug action problems. Anticancer peptides (ACPs) are selective and toxic to cancer cells and quickly produced. Thus, ACPs can be a satisfactory substitute for therapeutic drugs. We developed AI4ACP, a user-friendly web-server ACP predictor that can predict the anticancer property of query peptides, thus promoting the discovery of peptides with anticancer activity. ResultOur results revealed that the performance of our ACP predictor trained using the new ACP collection was superior to that of the available high-performance ACP predictors. ConclusionsAI4ACP is a user-friendly web-server ACP predictor that can be used to determine whether a query sequence is an ACP. This tool can be beneficial for drug development for cancer treatment. AI4ACP is freely accessible at https://axp.iis.sinica.edu.tw/AI4ACP/

cancer biology↗

bioRxiv2021

Developing an Antiviral Peptides Predictor with Generative Adversarial Network Data Augmentation

MotivationNew antiviral drugs are urgently needed because of emerging viral pathogens increasing severity and drug resistance. Antiviral peptides (AVPs) have multiple antiviral properties and are appealing candidates for antiviral drug development. We developed a sequence-based binary classifier to identify whether an unknown short peptide has AVP activity. We collected AVP sequence data from six existing databases. We used a generative adversarial network to augment the number of AVPs in the positive training dataset and allow our deep convolutional neural network model to train on more data. ResultsOur classifier achieved outstanding performance on the testing dataset compared with other state-of-the-art classifiers. We deployed our trained classifier on a user-friendly web server. Availability and implementationAI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/ Contactcylin@iis.sinica.edu.tw Supplementary informationSupplementary data is also available.

microbiology↗

bioRxiv2021

Discovering Novel Antimicrobial Peptides in Generative Adversarial Network

Due to the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) can become ideal for next-generation antibiotics. This study trained a deep convolutional generative adversarial network (GAN) with known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them named GAN-pep 1[~]8 were chosen to be synthesized for further experiments. Disk diffusion testing and minimum inhibitory concentration (MIC) determination were used to determine the antibacterial effects of the synthesized GAN-designed peptides. Seven out of the eight synthesized GAN-designed peptides showed antibacterial activities. Additionally, GAN-pep 3 and GAN-pep 8 had a broad spectrum of antibacterial effects. Both of them were also effective against antibiotic-resistant bacteria strains such as methicillin-resistant Staphylococcus aureus (S. aureus) and carbapenem-resistant Pseudomonas aeruginosa (P. aeruginosa). GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria.

microbiology↗

bioRxiv2020

AI4AMP: Sequence-based antimicrobial peptides predictor using physicochemical properties-based encoding method and deep learning

MotivationAntimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitution for antibiotics. However, AMPs discovery through traditional wet-lab research is expensive and inefficient. Thus, we developed AI4AMP, a user-friendly web-server that provides an accurate prediction of the antimicrobial activity of a given protein sequence, to accelerate the process of AMP discovery. ResultsOur results show that our prediction model is superior to the existing AMP predictors. AvailabilityAI4AMP is freely accessible at http://symbiosis.iis.sinica.edu.tw/PC_6/ Contactcylin@iis.sinica.edu.tw

microbiology↗