Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.

Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experimentally verified bioactive peptides has increased significantly, various of in silico approaches are imperative for investigating the characteristics of ACPs. However, the lack of methods for investigating the differences in physicochemical properties of ACPs. In this study, we compared the N- and C-terminal amino acid composition for each peptide, there are three major subtypes of ACPs that are defined based on the distribution of positively charged residues. For the first time, we were motivated to develop a two-step machine learning model for identification of the subtypes of ACPs, which classify the input data into the corresponding group before applying the classifier. Further, to improve the predictive power, the hybrid feature sets were considered for prediction. Evaluation by five-fold cross-validation showed that the two-step model trained with sequence-based features and physicochemical properties was most effective in discriminating between ACPs and non-ACPs. The two-step model trained with the hybrid features performed well, with a sensitivity of 86.75%, a specificity of 85.75%, an accuracy of 86.08%, and a Matthews Correlation Coefficient value of 0.703. Furthermore, the model also consistently provides the effective performance in independent testing set, with sensitivity of 77.6%, specificity of 94.74%, accuracy of 88.99% and the MCC value reached 0.75. Finally, the two-step model has been implemented as a web-based tool, namely iDACP, which is now freely available at http://mer.hc.mmh.org.tw/iDACP/ .

Comprehensive Cohort Analysis of Mutational Spectrum in Early Onset Breast Cancer Patients.

Early onset breast cancer (EOBC), diagnosed at age ~40 or younger, is associated with a poorer prognosis and higher mortality rate compared to breast cancer diagnosed at age 50 or older. EOBC poses a serious threat to public health and requires in-depth investigation. We studied a cohort comprising 90 Taiwanese female patients, aiming to unravel the underlying mechanisms of EOBC etiopathogenesis. Sequence data generated by whole-exome sequencing (WES) and whole-genome sequencing (WGS) from white blood cell (WBC)-tumor pairs were analyzed to identify somatic missense mutations, copy number variations (CNVs) and germline missense mutations. Similar to regular breast cancer, the key somatic mutation-susceptibility genes of EOBC include TP53 (40% prevalence), PIK3CA (37%), GATA3 (17%) and KMT2C (17%), which are frequently reported in breast cancer; however, the structural protein-coding genes MUC17 (19%), FLG (16%) and NEBL (11%) show a significantly higher prevalence in EOBC. Furthermore, the top 2 genes harboring EOBC germline mutations, MUC16 (19%) and KRT18 (19%), encode structural proteins. Compared to conventional breast cancer, an unexpectedly higher number of EOBC susceptibility genes encode structural proteins. We suspect that mutations in structural proteins may increase physical permeability to environmental hormones and carcinogens and cause breast cancer to occur at a young age.

Analysis of microbial sequences in plasma cell-free DNA for early-onset breast cancer patients and healthy females.

BACKGROUND: Cell-free circulating DNA (cfDNA) is becoming a useful biopsy for noninvasive diagnosis of diseases. Microbial sequences in plasma cfDNA may provide important information to improve prognosis and treatment. We have developed a stringent method to identify microbial species via microbial cfDNA in the blood plasma of early-onset breast cancer (EOBC) patients and healthy females. Empirically, microbe-originated sequence reads were identified by mapping non-human PE reads in cfDNA libraries to microbial databases. Those mapped concordantly to unique microbial species were assembled into contigs, which were subsequently aligned to the same databases. Microbial species uniquely aligned were identified and compared across all individuals on MCRPM (Microbial CfDNA Reads Per Million quality PE reads) basis. RESULTS: The predominant microbial cfDNAs in all plasma samples examined are originated from bacteria and these bacteria were limited to only a few genera. Among those, Acinetobacter johnsonii XBB1 and low levels of Mycobacterium spp. were commonly found in all healthy females, but also present in an EOBC patient. Compared to those in healthy counterparts, bacterial species in EOBC patients are more diverse and more likely to present at high levels. Among these three EOBC patients tested, a patient who has record high titer (2,724 MCRPM) of Pseudomonas mendocina together with 8.82 MCRPM of Pannonibacter phragmitetus has passed away; another patient infected by multiple Sphingomonas species remains alive; while the third patient who has similar microbial species (Acinetobacter johnsonii XBB1) commonly seen in normal controls is having a normal life. CONCLUSIONS: Our preliminary data on the profiles of microbial cfDNA sequences suggested that it may have some prognostic value in cancer patients. Validation in larger number of patients is warranted.

dbSNO 2.0: a resource for exploring structural environment, functional and disease association and regulatory network of protein S-nitrosylation.

Given the increasing number of proteins reported to be regulated by S-nitrosylation (SNO), it is considered to act, in a manner analogous to phosphorylation, as a pleiotropic regulator that elicits dual effects to regulate diverse pathophysiological processes by altering protein function, stability, and conformation change in various cancers and human disorders. Due to its importance in regulating protein functions and cell signaling, dbSNO (http://dbSNO.mbc.nctu.edu.tw) is extended as a resource for exploring structural environment of SNO substrate sites and regulatory networks of S-nitrosylated proteins. An increasing interest in the structural environment of PTM substrate sites motivated us to map all manually curated SNO peptides (4165 SNO sites within 2277 proteins) to PDB protein entries by sequence identity, which provides the information of spatial amino acid composition, solvent-accessible surface area, spatially neighboring amino acids, and side chain orientation for 298 substrate cysteine residues. Additionally, the annotations of protein molecular functions, biological processes, functional domains and human diseases are integrated to explore the functional and disease associations for S-nitrosoproteome. In this update, users are allowed to search a group of interested proteins/genes and the system reconstructs the SNO regulatory network based on the information of metabolic pathways and protein-protein interactions. Most importantly, an endogenous yet pathophysiological S-nitrosoproteomic dataset from colorectal cancer patients was adopted to demonstrate that dbSNO could discover potential SNO proteins involving in the regulation of NO signaling for cancer pathways.