Computational Screening for the Dipeptidyl Peptidase-IV Inhibitory Peptides from Putative Hemp Seed Hydrolyzed Peptidome as a Potential Antidiabetic Agent.

Dipeptidyl peptidase-IV (DPPIV) inhibitory peptides are a class of antihyperglycemic drugs used in the treatment of type 2 diabetes mellitus, a metabolic disorder resulting from reduced levels of the incretin hormone GLP-1. Given that DPPIV degrades incretin, a key regulator of blood sugar levels, various antidiabetic medications that inhibit DPPIV, such as vildagliptin, sitagliptin, and linagliptin, are employed. However, the potential side effects of these drugs remain a matter of debate. Therefore, we aimed to investigate food-derived peptides from Cannabis sativa (hemp) seeds. Our developed bioinformatics pipeline was used to identify the putative hydrolyzed peptidome of three highly abundant proteins: albumin, edestin, and vicilin. These proteins were subjected to in silico digestion by different proteases (trypsin, chymotrypsin, and pepsin) and then screened for DPPIV inhibitory peptides using IDPPIV-SCM. To assess potential adverse effects, several prediction tools, namely, TOXINpred, AllerCatPro, and HemoPred, were employed to evaluate toxicity, allergenicity, and hemolytic effects, respectively. COPID was used to determine the amino acid composition. Molecular docking was performed using GalaxyPepDock and HPEPDOCK, 3D visualizations were conducted using the UCSF Chimera program, and MD simulations were carried out with AMBER20 MD software. Based on the predictive outcomes, FNVDTE from edestin and EAQPST from vicilin emerged as promising candidates for DPPIV inhibitors. We anticipate that our findings may pave the way for the development of alternative DPPIV inhibitors.

TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides.

BACKGROUND: Tyrosinase is an enzyme involved in melanin production in the skin. Several hyperpigmentation disorders involve the overproduction of melanin and instability of tyrosinase activity resulting in darker, discolored patches on the skin. Therefore, discovering tyrosinase inhibitory peptides (TIPs) is of great significance for basic research and clinical treatments. However, the identification of TIPs using experimental methods is generally cost-ineffective and time-consuming. RESULTS: Herein, a stacked ensemble learning approach, called TIPred, is proposed for the accurate and quick identification of TIPs by using sequence information. TIPred explored a comprehensive set of various baseline models derived from well-known machine learning (ML) algorithms and heterogeneous feature encoding schemes from multiple perspectives, such as chemical structure properties, physicochemical properties, and composition information. Subsequently, 130 baseline models were trained and optimized to create new probabilistic features. Finally, the feature selection approach was utilized to determine the optimal feature vector for developing TIPred. Both tenfold cross-validation and independent test methods were employed to assess the predictive capability of TIPred by using the stacking strategy. Experimental results showed that TIPred significantly outperformed the state-of-the-art method in terms of the independent test, with an accuracy of 0.923, MCC of 0.757 and an AUC of 0.977. CONCLUSIONS: The proposed TIPred approach could be a valuable tool for rapidly discovering novel TIPs and effectively identifying potential TIP candidates for follow-up experimental validation. Moreover, an online webserver of TIPred is publicly available at http://pmlabstack.pythonanywhere.com/TIPred .

Computer-Aided Virtual Screening and In Vitro Validation of Biomimetic Tyrosinase Inhibitory Peptides from Abalone Peptidome.

Hyperpigmentation is a medical and cosmetic problem caused by an excess accumulation of melanin or the overexpression of the enzyme tyrosinase, leading to several skin disorders, i.e., freckles, melasma, and skin cancer. Tyrosinase is a key enzyme in melanogenesis and thus a target for reducing melanin production. Although abalone is a good source of bioactive peptides that have been used for several properties including depigmentation, the available information on the anti-tyrosinase property of abalone peptides remains insufficient. This study investigated the anti-tyrosinase properties of Haliotis diversicolor tyrosinase inhibitory peptides (hdTIPs) based on mushroom tyrosinase, cellular tyrosinase, and melanin content assays. The binding conformation between peptides and tyrosinase was also examined by molecular docking and dynamics study. KNN1 showed a high potent inhibitory effect on mushroom tyrosinase with an IC50 of 70.83 µM. Moreover, our selected hdTIPs could inhibit melanin production through the reductions in tyrosinase activity and reactive oxygen species (ROS) levels by enhancing the antioxidative enzymes. RF1 showed the highest activity on both cellular tyrosinase inhibition and ROS reduction. leading to the lower melanin content in B16F10 murine melanoma cells. Accordingly, it can be assumed that our selected peptides exhibited high potential in medical cosmetology applications.

Effect of conditioned media from Aeromonas caviae on the transcriptomic changes of the porcine isolates of Pasteurella multocida.

BACKGROUND: Pasteurella multocida is an opportunistic pathogen causing porcine respiratory diseases by co-infections with other bacterial and viral pathogens. Various bacterial genera isolated from porcine respiratory tracts were shown to inhibit the growth of the porcine isolates of P. multocida. However, molecular mechanisms during the interaction between P. multocida and these commensal bacteria had not been examined.  METHODS: This study aimed to investigate the interaction between two porcine isolates of P. multocida (PM2 for type D and PM7 for type A) with Aeromonas caviae selected from the previously published work by co-culturing P. multocida in the conditioned media prepared from A. caviae growth and examining transcriptomic changes using RNA sequencing and bioinformatics analysis.  RESULTS: In total, 629 differentially expressed genes were observed in the isolate with capsular type D, while 110 genes were significantly shown in type A. High expression of genes required for energy metabolisms, nutrient uptakes, and quorum sensing were keys to the growth and adaptation to the conditioned media, together with the decreased expression of those in the unurgent pathways, including translation and antibacterial resistance. CONCLUSION: This transcriptomic analysis also displayed the distinct capability of the two isolates of P. multocida and the preference of the capsular type A isolate in response to the tough environment of the A. caviae conditioned media. Therefore, controlling the environmental sensing and nutrient acquisition mechanisms of P. multocida would possibly prevent the overpopulation of these bacteria and reduce the chance of becoming opportunistic pathogens.

Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides.

The blood-brain barrier (BBB) is the primary barrier with a highly selective semipermeable border between blood vascular endothelial cells and the central nervous system. Since BBB can prevent drugs circulating in the blood from crossing into the interstitial fluid of the brain where neurons reside, many researchers are working hard on developing drug delivery systems to penetrate the BBB which currently poses a challenge. Thus, blood-brain barrier penetrating peptides (B3PPs) are an alternative neurotherapeutic for brain-related disorder since they can facilitate drug delivery into the brain. In the meanwhile, developing computational methods that are effective for both the identification and characterization of B3PPs in a cost-effective manner plays an important role for basic reach and in the pharmaceutical industry. Even though few computational methods for B3PP identification have been developed, their performance might fail in terms of generalization ability and interpretability. In this study, a novel and efficient scoring card method-based predictor (termed SCMB3PP) is presented for improving B3PP identification and characterization. To overcome the limitation of black-box computational approaches, the SCMB3PP predictor can automatically estimate amino acid and dipeptide propensities to be B3PPs. Both cross-validation and independent tests indicate that SCMB3PP can achieve impressive performance and outperform various popular machine learning-based methods and the existing methods on multiple independent test datasets. Furthermore, SCMB3PP-derived amino acid propensities were utilized to identify informative biophysical and biochemical properties for characterizing B3PPs. Finally, an online user-friendly web server ( http://pmlabstack.pythonanywhere.com/SCMB3PP ) is established to identify novel and potential B3PP cost-effectively. This novel computational approach is anticipated to facilitate the large-scale identification of high potential B3PP candidates for follow-up experimental validation.

Prediction of Antibacterial Peptides against Propionibacterium acnes from the Peptidomes of Achatina fulica Mucus Fractions.

Acne vulgaris is a common skin disease mainly caused by the Gram-positive pathogenic bacterium, Propionibacterium acnes. This bacterium stimulates the inflammation process in human sebaceous glands. The giant African snail (Achatina fulica) is an alien species that rapidly reproduces and seriously damages agricultural products in Thailand. There were several research reports on the medical and pharmaceutical benefits of these snail mucus peptides and proteins. This study aimed to in silico predict multifunctional bioactive peptides from A. fulica mucus peptidome using bioinformatic tools for the determination of antimicrobial (iAMPpred), anti-biofilm (dPABBs), cytotoxic (ToxinPred) and cell-membrane-penetrating (CPPpred) peptides. Three candidate peptides with the highest predictive score were selected and re-designed/modified to improve the required activities. Structural and physicochemical properties of six anti-P. acnes (APA) peptide candidates were performed using the PEP-FOLD3 program and the four previous tools. All candidates had a random coiled structure and were named APAP-1 ori, APAP-2 ori, APAP-3 ori, APAP-1 mod, APAP-2 mod, and APAP-3 mod. To validate the APA activity, these peptide candidates were synthesized and tested against six isolates of P. acnes. The modified APA peptides showed high APA activity on three isolates. Therefore, our biomimetic mucus peptides could be useful for preventing acne vulgaris and further examined on other activities important to medical and pharmaceutical applications.

Computer-Aided Screening for Potential Coronavirus 3-Chymotrypsin-like Protease (3CLpro) Inhibitory Peptides from Putative Hemp Seed Trypsinized Peptidome.

To control the COVID-19 pandemic, antivirals that specifically target the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently required. The 3-chymotrypsin-like protease (3CLpro) is a promising drug target since it functions as a catalytic dyad in hydrolyzing polyprotein during the viral life cycle. Bioactive peptides, especially food-derived peptides, have a variety of functional activities, including antiviral activity, and also have a potential therapeutic effect against COVID-19. In this study, the hemp seed trypsinized peptidome was subjected to computer-aided screening against the 3CLpro of SARS-CoV-2. Using predictive trypsinized products of the five major proteins in hemp seed (i.e., edestin 1, edestin 2, edestin 3, albumin, and vicilin), the putative hydrolyzed peptidome was established and used as the input dataset. To select the Cannabis sativa antiviral peptides (csAVPs), a predictive bioinformatic analysis was performed by three webserver screening programs: iAMPpred, AVPpred, and Meta-iAVP. The amino acid composition profile comparison was performed by COPid to screen for the non-toxic and non-allergenic candidates, ToxinPred and AllerTOP and AllergenFP, respectively. GalaxyPepDock and HPEPDOCK were employed to perform the molecular docking of all selected csAVPs to the 3CLpro of SARS-CoV-2. Only the top docking-scored candidate (csAVP4) was further analyzed by molecular dynamics simulation for 150 nanoseconds. Molecular docking and molecular dynamics revealed the potential ability and stability of csAVP4 to inhibit the 3CLpro catalytic domain with hydrogen bond formation in domain 2 with short bonding distances. In addition, these top ten candidate bioactive peptides contained hydrophilic amino acid residues and exhibited a positive net charge. We hope that our results may guide the future development of alternative therapeutics against COVID-19.

Interaction study of Pasteurella multocida with culturable aerobic bacteria isolated from porcine respiratory tracts using coculture in conditioned media.

BACKGROUND: The porcine respiratory tract harbours multiple microorganisms, and the interactions between these organisms could be associated with animal health status. Pasteurella multocida is a culturable facultative anaerobic bacterium isolated from healthy and diseased porcine respiratory tracts. The interaction between P. multocida and other aerobic commensal bacteria in the porcine respiratory tract is not well understood. This study aimed to determine the interactions between porcine P. multocida capsular serotype A and D strains and other culturable aerobic bacteria isolated from porcine respiratory tracts using a coculture assay in conditioned media followed by calculation of the growth rates and interaction parameters. RESULTS: One hundred and sixteen bacterial samples were isolated from five porcine respiratory tracts, and 93 isolates were identified and phylogenetically classified into fourteen genera based on 16S rRNA sequences. Thirteen isolates from Gram-negative bacterial genera and two isolates from the Gram-positive bacterial genus were selected for coculture with P. multocida. From 17 × 17 (289) interaction pairs, the majority of 220 pairs had negative interactions indicating competition for nutrients and space, while 17 pairs were identified as mild cooperative or positive interactions indicating their coexistence. All conditioned media, except those of Acinetobacter, could inhibit P. multocida growth. Conversely, the conditioned media of P. multocida also inhibited the growth of nine isolates plus themselves. CONCLUSION: Negative interaction was the major interactions among the coculture of these 15 representative isolates and the coculture with P. multocida. The conditioned media in this study might be further analysed to identify critical molecules and examined by the in vivo experiments. The study proposed the possibility of using these molecules in conditioned media to control P. multocida growth.

Identification of potential candidate genes involved in the sex determination cascade in an aquatic firefly, Sclerotia aquatilis (Coleoptera, Lampyridae).

Sexual differentiation, dimorphism, and courtship behavior are the downstream developmental programs of the sex determination cascade. The sex determination cascade in arthropods often involves key genes, transformer (tra), doublesex (dsx), transformer-2 (tra2), and fruitless (fru). These genes are conserved among insect taxa; however, they have never been reported in fireflies. In this study, the candidate genes for these key genes were identified for the first time in an aquatic firefly, Sclerotia aquatilis using transcriptome analysis. A comparative protein-protein interaction (PPI) network of sex determination cascade was reconstructed for S. aquatilis based on a network of a model insect, Drosophila melanogaster. Subsequently, a sex determination cascade in S. aquatilis was proposed based on the amino acid sequence structures and expression profiles of these candidates. This study describes the first efforts toward understanding the molecular control of sex determination cascade in fireflies.

Alternative metabolic routes in channeling xylose to cordycepin production of Cordyceps militaris identified by comparative transcriptome analysis.

The responsive mechanism of C. militaris TBRC7358 on xylose utilization was investigated by comparative analysis of transcriptomes, growth kinetics and cordycepin productions. The result showed that the culture grown on xylose exhibited high production yield of cordycepin on dry biomass. Comparing xylose to other carbon sources, a set of significantly up-regulated genes in xylose were enriched in pentose and glucuronate interconversion, and cordycepin biosynthesis. After validating up-regulated genes using quantitative real-time PCR, interestingly, putative alternative 3'-AMP-associated metabolic route on cordycepin biosynthesis was identified. Through reporter metabolites analysis of C. militaris, significant metabolites (e.g., AMP, glycine and L-glutamate) were identified guiding involvement of growth and cordycepin production. These findings suggested that there was a cooperative mechanism in transcriptional control of the supplying precursors pool directed towards the cordycepin biosynthesis through main and putative alternative metabolic routes for leverage of cell growth and cordycepin production on xylose of C. militaris strain TBRC7358.

K-Nearest Neighbor and Random Forest-Based Prediction of Putative Tyrosinase Inhibitory Peptides of Abalone Haliotis diversicolor.

Skin pigment disorders are common cosmetic and medical problems. Many known compounds inhibit the key melanin-producing enzyme, tyrosinase, but their use is limited due to side effects. Natural-derived peptides also display tyrosinase inhibition. Abalone is a good source of peptides, and the abalone proteins have been used widely in pharmaceutical and cosmetic products, but not for melanin inhibition. This study aimed to predict putative tyrosinase inhibitory peptides (TIPs) from abalone, Haliotis diversicolor, using k-nearest neighbor (kNN) and random forest (RF) algorithms. The kNN and RF predictors were trained and tested against 133 peptides with known anti-tyrosinase properties with 97% and 99% accuracy. The kNN predictor suggested 1075 putative TIPs and six TIPs from the RF predictor. Two helical peptides were predicted by both methods and showed possible interaction with the predicted structure of mushroom tyrosinase, similar to those of the known TIPs. These two peptides had arginine and aromatic amino acids, which were common to the known TIPs, suggesting non-competitive inhibition on the tyrosinase. Therefore, the first version of the TIP predictors could suggest a reasonable number of the TIP candidates for further experiments. More experimental data will be important for improving the performance of these predictors, and they can be extended to discover more TIPs from other organisms. The confirmation of TIPs in abalone will be a new commercial opportunity for abalone farmers and industry.

Virtual Screening for Biomimetic Anti-Cancer Peptides from Cordyceps militaris Putative Pepsinized Peptidome and Validation on Colon Cancer Cell Line.

Colorectal cancer is one of the leading causes of cancer-related death in Thailand and many other countries. The standard practice for curing this cancer is surgery with an adjuvant chemotherapy treatment. However, the unfavorable side effects of chemotherapeutic drugs are undeniable. Recently, protein hydrolysates and anticancer peptides have become popular alternative options for colon cancer treatment. Therefore, we aimed to screen and select the anticancer peptide candidates from the in silico pepsin hydrolysate of a Cordyceps militaris (CM) proteome using machine-learning-based prediction servers for anticancer prediction, i.e., AntiCP, iACP, and MLACP. The selected CM-anticancer peptide candidates could be an alternative treatment or co-treatment agent for colorectal cancer, reducing the use of chemotherapeutic drugs. To ensure the anticancer properties, an in vitro assay was performed with "CM-biomimetic peptides" on the non-metastatic colon cancer cell line (HT-29). According to the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay results from peptide candidate treatments at 0-400 µM, the IC50 doses of the CM-biomimetic peptide with no toxic and cancer-cell-penetrating ability, original C. militaris biomimetic peptide (C-ori), against the HT-29 cell line were 114.9 µM at 72 hours. The effects of C-ori compared to the doxorubicin, a conventional chemotherapeutic drug for colon cancer treatment, and the combination effects of both the CM-anticancer peptide and doxorubicin were observed. The results showed that C-ori increased the overall efficiency in the combination treatment with doxorubicin. According to the acridine orange/propidium iodine (AO/PI) staining assay, C-ori can induce apoptosis in HT-29 cells significantly, confirmed by chromatin condensation, membrane blebbing, apoptotic bodies, and late apoptosis which were observed under a fluorescence microscope.

Unveiling Putative Functions of Mucus Proteins and Their Tryptic Peptides in Seven Gastropod Species Using Comparative Proteomics and Machine Learning-Based Bioinformatics Predictions.

Gastropods are among the most diverse animals. Gastropod mucus contains several glycoproteins and peptides that vary by species and habitat. Some bioactive peptides from gastropod mucus were identified only in a few species. Therefore, using biochemical, mass spectrometric, and bioinformatics approaches, this study aimed to comprehensively identify putative bioactive peptides from the mucus proteomes of seven commonly found or commercially valuable gastropods. The mucus was collected in triplicate samples, and the proteins were separated by 1D-SDS-PAGE before tryptic digestion and peptide identification by nano LC-MS/MS. The mucus peptides were subsequently compared with R scripts. A total of 2818 different peptides constituting 1634 proteins from the mucus samples were identified, and 1218 of these peptides (43%) were core peptides found in the mucus of all examined species. Clustering and correspondence analyses of 1600 variable peptides showed unique mucous peptide patterns for each species. The high-throughput k-nearest neighbor and random forest-based prediction programs were developed with more than 95% averaged accuracy and could identify 11 functional categories of putative bioactive peptides and 268 peptides (9.5%) with at least five to seven bioactive properties. Antihypertensive, drug-delivering, and antiparasitic peptides were predominant. These peptides provide an understanding of gastropod mucus, and the putative bioactive peptides are expected to be experimentally validated for further medical, pharmaceutical, and cosmetic applications.

Iron-associated protein interaction networks reveal the key functional modules related to survival and virulence of Pasteurella multocida.

Pasteurella multocida causes respiratory infectious diseases in a multitude of birds and mammals. A number of virulence-associated genes were reported across different strains of P. multocida, including those involved in the iron transport and metabolism. Comparative iron-associated genes of P. multocida among different animal hosts towards their interaction networks have not been fully revealed. Therefore, this study aimed to identify the iron-associated genes from core- and pan-genomes of fourteen P. multocida strains and to construct iron-associated protein interaction networks using genome-scale network analysis which might be associated with the virulence. Results showed that these fourteen strains had 1587 genes in the core-genome and 3400 genes constituting their pan-genome. Out of these, 2651 genes associated with iron transport and metabolism were selected to construct the protein interaction networks and 361 genes were incorporated into the iron-associated protein interaction network (iPIN) consisting of nine different iron-associated functional modules. After comparing with the virulence factor database (VFDB), 21 virulence-associated proteins were determined and 11 of these belonged to the heme biosynthesis module. From this study, the core heme biosynthesis module and the core outer membrane hemoglobin receptor HgbA were proposed as candidate targets to design novel antibiotics and vaccines for preventing pasteurellosis across the serotypes or animal hosts for enhanced precision agriculture to ensure sustainability in food security.

Informatics for Metabolomics.

Metabolome profiling of biological systems has the powerful ability to provide the biological understanding of their metabolic functional states responding to the environmental factors or other perturbations. Tons of accumulative metabolomics data have thus been established since pre-metabolomics era. This is directly influenced by the high-throughput analytical techniques, especially mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques. Continuously, the significant numbers of informatics techniques for data processing, statistical analysis, and data mining have been developed. The following tools and databases are advanced for the metabolomics society which provide the useful metabolomics information, e.g., the chemical structures, mass spectrum patterns for peak identification, metabolite profiles, biological functions, dynamic metabolite changes, and biochemical transformations of thousands of small molecules. In this chapter, we aim to introduce overall metabolomics studies from pre- to post-metabolomics era and their impact on society. Directing on post-metabolomics era, we provide a conceptual framework of informatics techniques for metabolomics and show useful examples of techniques, tools, and databases for metabolomics data analysis starting from preprocessing toward functional interpretation. Throughout the framework of informatics techniques for metabolomics provided, it can be further used as a scaffold for translational biomedical research which can thus lead to reveal new metabolite biomarkers, potential metabolic targets, or key metabolic pathways for future disease therapy.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework.

Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew's correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins.Communicated by Ramaswamy H. Sarma.

Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction.

The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.

Rapid quantification of yeast lipid using microwave-assisted total lipid extraction and HPLC-CAD.

We here present simple and rapid methods for fast screening of yeast lipids in Saccharomyces cerevisiae. First we introduced a microwave-assisted technique for fast lipid extraction that allows the extraction of lipids within 10 min. The new method enhances extraction rate by 27 times, while maintaining product yields comparable to conventional methods (n = 14, P > 0.05). The recovery (n = 3) from spiking of synthetic standards were 92 ± 6% for cholesterol, 95 ± 4% for triacylglycerol, and 92 ± 4% for free fatty acids. Additionally, the new extraction method combines cell disruption and extraction in one step, and the approach, therefore, not only greatly simplifies sample handling but also reduces analysis time and minimizes sample loss during sample preparation. Second, we developed a chromatographic separation that allowed separation of neutral and polar lipids from the extracted samples within a single run. The separation was performed based on a three gradient solvent system combined with hydrophilic interaction liquid chromatography-HPLC followed by detection using a charged aerosol detector. The method was shown to be highly reproducible in terms of retention time of the analytes (intraday; 0.002-0.034% RSD; n = 10, interday; 0.04-1.35% RSD; n = 5) and peak area (intraday; 0.63-6% RSD; n = 10, interday; 4-12% RSD; n = 5).

Understanding Snail Mucus Biosynthesis and Shell Biomineralisation through Genomic Data Mining of the Reconstructed Carbohydrate and Glycan Metabolic Pathways of the Giant African Snail (Achatina fulica).

The giant African snail (Order Stylommatophora: Family Achatinidae), Achatina fulica (Bowdich, 1822), is the most significant and invasive land snail pest. The ecological adaptability of this snail involves high growth rate, reproductive capacity, and shell and mucus production, driven by several biochemical processes and metabolism. The available genomic information for A. fulica provides excellent opportunities to hinder the underlying processes of adaptation, mainly carbohydrate and glycan metabolic pathways toward the shell and mucus formation. The authors analysed the 1.78 Gb draft genomic contigs of A. fulica to identify enzyme-coding genes and reconstruct biochemical pathways related to the carbohydrate and glycan metabolism using a designed bioinformatic workflow. Three hundred and seventy-seven enzymes involved in the carbohydrate and glycan metabolic pathways were identified based on the KEGG pathway reference in combination with protein sequence comparison, structural analysis, and manual curation. Fourteen complete pathways of carbohydrate metabolism and seven complete pathways of glycan metabolism supported the nutrient acquisition and production of the mucus proteoglycans. Increased copy numbers of amylases, cellulases, and chitinases highlighted the snail advantage in food consumption and fast growth rate. The ascorbate biosynthesis pathway identified from the carbohydrate metabolic pathways of A. fulica was involved in the shell biomineralisation process in association with the collagen protein network, carbonic anhydrases, tyrosinases, and several ion transporters. Thus, our bioinformatic workflow was able to reconstruct carbohydrate metabolism, mucus biosynthesis, and shell biomineralisation pathways from the A. fulica genome and transcriptome data. These findings could reveal several evolutionary advantages of the A. fulica snail, and will benefit the discovery of valuable enzymes for industrial and medical applications.

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus.

Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.