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.

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.

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 .

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.

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.

PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning.

Quorum sensing peptides (QSPs) are microbial signaling molecules involved in several cellular processes, such as cellular communication, virulence expression, bioluminescence, and swarming, in various bacterial species. Understanding QSPs is essential for identifying novel drug targets for controlling bacterial populations and pathogenicity. In this study, we present a novel computational approach (PSRQSP) for improving the prediction and analysis of QSPs. In PSRQSP, we develop a novel propensity score representation learning (PSR) scheme. Specifically, we utilized the PSR approach to extract and learn a comprehensive set of estimated propensities of 20 amino acids, 400 dipeptides, and 400 g-gap dipeptides from a pool of scoring card method-based models. Finally, to maximize the utility of the propensity scores, we explored a set of optimal propensity scores and combined them to construct a final meta-predictor. Our experimental results showed that combining multiview propensity scores was more beneficial for identifying QSPs than the conventional feature descriptors. Moreover, extensive benchmarking experiments based on the independent test were sufficient to demonstrate the predictive capability and effectiveness of PSRQSP by outperforming the conventional ML-based and existing methods, with an accuracy of 94.44% and AUC of 0.967. PSR-derived propensity scores were employed to determine the crucial physicochemical properties for a better understanding of the functional mechanisms of QSPs. Finally, we constructed an easy-to-use web server for the PSRQSP (http://pmlabstack.pythonanywhere.com/PSRQSP). PSRQSP is anticipated to be an efficient computational tool for accelerating the data-driven discovery of potential QSPs for drug discovery and development.

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.

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides.

Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanisms. Due to the worldwide major health problem concerning antimicrobial resistance, these peptides possess great therapeutic value owing to their low incidences of drug resistance as compared to conventional antibiotics. Although well-known experimental methods are able to precisely determine the antimalarial activity of peptides, these methods are still time-consuming and costly. Thus, machine learning (ML)-based methods that are capable of identifying AMAPs rapidly by using only sequence information would be beneficial for the high-throughput identification of AMAPs. In this study, we propose the first computational model (termed iAMAP-SCM) for the large-scale identification and characterization of peptides with antimalarial activity by using only sequence information. Specifically, we employed an interpretable scoring card method (SCM) to develop iAMAP-SCM and estimate propensities of 20 amino acids and 400 dipeptides to be AMAPs in a supervised manner. Experimental results showed that iAMAP-SCM could achieve a maximum accuracy and Matthew's coefficient correlation of 0.957 and 0.834, respectively, on the independent test dataset. In addition, SCM-derived propensities of 20 amino acids and selected physicochemical properties were used to provide an understanding of the functional mechanisms of AMAPs. Finally, a user-friendly online computational platform of iAMAP-SCM is publicly available at http://pmlabstack.pythonanywhere.com/iAMAP-SCM. The iAMAP-SCM predictor is anticipated to assist experimental scientists in the high-throughput identification of potential AMAP candidates for the treatment of malaria and other clinical 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.

Histological and histochemical characteristics of the oral, pharyngeal and accessory digestive organs in the water monitor lizard (Varanus salvator) from Thailand.

Srichairat, N., Taksintum, W., & Chumnanpuen, P. (2022). Histological and histochemical characteristics of the oral, pharyngeal and accessory digestive organs in the water monitor lizard (Varanus salvator) from Thailand. Anatomia Histologia Embryologia, 51, https://doi.org/10.1111/ahe.12846. The water monitor lizard (Varanus salvator) is largely distributed in Southeast Asia, especially Thailand, which the microanatomical structure studies on this particular species have been limited. Therefore, the purpose of this study is to examine and describe the oral, pharyngeal and accessory digestive organs' histological and histochemical characteristics. Histological and histochemical examinations were performed on the two specimens (male and female). The majority of V. salvator oral, pharyngeal and accessory digestive organs are similar to those of other vertebrates, according to the results of the histological investigation. However, several unique characteristics have been observed, such as the dentine lamella (plicidentine fold) at the tooth root structure was discovered to be attached to the jaw bone, the absence of lingual papillae and taste buds on the tongue, and the absence of inner surface folding structures in the gallbladder. Regarding mucin histochemical investigation, acid and neutral mucins were discovered in the pharyngeal glands of V. salvator, whereas only acid mucin was found in the mandibular salivary glands. This study reveals the histological and histochemical characteristics of V. salvator oral, pharyngeal and accessory digestive organs, which could be employed to improve digestive tract function, as well as the necessity of understanding animal digestive processes.

Virtual Screening for SARS-CoV-2 Main Protease Inhibitory Peptides from the Putative Hydrolyzed Peptidome of Rice Bran.

The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the loss of life and has affected the life quality, economy, and lifestyle. The SARS-CoV-2 main protease (Mpro), which hydrolyzes the polyprotein, is an interesting antiviral target to inhibit the spreading mechanism of COVID-19. Through predictive digestion, the peptidomes of the four major proteins in rice bran, albumin, glutelin, globulin, and prolamin, with three protease enzymes (pepsin, trypsin, and chymotrypsin), the putative hydrolyzed peptidome was established and used as the input dataset. Then, the prediction of the antiviral peptides (AVPs) was performed by online bioinformatics tools, i.e., AVPpred, Meta-iAVP, AMPfun, and ENNAVIA programs. The amino acid composition and cytotoxicity of candidate AVPs were analyzed by COPid and ToxinPred, respectively. The ten top-ranked antiviral peptides were selected and docked to the SARS-CoV-2 main protease using GalaxyPepDock. Only the top docking scored candidate (AVP4) was further analyzed by molecular dynamics simulation for one nanosecond. According to the bioinformatic analysis results, the candidate SARS-CoV-2 main protease inhibitory peptides were 7-33 amino acid residues and formed hydrogen bonds at Thr22-24, Glu154, and Thr178 in domain 2 with short bonding distances. In addition, these top-ten candidate bioactive peptides contain hydrophilic amino acid residues and have a positive net charge. We hope that this study will provide a potential starting point for peptide-based therapeutic agents against COVID-19.

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.

Enhancing Genome-Scale Model by Integrative Exometabolome and Transcriptome: Unveiling Carbon Assimilation towards Sphingolipid Biosynthetic Capability of Cordyceps militaris.

Cordyceps militaris is an industrially important fungus, which is often used in Asia as traditional medicine. There has been a published genome-scale metabolic model (GSMM) of C. militaris useful for predicting its growth behaviors; however, lipid metabolism, which plays a vital role in cellular functions, remains incomplete in the GSMM of C. militaris. A comprehensive study on C. militaris was thus performed by enhancing GSMM through integrative analysis of metabolic footprint and transcriptome data. Through the enhanced GSMM of C. militaris (called iPC1469), it contained 1469 genes, 1904 metabolic reactions and 1229 metabolites. After model evaluation, in silico growth simulation results agreed well with the experimental data of the fungal growths on different carbon sources. Beyond the model-driven integrative data analysis, interestingly, we found key metabolic responses in alteration of lipid metabolism in C. militaris upon different carbon sources. The sphingoid bases (e.g., sphinganine, sphingosine, and phytosphingosine) and ceramide were statistically significant accumulated in the xylose culture when compared with other cultures; this study suggests that the sphingolipid biosynthetic capability in C. militaris was dependent on the carbon source assimilated for cell growth; this finding provides a comprehensive basis for the sphingolipid biosynthesis in C. militaris that can help to further redesign its metabolic control for medicinal and functional food applications.

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.

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.

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.

Comparison of Hyaluronic Acid Biosynthetic Genes From Different Strains of Pasteurella multocida.

Pasteurella multocida produces a capsule composed of different polysaccharides according to the capsular serotype (A, B, D, E, and F). Hyaluronic acid (HA) is a component of certain capsular types of this bacterium, especially capsular type A. Previously, 2 HA biosynthetic genes from a capsular type A strain were studied for the industrial-scale improvement of HA production. Molecular comparison of these genes across different capsular serotypes of P multocida has not been reported. This study aimed to compare 8 HA biosynthetic genes (pgi, pgm, galU, hyaC, glmS, glmM, glmU, and hyaD) of 22 P multocida strains (A:B:D:F = 6:6:6:4) with those of other organisms using sequence and structural bioinformatics analyses. These 8 genes showed a high level of within-species similarity (98%-99%) compared with other organisms. Only the last gene of 4 strains with capsular type F (HN07, PM70, HNF01, and HNF02) significantly differed from those of other strains (82%). Analysis of amino acid patterns together with phylogenetic results showed that the HA biosynthetic genes of the type A were closely related within the group. The genes in the capsular type F strain were notably similar to those of the capsular type A strain. Protein structural analysis supported structural similarities of the encoded enzymes between the strains of capsular types A, B, D, and F, except for the Pgm, GlmS, GlmU, and HyaD proteins. Our bioinformatics analytic workflow proposed that variations observed within these genes could be useful for genetic engineering-based improvement of hyaluronic acid-producing enzymes.

A study of the aphrodisiac properties of Cordyceps militaris in streptozotocin-induced diabetic male rats.

BACKGROUND AND AIM: Cordyceps militaris (CM) is a fungus that has been used to enhance aphrodisiac activity in men, but to date, no studies have focused on its antidiabetic properties. This study aimed to investigate the effects of CM on reproductive performance of streptozotocin (STZ)-induced diabetic male rats. MATERIALS AND METHODS: Six-week-old Wistar rats were randomly divided into four groups: control Group 1 consisting of healthy rats; Group 2, healthy rats treated with CM (100 mg/kg); Group 3, diabetic untreated rats; and Group 4, diabetic rats treated with CM (100 mg/kg). Rats were orally administered with vehicle or CM for 21 days. The body weight, blood glucose level, food intake, epididymal sperm parameter, sexual behavior, serum testosterone level, and antioxidant parameters were determined. RESULTS: The results indicated that CM treatment in STZ-induced diabetic rats significantly improved the epididymal sperm parameter and serum testosterone level and, in turn, their copulatory behavior. CM treatment in diabetic rats significantly ameliorated malondialdehyde level and significantly improved the glutathione and catalase levels. CONCLUSION: These results provide new information on the pharmacological properties of CM in ameliorating testicular damage due to oxidative stress and improving sexual performance in diabetic male rats.