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Watshara Shoombuatong,1 Veda Prachayasittikul,1,2 Nuttapat Anuwongcharoen,1 Napat Songtawee,1 Teerawat Monnor,1 Supaluk Prachayasittikul,1 Virapong Prachayasittikul,2 Chanin Nantasenamat1,2 1Center of Data Mining and Biomedical Informatics, 2Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand Abstract: This study represents the first large-scale study on the chemical space of inhibitors of dipeptidyl peptidase-4 (DPP4), which is a potential therapeutic protein target for the treatment of diabetes mellitus. Herein, a large set of 2,937 compounds evaluated for their ability to inhibit DPP4 was compiled from the literature. Molecular descriptors were generated from the geometrically optimized low-energy conformers of these compounds at the semiempirical AM1 level. The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 µM and greater than 10 µM, respectively. Decision tree analysis revealed the importance of molecular weight, total energy of a molecule, topological polar surface area, lowest unoccupied molecular orbital, and number of hydrogen-bond donors, which correspond to molecular size, energy, surface polarity, electron acceptors, and hydrogen bond donors, respectively. The prediction model was subjected to rigorous independent testing via three external sets. Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties. Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues. The results of this study are anticipated to be useful in guiding the rational design of novel and robust DPP4 inhibitors for the treatment of diabetes. Keywords: QSAR, decision tree, scaffold analysis, fragment analysis, antidiabetic, molecular docking, rational drug design

Veda Prachayasittikul,1 Ratchanok Pingaew,2 Chanin Nantasenamat,3 Supaluk Prachayasittikul,3 Somsak Ruchirawat,4,5 Virapong Prachayasittikul1 1Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand; 2Department of Chemistry, Faculty of Science, Srinakharinwirot University, Bangkok, Thailand; 3Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand; 4Laboratory of Medicinal Chemistry, Chulabhorn Research Institute, 5Chulabhorn Graduate Institute, Bangkok, Thailand Purpose: Estrogens play important roles in the pathogenesis and progression of breast cancer as well as estrogen-related diseases. Aromatase is a key enzyme in the rate-limiting step of estrogen production, in which its inhibition is one strategy for controlling estrogen levels to improve prognosis of estrogen-related cancers and diseases. Herein, a series of metal (Mn, Cu, and Ni) complexes of 8-hydroxyquinoline (8HQ) and uracil derivatives (4–9) were investigated for their aromatase inhibitory and cytotoxic activities. Methods: The aromatase inhibition assay was performed according to a Gentest™ kit using CYP19 enzyme, wherein ketoconazole and letrozole were used as reference drugs. The cytotoxicity was tested on normal embryonic lung cells (MRC-5) using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Results: Only Cu complexes (6 and 9) exhibited aromatase inhibitory effect with IC50 0.30 and 1.7 µM, respectively. Cytotoxicity test against MRC-5 cells showed that Mn and Cu complexes (5 and 6), as well as free ligand 8HQ, exhibited activity with IC50 range 0.74–6.27 µM. Conclusion: Cu complexes (6 and 9) were found to act as a novel class of aromatase inhibitor. Our findings suggest that these 8HQ–Cu–uracil complexes are promising agents that could be potentially developed as a selective anticancer agent for breast cancer and other estrogen-related diseases. Keywords: aromatase inhibitor, anticancer, metal-based compound

Diabetes mellitus is a chronic disease that affects > 180 million people worldwide. It is persistent in Thai communities in spite of much effort in prevention and control. This study examined the knowledge capacity of villagers in the Klongmai community of Nakhon Pathom, Thailand, regarding diabetes by way of action research. A health status assessment and a survey of the community were carried out and used as the basis for designing an educational video on diabetes that is accessible regardless of age and educational background. Evaluations of the pre- and poststudy questionnaires were carried out using statistical analysis. The results indicated that the devised educational materials were effective in encouraging the community's self-awareness and perception of diabetes at the significance level of 0.05. Most importantly, the participants demonstrated proficiency in adapting the knowledge gained from the workshop to their own lifestyle. [ABSTRACT FROM AUTHOR]

Osteoanabolic agents, or drugs that promote bone formation, have gained considerable attention for osteoporosis management due to their curative and preventive potentials. Sphingosine-1-phosphate receptor 2 (S1PR2) is an attractive drug target, in which its activation leads to osteogenesis-promoting effect. Nitrogen-containing heterocyclic scaffolds (i.e., quinoxaline and indole) are promising pharmacophores possessing diverse bioactivities and were reported as S1PR2 activators. Quantitative structure-activity relationship (QSAR) modeling is a computational approach well-known as a fundamental tool for facilitating successful drug development. This study demonstrates the discovery of new S1PR2 activators using computational-driven rational design. Herein, an original dataset of nitrogen-containing S1PR2 activators was collected from ChEMBL database. The retrieved dataset was separated into two datasets according to their core scaffolds (i.e., quinoxaline and indole). QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. The constructed models also revealed key properties playing essential roles for potent S1PR2 activation, such as Van der Waals volume (R2v+ and E3v), mass (MATS5m and Km), electronegativity (H3e), and number of 5-membered rings (nR05). Subsequently, the constructed models were further employed to guide rational design and predict S1PR2 activating effects of an additional set of 752 structurally modified compounds. Most of the modified compounds were predicted to have higher potency than their parents, and a set of promising potent newly designed compounds was highlighted. Additionally, drug-likeness prediction was performed to reveal that most of the newly designed compounds are druggable compounds with possibility for further development., Competing Interests: There are no conflicts to declare., (Copyright © 2024 Tangporncharoen et al.)

Angiotensin-converting enzyme inhibitors (ACEIs) play a crucial role in treating conditions such as hypertension, heart failure, and kidney diseases. Nevertheless, the ACEIs currently available on the market are linked to a variety of adverse effects including renal insufficiency, which restricts their usage. There is thus an urgent need to optimize the currently available ACEIs. This study represents a structure-activity relationship investigation of ACEIs, employing machine learning to analyze data sets sourced from the ChEMBL database. Exploratory data analysis was performed to visualize the physicochemical properties of compounds by investigating the distributions, patterns, and statistical significance among the different bioactivity groups. Further scaffold analysis has identified 9 representative Murcko scaffolds with frequencies ≥10. Scaffold diversity has revealed that active ACEIs had more scaffold diversity than their intermediate and inactive counterparts, thereby indicating the significance of performing lead optimization on scaffolds of active ACEIs. Scaffolds 1, 3, 6, and 8 are unfavorable in comparison with scaffolds 2, 3, 5, 7, and 9. QSAR investigation of compiled data sets consisting of 549 compounds led to the selection of Mordred descriptor and Random Forest algorithm as the best model, which afforded robust model performance (accuracy: 0.981, 0.77, and 0.745; MCC: 0.972, 0.658, and 0.617 for the training set, 10-fold cross-validation set, and testing set, respectively). To enhance the model's robustness and predictability, we reduced the chemical diversity of the input compounds by using the 9 most prevalent Murcko scaffold-matched compounds (comprising a total of 168) followed by a subsequent QSAR model investigation using Mordred descriptor and extremely gradient boost algorithm (accuracy: 0.973, 0.849, and 0.823; MCC: 0.959, 0.786, and 0.742 for the training set, 10-fold cross-validation set, and testing set, respectively). Further illustration of the structure-activity relationship using SALI plots has enabled the identification of clusters of compounds that create activity cliffs. These findings, as presented in this study, contribute to the advancement of drug discovery and the optimization of ACEIs., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Antimicrobial resistance (AMR) has emerged as one of the global threats to human health in the 21st century. Drug discovery of inhibitors against novel targets rather than conventional bacterial targets has been considered an inevitable strategy for the growing threat of AMR infections. In this study, we applied quantitative structure-activity relationship (QSAR) modeling to the LpxC inhibitors to predict the inhibitory activity. In addition, we performed various cheminformatics analysis consisting of the exploration of the chemical space, identification of chemotypes, performing structure-activity landscape and activity cliffs as well as construction of the Structure-Activity Similarity (SAS) map. We built a total of 24 QSAR classification models using PubChem and MACCS fingerprint with 12 various machine learning algorithms. The best model with PubChem fingerprint is the Extremely Gradient Boost model (accuracy on the training set: 0.937; accuracy on the 10-fold cross-validation set: 0.795; accuracy on the test set: 0.799). Furthermore, it was found that the best model using the MACCS fingerprint was the Random Forest model (accuracy on the training set: 0.955; accuracy on the 10-fold cross-validation set: 0.803; accuracy on the test set: 0.785). In addition, we have identified eight consensus activity cliff generators that are highly informative for further SAR investigations. It is hoped that findings presented herein can provide guidance for further lead optimization of LpxC inhibitors., Competing Interests: The authors declare that no competing interests exist., (Copyright © 2023 Yu et al.)

Prostate cancer (PCa) is a major leading cause of mortality of cancer among males. There have been numerous studies to develop antagonists against androgen receptor (AR), a crucial therapeutic target for PCa. This study is a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure-activity relationship, and landscape of human AR antagonists. There are 1678 molecules as final data sets. Chemical space visualization by physicochemical property visualization has demonstrated that molecules from the potent/active class generally have a mildly smaller molecular weight (MW), octanol-water partition coefficient (log  P ), number of hydrogen-bond acceptors (nHA), number of rotatable bonds (nRot), and topological polar surface area (TPSA) than molecules from intermediate/inactive class. The chemical space visualization in the principal component analysis (PCA) plot shows significant overlapping distributions between potent/active class molecules and intermediate/inactive class molecules; potent/active class molecules are intensively distributed, while intermediate/inactive class molecules are widely and sparsely distributed. Murcko scaffold analysis has shown low scaffold diversity in general, and scaffold diversity of potent/active class molecules is even lower than intermediate/inactive class molecules, indicating the necessity for developing molecules with novel scaffolds. Furthermore, scaffold visualization has identified 16 representative Murcko scaffolds. Among them, scaffolds 1, 2, 3, 4, 7, 8, 10, 11, 15, and 16 are highly favorable scaffolds due to their high scaffold enrichment factor values. Based on scaffold analysis, their local structure-activity relationships (SARs) were investigated and summarized. In addition, the global SAR landscape was explored by quantitative structure-activity relationship (QSAR) modelings and structure-activity landscape visualization. A QSAR classification model incorporating all of the 1678 molecules stands out as the best model from a total of 12 candidate models for AR antagonists (built on PubChem fingerprint, extra trees algorithm, accuracy for training set: 0.935, 10-fold cross-validation set: 0.735 and test set: 0.756). Deeper insights into the structure-activity landscape highlighted a total of seven significant activity cliff (AC) generators (ChEMBL molecule IDs: 160257, 418198, 4082265, 348918, 390728, 4080698, and 6530), which provide valuable SAR information for medicinal chemistry. The findings in this study provide new insights and guidelines for hit identification and lead optimization for the development of novel AR antagonists., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Cytochrome P450 17A1 (CYP17A1) is one of the key enzymes in steroidogenesis that produces dehydroepiandrosterone (DHEA) from cholesterol. Abnormal DHEA production may lead to the progression of severe diseases, such as prostatic and breast cancers. Thus, CYP17A1 is a druggable target for anti-cancer molecule development. In this study, cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling were applied on a set of 962 CYP17A1 inhibitors (i.e., consisting of 279 steroidal and 683 nonsteroidal inhibitors) compiled from the ChEMBL database. For steroidal inhibitors, a QSAR classification model built using the PubChem fingerprint along with the extra trees algorithm achieved the best performance, reflected by the accuracy values of 0.933, 0.818, and 0.833 for the training, cross-validation, and test sets, respectively. For nonsteroidal inhibitors, a systematic cheminformatic analysis was applied for exploring the chemical space, Murcko scaffolds, and structure-activity relationships (SARs) for visualizing distributions, patterns, and representative scaffolds for drug discoveries. Furthermore, seven total QSAR classification models were established based on the nonsteroidal scaffolds, and two activity cliff (AC) generators were identified. The best performing model out of these seven was model VIII, which is built upon the PubChem fingerprint along with the random forest algorithm. It achieved a robust accuracy across the training set, the cross-validation set, and the test set, i.e., 0.96, 0.92, and 0.913, respectively. It is anticipated that the results presented herein would be instrumental for further CYP17A1 inhibitor drug discovery efforts.

Purpose: The uppermost segment of the cervical vertebra or atlas (C1) is a critically important anatomical structure, housing the medulla oblongata and containing the grooves for the C1 spinal nerve and the vertebral vessels. Variations of the C1 vertebra can affect upper spine stability, and morphometric parameters have been reported to differ by population. However, there are few data regarding these parameters in Thais. The use of this bone to predict sex and age has never been reported., Methods: This study aimed to examine C1 morphometry and determine its ability to predict sex. Twelve diameter parameters were taken from the C1 vertebrae of identified skeletons (n = 104, males [n, 54], females [n, 50]). Correlation analysis was also performed for sex and age, which were predicted using machine learning algorithms., Results: The results showed that 8 of the 12 measured parameters were significantly longer in the male atlas (p < 0.05), while the remaining 4 (distance between both medial-most edges of the transverse foramen, transverse dimension of the superior articular surface, frontal plane passing through the canal's midpoint, and anteroposterior dimension of the inferior articular surface) did not differ significantly by sex. There was no statistically significant difference in these parameters on the lateral side. The decision stump classifier was trained on C1 parameters, and the resulting model could predict sex with 82.6% accuracy (root mean square error = 0.38)., Conclusion: Assertation of the morphometric parameters of the atlas is important for preoperative assessment, especially for the treatment of atlas dislocation. Our findings also highlighted the potential use of atlas measurements for sex prediction., (© 2023. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)

Cancer is the leading cause of death worldwide, resulting in the mortality of more than 10 million people in 2020, according to Global Cancer Statistics 2020. A potential cancer therapy involves targeting the DNA repair process by inhibiting PARP-1. In this study, classification models were constructed using a non-redundant set of 2018 PARP-1 inhibitors. Briefly, compounds were described by 12 fingerprint types and built using the random forest algorithm concomitant with various sampling approaches. Results indicated that PubChem with an oversampling approach yielded the best performance, with a Matthews correlation coefficient > 0.7 while also affording interpretable molecular features. Moreover, feature importance, as determined from the Gini index, revealed that the aromatic/cyclic/heterocyclic moiety, nitrogen-containing fingerprints, and the ether/aldehyde/alcohol moiety were important for PARP-1 inhibition. Finally, our predictive model was deployed as a web application called PARP1pred and is publicly available at https://parp1pred.streamlitapp.com, allowing users to predict the biological activity of query compounds using their SMILES notation as the input. It is anticipated that the model described herein will aid in the discovery of effective PARP-1 inhibitors., (Copyright © 2023 Lerksuthirat et al.)

Despite the arsenal of existing cancer therapies, the ongoing recurrence and new cases of cancer pose a serious health concern that necessitates the development of new and effective treatments. Cancer immunotherapy, which uses the body's immune system to combat cancer, is a promising treatment option. As a result, in silico methods for identifying and characterizing tumor T cell antigens (TTCAs) would be useful for better understanding their functional mechanisms. Although few computational methods for TTCA identification have been developed, their lack of model interpretability is a major drawback. Thus, developing computational methods for the effective identification and characterization of TTCAs is a critical endeavor. PSRTTCA, a new machine learning (ML)-based approach for improving the identification and characterization of TTCAs based on their primary sequences, is proposed in this study. Specifically, we introduce a new propensity score representation learning algorithm that allows one to generate various sets of propensity scores of amino acids, dipeptides, and g-gap dipeptides to be TTCAs. To enhance the predictive performance, optimal sets of variant propensity scores were determined and fed into the final meta-predictor (PSRTTCA). Benchmarking results revealed that PSRTTCA was a more precise and promising tool for the identification and characterization of TTCAs than conventional ML classifiers and existing methods. Furthermore, PSR-derived propensities of amino acids in becoming TTCAs are used to reveal the relationship between TTCAs and their informative physicochemical properties in order to provide insights into TTCA characteristics. Finally, a user-friendly online computational platform of PSRTTCA is publicly available at http://pmlabstack.pythonanywhere.com/PSRTTCA. The PSRTTCA predictor is anticipated to facilitate community-wide efforts in accelerating the discovery of novel TTCAs for cancer immunotherapy and other clinical applications., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The emergence of New Delhi metallo-beta-lactamase-1 (NDM-1) has conferred enteric bacteria resistance to almost all beta-lactam antibiotics. Its capability of horizontal transfer through plasmids, amongst humans, animal reservoirs and the environment, has added up to the totality of antimicrobial resistance control, animal husbandry and food safety. Thus far, there have been no effective drugs for neutralizing NDM-1. This study explores the structure-activity relationship of NDM-1 inhibitors. IC 50 values of NDM-1 inhibitors were compiled from both the ChEMBL database and literature. After curation, a final set of 686 inhibitors were used for machine learning model building using the random forest algorithm against 12 sets of molecular fingerprints. Benchmark results indicated that the KlekotaRothCount fingerprint provided the best overall performance with an accuracy of 0.978 and 0.778 for the training and testing set, respectively. Model interpretation revealed that nitrogen-containing features (KRFPC 4080, KRFPC 3882, KRFPC 677, KRFPC 3608, KRFPC 3750, KRFPC 4287 and KRFPC 3943), sulfur-containing substructures (KRFPC 2855 and KRFPC 4843), aromatic features (KRFPC 1566, KRFPC 1564, KRFPC 1642, KRFPC 3608, KRFPC 4287 and KRFPC 3943), carbonyl features (KRFPC 1193 and KRFPC 3025), aliphatic features (KRFPC 2975, KRFPC 297, KRFPC 3224 and KRFPC 669) are features contributing to NDM-1 inhibitory activity. It is anticipated that findings from this study would help facilitate the drug discovery of NDM-1 inhibitors by providing guidelines for further lead optimization., (Copyright © 2022 Yu et al.)

The development of efficient and effective bioinformatics tools and pipelines for identifying peptides with dipeptidyl peptidase IV (DPP-IV) inhibitory activities from large-scale protein datasets is of great importance for the discovery and development of potential and promising antidiabetic drugs. In this study, we present a novel stacking-based ensemble learning predictor (termed StackDPPIV) designed for identification of DPP-IV inhibitory peptides. Unlike the existing method, which is based on single-feature-based methods, we combined five popular machine learning algorithms in conjunction with ten different feature encodings from multiple perspectives to generate a pool of various baseline models. Subsequently, the probabilistic features derived from these baseline models were systematically integrated and deemed as new feature representations. Finally, in order to improve the predictive performance, the genetic algorithm based on the self-assessment-report was utilized to determine a set of informative probabilistic features and then used the optimal one for developing the final meta-predictor (StackDPPIV). Experiment results demonstrated that StackDPPIV could outperform its constituent baseline models on both the training and independent datasets. Furthermore, StackDPPIV achieved an accuracy of 0.891, MCC of 0.784 and AUC of 0.961, which were 9.4%, 19.0% and 11.4%, respectively, higher than that of the existing method on the independent test. Feature analysis demonstrated that our feature representations had more discriminative ability as compared to conventional feature descriptors, which highlights the combination of different features was essential for the performance improvement. In order to implement the proposed predictor, we had built a user-friendly online web server at http://pmlabstack.pythonanywhere.com/StackDPPIV., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL . It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins., (© 2022. The Author(s).)

Alzheimer's disease (AD) is one of the most common forms of dementia and is associated with a decline in cognitive function and language ability. The deficiency of the cholinergic neurotransmitter known as acetylcholine (ACh) is associated with AD. Acetylcholinesterase (AChE) hydrolyses ACh and inhibits the cholinergic transmission. Furthermore, both AChE and butyrylcholinesterase (BChE) plays important roles in early and late stages of AD. Therefore, the inhibition of either or both cholinesterase enzymes represent a promising therapeutic route for treating AD. In this study, a large-scale classification structure-activity relationship model was developed to predict cholinesterase inhibitory activities as well as revealing important substructures governing their activities. Herein, a non-redundant dataset constituting 985 and 1056 compounds for AChE and BChE, respectively, was obtained from the ChEMBL database. These inhibitors were described by 12 sets of molecular fingerprints and predictive models were developed using the random forest algorithm. Evaluation of the model performance by means of Matthews correlation coefficient and consideration of the model's interpretability indicated that the SubstructureCount fingerprint was the most robust with five-fold cross-validated MCC of [0.76, 0.82] for AChE and BChE, respectively, and test MCC of [0.73, 0.97]. Feature interpretation revealed that the aromatic ring system, heterocyclic nitrogen containing compounds and amines are important for cholinesterase inhibition. Finally, the model was deployed as a publicly available webserver called the ABCpred at http://codes.bio/abcpred/ ., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

DNA barcodes are regarded as hereditary succession codes that serve as a recognition marker to address several queries relating to the identification, classification, community ecology, and evolution of certain functional traits in organisms. The mitochondrial cytochrome c oxidase 1 ( CO1 ) gene as a DNA barcode is highly efficient for discriminating vertebrate and invertebrate animal species. Similarly, different specific markers are used for other organisms, including ribulose bisphosphate carboxylase ( rbcL ), maturase kinase ( matK ), transfer RNA-H and photosystem II D1-ApbsArabidopsis thaliana ( trnH - psbA ), and internal transcribed spacer (ITS) for plant species; 16S ribosomal RNA (16S rRNA), elongation factor Tu gene ( Tuf gene), and chaperonin for bacterial strains; and nuclear ITS for fungal strains. Nevertheless, the taxon coverage of reference sequences is far from complete for genus or species-level identification. Applying the next-generation sequencing approach to the parallel acquisition of DNA barcode sequences could greatly expand the potential for library preparation or accurate identification in biodiversity research. Overall, this review articulates on the DNA barcoding technology as applied to different organisms, its universality, applicability, and innovative approach to handling DNA-based species identification., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2022 Sarfraz Ahmed et al.)

Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs' functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties.

Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs., (Copyright © 2022 Kabir et al.)

Cancer is one of the leading causes of death worldwide and the underlying angiogenesis represents one of the hallmarks of cancer. Efforts are already under way for the discovery of anti-angiogenic peptides (AAPs) as a promising therapeutic route, which tackle the formation of new blood vessels. As such, the identification of AAPs constitutes a viable path for understanding their mechanistic properties pertinent for the discovery of new anti-cancer drugs. In spite of the abundance of peptide sequences in public databases, experimental efforts in the identification of anti-angiogenic peptides have progressed very slowly owing to high expenditures and laborious nature. Owing to its inherent ability to make sense of large volumes of data, machine learning (ML) represents a lucrative technique that can be harnessed for peptide-based drug discovery. In this review, we conducted a comprehensive and comparative analysis of ML-based AAP predictors in terms of their employed feature descriptors, ML algorithms, cross-validation methods and prediction performance. Moreover, the common framework of these AAP predictors and their inherent weaknesses are also discussed. Particularly, we explore future perspectives for improving the prediction accuracy and model interpretability, which represent an interesting avenue for overcoming some of the inherent weaknesses of existing AAP predictors. We anticipate that this review would assist researchers in the rapid screening and identification of promising AAPs for clinical use., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Introduction: Thyroid ima artery is a variant artery found on the anterior surface of the trachea. The aim of this meta-analysis was to obtain pooled prevalence data of the thyroid ima artery and discuss its clinical importance especially for tracheostomy., Methods: A systematic literature search was performed through five electronic databases until May 2021. A set of inclusion and exclusion criteria based on AQUA guidelines were used to select relevant studies. Meta-analysis, subgroup analyses, meta-regression, and tests for publication bias were performed. Factors that influence the prevalence of the thyroid ima artery were detected using simple and interpretable machine learning (linear regression and K means)., Results: Thirty-six studies with a total of 4335 subjects met the inclusion criteria. The prevalence of the thyroid ima artery was 3.8% (95% CI: 0.027-0.049, I 2 =56.2%). Machine learning identified age, region and year of publication as potential covariates. Subgroup analysis showed that the prevalence of the thyroid ima artery was 4.5 times higher in fetuses (14.8%) than adults (3.3%) (z=-6.76, p<0.01). There was a significant negative correlation between the adult prevalence of the thyroid ima artery and the year of publication (Pearson's r=-0.354, p=0.040) thereby suggesting a decline in thyroid ima artery prevalence over time. This artery, if present, may originate from the brachiocephalic trunk (74%), right common carotid artery (9.6%), arch of aorta (7.7%), right internal thoracic artery (4.8%), left common carotid artery (1.9%) and left internal thoracic artery (1.9%)., Conclusion: In addition to evidence-based synthesis of the thyroid ima artery, this study is the first ever study to report the decreasing prevalence over time of a human body structure in the postnatal life. Knowledge of the thyroid ima artery is of vital importance for surgeons to avoid accidental hemorrhage during tracheostomy., (Copyright © 2021 Elsevier GmbH. All rights reserved.)

Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs., (© 2021. The Author(s).)

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online.&nbsp;It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.

Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. Materials and methods: One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. Results: Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. Conclusions: Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age.

The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Host defense peptides are promising candidates for the development of novel antibiotics. To realize their therapeutic potential, high levels of target selectivity is essential. This study aims to identify factors governing selectivity via the use of the random forest algorithm for correlating peptide sequence information with their bioactivity data. Satisfactory predictive models were achieved from out-of-bag prediction that yielded accuracies and Matthew's correlation coefficients in excess of 0.80 and 0.57, respectively. Model interpretation through the use of variable importance metrics and partial dependence plots indicated that the selectivity was heavily influenced by the composition and distribution patterns of molecular charge and solubility related parameters. Furthermore, the three investigated bacterial target species (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) likely had a significant influence on how selectivity was realized as there appears to be a similar underlying selectivity mechanism on the basis of charge-solubility properties (i.e. but which is tailored according to the target in question)., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Betulinic acid (BA) is a pentacyclic triterpene usually isolated from botanical sources. Numerous studies have reported the inhibitory effect of BA against human colorectal cancer cells (CRC). However, its effect on the expression of the molecular chaperone HSPA is unclear. The aim of this research is to investigate the anti-cancer activities of BA purified from Piper retrofractum and study its effect on the expression of HSPA in colorectal cancer HCT116 and SW480 cells. The viability of both cancer cells was reduced after they were treated with an increasing dosage of BA. Flow cytometry assay revealed that levels of cell apoptosis significantly increased after incubation with BA in both cancer cells. Pro-apoptotic markers including Bax, cleaved-caspase-3 and cleaved-caspase-9 were increased while anti-apoptotic marker Bcl-2 was decreased after BA treatment. Western blot also showed that the expression of HSPA fluctuated upon BA treatment, whereby HSPA was increased at lower BA concentrations while at higher BA concentrations HSPA expression was decreased. Preliminary molecular docking assay showed that BA can bind to the nucleotide binding domain of the HSP70 at its ADP-bound state of the HSP70. Although further research is needed to comprehend the BA-HSPA interaction, our findings indicate that BA can be considered as potential candidate for the development of new treatment for colorectal cancer.

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Motivation: The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desirable., Results: In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with an accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of 8.0% accuracy and 16.0% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research., Availabilityand Implementation: The user-friendly web server of the proposed BERT4Bitter is freely accessible at http://pmlab.pythonanywhere.com/BERT4Bitter., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.

Estrogen receptors alpha and beta (ERα and ERβ) are responsible for breast cancer metastasis through their involvement of clinical outcomes. Estradiol and hormone replacement therapy targets both ERs, but this often leads to an increased risk of breast and endometrial cancers as well as thromboembolism. A major challenge is posed for the development of compounds possessing ER subtype specificity. Herein, we present a large-scale classification structure-activity relationship (CSAR) study of inhibitors from the ChEMBL database which consisted of an initial set of 11,618 compounds for ERα and 7,810 compounds for ERβ. The IC 50 was selected as the bioactivity unit for further investigation and after the data curation process, this led to a final data set of 1,593 and 1,281 compounds for ERα and ERβ, respectively. We employed the random forest (RF) algorithm for model building and of the 12 fingerprint types, models built using the PubChem fingerprint was the most robust (Ac of 94.65% and 92.25% and Matthews correlation coefficient (MCC) of 89% and 76% for ERα and ERβ, respectively) and therefore selected for feature interpretation. Results indicated the importance of features pertaining to aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. Finally, the model was deployed as the publicly available web server called ERpred at http://codes.bio/erpred where users can submit SMILES notation as the input query for prediction of the bioactivity against ERα and ERβ., Competing Interests: The authors declare that they have no competing interests., (© 2021 Schaduangrat et al.)

Background: Accumulating studies have confirmed that oxidative stress leads to the death of neuronal cells and is associated with the progression of neurodegenerative diseases, including Alzheimer's disease (AD). Despite the compelling evidence, there is a drawback to the use of the antioxidant approach for AD treatment, partly due to limited blood-brain barrier (BBB) permeability. Phytosterol is known to exhibit BBB penetration and exerts various bioactivities such as antioxidant and anticancer effects, and displays a potential treatment for dyslipidemia, cardiovascular disease, and dementia. Objective: In this study, the protective effects of stigmasterol, a phytosterol compound, on cell death induced by hydrogen peroxide (H 2 O 2 ) were examined in vitro using human neuronal cells (SH-SY5Y cells). Methods: MTT assay, reactive oxygen species measurement, mitochondrial membrane potential assay, apoptotic cell measurement, and protein expression profiles were performed to determine the neuroprotective properties of stigmasterol. Results: H 2 O 2 exposure significantly increased the levels of reactive oxygen species (ROS) within the cells thereby inducing apoptosis. On the contrary, pretreatment with stigmasterol maintained ROS levels inside the cells and prevented oxidative stress-induced cell death. It was found that pre-incubation with stigmasterol also facilitated the upregulation of forkhead box O (FoxO) 3a, catalase, and anti-apoptotic protein B-cell lymphoma 2 (Bcl-2) in the neurons. In addition, the expression levels of sirtuin 1 (SIRT1) were also increased while acetylated lysine levels were decreased, indicating that SIRT1 activity was stimulated by stigmasterol, and the result was comparable with the known SIRT1 activator, resveratrol. Conclusion: Taken together, these results suggest that stigmasterol could be potentially useful to alleviate neurodegeneration induced by oxidative stress., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Pratiwi, Nantasenamat, Ruankham, Suwanjang, Prachayasittikul, Prachayasittikul and Phopin.)

Leukemia is a group of cancer caused by the abnormal proliferation and differentiation of hematopoietic stem cells. Efforts geared toward effective therapeutic strategies with minimal side effects are underway. Peptides derived from natural resources have recently gained special attention as alternative chemotherapeutic agents due to their minimal adverse effects. In the present study, the aim was to isolate peptides from garlic (Allium sativum) and investigate their anticancer activity against leukemic cell lines. The protein extract of A. sativum was pepsin-digested to obtain protein hydrolysate followed by sequential purification methods. A novel anticancer peptide, VKLRSLLCS (VS-9), was identified and characterized by mass spectrometric analysis. The peptide was demonstrated to significantly inhibit the cell proliferation of MOLT-4 and K562 leukemic cell lines while exhibiting minimal inhibition against normal PBMC. Particularly, VS-9 could induce apoptosis and upregulate mRNA levels of caspase 3, caspase 8, caspase 9, and Bax while downregulating Bcl-2, Bcl-xL, and Bcl-w. Molecular docking of VS-9 with the anti-apoptotic Bcl-2 protein family suggested that VS-9 could bind the binding groove of the BH3 domain on target proteins. Protein-peptide interaction analysis by affinity chromatography and LC-MS/MS further showed that VS-9 could bind Bcl-2 proteins. Results suggest VS-9 as a potential garlic-derived novel anticancer peptide possessing apoptosis-inducing properties against leukemic cell lines via anti-apoptotic Bcl-2 protein family., (© 2021 John Wiley & Sons Ltd.)

The pathological finding of amyloid-β (Aβ) aggregates is thought to be a leading cause of untreated Alzheimer's disease (AD). In this study, we isolated 2-butoxytetrahydrofuran (2-BTHF), a small cyclic ether, from Holothuria scabra and demonstrated its therapeutic potential against AD through the attenuation of Aβ aggregation in a transgenic Caenorhabditis elegans model. Our results revealed that amongst the five H. scabra isolated compounds, 2-BTHF was shown to be the most effective in suppressing worm paralysis caused by Aβ toxicity and in expressing strong neuroprotection in CL4176 and CL2355 strains, respectively. An immunoblot analysis showed that CL4176 and CL2006 treated with 2-BTHF showed no effect on the level of Aβ monomers but significantly reduced the toxic oligomeric form and the amount of 1,4-bis(3-carboxy-hydroxy-phenylethenyl)-benzene (X-34)-positive fibril deposits. This concurrently occurred with a reduction of reactive oxygen species (ROS) in the treated CL4176 worms. Mechanistically, heat shock factor 1 (HSF-1) (at residues histidine 63 (HIS63) and glutamine 72 (GLN72)) was shown to be 2-BTHF's potential target that might contribute to an increased expression of autophagy-related genes required for the breakdown of the Aβ aggregate, thus attenuating its toxicity. In conclusion, 2-BTHF from H. scabra could protect C. elegans from Aβ toxicity by suppressing its aggregation via an HSF-1-regulated autophagic pathway and has been implicated as a potential drug for AD.

Background and Objectives: Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool., Methods: A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment., Results: A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85-91)), and sensitivity (84% (95% CI 76-89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86-93)) where Logistic Regression demonstrates the highest (99% (95% CI 97-99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%)., Conclusion: The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available., Trial Registration: NCT04208789., Competing Interests: The authors have declared that no competing interests exist.

In this study, a key issue to be addressed is the safe disposal of hybridoma instability. Hybridoma technology was used to produce anti-O. viverrini monoclonal antibody. Previous studies have shown that antibody production via antibody phage display can sustain the hybridoma technique. This paper presents the utility of antibody phage display technology for producing the phage displayed KKU505 Fab fragment and using experiments in concomitant with molecular simulation for characterization. The phage displayed KKU505 Fab fragment and characterization were successfully carried out. The KKU505 hybridoma cell line producing anti-O. viverrini antibody predicted to bind to myosin was used to synthesize cDNA so as to amplify the heavy chain and the light chain sequences. The KKU505 displayed phage was constructed and characterized by a molecular modeling in which the KKU505 Fab fragment and -O. viverrini myosin head were docked computationally and it is assumed that the Fab fragment was specific to -O. viverrini on the basis of mass spectrometry and Western blot. This complex interaction was confirmed by molecular simulation. Furthermore, the KKU505 displayed phage was validated using indirect enzyme-linked immunosorbent assays (ELISA) and immunohistochemistry. It is worthy to note that ELISA and immunohistochemistry results confirmed that the Fab fragment was specific to the -O. viverrini antigen. Results indicated that the approach presented herein can generate anti-O. viverrini antibody via the phage display technology. This study integrates the use of phage display technology together with molecular simulation for further development of monoclonal antibody production. Furthermore, the presented work has profound implications for antibody production, particularly by solving the problem of hybridoma stability issues., Competing Interests: The authors have declared that no competing interests exist.

As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+-dependent histone deacetylases which play important functional roles in the removal of the acetyl group of acetyllysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors., Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles., Methods: Simplified molecular-input line-entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The dataset was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration, and external sets., Results: Statistical indices for the evaluation of QSAR models suggested the good statistical quality of models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e., promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved in Sirt1 and Sirt2 inhibitors., Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic properties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algorithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Fast, accurate identification and characterization of amyloid proteins at a large-scale is essential for understating their role in therapeutic intervention strategies. As a matter of fact, there exist only one in silico model for amyloid protein identification using the random forest (RF) model in conjunction with various feature types namely the RFAmy. However, it suffers from low interpretability for biologists. Thus, it is highly desirable to develop a simple and easily interpretable prediction method with robust accuracy as compared to the existing complicated model. In this study, we propose iAMY-SCM, the first scoring card method-based predictor for predicting and analyzing amyloid proteins. Herein, the iAMY-SCM made use of a simple weighted-sum function in conjunction with the propensity scores of dipeptides for the amyloid protein identification. Cross-validation results indicated that iAMY-SCM provided an accuracy of 0.895 that corresponded to 10-22% higher performance than that of widely used machine learning models. Furthermore, iAMY-SCM achieving an accuracy of 0.827 as evaluated by an independent test, which was found to be comparable to that of RFAmy and was approximately 9-13% higher than widely used machine learning models. Furthermore, the analysis of estimated propensity scores of amino acids and dipeptides were performed to provide insights into the biophysical and biochemical properties of amyloid proteins. As such, this demonstrates that the proposed iAMY-SCM is efficient and reliable in terms of simplicity, interpretability and implementation. To facilitate ease of use of the proposed iAMY-SCM, a user-friendly and publicly accessible web server at http://camt.pythonanywhere.com/iAMY-SCM has been established. We anticipate that that iAMY-SCM will be an important tool for facilitating the large-scale prediction and characterization of amyloid protein., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Umami or the taste of monosodium glutamate represents one of the major attractive taste modalities in humans. Therefore, knowledge about biophysical and biochemical properties of the umami taste is important for both scientific research and the food industry. Experimental approaches for predicting umami peptides are labor intensive, time consuming, and expensive. To date, computational models for the prediction and analysis of umami peptides as a function of sequence information have not been developed yet. In this study, we have proposed the first sequence-based predictor named iUmami-SCM using primary sequence information for the identification and characterization of umami peptides. iUmami-SCM utilized a newly developed scoring card method (SCM) in conjunction with the propensity scores of amino acids and dipeptide. Our predictor demonstrated excellent prediction performance ability for predicting umami peptides as well as outperforming other commonly used machine learning classifiers. Particularly, iUmami-SCM afforded the highest accuracy and Matthews correlation coefficient of 0.865 and 0.679, respectively, on an independent data set. Furthermore, the analysis of SCM-derived propensity scores was performed so as to provide a more in-depth understanding and knowledge of biophysical and biochemical properties of umami intensities of peptides. To develop a convenient bioinformatics tool, the best model is deployed as a web server that is made publicly available at http://camt.pythonanywhere.com/iUmami-SCM. The iUmami-SCM, as presented herein, serves as a powerful computational technique for large-scale umami peptide identification as well as facilitating the interpretation of umami peptides.