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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.

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.

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. IC50 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.

Purpose – The Chulalongkorn-Hasanuddin Rifampicin-Resistant Tuberculosis Screening Tool (CUHAS-ROBUST) is an artificial intelligence–based (AI–based) application for rifampicin-resistant tuberculosis (RR-TB) screening. This study aims to elaborate on the drug-resistant TB (DR-TB) problem and the impact of CUHAS-ROBUST implementation on RR-TB screening. Design/methodology/approach – A qualitative approach with content analysis was performed from September 2020 to October 2020. Medical staff from the primary care center were invited online for application trials and in-depth video call interviews. Transcripts were derived as a data source. An inductive thematic data saturation technique was conducted. Descriptive data of participants, user experience and the impact on the health service were summarized Findings – A total of 33 participants were selected from eight major islands in Indonesia. The findings show that DR-TB is a new threat, and its diagnosis faces obstacles particularly prolonged waiting time and inevitable delayed treatment. Despite overcoming the RR-TB screening problems with fast prediction, the dubious screening performance, and the reliability of data collection for input parameters were the main concerns of CUHAS-ROBUST. Nevertheless, this application increases the confidence in decision-making, promotes medical procedure compliance, active surveillance and enhancing a low-cost screening approach. Originality/value – The CUHAS-ROBUST achieved its purpose as a tool for clinical decision-making in RR-TB screening. Moreover, this study demonstrates AI roles in enhancing health-care quality and boost public health efforts against tuberculosis.

Abstract 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.

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.

Abstract 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.

Abstract 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.

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.

Objectives: Effective methods for diagnosing urogenital tuberculosis (UGTB) are important for its clinical management. Therefore, we undertook a systematic review to assess the performance of the urine-based Xpert MTB/RIF assay for UGTB. Methods: PubMed, Embase, Web of Science, the Cochrane library, and Scopus were systematically searched up to July 30, 2019. A hierarchical summary receiver operating characteristic (HSROC) was applied to calculate the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and odds ratio (OR) for the diagnostic accuracy of the Xpert test. Results: Our search identified 858 unique articles from which 69 studies were selected for full-text revision, with 12 studies meeting the inclusion criteria. Eleven studies comprising 1202 samples compared Xpert with mycobacterial culture, while 924 samples from eight studies compared it with a composite reference standard (CRS). The values for pooled sensitivity, specificity, PLR, NLR, and OR were 0.89, 0.95, 20.1, 0.18, and 159.53, respectively, when compared with the mycobacterial culture. Likewise, when compared with a CRS, the respective pooled sensitivity, specificity, PLR, NLR, and OR values were 0.55, 0.99, 40.67, 0.43, and 166.17, thereby suggesting a high level of accuracy for diagnosing UGTB. A meta-regression and sub-group analysis of TB-burden countries, study design, decontamination, concentration, and reference standard could not explain the heterogeneity (p > 0.05) in the diagnostic efficiency. Conclusions: Our results suggested that Xpert is a promising diagnostic tool for the diagnosis of UGTB via urine specimen.

Abstract The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

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 IC50 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.

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 (H2O2) 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: H2O2 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.

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.

Background and objectivesDiagnosis 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.MethodsA 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.ResultsA 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%).ConclusionThe 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 registrationNCT04208789.

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.

The continued and general rise of antibiotic resistance in pathogenic microbes is a well-recognized global threat. Host defense peptides (HDPs), a component of the innate immune system have demonstrated promising potential to become a next generation antibiotic effective against a plethora of pathogens. While the effectiveness of antimicrobial HDPs has been extensively demonstrated in experimental studies, theoretical insights on the mechanism by which these peptides function is comparably limited. In particular, experimental studies of AMP mechanisms are limited in the number of different peptides investigated and the type of peptide parameters considered. This study makes use of the random forest algorithm for classifying the antimicrobial activity as well for identifying molecular descriptors underpinning the antimicrobial activity of investigated peptides. Subsequent manual interpretation of the identified important descriptors revealed that polarity-solubility are necessary for the membrane lytic antimicrobial activity of HDPs.

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. 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.

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.

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.

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.

Mesenchymal stem cell (MSC) is a type of stem cell that is capable of differentiating into osteoblasts and adipocytes. The pathological perturbation of MSC fate determination is well demonstrated by the replacement of bone tissues with fat in those with osteoporosis and osteopenia. Cell fate determination can be regulated by epigenetic and post-transcriptional mechanisms. MicroRNAs (miRNAs) are small endogenous non-coding RNA molecules that mediates the post-transcriptional regulation of genes expression. We hypothesized that miRNA specified to PPARγ, a major transcription factor of adipogenesis, is responsible for the differentiation of MSCs into osteoblasts. Candidate miRNA that is responsible for target gene inhibition was identified from the miRNA database via bioinformatic analyses. In this study, miR-130a and miR-27b were selected for investigation on their role in specifically binding to peroxisome proliferator-activated receptor γ (PPARγ) via in vitro osteogenesis of human MSCs. During osteogenic differentiation of human MSCs, the expression level of miR-130a and miR-27b were found to be upregulated. In the meanwhile, adipogenic marker genes (PPARγ and C/EBPβ) were found to decrease, which is in contrary to the increased expression of osteogenic marker genes (RUNX2 and Osterix). MSCs were transfected with mimics and inhibitors of miR-130a and miR-27b during in vitro osteogenesis followed by evaluation for the presence of osteogenic markers via quantitative gene expression, Western blot analysis and alkaline phosphatase activity assay. The overexpression of miR-130a and miR-27b is shown to enhance osteogenesis by increasing the gene expression of RUNX2 and Osterix, the protein expression of RUNX2, COL1A1, and Osterix as well as the alkaline phosphatase activity. Taken altogether, these results suggested that miR-130a and miR-27b could promote osteogenesis in human MSCs by targeting the PPARγ.

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In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.

Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes.Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes.Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms.Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets.

Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.

Cancer remains one of the major causes of death worldwide. Angiogenesis is crucial for the pathogenesis of various human diseases, especially solid tumors. The discovery of anti-angiogenic peptides is a promising therapeutic route for cancer treatment. Thus, reliably identifying anti-angiogenic peptides is extremely important for understanding their biophysical and biochemical properties that serve as the basis for the discovery of new anti-cancer drugs. This study aims to develop an efficient and interpretable computational model called TargetAntiAngio for predicting and characterizing anti-angiogenic peptides. TargetAntiAngio was developed using the random forest classifier in conjunction with various classes of peptide features. It was observed via an independent validation test that TargetAntiAngio can identify anti-angiogenic peptides with an average accuracy of 77.50% on an objective benchmark dataset. Comparisons demonstrated that TargetAntiAngio is superior to other existing methods. In addition, results revealed the following important characteristics of anti-angiogenic peptides: (i) disulfide bond forming Cys residues play an important role for inhibiting blood vessel proliferation; (ii) Cys located at the C-terminal domain can decrease endothelial formatting activity and suppress tumor growth; and (iii) Cyclic disulfide-rich peptides contribute to the inhibition of angiogenesis and cell migration, selectivity and stability. Finally, for the convenience of experimental scientists, the TargetAntiAngio web server was established and made freely available online.

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R2, ${Q}_{\mathrm{CV }}^{2}$ Q CV 2 and ${Q}_{\mathrm{Ext}}^{2}$ Q Ext 2 values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R2, ${Q}_{\mathrm{CV }}^{2}$ Q CV 2 and ${Q}_{\mathrm{Ext}}^{2}$ Q Ext 2 values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding, π–π stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure–activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.

MicroRNAs (miRNAs) are small endogenous noncoding RNAs that play an instrumental role in post-transcriptional modulation of gene expression. Genes related to osteogenesis (i.e., RUNX2, COL1A1 and OSX) is important in controlling the differentiation of mesenchymal stem cells (MSCs) to bone tissues. The regulated expression level of miRNAs is critically important for the differentiation of MSCs to preosteoblasts. The understanding of miRNA regulation in osteogenesis could be applied for future applications in bone defects. Therefore, this study aims to shed light on the mechanistic pathway underlying osteogenesis by predicting miRNAs that may modulate this pathway. This study investigates RUNX2, which is a major transcription factor for osteogenesis that drives MSCs into preosteoblasts. Three different prediction tools were employed for identifying miRNAs related to osteogenesis using the 3’UTR of RUNX2 as the target gene. Of the 1,023 miRNAs, 70 miRNAs were found by at least two of the tools. Candidate miRNAs were then selected based on their free energy values, followed by assessing the probability of target accessibility. The results showed that miRNAs 23b, 23a, 30b, 143, 203, 217, and 221 could regulate the RUNX2 gene during the differentiation of MSCs to preosteoblasts.

The fight against the emergence of mutant influenza strains has led to the screening of an increasing number of compounds for inhibitory activity against influenza neuraminidase. This study explores the chemical space of neuraminidase inhibitors (NAIs), which provides an opportunity to obtain further molecular insights regarding the underlying basis of their bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the literature. Molecular and quantum chemical descriptors were obtained from low-energy conformational structures geometrically optimized at the PM6 level. The bioactivities of NAIs were classified as active or inactive according to their half maximum inhibitory concentration (IC50) value in which IC50 < 1µM and ≥ 10µM were defined as active and inactive compounds, respectively. Interpretable decision rules were derived from a quantitative structure–activity relationship (QSAR) model established using a set of substructure descriptors via decision tree analysis. Univariate analysis, feature importance analysis from decision tree modeling and molecular scaffold analysis were performed on both data sets for discriminating important structural features amongst active and inactive NAIs. Good predictive performance was achieved as deduced from accuracy and Matthews correlation coefficient values in excess of 81% and 0.58, respectively, for both influenza A and B NAIs. Furthermore, molecular docking was employed to investigate the binding modes and their moiety preferences of active NAIs against both influenza A and B neuraminidases. Moreover, novel NAIs with robust binding fitness towards influenza A and B neuraminidase were generated via combinatorial library enumeration and their binding fitness was on par or better than FDA-approved drugs. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.

Aromatase is an enzyme that plays a critical role in the development of estrogen receptor positive breast cancer. As aromatase catalyzes the aromatization of androstenedione to estrone, a naturally occurring estrogen, it is a promising drug target for therapeutic management. The undesirable effects found in aromatase inhibitors (AIs) that are in clinical use necessitate the discovery of novel AIs with higher selectivity, less toxicity and improving potency. In this study, we elucidate the binding mode of all three generations of AI drugs to the crystal structure of aromatase by means of molecular docking. It was demonstrated that the docking protocol could reliably reproduce the interaction of aromatase with its substrate with an RMSD of 1.350 Å. The docking study revealed that polar (D309, T310, S478 and M374), aromatic (F134, F221 and W224) and non-polar (A306, A307, V370, L372 and L477) residues were important for interacting with the AIs. The insights gained from the study herein have great potential for the design of novel AIs.

Nicotinic acid, known as vitamin B3, is an effective lipid lowering drug and intense cutaneous vasodilator. This study reports the effect of 2-(1-adamantylthio)nicotinic acid (6) and its amide 7 and nitrile analog 8 on phenylephrine-induced contraction of rat thoracic aorta as well as antioxidative activity. It was found that the tested thionicotinic acid analogs 6-8 exerted maximal vasorelaxation in a dose-dependent manner, but their effects were less than acetylcholine (ACh)-induced nitric oxide (NO) vasorelaxation. The vasorelaxations were reduced, apparently, in both NG-nitro-L-arginine methyl ester (L-NAME) and indomethacin (INDO). Synergistic effects were observed in the presence of L-NAME plus INDO, leading to loss of vasorelaxation of both the ACh and the tested nicotinic acids. Complete loss of the vasorelaxation was noted under removal of endothelial cells. This infers that the vasorelaxations are mediated partially by endothelium-induced NO and prostacyclin. The thionicotinic acid analogs all exhibited antioxidant properties in both 2,2-diphenyl-1-picrylhydrazyl (DPPH) and superoxide dismutase (SOD) assays. Significantly, the thionicotinic acid 6 is the most potent vasorelaxant with ED50 of 21.3 nM and is the most potent antioxidant (as discerned from DPPH assay). Molecular modeling was also used to provide mechanistic insights into the vasorelaxant and antioxidative activities. The findings reveal that the thionicotinic acid analogs are a novel class of vasorelaxant and antioxidant compounds which have potential to be further developed as promising therapeutics.

Molecular imprinting is a technology that facilitates the production of artificial receptors toward compounds of interest. The molecularly imprinted polymers act as artificial antibodies, artificial receptors, or artificial enzymes with the added benefit over their biological counterparts of being highly durable. In this study, we prepared molecularly imprinted polymers for the purpose of binding specifically to tocopherol (vitamin E) and its derivative, tocopherol acetate. Binding of the imprinted polymers to the template was found to be two times greater than that of the control, non-imprinted polymers, when using only 10 mg of polymers. Optimization of the rebinding solvent indicated that ethanol-water at a molar ratio of 6:4 (v/v) was the best solvent system as it enhanced the rebinding performance of the imprinted polymers toward both tocopherol and tocopherol acetate with a binding capacity of approximately 2 mg/g of polymer. Furthermore, imprinted nanospheres against tocopherol was successfully prepared by precipitation polymerization with ethanol-water at a molar ratio of 8:2 (v/v) as the optimal rebinding solvent. Computer simulation was also performed to provide mechanistic insights on the binding mode of template-monomer complexes. Such polymers show high potential for industrial and medical applications, particularly for selective separation of tocopherol and derivatives.

Bacterial lipopolysaccharides (LPS), also known as endotoxins, are major structural components of the outer membrane of Gram-negative bacteria that serve as a barrier and protective shield between them and their surrounding environment. LPS is considered to be a major virulence factor as it strongly stimulates the secretion of pro-inflammatory cytokines which mediate the host immune response and culminating in septic shock. Quantitative structure-activity relationship studies of the LPS neutralization activities of anti-endotoxins were performed using charge and quantum chemical descriptors. Artificial neural network implementing the back-propagation algorithm was selected for the multivariate analysis. The predicted activities from leave-one-out cross-validation were well correlated with the experimental values as observed from the correlation coefficient and root mean square error of 0.930 and 0.162, respectively. Similarly, the external testing set also yielded good predictivity with correlation coefficient and root mean square error of 0.983 and 0.130. The model holds great potential for the rational design of novel and robust compounds with enhanced neutralization activity.

Nicotinic acid (also known as vitamin B3) is a dietary element essential for physiological and antihyperlipidemic functions. This study reports the synthesis of novel mixed ligand complexes of copper with nicotinic and other select carboxylic acids (phthalic, salicylic and anthranilic acids). The tested copper complexes exhibited superoxide dismutase (SOD) mimetic activity and antimicrobial activity against Bacillus subtilis ATCC 6633, with a minimum inhibition concentration of 256 μg/mL. Copper complex of nicotinic-phthalic acids (CuNA/Ph) was the most potent with a SOD mimetic activity of IC50 34.42 μM. The SOD activities were observed to correlate well with the theoretical parameters as calculated using density functional theory (DFT) at the B3LYP/LANL2DZ level of theory. Interestingly, the SOD activity of the copper complex CuNA/Ph was positively correlated with the electron affinity (EA) value. The two quantum chemical parameters, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), were shown to be appropriate for understanding the mechanism of the metal complexes as their calculated energies show good correlation with the SOD activity. Moreover, copper complex with the highest SOD activity were shown to possess the lowest HOMO energy. These findings demonstrate a great potential for the development of value-added metallovitamin-based therapeutics.

Molecular imprinting is one of the most efficient methods for preparing synthetic receptors that possess user defined recognition properties. Despite general success of non-covalent imprinting for a large variety of templates, some groups of compounds remain difficult to tackle due to their structural complexity. In this study we investigate preparation of molecularly imprinted polymers that can bind sulfonamide compounds, which represent important drug candidates. Compared to the biological system that utilizes metal coordinated interaction, the imprinted polymer provided pronounced selectivity when hydrogen bond interaction was employed in an organic solvent. Computer simulation of the interaction between the sulfonamide template and functional monomers pointed out that although methacrylic acid had strong interaction energy with the template, it also possessed high non-specific interaction with the solvent molecules of tetrahydrofuran as well as being prone to self-complexation. On the other hand, 1-vinyl-imidazole was suitable for imprinting sulfonamides as it did not cross-react with the solvent molecules or engage in self-complexation structures.

Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.

A unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created and made publicly available under the Bioclipse Decision Support open source system at www.cyp450model.org. In regards to the proteochemometric modeling we represented the chemical compounds by molecular signature descriptors and the CYP-isoforms by alignment-independent description of composition and transition of amino acid properties of their protein primary sequences. The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. The model showed excellent predictive ability with internal AUC = 0.923 and an external AUC = 0.940, as evaluated on a large external dataset. The advantage of PCM models is their extensibility making it possible to extend our model for new CYP isoforms and polymorphic CYP forms. A key benefit of PCM is that all proteins are confined in one single model, which makes it generally more stable and predictive as compared with single target models. The inclusion of the model in Bioclipse Decision Support makes it possible to make virtual instantaneous predictions (∼100 ms per prediction) while interactively drawing or modifying chemical structures in the Bioclipse chemical structure editor.

Hemoglobin (Hb) plays a critical role in human physiological function by transporting O2. Hb is safe and inert within the confinement of the red blood cell but becomes reactive and toxic upon hemolysis. Haptoglobin (Hp) is an acute-phase serum protein that scavenges Hb and the resulting Hb-Hp complex is subjected to CD163-mediated endocytosis by macrophages. The interaction between Hb and Hp is extraordinarily strong and largely irreversible. As the structural details of the human Hb-Hp complex are not yet available, this study reports for the first time on insights of the binding modalities and molecular details of the human Hb-Hp interaction by means of protein-protein docking. Furthermore, residues that are pertinent for complex formation were identified by computational alanine scanning mutagenesis. Results revealed that the surface of the binding interface of Hb-Hp is not flat and protrudes into each binding partner. It was also observed that the secondary structures at the Hb-Hp interface are oriented as coils and α-helices. When dissecting the interface in more detail, it is obvious that several tyrosine residues of Hb, particularly β145Tyr, α42Tyr and α140Tyr, are buried in the complex and protected from further oxidative reactions. Such finding opens up new avenues for the design of Hp mimics which may be used as alternative clinical Hb scavengers.

Host defense peptides (HDPs) are positively-charged and amphipathic components of the innate immune system that have demonstrated great potential to become the next generation of broad spectrum therapeutic agents effective against a vast array of pathogens and tumor. As such, many approaches have been taken to improve the therapeutic efficacy of HDPs. Amongst these methods, the incorporation of d-amino acids (d-AA) is an approach that has demonstrated consistent success in improving HDPs. Although, virtually all HDP review articles briefly mentioned about the role of d-AA, however it is rather surprising that no systematic review specifically dedicated to this topic exists. Given the impact that d-AA incorporation has on HDPs, this review aims to fill that void with a systematic discussion of the impact of d-AA on HDPs.