Polyketides (PKs) are a widespread class of secondary metabolites with recognised pharmacological properties. These molecules are abundantly produced in the marine environment, especially by dinoflagellate-photosynthetic organisms able to produce several PKs, including neurotoxins, cytotoxins, and immunomodulating agents. The biosynthesis of these compounds is driven by a conserved enzymatic process involving polyketide synthase complexes. Different genera of dinoflagellates produce PKs. Among them, dinoflagellates of the genus Amphidinium are of particular interest due to its ability to produce the following two major families of PKs: amphidinolides and amphidinols. These compounds display remarkable biological activities, including anticancer, antimicrobial, and antifungal effects, making them attractive targets for pharmaceutical research and development. However, the natural yield of Amphidinium-derived polyketides (APKs) is generally low, limiting their potential for sustainable molecular farming. This challenge has prompted interest in developing biotechnological strategies to enhance their production. This review aims to define the current state of studies about APKs, starting from their initial discoveries to the recent understanding of their biosynthetic pathways. Additionally, it summarizes the structures of compounds discovered, highlights their biotechnological potential, and discusses novel trends in their production.
The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced malware, such as polymorphic and metamorphic variants. To address these challenges, this study introduces a hybrid deep learning model (DBN-GRU) that integrates Deep Belief Networks (DBN) for static analysis and Gated Recurrent Units (GRU) for dynamic behavior modeling to enhance malware detection accuracy and efficiency. The model extracts static features (permissions, API calls, intent filters) and dynamic features (system calls, network activity, inter-process communication) from Android APKs, enabling a comprehensive analysis of application behavior.The proposed model was trained and tested on the Drebin dataset, which includes 129,013 applications (5,560 malware and 123,453 benign).Performance evaluation against NMLA-AMDCEF, MalVulDroid, and LinRegDroid demonstrated that DBN-GRU achieved 98.7% accuracy, 98.5% precision, 98.9% recall, and an AUC of 0.99, outperforming conventional models.In addition, it exhibits faster preprocessing, feature extraction, and malware classification times, making it suitable for real-time deployment.By bridging static and dynamic detection methodologies, the DBN-GRU enhances malware detection capabilities while reducing false positives and computational overhead.These findings confirm the applicability of the proposed model in real-world Android security applications, offering a scalable and high-performance malware detection solution.
Multiple dysregulated pathways are behind the pathogenesis of neurodegenerative diseases (NDDs); however, the crucial targets are still unknown. Oxidative stress, apoptosis, autophagy, and inflammation are the most dominant pathways that strongly influence neurodegeneration. In this way, targeting the Ras/Raf/mitogen-activated protein kinases (MAPKs) pathway appears to be a developing strategy for combating NDDs like Parkinson's disease, Alzheimer's disease, stroke, aging, and other NDDs. Accordingly, plant secondary metabolites have shown promising potentials for the simultaneous modulation of the Ras/Raf/MAPKs pathway and play an essential role in NDDs. MAPKs include p38 MAPK, extracellular signal-regulated kinase 1/2 (ERK 1/2), and c-Jun N-terminal kinase (JNK), which are important molecular players in neurodegeneration. Ras/Raf, which is located the upstream of MAPK pathway influences the initiation and progression of neurodegeneration and is regulated by natural products. Thus, the present study aimed to investigate the neuroprotective roles of plant- and marine-derived secondary metabolites against several NDDs through the modulation of the Ras/Raf/MAPK signaling pathway. A systematic and comprehensive review was performed to highlight the modulatory roles of natural products on the Ras/Raf/MAPK signaling pathway in NDDs, according to the PRISMA guideline, using scholarly electronic databases, including PubMed, Scopus, and Web of Sciences. Associated reference lists were also searched for the literature review. From a total of 1495 results, finally 107 articles were included in the present study. The results show that several natural compounds such as alkaloid, phenolic, terpenoids, and nanoformulation were shown to have modulatory effects on the Ras/Raf/MAPKs pathway. Natural products are promising multi-targeted agents with on NDDs through Ras/Raf/MAPKs pathway. Nevertheless, additional and complementary studies are necessary to check its efficacy and potential side effects.
Protein kinases are a diverse superfamily of proteins common to organisms across the tree of life that are typically involved in signal transduction, allowing organisms to sense and respond to biotic or abiotic environmental factors. They have important roles in organismal physiology, including development, reproduction, acclimation to environmental stress, while their dysregulation can lead to disease, including several forms of cancer. Identifying the complement of protein kinases (the kinome) of any organism is useful for understanding its physiological capabilities, limitations and adaptations to environmental stress. The increasing availability of genomes makes it now possible to examine and compare the kinomes across a broad diversity of organisms. Here we present a pipeline respecting the FAIR principles (findable, accessible, interoperable and reusable) that facilitates the search and identification of protein kinases from a predicted proteome, and classifies them according to group of serine/threonine/tyrosine protein kinases present in eukaryotes. KiNext is a Nextflow pipeline that regroups a number of existing bioinformatic tools to search for and classify the protein kinases of an organism in a reproducible manner, starting from a set of amino acid sequences. Conventional eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs) are identified by using Hidden Markov Models (HMMs) generated from the catalytic domains of kinases. Furthermore, KiNext categorizes ePKs into the eight kinase groups by employing dedicated Hidden Markov Models (HMMs) tailored for each group. The performance of the KiNext pipeline was validated against previously identified kinomes obtained with other tools that were already published for two marine species, the Pacific oyster Crassostrea gigas and the unicellular green alga Ostreoccocus tauri. KiNext outperformed previous results by finding previously unidentified kinases and by attributing a large proportion of previously unclassified kinases to a group in both species. These results demonstrate improvements in kinase identification and classification, all while providing traceability and reproducibility of results in a FAIR pipeline. The default HMM models provided with KiNext are most suitable for eukaryotes, but the pipeline can be easily modified to include HMM models for other taxa of interest. The KiNext pipeline enables efficient and reproducible identification of kinomes based on predicted amino acid sequences (i.e. proteomes). KiNext was designed to be easy to use, automated, portable and scalable.
Robotic adoption in knee surgery has yielded several benefits, but its application in patellofemoral arthroplasty (PFA) remains barely reported. The purpose of this study was to determine implant survival, patient satisfaction, and functional outcomes after robotic-assisted PFA at an intermediate follow-up. This prospective analysis targeted 18 knees of 16 consecutive patients who underwent robot-aided PFA with three-year minimum follow-up (range, 3 to 6 years). Each patient was evaluated collecting pre-operative and post-operative medical record data, including range of motion, radiographic images, and multiple scores, such as VAS, APKS, and OKS. At surgery, the mean age was 55.4 years ± 14.4 (range, 32 to 78 years), and the mean BMI was 26.8 kg/m² ±5.2 (range, 20 to 36). Etiologies of patellofemoral osteoarthritis included idiopathic degeneration (28%), post-traumatic (33%), and dysplasia (39%). Pre-implantation scores were VAS 7.9 ± 1.4, AKPS 34.6 ± 23.3, and OKS 17.3 ± 10.3. One implant was revised with primary total knee arthroplasty for osteoarthritis progression. Clinical and radiographic follow-up showed no signs of loosening or infection. The maximum flexion reached an average of 131.1°±10.5° (range, 110° to 145°), accompanied by significantly improved score results (P-value < 0.01): VAS 1.1 ± 1.4, AKPS 90.2 ± 8.6, and OKS 46.3 ± 1.8. At 3 years after robotic assisted patellofemoral arthroplasty, excellent implant survival and patient satisfaction rates can be expected along with significantly improved functional and pain control outcomes. Although the limitations imposed by the restricted cohort, these findings indicate that robotic assistance in PFA is both safe and effective at intermediate follow-up.
With the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers. In this paper, we have created AndroDex: an Android malware dataset containing a total of 24,746 samples that belong to more than 180 malware families. These samples are based on .dex images that truly reflect the characteristics of malware. To construct this dataset, we first downloaded the APKs of the malware, applied obfuscation techniques, and then converted them into images. We believe this dataset will significantly enhance a series of research studies, including Android malware detection and classification, and it will also boost deep learning classification efforts, among others. The main objective of creating images based on the Android dataset is to help other malware researchers better understand how malware works. Additionally, an important result of this study is that most malware nowadays employs obfuscation techniques to hide their malicious activities. However, malware images can overcome such issues. The main limitation of this dataset is that it contains images based on .dex files that are based on static analysis. However, dynamic analysis takes time, therefore, to overcome the issue of time and space this dataset can be used for the initial examination of any .apk files.
Computational analysis of protein-protein interaction provided the crucial information to increase the binding affinity without a change in basic conformation. Several docking programs were used to predict the near-native poses of the protein-protein complex in 10 top-rankings. The universal criteria for discriminating the near-native pose are not available since there are several classes of recognition protein. Currently, the explicit criteria for identifying the near-native pose of ankyrin-protein complexes (APKs) have not been reported yet. In this study, we established an ensemble computational model for discriminating the near-native docking pose of APKs named "AnkPlex". A dataset of APKs was generated from seven X-ray APKs, which consisted of 3 internal domains, using the reliable docking tool ZDOCK. The dataset was composed of 669 and 44,334 near-native and non-near-native poses, respectively, and it was used to generate eleven informative features. Subsequently, a re-scoring rank was generated by AnkPlex using a combination of a decision tree algorithm and logistic regression. AnkPlex achieved superior efficiency with ≥1 near-native complexes in the 10 top-rankings for nine X-ray complexes compared to ZDOCK, which only obtained six X-ray complexes. In addition, feature analysis demonstrated that the van der Waals feature was the dominant near-native pose out of the potential ankyrin-protein docking poses. The AnkPlex model achieved a success at predicting near-native docking poses and led to the discovery of informative characteristics that could further improve our understanding of the ankyrin-protein complex. Our computational study could be useful for predicting the near-native poses of binding proteins and desired targets, especially for ankyrin-protein complexes. The AnkPlex web server is freely accessible at http://ankplex.ams.cmu.ac.th .
Microsporidia have attracted considerable attention because they infect a wide range of hosts, from invertebrates to vertebrates, and cause serious human diseases and major economic losses in the livestock industry. There are no prospective drugs to counteract this pathogen. Eukaryotic protein kinases (ePKs) play a central role in regulating many essential cellular processes and are therefore potential drug targets. In this study, a comprehensive summary and comparative analysis of the protein kinases in four microsporidia—Enterocytozoon bieneusi, Encephalitozoon cuniculi, Nosema bombycis and Nosema ceranae—was performed. The results show that there are 34 ePKs and 4 atypical protein kinases (aPKs) in E. bieneusi, 29 ePKs and 6 aPKs in E. cuniculi, 41 ePKs and 5 aPKs in N. bombycis, and 27 ePKs and 4 aPKs in N. ceranae. These data support the previous conclusion that the microsporidian kinome is the smallest eukaryotic kinome. Microsporidian kinomes contain only serine-threonine kinases and do not contain receptor-like and tyrosine kinases. Many of the kinases related to nutrient and energy signaling and the stress response have been lost in microsporidian kinomes. However, cell cycle-, development- and growth-related kinases, which are important to parasites, are well conserved. This reduction of the microsporidian kinome is in good agreement with genome compaction, but kinome density is negatively correlated with proteome size. Furthermore, the protein kinases in each microsporidian genome are under strong purifying selection pressure. No remarkable differences in kinase family classification, domain features, gain and/or loss, and selective pressure were observed in these four species. Although microsporidia adapt to different host types, the coevolution of microsporidia and their hosts was not clearly reflected in the protein kinases. Overall, this study enriches and updates the microsporidian protein kinase database and may provide valuable information and candidate targets for the design of treatments for pathogenic diseases.
The number of applications prepared for use on mobile devices has increased rapidly with the widespread use of the Android OS. This has resulted in the undesired installation of Android application packages (APKs) that violate user privacy or are malicious. The increasing similarity between Android malware and benign applications makes it difficult to distinguish them from each other and causes a situation of concern for users. In this study, FG-Droid, a machine-learning based classifier, using the method of grouping the features obtained by static analysis, was proposed. It was created because of experiments with machine learning (ML), deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)-based models using Drebin, Genome, and Arslan datasets. The experimental results revealed that FG-Droid achieved a 97.7% area under the receiver operating characteristic (ROC) curve (AUC) score with a vector including only 11 static features and the ExtraTree algorithm. While reaching a high classification rate, only 0.063 seconds were needed for analysis per application. This means that the proposed feature selection method is faster than all traditional feature selection methods, and FG-Droid is one of the tools to date with the shortest analysis time per application. As a result, an efficient classifier with few features, low analysis time, and high classification success was developed using a unique feature grouping method.
Atypical protein kinases (aPKs) include proteins known to be involved in the phosphorylation-mediated regulation of a wide variety of cellular processes, as well as some for which the function is, as yet, unknown. At present, 13 families of aPKs have been identified in the human genome. This review briefly summarizes their known properties, but concentrates in particular on the RIO family of aPKs. Representatives of this family are present in organisms varying from archaea to humans. All these organisms contain at least two RIO proteins, Rio1 and Rio2, but a third Rio3 group is present in multicellular eukaryotes. Crystal structures of A. fulgidus Rio1 and Rio2 have shown that whereas the overall fold of these enzymes resembles typical protein kinases, some of the kinase structural domains, particularly those involved in peptide substrate binding, are not present. The mode of binding of nucleotides also differs from other kinases. While the enzymatic activity of Rio1 and Rio2 has been demonstrated and both have been shown to be essential in S. cerevisiae and required for proper cell cycle progression and chromosome maintenance, the biological substrates of RIO proteins still remain to be identified.
Kinases are attractive anticancer targets due to their central role in the growth, survival, and therapy resistance of tumor cells. This review explores the two primary kinase classes, the eukaryotic protein kinases (ePKs) and the atypical protein kinases (aPKs), and provides a structure-centered comparison of their sequences, structures, hydrophobic spines, mutation and SNP hotspots, and inhibitor interaction patterns. Despite the limited sequence similarity between these two classes, atypical kinases commonly share the archetypical kinase fold but lack conserved eukaryotic kinase motifs and possess altered hydrophobic spines. Furthermore, atypical kinase inhibitors explore only a limited number of binding modes both inside and outside the orthosteric binding site. The distribution of genetic variations in both classes shows multiple ways they can interfere with kinase inhibitor binding. This multilayered review provides a research framework bridging the eukaryotic and atypical kinase classes.
The data in this article have been collaborated from mainly four sources- Google Playstore, Wandoujia (third party app store market), AMD and Androzoo. These data include ~85,000 APKs (Android Package Kit), both malicious and benign from these data sources. Static and dynamic features are extracted from these APK files, and then supervised machines learning algorithms are employed for malware detection in Android. This data article also provides the Python code for data analysis. For feature extraction, a generic algorithm has also been incorporated, thereby, selecting important and relevant feature subset. Conclusive results obtained from this data set are further comprehended and interpreted in our latest research study "A Novel Parallel Classifier Scheme for Vulnerability Detection in Android" (Garg et al., 2018). This proved to be precious contribution for ensembling classifiers in machine learning to detect malware in Android.
Oomycetes are a large group of economically and ecologically important species. Its most notorious member is Phytophthora infestans, the cause of the devastating potato late blight disease. The life cycle of P. infestans involves hyphae which differentiate into spores used for dispersal and host infection. Protein phosphorylation likely plays crucial roles in these stages, and to help understand this we present here a genome-wide analysis of the protein kinases of P. infestans and several relatives. The study also provides new insight into kinase evolution since oomycetes are taxonomically distant from organisms with well-characterized kinomes. Bioinformatic searches of the genomes of P. infestans, P. ramorum, and P. sojae reveal they have similar kinomes, which for P. infestans contains 354 eukaryotic protein kinases (ePKs) and 18 atypical kinases (aPKs), equaling 2% of total genes. After refining gene models, most were classifiable into families seen in other eukaryotes. Some ePK families are nevertheless unusual, especially the tyrosine kinase-like (TKL) group which includes large oomycete-specific subfamilies. Also identified were two tyrosine kinases, which are rare in non-metazoans. Several ePKs bear accessory domains not identified previously on kinases, such as cyclin-dependent kinases with integral cyclin domains. Most ePKs lack accessory domains, implying that many are regulated transcriptionally. This was confirmed by mRNA expression-profiling studies that showed that two-thirds vary significantly between hyphae, sporangia, and zoospores. Comparisons to neighboring taxa (apicomplexans, ciliates, diatoms) revealed both clade-specific and conserved features, and multiple connections to plant kinases were observed. The kinome of Hyaloperonospora arabidopsidis, an oomycete with a simpler life cycle than P. infestans, was found to be one-third smaller. Some differences may be attributable to gene clustering, which facilitates subfamily expansion (or loss) through unequal crossing-over. The large sizes of the Phytophthora kinomes imply that phosphorylation plays major roles in their life cycles. Their kinomes also include many novel ePKs, some specific to oomycetes or shared with neighboring groups. Little experimentation to date has addressed the biological functions of oomycete kinases, but this should be stimulated by the structural, evolutionary, and expression data presented here. This may lead to targets for disease control.
Protein kinases are key enzymes that regulate a wide range of cellular processes, including cell-cycle progression, transcription, DNA replication and metabolic functions. These enzymes catalyse the transfer of phosphates to serine, threonine and tyrosine residues, thus playing functional roles in reversible protein phosphorylation. There are two main groups, namely eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs); RIO kinases belong to the latter group. While there is some information about RIO kinases and their roles in animals, nothing is known about them in parasites. This is the first study to characterise a RIO1 kinase from any parasite. A full-length cDNA (Tv-rio-1) encoding a RIO1 protein kinase (Tv-RIO1) was isolated from the economically important parasitic nematode Trichostrongylus vitrinus (Order Strongylida). The uninterrupted open reading frame (ORF) of 1476 nucleotides encoded a protein of 491 amino acids, containing the characteristic RIO1 motif LVHADLSEYNTL. Tv-rio-1 was transcribed at the highest level in the third-stage larva (L3), and a higher level in adult females than in males. Comparison with homologues from other organisms showed that protein Tv-RIO1 had significant homology to related proteins from a range of metazoans and plants. Amino acid sequence identity was most pronounced in the ATP-binding motif, active site and metal binding loop. Phylogenetic analyses of selected amino acid sequence data revealed Tv-RIO1 to be most closely related to the proteins in the species of Caenorhabditis. A structural model of Tv-RIO1 was constructed and compared with the published crystal structure of RIO1 of Archaeoglobus fulgidus (Af-Rio1). This study provides the first insights into the RIO1 protein kinases of nematodes, and a foundation for further investigations into the biochemical and functional roles of this molecule in biological processes in parasitic nematodes.
A family of plant kinases containing ankyrin-repeats, the Ankyrin-Protein Kinases (APKs), shows structural resemblance to mammalian Integrin-Linked Kinases (ILKs), key regulators of mammalian cell adhesion. MsAPK1 expression is induced by osmotic stress in roots of Medicago sativa (L.) plants. The Escherichia coli-purified MsAPK1 could only phosphorylate tubulin among a variety of substrates and the enzymatic activity was strictly dependent on Mn2+. MsAPK1 is highly related to two APK genes in Arabidopsis thaliana (L.), AtAPK1 and AtAPK2. Promoter-GUS fusions assays revealed that the Arabidopsis APK genes show distinct expression patterns in roots and hypocotyls. Although Medicago truncatula (L.) plants affected in MsAPK1 expression could not be obtained using in vitro regeneration, A. thaliana plants expressing MsAPK1 or a mutant MsAPK1 protein, in which the conserved aspartate 315 of the kinase catalytic domain was replaced by asparagines (DN-lines), developed normally. The DN mutant lines showed increased capacity to develop adventitious roots when compared with control or MsAPK1-expressing plants. APK-mediated signalling may therefore link perception of external abiotic signals and the microtubule cytoskeleton, and influence adventitious root development.
The trypanosomatids Leishmania major, Trypanosoma brucei and Trypanosoma cruzi cause some of the most debilitating diseases of humankind: cutaneous leishmaniasis, African sleeping sickness, and Chagas disease. These protozoa possess complex life cycles that involve development in mammalian and insect hosts, and a tightly coordinated cell cycle ensures propagation of the highly polarized cells. However, the ways in which the parasites respond to their environment and coordinate intracellular processes are poorly understood. As a part of an effort to understand parasite signaling functions, we report the results of a genome-wide analysis of protein kinases (PKs) of these three trypanosomatids. Bioinformatic searches of the trypanosomatid genomes for eukaryotic PKs (ePKs) and atypical PKs (aPKs) revealed a total of 176 PKs in T. brucei, 190 in T. cruzi and 199 in L. major, most of which are orthologous across the three species. This is approximately 30% of the number in the human host and double that of the malaria parasite, Plasmodium falciparum. The representation of various groups of ePKs differs significantly as compared to humans: trypanosomatids lack receptor-linked tyrosine and tyrosine kinase-like kinases, although they do possess dual-specificity kinases. A relative expansion of the CMGC, STE and NEK groups has occurred. A large number of unique ePKs show no strong affinity to any known group. The trypanosomatids possess few ePKs with predicted transmembrane domains, suggesting that receptor ePKs are rare. Accessory Pfam domains, which are frequently present in human ePKs, are uncommon in trypanosomatid ePKs. Trypanosomatids possess a large set of PKs, comprising approximately 2% of each genome, suggesting a key role for phosphorylation in parasite biology. Whilst it was possible to place most of the trypanosomatid ePKs into the seven established groups using bioinformatic analyses, it has not been possible to ascribe function based solely on sequence similarity. Hence the connection of stimuli to protein phosphorylation networks remains enigmatic. The presence of numerous PKs with significant sequence similarity to known drug targets, as well as a large number of unusual kinases that might represent novel targets, strongly argue for functional analysis of these molecules.
BACKGROUND: Anal smears are increasingly being used as a screening test for anal squamous intraepithelial lesions (ASILs). This study was undertaken to assess the usefulness and limitations of anal smears in screening for ASILs. METHODS: The cytomorphological features of 200 consecutive anal smears collected in liquid medium from 198 patients were studied and findings were correlated with results of surgical biopsies and/or repeat smears that became available for 71 patients within six months. RESULTS: Adequate cellularity was defined as an average of 6 or more nucleated squamous cells/hpf. A glandular/transitional component was not required for adequacy. Dysplastic cells, atypical parakeratotic cells and bi/multinucleated cells were frequent findings in ASIL while koilocytes were infrequent. Smears from LSIL cases most frequently showed mildly dysplastic and bi/multinucleate squamous cells followed by parakeratotic cells (PK), atypical parakeratotic cells (APK), and koilocytes. HSIL smears contained squamous cells with features of moderate/severe dysplasia and many APKs. Features of LSIL were also found in most HSIL smears. CONCLUSIONS: In this study liquid based anal smears had a high sensitivity (98%) for detection of ASIL but a low specificity (50%) for predicting the severity of the abnormality in subsequent biopsy. Patients with cytologic diagnoses of ASC-US and LSIL had a significant risk (46-56%) of HSIL at biopsy. We suggest that all patients with a diagnosis of ASC-US and above be recommended for high resolution anoscopy with biopsy.
Interaction between Medicago spp. and Sinorhizobium meliloti leads to the development of a novel organ, the root nodule. A gene, Msapk1, encoding a novel type of plant protein kinase containing a N-terminal region with an ankyrin domain, was identified and shown to be expressed both in S. meliloti-infected and spontaneous nodules in alfalfa. This gene is not exclusively associated to nodulation since its expression was detected in other plant organs. Several genes coding for ankyrin protein kinases (APKs) were detected in various plants and animals. Three closest A. thaliana homologues of Msapk1 were identified in databases and two of them were shown to express differentially in various organs using gene-specific RT-PCR. In contrast, Southern analysis suggests that a single-copy gene exists in diploid M. truncatula. By screening a M. truncatula BAC library the Mtapk1 genomic region was isolated and sequenced. Two neighbouring genes showing homologies to previously identified sequences in data banks were detected in the vicinity of the Mtapk1 gene and compared to similar regions of the three Atapk genes. The distribution of exons/introns was the same for all expressed genes of both species although Mtapk1 contained larger introns. Upon osmotic stress Msapk1 expression was induced in roots of alfalfa starting from three hours up to two days of treatment. These data suggest that Msapk1, involved in alfalfa osmotic stress responses, belongs to a novel class of plant protein kinases.