Recent Advances in Conotoxin Classification by Using Machine Learning Methods

AScirRNA: A novel computational approach to discover abiotic stress-responsive circular RNAs in plant genome

In the realm of plant biology, understanding the intricate regulatory mechanisms governing stress responses stands as a pivotal pursuit. Circular RNAs (circRNAs), emerging as critical players in gene regulation, have garnered attention in recent days for their potential roles in abiotic stress adaptation. A comprehensive grasp of circRNAs' functions in stress response offers avenues for breeders to manipulating plants to develop abiotic stress resistant crop cultivars to thrive in challenging climates. This study pioneers a machine learning-based model for predicting abiotic stress-responsive circRNAs. The K-tuple nucleotide composition (KNC) and Pseudo KNC (PKNC) features were utilized to numerically represent circRNAs. Three different feature selection strategies were employed to select relevant and non-redundant features. Eight shallow and four deep learning algorithms were evaluated to build the final predictive model. Following five-fold cross-validation process, XGBoost learning algorithm demonstrated superior performance with LightGBM-chosen 260 KNC features (Accuracy: 74.55 %, auROC: 81.23 %, auPRC: 76.52 %) and 160 PKNC features (Accuracy: 74.32 %, auROC: 81.04 %, auPRC: 76.43 %), over other combinations of learning algorithms and feature selection techniques. Further, the robustness of the developed models were evaluated using an independent test dataset, where the overall accuracy, auROC and auPRC were found to be 73.13 %, 72.34 % and 72.68 % for KNC feature set and 73.52 %, 79.53 % and 73.09 % for PKNC feature set, respectively. This computational approach was also integrated into an online prediction tool, AScirRNA (https://iasri-sg.icar.gov.in/ascirna/) for easy prediction by the users. Both the proposed model and the developed tool are poised to augment ongoing efforts in identifying stress-responsive circRNAs in plants.

Potential Ancestral Conoidean Toxins in the Venom Cocktail of the Carnivorous Snail Raphitoma purpurea (Montagu, 1803) (Neogastropoda: Raphitomidae)

Venomous marine gastropods of the superfamily Conoidea possess a rich arsenal of toxins, including neuroactive toxins. Venom adaptations might have played a fundamental role in the radiation of conoideans; nevertheless, there is still no knowledge about the venom of the most diversified family of the group: Raphitomidae Bellardi, 1875. In this study, transcriptomes were produced from the carcase, salivary glands, and proximal and distal venom ducts of the northeastern Atlantic species Raphitoma purpurea (Montagu, 1803). Using a gut barcoding approach, we were also able to report, for the first time, molecular evidence of a vermivorous diet for the genus. Transcriptomic analyses revealed over a hundred putative venom components (PVC), including 69 neurotoxins. Twenty novel toxin families, including some with high levels of expansion, were discovered. No significant difference was observed between the distal and proximal venom duct secretions. Peptides related to cone snail toxins (Cerm06, Pgam02, and turritoxin) and other venom-related proteins (disulfide isomerase and elevenin) were retrieved from the salivary glands. These salivary venom components may constitute ancestral adaptations for venom production in conoideans. Although often neglected, salivary gland secretions are of extreme importance for understanding the evolutionary history of conoidean venom.

Voltage-Gated K+ Channel Modulation by Marine Toxins: Pharmacological Innovations and Therapeutic Opportunities

Bioactive compounds are abundant in animals originating from marine ecosystems. Ion channels, which include sodium, potassium, calcium, and chloride, together with their numerous variants and subtypes, are the primary molecular targets of the latter. Based on their cellular targets, these venom compounds show a range of potencies and selectivity and may have some therapeutic properties. Due to their potential as medications to treat a range of (human) diseases, including pain, autoimmune disorders, and neurological diseases, marine molecules have been the focus of several studies over the last ten years. The aim of this review is on the various facets of marine (or marine-derived) molecules, ranging from structural characterization and discovery to pharmacology, culminating in the development of some "novel" candidate chemotherapeutic drugs that target potassium channels.

DeTox: a pipeline for the detection of toxins in venomous organisms

Venomous organisms have independently evolved the ability to produce toxins 101 times during their evolutionary history, resulting in over 200 000 venomous species. Collectively, these species produce millions of toxins, making them a valuable resource for bioprospecting and understanding the evolutionary mechanisms underlying genetic diversification. RNA-seq is the preferred method for characterizing toxin repertoires, but the analysis of the resulting data remains challenging. While early approaches relied on similarity-based mapping to known toxin databases, recent studies have highlighted the importance of structural features for toxin detection. The few existing pipelines lack an integration between these complementary approaches, and tend to be difficult to run for non-experienced users. To address these issues, we developed DeTox, a comprehensive and user-friendly tool for toxin research. It combines fast execution, parallelization and customization of parameters. DeTox was tested on published transcriptomes from gastropod mollusks, cnidarians and snakes, retrieving most putative toxins from the original articles and identifying additional peptides as potential toxins to be confirmed through manual annotation and eventually proteomic analysis. By integrating a structure-based search with similarity-based approaches, DeTox allows the comprehensive characterization of toxin repertoire in poorly-known taxa. The effect of the taxonomic bias in existing databases is minimized in DeTox, as mirrored in the detection of unique and divergent toxins that would have been overlooked by similarity-based methods. DeTox streamlines toxin annotation, providing a valuable tool for efficient identification of venom components that will enhance venom research in neglected taxa.

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants

Abiotic stresses have become a major challenge in recent years due to their pervasive nature and shocking impacts on plant growth, development, and quality. MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of specific abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational model for prediction of miRNAs associated with four specific abiotic stresses such as cold, drought, heat and salt. The pseudo K-tuple nucleotide compositional features of Kmer size 1 to 5 were used to represent miRNAs in numeric form. Feature selection strategy was employed to select important features. With the selected feature sets, support vector machine (SVM) achieved the highest cross-validation accuracy in all four abiotic stress conditions. The highest cross-validated prediction accuracies in terms of area under precision-recall curve were found to be 90.15, 90.09, 87.71, and 89.25% for cold, drought, heat and salt respectively. Overall prediction accuracies for the independent dataset were respectively observed 84.57, 80.62, 80.38 and 82.78%, for the abiotic stresses. The SVM was also seen to outperform different deep learning models for prediction of abiotic stress-responsive miRNAs. To implement our method with ease, an online prediction server "ASmiR" has been established at https://iasri-sg.icar.gov.in/asmir/ . The proposed computational model and the developed prediction tool are believed to supplement the existing effort for identification of specific abiotic stress-responsive miRNAs in plants.

Immunosensors for Assay of Toxic Biological Warfare Agents

An immunosensor for the assay of toxic biological warfare agents is a biosensor suitable for detecting hazardous substances such as aflatoxin, botulinum toxin, ricin, Shiga toxin, and others. The application of immunosensors is used in outdoor assays, point-of-care tests, as a spare method for more expensive devices, and even in the laboratory as a standard analytical method. Some immunosensors, such as automated flow-through analyzers or lateral flow tests, have been successfully commercialized as tools for toxins assay, but the research is ongoing. New devices are being developed, and the use of advanced materials and assay techniques make immunosensors highly competitive analytical devices in the field of toxic biological warfare agents assay. This review summarizes facts about current applications and new trends of immunosensors regarding recent papers in this area.

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development

Cone snail venoms have been considered a valuable treasure for international scientists and businessmen, mainly due to their pharmacological applications in development of marine drugs for treatment of various human diseases. To date, around 800 Conus species are recorded, and each of them produces over 1,000 venom peptides (termed as conopeptides or conotoxins). This reflects the high diversity and complexity of cone snails, although most of their venoms are still uncharacterized. Advanced multiomics (such as genomics, transcriptomics, and proteomics) approaches have been recently developed to mine diverse Conus venom samples, with the main aim to predict and identify potentially interesting conopeptides in an efficient way. Some bioinformatics techniques have been applied to predict and design novel conopeptide sequences, related targets, and their binding modes. This review provides an overview of current knowledge on the high diversity of conopeptides and multiomics advances in high-throughput prediction of novel conopeptide sequences, as well as molecular modeling and design of potential drugs based on the predicted or validated interactions between these toxins and their molecular targets.

Synthesis and evaluation of a novel analgesic conotoxin Lt7b that inhibits calcium currents and increases sodium currents

Conotoxins are promising neuropharmacological tools and drug candidates due to their high efficiency and specificity in targeting ion channels or neurotransmitter receptors. In this study, a novel O₂‐superfamily conotoxin, Lt7b, was synthesized and its pharmacological functions were evaluated. Lt7b with three modified amino acids and three disulfide bonds was successfully synthesized. CD spectra showed that Lt7b had a typical α‐helix in the secondary structure. Patch clamp experiments on rat DRG neurons showed that Lt7b could significantly inhibit calcium currents with an IC₅₀ value of 856 ± 95 nM. Meanwhile, 10 μM Lt7b could significantly increase the sodium currents by 77 ± 8%, but it had no obvious effects on the potassium currents in DRG neurons. In addition, patch clamp experiments on ion channel subtypes showed that 10 μM Lt7b could inhibit 7.0 ± 1.2%, 8.0 ± 1.5%, 4.6 ± 3.4%, and 9.5 ± 0.1% of the hCa_v1.2, hCa_v2.1, hCa_v2.2, and hCa_v3.2 currents, respectively, while it did not increase the rNa_v1.7, rNa_v1.8, hNa_v1.5, hNa_v1.7, and hNa_v1.8 currents. Lt7b had no obvious toxicity to HaCaT and ND7/23 cells up to 1 mM and significantly increased the pain threshold at the testing time of 0.5–4 h in a dose‐dependent manner in the mouse hotplate assay. This novel conotoxin Lt7b may be a useful tool for ion channel studies and analgesic drug development.

Comprehensive analysis reveals a 5-gene signature and immune cell infiltration in Alzheimer’s disease with qPCR validation

Over 50 million people around the world currently are suffering from Alzheimer’s disease (AD) without any effective therapy. Neuroinflammation plays a pivotal role in AD, which leads us to probe the profile of immune cell infiltration in AD. Here, we analyzed a microarray dataset (GSE44770) containing 115 AD and 115 control samples to determine biomarkers and immune infiltration characteristics of AD by multiple bioinformatics methods. First, we identified 3,840 DEGs (1892 upregulated and 1948 downregulated) by using the limma package and 2,697 hub genes by constructing a weighted gene correlation network, and they had a total of 2,167 intersecting genes. Second, combining the LASSO logistic regression and SVM-RFE, we obtained five biomarkers (DGKG, MAP3K7IP2, NFKBIE, VIP, and PCCB), which may reveal the key pathogenetic features of AD and serve as diagnostic markers assessed by the ROC curve (AUC = 0.9716) and validation of another AD dataset (GSE33000) (AUC = 0.9388). Third, immune cell infiltration analysis revealed that compared with control samples, plasma cells, CD8 T cells, T follicular helper cells, and activated NK cells infiltrated less in AD; Monocytes, M2 macrophages, and neutrophils infiltrated more in AD. Neutrophils and activated NK cells demonstrated the most significant and negative correlation. Then, Spearman correlation analysis between the five biomarkers and immune infiltrating cells revealed that all of them were significantly associated with plasma cells. Finally, mRNA levels of VIP and PCCB were conformed in a murine AD model. In conclusion, DGKG, MAP3K7IP2, NFKBIE, VIP, and PCCB may be used as diagnostic markers of AD, and the disruption of the delicate immune balance may be a key process in the onset and development of AD.

TMPpred: A support vector machine-based thermophilic protein identifier

MOTIVATION

The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics.

RESULTS

Different from other methods that only pursue the performance of models, we aim to find important features so as to provide a powerful reference for in vitro experiments. We transformed this problem into a binary classification problem, that is, the distinction between thermophilic proteins and nonthermophilic proteins. Using support vector machine-based model construction and analysis, we inferred that Gly, Ala, Ser and Thr may be the most important components at the residue level that determine the thermal stability of proteins. It is also noteworthy that our proposed model obtains an Sn of 0.892, an Sp of 0.857, an ACC of 0.87566 and an AUC of 0.874. To facilitate other researchers, we wrapped our model and deployed it as a web server, which is accessible at http://112.124.26.17:7000/TMPpred/index.html.

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server “ASRmiRNA” has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.

Preparation and Functional Identification of a Novel Conotoxin QcMNCL-XIII0.1 from Conus quercinus

Conotoxins are tools used by marine Conus snails to hunt and are a significant repository for marine drug research. Conotoxins highly selectively coordinate different subtypes of various ion channels, and a few have been used in pain management. Although more than 8000 conotoxin genes have been found, the biological activity and function of most have not yet been examined. In this report, we selected the toxin gene QcMNCL-XIII0.1 from our previous investigation and studied it in vitro. First, we successfully prepared active recombinant QcMNCL-XIII0.1 using a TrxA (Thioredoxin A)-assisted folding expression vector based on genetic engineering technology. Animal experiments showed that the recombinant QcMNCL-XIII0.1 exhibited nerve conduction inhibition similar to that of pethidine hydrochloride. With flow cytometry combined fluorescent probe Fluo-4 AM, we found that 10 ng/μL recombinant QcMNCL-XIII0.1 inhibited the fluorescence intensity by 31.07% in the 293T cell model transfected with Cav3.1, implying an interaction between α1G T-type calcium channel protein and recombinant QcMNCL-XIII0.1. This toxin could be an important drug in biomedical research and medicine for pain control.

Mining the Royal Jelly Proteins: Combinatorial Hexapeptide Ligand Library Significantly Improves the MS-Based Proteomic Identification in Complex Biological Samples

Royal jelly (RJ) is a complex, creamy secretion produced by the glands of worker bees. Due to its health-promoting properties, it is used by humans as a dietary supplement. However, RJ compounds are not fully characterized yet. Hence, in this research, we aimed to broaden the knowledge of the proteomic composition of fresh RJ. Water extracts of the samples were pre-treated using combinatorial hexapeptide ligand libraries (ProteoMiner^TM kit), trypsin-digested, and analyzed by a nanoLC-MALDI-TOF/TOF MS system. To check the ProteoMiner^TM performance in the MS-based protein identification, we also examined RJ extracts that were not prepared with the ProteoMiner^TM kit. We identified a total of 86 proteins taxonomically classified to Apis spp. (bees). Among them, 74 proteins were detected in RJ extracts pre-treated with ProteoMiner^TM kit, and only 50 proteins were found in extracts non-enriched with this technique. Ten of the identified features were hypothetical proteins whose existence has been predicted, but any experimental evidence proves their in vivo expression. Additionally, we detected four uncharacterized proteins of unknown functions. The results of this research indicate that the ProteoMiner^TM strategy improves proteomic identification in complex biological samples. Broadening the knowledge of RJ composition may contribute to the development of standards and regulations, enhancing the quality of RJ, and consequently, the safety of its supplementation.

MRMD2.0: A Python Tool for Machine Learning with Feature Ranking and Reduction

Aims:

The study aims to find a way to reduce the dimensionality of the dataset.

Background:

Dimensionality reduction is the key issue of the machine learning process. It does not only improve the prediction performance but also could recommend the intrinsic features and help to explore the biological expression of the machine learning “black box”.

Objective:

A variety of feature selection algorithms are used to select data features to achieve dimensionality reduction.

Methods:

First, MRMD2.0 integrated 7 different popular feature ranking algorithms with PageRank strategy. Second, optimized dimensionality was detected with forward adding strategy.

Result:

We have achieved good results in our experiments.

Conclusion:

Several works have been tested with MRMD2.0. It showed well performance. Otherwise, it also can draw the performance curves according to the feature dimensionality. If users want to sacrifice accuracy for fewer features, they can select the dimensionality from the performance curves.

Other:

We developed friendly python tools together with the web server. The users could upload their csv, arff or libsvm format files. Then the webserver would help to rank features and find the optimized dimensionality.

eHSCPr discriminating the cell identity involved in endothelial to hematopoietic transition

MOTIVATION

Hematopoietic stem cells (HSCs) give rise to all blood cells and play a vital role throughout the whole lifespan through their pluripotency and self-renewal properties. Accurately identifying the stages of early HSCs is extremely important, as it may open up new prospects for extracorporeal blood research. Existing experimental techniques for identifying the early stages of HSCs development are time-consuming and expensive. Machine learning has shown its excellence in massive single-cell data processing and it is desirable to develop related computational models as good complements to experimental techniques.

RESULTS

In this study, we presented a novel predictor called eHSCPr specifically for predicting the early stages of HSCs development. To reveal the distinct genes at each developmental stage of HSCs, we compared F-score with three state-of-art differential gene selection methods (limma, DESeq2, edgeR) and evaluated their performance. F-score captured the more critical surface markers of endothelial cells and hematopoietic cells, and the area under receiver operating characteristic curve (ROC) value was 0.987. Based on SVM, the 10-fold cross-validation accuracy of eHSCpr in the independent dataset and the training dataset reached 94.84% and 94.19%, respectively. Importantly, we performed transcription analysis on the F-score gene set, which indeed further enriched the signal markers of HSCs development stages. eHSCPr can be a powerful tool for predicting early stages of HSCs development, facilitating hypothesis-driven experimental design and providing crucial clues for the in vitro blood regeneration studies.

AVAILABILITY

http://bioinfor.imu.edu.cn/ehscpr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

μ-conotoxin TsIIIA, a peptide inhibitor of human voltage-gated sodium channel hNav1.8

TsIIIA, the first μ-conotoxin from Conus tessulatus, can selectively inhibit rat tetrodotoxin-resistant sodium channels. TsIIIA also shows potent analgesic activity in a mice hotplate analgesic assay, but its effect on human sodium channels remains unknown. In this study, eight human sodium channel subtypes, hNa_v1.1- hNa_v1.8, were expressed in HEK293 or ND7/23 cells and tested on the chemically synthesized TsIIIA. Patch clamp experiments showed that 10 μM TsIIIA had no effects on the tetrodotoxin-sensitive hNa_v1.1, hNa_v1.2, hNa_v1.3, hNa_v1.4, hNa_v1.6 and hNa_v1.7, as well as tetrodotoxin-resistant hNa_v1.5. For tetrodotoxin-resistant hNa_v1.8, concentrations of 1, 5 and 10 μM TsIIIA reduced the hNa_v1.8 currents to 59.26%, 36.21% and 24.93% respectively. Further detailed dose-effect experiments showed that TsIIIA inhibited hNa_v1.8 currents with an IC₅₀ value of 2.11 μM. In addition, 2 μM TsIIIA did not induce a shift in the current-voltage relationship of hNa_v1.8. Taken together, the hNa_v1.8 peptide inhibitor TsIIIA provides a pharmacological probe for sodium channels and a potential therapeutic agent for pain.

Neurobiological activity of conotoxins via sodium channel modulation

Conotoxins (CnTX) are bioactive peptides produced by marine molluscs belonging to Conus genus. The biochemical structure of these venomous peptides is characterized by a low number of amino acids linked with disulfide bonds formed by a high degree of post-translational modifications and glycosylation steps which increase the diversity and rate of evolution of these molecules. CnTX different isoforms are known to target ion channels and, in particular, voltage-gated sodium (Na⁺) channels (Na_v channels). These are transmembrane proteins fundamental in excitable cells for generating the depolarization of plasma membrane potential known as action potential which propagates electrical signals in muscles and nerves for physiological functions. Disorders in Na_v channel activity have been shown to induce neurological pathologies and pain states. Here, we describe the current knowledge of CnTX isoform modulation of the Na_v channel activity, the mechanism of action and the potential therapeutic use of these toxins in counteracting neurological dysfunctions.

A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods

Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.

Conotoxins: Chemistry and Biology

The venom of the marine predatory cone snails (genus Conus) has evolved for prey capture and defense, providing the basis for survival and rapid diversification of the now estimated 750+ species. A typical Conus venom contains hundreds to thousands of bioactive peptides known as conotoxins. These mostly disulfide-rich and well-structured peptides act on a wide range of targets such as ion channels, G protein-coupled receptors, transporters, and enzymes. Conotoxins are of interest to neuroscientists as well as drug developers due to their exquisite potency and selectivity, not just against prey but also mammalian targets, thereby providing a rich source of molecular probes and therapeutic leads. The rise of integrated venomics has accelerated conotoxin discovery with now well over 10,000 conotoxin sequences published. However, their structural and pharmacological characterization lags considerably behind. In this review, we highlight the diversity of new conotoxins uncovered since 2014, their three-dimensional structures and folds, novel chemical approaches to their syntheses, and their value as pharmacological tools to unravel complex biology. Additionally, we discuss challenges and future directions for the field.

Classification of drug molecules for oxidative stress signalling pathway

In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers’ disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2‐antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE signalling pathway and to propose a multi‐level prediction scheme for the same is the main aim of the study as it contributes in a big amount to the improvement of oxidative stress in humans. Applying the process of knowledge discovery from data, required knowledge is gathered and then machine learning techniques are applied to propose a multi‐level scheme. The validation of the proposed scheme is done using the K‐fold cross‐validation method and an accuracy of 90% is achieved for prediction of activity score for ARE molecules which determine their power to refine oxidative stress.

Structural and Functional Analyses of Cone Snail Toxins

Cone snails are marine gastropod mollusks with one of the most powerful venoms in nature. The toxins, named conotoxins, must act quickly on the cone snails´ prey due to the fact that snails are extremely slow, reducing their hunting capability. Therefore, the characteristics of conotoxins have become the object of investigation, and as a result medicines have been developed or are in the trialing process. Conotoxins interact with transmembrane proteins, showing specificity and potency. They target ion channels and ionotropic receptors with greater regularity, and when interaction occurs, there is immediate physiological decompensation. In this review we aimed to evaluate the structural features of conotoxins and the relationship with their target types.

Proteome based de novo sequencing of novel conotoxins from marine molluscivorous cone snail Conus amadis and neurological activities of its natural venom in zebrafish model

Conus amadis is a carnivorous snail found abundantly in coastal waters of India. They are equipped with potent chemical arsenal made of neurotoxic peptide concoction used for predation and competition. In this study, we have identified 19 novel conotoxins containing 1, 2 & 3 disulfides, belonging to different classes, from a molluscivorous cone snail Conus amadis using proteome based MALDI-TOF and LC-MS-MS analysis. Among them, 2 novel contryphans, 3 T-superfamily conotoxin, 2 A-superfamily conotoxins and 2 Mini M-Superfamily conotoxins were sequenced to its amino acid level from the fragmented spectrum of singly and doubly charged parent ions using de novo sequencing strategies. ama1054, a contryphan peptide toxin, possesses post translationally modified bromo tryptophan at its seventh position. Except ama1251, all the sequenced peptide toxins possess modified C-terminal amidation. Moreover, we have screened the crude venom for the presence of biological function in zebrafish model. Crude venom exhibited anticonvulsant properties in pentylenetetrazole-induced seizure in zebrafish larvae which suggested anti-epileptic properties of the venom cocktail. Acetyl cholinesterase activity was also identified in the venom complex.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

The identification of therapeutic biomarkers predictive of drug response is crucial in personalized medicine. A number of computational models to predict response of anti-cancer drugs have been developed as the establishment of several pharmacogenomics screening databases. In our study, we proposed a deep cascaded forest model, Deep-Resp-Forest, to classify the anti-cancer drug response as "sensitive" or "resistant". We made three contributions in this study. Firstly, diverse molecular data could be effectively integrated to provide more information than single type of data for the classification. Combination of two types of data were tested here. Secondly, two structures based on the multi-grained scanning to transform the raw features into high-dimensional feature vectors and integrate the diverse data were proposed in our study. Thirdly, the original deep and time-consuming architecture of cascade forest was improved by a feature optimization operation, which emphasized the most discriminative features across layers. We evaluated the proposed method on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) data sets and then compared with the Support Vector Machine. The proposed Deep-Resp-Forest has demonstrated the promising use of deep learning and deep forest approach on the drug response prediction tasks. The R implementation for running our experiments is available athttps://github.com/RanSuLab/Deep-Resp-Forest.

Alanine-Scanning Mutagenesis of α-Conotoxin GI Reveals the Residues Crucial for Activity at the Muscle Acetylcholine Receptor

Recently, the muscle-type nicotinic acetylcholine receptors (nAChRs) have been pursued as a potential target of several diseases, including myogenic disorders, muscle dystrophies and myasthenia gravis, etc. α-conotoxin GI isolated from Conus geographus selectively and potently inhibited the muscle-type nAChRs which can be developed as a tool to study them. Herein, alanine scanning mutagenesis was used to reveal the structure–activity relationship (SAR) between GI and mouse α1β1δε nAChRs. The Pro⁵, Gly⁸, Arg⁹, and Tyr¹¹ were proved to be the critical residues for receptor inhibiting as the alanine (Ala) replacement led to a significant potency loss on mouse α1β1δε nAChR. On the contrary, substituting Asn⁴, His¹⁰ and Ser¹² with Ala respectively did not affect its activity. Interestingly, the [E1A] GI analogue exhibited a three-fold potency for mouse α1β1δε nAChR, whereas it obviously decreased potency at rat α9α10 nAChR compared to wildtype GI. Molecular dynamic simulations also suggest that loop2 of GI significantly affects the interaction with α1β1δε nAChR, and Tyr¹¹ of GI is a critical residue binding with three hydrophobic amino acids of the δ subunit, including Leu⁹³, Tyr⁹⁵ and Leu¹⁰³. Our research elucidates the interaction of GI and mouse α1β1δε nAChR in detail that will help to develop the novel analogues of GI.

Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics

The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understanding the cell-division cycle and gene expression regulation so that scholars can develop a new strategy against genetic diseases by using the knowledge of DNA replication. Thus, the accurate identification of ORIs will provide key clues for DNA replication research and clinical medicine. Although, the conventional experiments could provide accurate results, they are time-consuming and cost ineffective. On the contrary, bioinformatics-based methods can overcome these shortcomings. Especially, with the emergence of DNA sequences in the post-genomic era, it is highly expected to develop high throughput tools to identify ORIs based on sequence information. In this review, we will summarize the current progress in computational prediction of eukaryotic ORIs including the collection of benchmark dataset, the application of machine learning-based techniques, the results obtained by these methods, and the construction of web servers. Finally, we gave the future perspectives on ORIs prediction. The review provided readers with a whole background of ORIs prediction based on machine learning methods, which will be helpful for researchers to study DNA replication in-depth and drug therapy of genetic defect.

Sequence clustering in bioinformatics: an empirical study

Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/∼lg/clustering/.

A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.

Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens

2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2'-O-methylation sites. In this study, based on the experimental 2'-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2'-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%.

Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods

Accurate identification of phage virion protein is not only a key step for understanding the function of the phage virion protein but also helpful for further understanding the lysis mechanism of the bacterial cell. Since traditional experimental methods are time-consuming and costly for identifying phage virion proteins, it is extremely urgent to apply machine learning methods to accurately and efficiently identify phage virion proteins. In this work, a support vector machine (SVM) based method was proposed by mixing multiple sets of optimal g-gap dipeptide compositions. The analysis of variance (ANOVA) and the minimal-redundancy-maximal-relevance (mRMR) with an increment feature selection (IFS) were applied to single out the optimal feature set. In the five-fold cross-validation test, the proposed method achieved an overall accuracy of 87.95%. We believe that the proposed method will become an efficient and powerful method for scientists concerning phage virion proteins.

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews' correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2-5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.

RFAmyloid: A Web Server for Predicting Amyloid Proteins

Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer’s disease and Creutzfeldt–Jakob’s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy’s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http://server.malab.cn/RFAmyloid/.

Comparison of Strategies to Overcome Drug Resistance: Learning from Various Kingdoms

Drug resistance, especially antibiotic resistance, is a growing threat to human health. To overcome this problem, it is significant to know precisely the mechanisms of drug resistance and/or self-resistance in various kingdoms, from bacteria through plants to animals, once more. This review compares the molecular mechanisms of the resistance against phycotoxins, toxins from marine and terrestrial animals, plants and fungi, and antibiotics. The results reveal that each kingdom possesses the characteristic features. The main mechanisms in each kingdom are transporters/efflux pumps in phycotoxins, mutation and modification of targets and sequestration in marine and terrestrial animal toxins, ABC transporters and sequestration in plant toxins, transporters in fungal toxins, and various or mixed mechanisms in antibiotics. Antibiotic producers in particular make tremendous efforts for avoiding suicide, and are more flexible and adaptable to the changes of environments. With these features in mind, potential alternative strategies to overcome these resistance problems are discussed. This paper will provide clues for solving the issues of drug resistance.

iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites

RNA modifications are additions of chemical groups to nucleotides or their local structural changes. Knowledge about the occurrence sites of these modifications is essential for in-depth understanding of the biological functions and mechanisms and for treating some genomic diseases as well. With the avalanche of RNA sequences generated in the post-genomic age, many computational methods have been proposed for identifying various types of RNA modifications one by one. However, so far no method whatsoever has been developed for simultaneously identifying several different types of RNA modifications. To address such a challenge, we developed a predictor called "iRNA-3typeA," by which we can simultaneously identify the occurrence sites of the following three most frequently observed modifications in RNA: (1) N1-methyladenosine (m1A), (2) N6-methyladenosine (m6A), and (3) adenosine to inosine (A-to-I). It has been shown via rigorous cross-validations for the RNA sequences from Homo sapiens and Mus musculus transcriptomes that the success rates achieved by the powerful new predictor are quite high. For the convenience of broad experimental scientists, a user-friendly web server for iRNA-3typeA has been established at http://lin-group.cn/server/iRNA-3typeA/. It is anticipated that iRNA-3typeA may become a useful high throughput tool for genome analysis.

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

HBPred: a tool to identify growth hormone-binding proteins

Hormone-binding protein (HBP) is a kind of soluble carrier protein and can selectively and non-covalently interact with hormone. HBP plays an important role in life growth, but its function is still unclear. Correct recognition of HBPs is the first step to further study their function and understand their biological process. However, it is difficult to correctly recognize HBPs from more and more proteins through traditional biochemical experiments because of high experimental cost and long experimental period. To overcome these disadvantages, we designed a computational method for identifying HBPs accurately in the study. At first, we collected HBP data from UniProt to establish a high-quality benchmark dataset. Based on the dataset, the dipeptide composition was extracted from HBP residue sequences. In order to find out the optimal features to provide key clues for HBP identification, the analysis of various (ANOVA) was performed for feature ranking. The optimal features were selected through the incremental feature selection strategy. Subsequently, the features were inputted into support vector machine (SVM) for prediction model construction. Jackknife cross-validation results showed that 88.6% HBPs and 81.3% non-HBPs were correctly recognized, suggesting that our proposed model was powerful. This study provides a new strategy to identify HBPs. Moreover, based on the proposed model, we established a webserver called HBPred, which could be freely accessed at http://lin-group.cn/server/HBPred.

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.