Wet-lab Experiments Research Articles

Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis.

Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductance regulator [CFTR] in organoids). We describe steps for wet-lab experiments, image acquisition, and CFTR function analysis by DETECTOR. We also detail procedures for applying pre-trained models and training custom models on new customized datasets. For complete details on the use and execution of this protocol, refer to Bulcaen etal.1.

Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences. In this paper, we propose a Deep Novel Mutation Search (DNMS) method, using deep neural networks, to model protein sequence for mutation prediction. We use SARS-CoV-2 spike protein as the target and use a protein language model to predict novel mutations. Different from existing research which is often limited to mutating the reference sequence for prediction, we propose a parent-child mutation prediction paradigm where a parent sequence is modeled for mutation prediction. Because mutations introduce changing context to the underlying sequence, DNMS models three aspects of the protein sequences: semantic changes, grammatical changes, and attention changes, each modeling protein sequence aspects from shifting of semantics, grammar coherence, and amino-acid interactions in latent space. A ranking approach is proposed to combine all three aspects to capture mutations demonstrating evolving traits, in accordance with real-world SARS-CoV-2 spike protein sequence evolution. DNMS can be adopted for an early warning variant detection system, creating public health awareness of future SARS-CoV-2 mutations.

Preventive effects of thymoquinone on glyco-nitro-oxidized human fibrinogen: A comprehensive biophysical study projecting possible therapeutic role in diabetes and associated complications.

Persistence of long-term hyperglycemia results in the glyco-oxidation of plasma proteins, which is considered to be a significant factor in metabolic dysfunction, linking hyperglycemia to the emergence of vascular complications. Methylglyoxal (MGO), a dicarbonyl species formed excessively under diabetes, elevates the oxidative stress, enhancing the generation of superoxide anion, which ultimately reacts with nitric oxide (NO•) to form peroxynitrite (PON). PON, being a powerful nitro-oxidizing agent distorts protein structure, hampering its function. This article describes the binding mechanism of thymoquinone (TQ) to fibrinogen (Fg) and its protective effects under simultaneous glyco-nitro-oxidation. Thermodynamic investigations revealed hydrogen bonding and Vander Waal interactions stabilise the complex, confirming its spontaneity and exothermic nature. TQ-induced micro-environmental and structural alterations in fibrinogen were observed by synchronous, 3-D fluorescence maps, and red edge excitation shift (REES). Molecular docking confirmed the wet lab experiments. Previous studies have shown that glycation, as well as nitro-oxidation, modifies the key residues of fibrinogen, leading to its aggregation. Our findings showed that TQ prevented MGO + PON-induced damage to fibrinogen. The current study analyzed the protective effects of TQ on glyco-nitro-oxidized fibrinogen using various biochemical, spectroscopic, and computational methods. NBT assay and carbonyl content revealed glyco-nitro-oxidation-mediated oxidative stress, which was effectively mitigated by TQ in a concentration-dependent manner. The secondary structural alterations in fibrinogen were prevented by TQ as observed by circular dichroism (CD) and Fourier transform infrared spectroscopy (FTIR). Moreover, multiple assays and electron microscopy confirmed structural perturbations leading to the development of fibrillar aggregates that were reduced in TQ treated samples. Our findings project TQ as a potent protective agent against hyperglycemia and related human complications.

Ecogenomic insights into the resilience of keystone Blastococcus Species in extreme environments: a comprehensive analysis

BackgroundThe stone-dwelling genus Blastococcus plays a key role in ecosystems facing extreme conditions such as drought, salinity, alkalinity, and heavy metal contamination. Despite its ecological significance, little is known about the genomic factors underpinning its adaptability and resilience in such harsh environments. This study investigates the genomic basis of Blastococcus's adaptability within its specific microniches, offering insights into its potential for biotechnological applications.ResultsComprehensive pangenome analysis revealed that Blastococcus possesses a highly dynamic genetic composition, characterized by a small core genome and a large accessory genome, indicating significant genomic plasticity. Ecogenomic assessments highlighted the genus's capabilities in substrate degradation, nutrient transport, and stress tolerance, particularly on stone surfaces and archaeological sites. The strains also exhibited plant growth-promoting traits, enhanced heavy metal resistance, and the ability to degrade environmental pollutants, positioning Blastococcus as a candidate for sustainable agriculture and bioremediation. Interestingly, no correlation was found between the ecological or plant growth-promoting traits (PGPR) of the strains and their isolation source, suggesting that these traits are not linked to their specific environments.ConclusionsThis research highlights the ecological and biotechnological potential of Blastococcus species in ecosystem health, soil fertility improvement, and stress mitigation strategies. It calls for further studies on the adaptation mechanisms of the genus, emphasizing the need to validate these findings through wet lab experiments. This study enhances our understanding of microbial ecology in extreme environments and supports the use of Blastococcus in environmental management, particularly in soil remediation and sustainable agricultural practices.

Predicting circRNA-Disease Associations through Multisource Domain-Aware Embeddings and Feature Projection Networks.

Recent studies have highlighted the significant role of circular RNAs (circRNAs) in various diseases. Accurately predicting circRNA-disease associations is crucial for understanding their biological functions and disease mechanisms. This work introduces the MNDCDA method, designed to address the challenges posed by the limited number of known circRNA-disease associations and the high cost of biological experiments. MNDCDA integrates multiple biological data sources with neighborhood-aware embedding models and deep feature projection networks to predict potential pathways linking circRNAs to diseases. Initially, comprehensive biometric data are used to construct four similarity networks, forming a diverse circRNA-disease interaction framework. Next, a neighborhood-aware embedding model captures structural information about circRNAs and diseases, while deep feature projection networks learn high-order feature interactions and nonlinear connections. Finally, a bilinear decoder identifies novel associations between circRNAs and diseases. The MNDCDA model achieved an AUC of 0.9070 on a constructed benchmark dataset. In case studies, 25 out of 30 predicted circRNA-disease pairs were validated through wet lab experiments and published literature. These extensive experimental results demonstrate that MNDCDA is a robust computational tool for predicting circRNA-disease associations, providing valuable insights while helping to reduce research costs.

Active learning-assisted directed evolution

Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. We apply ALDE to an engineering landscape that is challenging for DE: optimization of five epistatic residues in the active site of an enzyme. In three rounds of wet-lab experimentation, we improve the yield of a desired product of a non-native cyclopropanation reaction from 12% to 93%. We also perform computational simulations on existing protein sequence-fitness datasets to support our argument that ALDE can be more effective than DE. Overall, ALDE is a practical and broadly applicable strategy to unlock improved protein engineering outcomes.

Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations

BackgroundmiRNAs (microRNAs) are endogenous RNAs with lengths of 18 to 24 nucleotides and play critical roles in gene regulation and disease progression. Although traditional wet-lab experiments provide direct evidence for miRNA-disease associations, they are often time-consuming and complicated to analyze by current bioinformatics tools. In recent years, machine learning (ML) and deep learning (DL) techniques are powerful tools to analyze large-scale biological data. Hence, developing a model to predict, identify, and rank connections in miRNAs and diseases can significantly enhance the precision and efficiency in investigating the relationships between miRNAs and diseases.ResultsIn this study, we utilized miRNA-disease association data obtained by biotechnological experiments to develop a DL model for miRNA-disease associations. To improve the accuracy of prediction in this model, we introduced two labeling strategies, weight-based and majority-based definitions, to classify miRNA-disease associations. After preprocessing, data was trained with a novel model combining gated recurrent units (GRU) and graph convolutional network (GCN) to predict the level of miRNA-disease associations. The miRNA-disease association datasets were from HMDD (the Human miRNA Disease Database) and categorized by two distinct labeling approaches, weight-based definitions and majority-based definitions. We classified the miRNA-disease associations into three groups, “upregulated”, “downregulated” and “nonspecific”, by regression analysis and multiclass classification. This GRU-GCN coordinated model achieved a robust area under the curve (AUC) score of 0.8 in all datasets, demonstrating the efficacy in predicting potential miRNA-disease relationships.ConclusionsBy introducing innovative label-preprocessing methods, this study addressed the relationships between miRNAs and diseases, and improved the ambiguity of the results in different experiments. Based on these refined label definitions, we developed a DL-based model to refine and predict the results of associations between miRNAs and diseases. This model offers a valuable tool for complementing traditional experimental methods and enhancing our understanding of miRNA-related disease mechanisms.

Interpreting Lung Cancer Health Disparity at Transcriptome Level.

Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Understanding the genetic factors of these disparities is critical for developing targeted treatment therapies. This study aims to identify both patient-specific and cohort-specific biomarker genes that contribute to lung cancer health disparities among African American males (AAMs), European American males (EAMs), African American females (AAFs), and European American females (EAFs). The real-world data is highly imbalanced with respect to race, and the lung cancer dataset is no exception. So, classification with race labels will generate highly biased results toward the larger cohort. We developed a computational framework by designing the classification problems with disease conditions instead of races and leveraging the local interpretability of explainable AI, SHAP (SHapley Additive exPlanations). This study used three disease conditions of lung cancer, including Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), and Healthy samples (HEALTHY) to design four classification tasks: one 3-class problem (LUAD-LUSC-HEALTHY) and three 2-class problems (LUAD-LUSC, LUAD-HEALTHY, and LUSC-HEALTHY). This multiple-classification approach allows a LUAD patient to be interrogated via three classification problems, namely LUAD-LUSC-HEALTHY, LUAD-LUSC, and LUAD-HEALTHY, thus providing a robust approach of retrieving disparity information for individual patients through the local interpretation of SHAP. The proposed method successfully discovered the sets of genes and pathways related to health disparities in lung cancer between two cohorts, including AAMs vs. EAMs, AAFs vs. EAFs, AAMs vs. AAFs, and EAMs vs. EAFs. The discovered list of genes and pathways provide a short list for biological scientists to conduct wet lab experiment.

A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides.

Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive. Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by using these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review focuses on several ML-based CPP prediction tools. We discussed the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was conducted based on their algorithms, dataset size, feature encoding, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will have a positive impact on future research on drug delivery and therapeutics.

In-silico Analysis of Peroxidase from Raphanus Sativus

Background:: In-silico study plays an important role in bioinformatics. It is a fastexpanding field to modelling, predicting and explaining biological activity at the molecular level using computational methods. Peroxidases are heme or non-heme-containing key antioxidant enzyme belonging to the oxidoreductase family. They can bioremediate the different environmental pollutants such as dioxins, petroleum hydrocarbons, synthetic dyes, herbicides, pesticides, chlorinated hydrocarbons, different phenolic and nonphenolic compounds etc. The current work aims to extend knowledge among researchers in better understanding structure of peroxidase purified from Raphanus sativus by analysing it’s physicochemical properties, secondary structure prediction, and 3D modelling of protein sequences and its validation using a variety of conventional computational methods. Objective:: Using bioinformatics techniques, it is feasible to figure out the relationship between sequence, structure, and function using enzyme protein sequences. To improve catalytic efficacy, thermostability, structure prediction, and validation, in-silico studies of the protein sequences of several industrially important enzymes have been performed recently. Method:: The physical and chemical parameters of radish peroxidase was analysed by using protparam tool-Expasy. SOPMA, SWISS MODEL, PROCHECK, ERRAT and Verify3D tools were used for structural analysis and validation of peroxidase protein sequence of Raphanus Sativus. The Molecular Evolutionary Genetics Analysis (MEGA 11) tool was used to align the protein sequences automatically and manually using the query sequence and peroxidase from various plant sources. Interaction of Radish peroxidase with the different organic substrates like guaiacol, o-cresol, mcresol, p-cresol, hydroquinone, catechol, resorcinol, benzaldehyde and aniline were analysed by molecular docking technique. Result:: This research gave critical information regarding the properties and functioning of Raphanus sativus peroxidase. The computational molecular weight for the query protein sequence of radish peroxidase was found to be 37.503 KDa. The analysis of secondary structure prediction using SOPMA tool revealed that random coil (Cc) was present in the highest percentage as 39.18 %. From the instability index (II) value and the aliphatic index value it was confirmed that the protein was slightly unstable but thermally stable in a wide range of temperature. The phylogenetic tree constructed by Molecular Evolutionary Genetics Analysis (MEGA 11) server revealed that the peroxidase and other plant peroxidases had been evolved from a common ancestor. Molecular docking analysis revealed that all the ligand had binding energy > -4.0 Kcal/mol. The interaction involved in the docking of radish peroxidase with selected ligands were conventional hydrogen bond, pi-cation, alkyl, pi-alkyl, pi-pi stacked, pi-sigma, carbon hydrogen bond, pi-lone pair. Conclusion:: In summary, these in-silico investigations provide a strong basis for carrying out wetlab experiments to boost production, research for novel sources with a metagenomics strategy and attempt directed evolution to include desired functional features. From this study, we found the active site of the enzyme and the key amino acid residues that are used in the enzyme-ligand interaction. The novel information presented in this work will promote proteomics research and the development of novel bioinformatics techniques. The investigation of enzyme-ligand interactions will aid in the creation of a fresh approach to the synthesis of organic molecules.

Automatic collaborative learning for drug repositioning

Drug repositioning seeks to identify new therapeutic uses for existing drugs, accelerating development and reducing costs. While traditional wet lab experiments are costly, computational methods offer a low-cost, efficient alternative. Despite their potential, most research in this field has uncritically employed the standard message-passing mechanism of Graph Neural Network (GNN), limiting the assessment of collaborative effects on prediction accuracy. In this paper, we introduce a novel model, an automatic collaborative learning framework for drug repositioning. Initially, we propose a metric to measure the interaction levels among neighbors and integrate it with the intrinsic message-passing mechanism of GNN, thereby enhancing the impact of various collaborative effects on prediction accuracy. Furthermore, we introduce an advanced contrastive learning technique to align feature consistency between the disease–drug association space and the customized neighbor space. This approach leverages the inherent regularities across different feature dimensions to minimize feature redundancy. Extensive experiments conducted on three benchmark datasets demonstrate substantial improvements of this novel model over various state-of-the-art methods. Case studies further highlight the practical utility of this model.

Isorauhimbine and Vinburnine as Novel 5-HT2A Receptor Antagonists From Rauwolfia serpentina for the Treatment of Insomnia: An In Silico Investigation

Objective: This study aimed to identify and delineate the antagonistic effects of phytochemicals present in Rauwolfia serpentina (Indian snakehead) on the serotonin 2A receptor (5HT2AR) for insomnia treatment. Methods: Molecular docking and molecular dynamics simulations were performed to analyze the binding affinity and stability of protein–ligand complexes. The dynamic behavior of the complexes was studied via normal mode analysis performed via the iMODS server. The blood–brain barrier permeability of the phytochemicals was hence analyzed via the Brain or IntestinaL EstimateD permeation method (BOILED-Egg). The bioactivity of phytochemicals as “g-protein coupled receptor (GPCR) ligands” was analyzed via the MolInspiration server. Lipinski’s rule of five was used as a benchmark to assess the drug likeness of the phytochemicals. The SWISS ADME server was used to analyze the physiochemical properties of the selected phytocompounds. Furthermore, the preclinical efficacy and safety of the compounds were assessed via absorption, distribution, metabolism, excretion, and toxicity (ADME/T) analysis performed via the Deep-PK and ProTox 3.0 servers. Results: Cumulative results from various computational parameters yielded isorauhimbine and vinburnine as hit compounds because they are 5HT2AR antagonists. These phytochemicals exhibited promising docking scores, and their root mean square deviation, radius of gyration, and solvent accessible surface area confirmed their stability with the target protein. The ADME/T profile and drug-likeness values of these two compounds validated their safety, efficacy, and potential as drug candidates. Conclusion: We conclude that isorauhimbine and vinburnine stand out as potential phytochemicals from R. serpentina that could potentially serve as potential antagonistic drug candidates against 5HT2AR for insomnia treatment. However, these findings need to be validated through wet lab experiments to fully elucidate their therapeutic potential against insomnia.

Computational design of siRNA targeting <i>Homo sapiens</i> HER2 splice variant mRNA: A potential strategy for breast cancer intervention

This research focuses on an innovative approach utilizing in silico methods to design small interfering RNA (siRNA) targeting the HER2 splice variant mRNA in Homo sapiens. HER2 is known to be overexpressed in certain types of breast cancer, contributing to tumor progression and poor prognosis. By designing siRNA molecules that can specifically bind to and degrade HER2 mRNA, this study aims to reduce HER2 protein levels, thereby hindering the growth and spread of breast cancer cells. The in-silico design process involves identifying optimal siRNA sequences that maximize target specificity and minimize off-target effects, which is crucial for potential therapeutic applications. This approach represents a promising step towards personalized medicine in the treatment of breast cancer, offering a targeted strategy to combat this variant associated with aggressive disease. The methodology comprises the RNA computational tools used for the design, the selection criteria for siRNA candidates, and the potential implications of this research in a clinical setting. The resulting outcomes are 2D and 3D siRNA designs that could potentially silence HER2 mRNA through an in-silico approach. The leads were generated using a de novo modeling approach, with no existing template available in GenBank. Moreover, it is concluded that computational tools can generate sufficiently stable 2D and 3D RNA models that could be advanced for further molecular simulation studies. The benefit of this outcome is that it facilitates better preparation for wet laboratory experiments in siRNA assays, with future implementation in vivo and clinical trial settings.

A predictive language model for SARS-CoV-2 evolution

Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail. We constructed the “grammatical frameworks” of the available S1 sequences for dimension reduction and semantic representation to grasp the model’s latent regularity. The mutational profile, defined as the frequency of mutations, was introduced into the model to incorporate randomness. With this model, we successfully identified and validated several variants with significantly enhanced viral infectivity and immune evasion by wet-lab experiments. By inputting the sequence data from three different time points, we detected circulating strains or vital mutations for XBB.1.16, EG.5, JN.1, and BA.2.86 strains before their emergence. In addition, our results also predicted the previously unknown variants that may cause future epidemics. With both the data validation and experiment evidence, our study represents a fast-responding, concise, and promising language model, potentially generalizable to other viral pathogens, to forecast viral evolution and detect crucial hot mutation spots, thus warning the emerging variants that might raise public health concern.

Human-in-the-loop active learning for goal-oriented molecule generation

Machine learning (ML) systems have enabled the modelling of quantitative structure–property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules.Scientific contributionWe present an adaptable framework that integrates AL and human expertise to refine property predictors for goal-oriented molecule generation. This approach is robust to noise in human feedback and ensures that navigating chemical space with human-refined predictors leverages human insights to identify molecules that not only satisfy predicted property profiles but also score highly on oracle models. Additionally, it prioritizes practical characteristics such as drug-likeness, synthetic accessibility, and a favorable balance between exploring diverse chemical space and exploiting similarity to existing training data.

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

BackgroundThe development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to machine learning-based prediction. This transition enables a more expedient exploration of potential interactions between drugs and targets, leading to substantial savings in time and funding resources. However, existing methods still face several challenges, such as drug information loss, lack of calculation of the contribution of each modality, and lack of simulation regarding the drug–target binding mechanisms.ResultsWe propose MTAF–DTA, a method for drug–target binding affinity prediction to solve the above problems. The drug representation module extracts three modalities of features from drugs and uses an attention mechanism to update their respective contribution weights. Additionally, we design a Spiral-Attention Block (SAB) as drug–target feature fusion module based on multi-type attention mechanisms, facilitating a triple fusion process between them. The SAB, to some extent, simulates the interactions between drugs and targets, thereby enabling outstanding performance in the DTA task. Our regression task on the Davis and KIBA datasets demonstrates the predictive capability of MTAF–DTA, with CI and MSE metrics showing respective improvements of 1.1% and 9.2% over the state-of-the-art (SOTA) method in the novel target settings. Furthermore, downstream tasks further validate MTAF–DTA’s superiority in DTA prediction.ConclusionsExperimental results and case study demonstrate the superior performance of our approach in DTA prediction tasks, showing its potential in practical applications such as drug discovery and disease treatment.

A Comprehensive Genome Mining Analysis of Biosynthetic Gene Clusters in Pseudomonas sp. SXM-1

Very resistant pathogenic microorganisms have been reported to current antibiotics in the last decade. Therefore, there is a great need to understand not only resistance metabolism but also secondary metabolites of pathogenic microorganisms. Genome mining tools have so far been improved to understand secondary metabolites from biosynthetic gene clusters. Microorganisms predicted for their genomes and secondary metabolites using these tools are widely employed in pharmaceutical and industrial studies. Pseudomonas spp. are widely used in recombinant DNA technology to produce commercial products. Bioinformatics-based in silico tools significantly contribute to the discovery of new bioactive compounds for pharmacy and medicine. This study aims to conduct a comprehensive gene cluster analysis of the Pseudomonas sp. SXM-1 strain isolated from the coastal seawater of Xiamen Bay using antiSMASH (7.0.1). The accession number of Pseudomonas sp. SXM-1 strain was retrieved from NCBI. 14 regions were found, including non-ribosomal peptides metallophores (NRP-metallophore), nonribosomal peptide-synthetase (NRPS), NRPS-like, ribosomally synthesized and post-translationally modified peptide-like (RiPP-like), betalactone, nonribosomal peptide-synthetase (NRPS), ectoine and N-acetylglutaminylglutamine amide (NAGGN). Analysis of all 14 regions revealed secondary metabolites with potential applications in diverse fields. Microbiologists are strongly advised to conduct wet-lab experiments to validate the secondary metabolites discussed in this study.

Acacia nilotica (L.) Delile as New Potential Inhibitors of 2019 Novel Coronavirus (Covid-19): Molecular Docking Study

The COVID-19 pandemic caused by SARS-CoV-2 has created an urgent need for effective therapeutics and vaccines. This study aimed to investigate the inhibitory activity of Acacia nilotica, a medicinal plant commonly used to treat various diseases in tropical and subtropical regions, against SARS-CoV-2 main proteases (Mpro) and spike proteins. Based on published literature, 22 compounds derived from Acacia nilotica were selected and assessed for their drug likeliness using Lipinski's rule of five and the SwissADME web tool. The compounds that fulfilled Lipinski's rule were subjected to molecular docking with Mpro (PDB ID: 6LU7) and spike proteins (PDB ID: 6LXT) using the Molecular Operating Environment software MOE. Among the 13 compounds docked with the main proteases and spike proteins of SARS-CoV-2, catechin-5-O-gallate, catechin-7-gallate, cetechin-3-O-gallate, cetechin-4-O-gallate, and gallocatechin-7-gallate was demonstrated superior inhibitory activity against Mpro and spike proteins compared to hydroxychloroquine, dexamethasone, and favipiravir. These findings indicate Acacia nilotica's potential as a source for developing specific therapeutic agents against SARS-CoV-2, pending further validation through wet lab experiments.

Machine Learning Accelerates the Discovery of Epitope-based Dual-bioactive Peptides Against Skin Infections

Skin injuries and infections are an inevitable part of daily human life, particularly with chronic wounds, becoming an increasing socioeconomic burden. In treating skin infections and promoting wound healing, bioactive peptides may hold significant potential, particularly those possessing antimicrobial and anti-inflammatory properties. However, obtaining these peptides solely through traditional wet laboratory experiments is costly and time-consuming, and peptides identified by current computer-assisted predictive models largely lack validation of their effects via wet laboratory experiments. Consequently, this study aimed to integrate computer-assisted methods and traditional wet laboratory experiments to identify anti-inflammatory and antimicrobial peptides. We developed a computer-assisted mining pipeline to screen potential peptides from the epitopes of the major histocompatibility complex class II (MHC-II). The peptide AIMP1 was identified, with the ability to physically damage Escherichia coli by increasing bacterial cell membrane permeability and with the ability to inhibit inflammation by binding to endotoxin-lipopolysaccharide. Additionally, in an LPS-induced inflammation animal model, AIMP1 slightly increased levels of proinflammatory cytokines (TNF-α, IL-1β, and IL-6), and in a skin wound infection animal model, AIMP1 effectively accelerated healing, reduced levels of these pro-inflammatory cytokines, and showed no acute hepatotoxicity or nephrotoxicity. In conclusion, this study not only developed a computer-assisted mining pipeline for identifying anti-inflammatory and antimicrobial peptides but also successfully pinpointed the peptide AIMP1, demonstrating its therapeutic potential for skin injury treatment.

Ion channel classification through machine learning and protein language model embeddings.

Ion channels are critical membrane proteins that regulate ion flux across cellular membranes, influencing numerous biological functions. The resource-intensive nature of traditional wet lab experiments for ion channel identification has led to an increasing emphasis on computational techniques. This study extends our previous work on protein language models for ion channel prediction, significantly advancing the methodology and performance. We employ a comprehensive array of machine learning algorithms, including k-Nearest Neighbors, Random Forest, Support Vector Machines, and Feed-Forward Neural Networks, alongside a novel Convolutional Neural Network (CNN) approach. These methods leverage fine-tuned embeddings from ProtBERT, ProtBERT-BFD, and MembraneBERT to differentiate ion channels from non-ion channels. Our empirical findings demonstrate that TooT-BERT-CNN-C, which combines features from ProtBERT-BFD and a CNN, substantially surpasses existing benchmarks. On our original dataset, it achieves a Matthews Correlation Coefficient (MCC) of 0.8584 and an accuracy of 98.35 %. More impressively, on a newly curated, larger dataset (DS-Cv2), it attains an MCC of 0.9492 and an ROC AUC of 0.9968 on the independent test set. These results not only highlight the power of integrating protein language models with deep learning for ion channel classification but also underscore the importance of using up-to-date, comprehensive datasets in bioinformatics tasks. Our approach represents a significant advancement in computational methods for ion channel identification, with potential implications for accelerating research in ion channel biology and aiding drug discovery efforts.