QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy.

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure-activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of - 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing Data through Explainable Artificial Intelligence.

BACKGROUND: The incidence rate of oropharyngeal squamous cell carcinoma (OPSCC) worldwide is alarming. In the clinical community, there is a pressing necessity to comprehend the etiology of the OPSCC to facilitate the administration of effective treatments. METHODS: This study confers an integrative genomics approach for identifying key oncogenic drivers involved in the OPSCC pathogenesis. The dataset contains RNA-Sequencing (RNA-Seq) samples of 46 Human papillomavirus-positive head and neck squamous cell carcinoma and 25 normal Uvulopalatopharyngoplasty cases. The differential marker selection is performed between the groups with a log2FoldChange (FC) score of 2, adjusted p-value < 0.01, and screened 714 genes. The Particle Swarm Optimization (PSO) algorithm selects the candidate gene subset, reducing the size to 73. The state-of-the-art machine learning algorithms are trained with the differentially expressed genes and candidate subsets of PSO. RESULTS: The analysis of predictive models using Shapley Additive exPlanations revealed that seven genes significantly contribute to the model's performance. These include ECT2, LAMC2, and DSG2, which predominantly influence differentiating between sample groups. They were followed in importance by FAT1, PLOD2, COL1A1, and PLAU. The Random Forest and Bayes Net algorithms also achieved perfect validation scores when using PSO features. Furthermore, gene set enrichment analysis, protein-protein interactions, and disease ontology mining revealed a significant association between these genes and the target condition. As indicated by Shapley Additive exPlanations (SHAPs), the survival analysis of three key genes unveiled strong over-expression in the samples from "The Cancer Genome Atlas". CONCLUSIONS: Our findings elucidate critical oncogenic drivers in OPSCC, offering vital insights for developing targeted therapies and enhancing understanding its pathogenesis.

Antibiotic discovery with artificial intelligence for the treatment of Acinetobacter baumannii infections.

Global challenges presented by multidrug-resistant Acinetobacter baumannii infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in A. baumannii. Here, a library of 11,648 natural compounds was subjected to a primary screening using quantitative structure-activity relationship (QSAR) models generated from a ChEMBL data set with >7,000 compounds with their reported minimal inhibitory concentration (MIC) values against A. baumannii followed by a structure-based virtual screening against OmpW. In silico pharmacokinetic evaluation was conducted to assess the drug-likeness of these compounds. The ten highest-ranking compounds were found to bind with an energy score ranging from -7.8 to -7.0 kcal/mol where most of them belonged to curcuminoids. To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethoxycurcumin, was tested against a panel of A. baumannii strains to determine its antibacterial activity using microdilution and time-kill curve assays. To validate whether the compound binds to the selected target, an OmpW-deficient mutant was studied and compared with the wild type. Our results demonstrate that demethoxycurcumin in monotherapy and in combination with colistin is active against all A. baumannii strains. Finally, the compound was found to significantly reduce the A. baumannii interaction with host cells, suggesting its anti-virulence properties. Collectively, this study demonstrates machine learning as a promising strategy for the discovery of curcuminoids as antimicrobial agents for combating A. baumannii infections. IMPORTANCE: Acinetobacter baumannii presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in A. baumannii and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against A. baumannii, including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing A. baumannii interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against A. baumannii infections, offering a promising strategy to address antibiotic resistance.

Expression analysis and mapping of Viral-Host Protein interactions of Poxviridae suggests a lead candidate molecule targeting Mpox.

BACKGROUND: Monkeypox (Mpox) is an important human pathogen without etiological treatment. A viral-host interactome study may advance our understanding of molecular pathogenesis and lead to the discovery of suitable therapeutic targets. METHODS: GEO Expression datasets characterizing mRNA profile changes in different host responses to poxviruses were analyzed for shared pathway identification, and then, the Protein-protein interaction (PPI) maps were built. The viral gene expression datasets of Monkeypox virus (MPXV) and Vaccinia virus (VACV) were used to identify the significant viral genes and further investigated for their binding to the library of targeting molecules. RESULTS: Infection with MPXV interferes with various cellular pathways, including interleukin and MAPK signaling. While most host differentially expressed genes (DEGs) are predominantly downregulated upon infection, marked enrichments in histone modifiers and immune-related genes were observed. PPI analysis revealed a set of novel virus-specific protein interactions for the genes in the above functional clusters. The viral DEGs exhibited variable expression patterns in three studied cell types: primary human monocytes, primary human fibroblast, and HeLa, resulting in 118 commonly deregulated proteins. Poxvirus proteins C6R derived protein K7 and K7R of MPXV and VACV were prioritized as targets for potential therapeutic interventions based on their histone-regulating and immunosuppressive properties. In the computational docking and Molecular Dynamics (MD) experiments, these proteins were shown to bind the candidate small molecule S3I-201, which was further prioritized for lead development. RESULTS: MPXV circumvents cellular antiviral defenses by engaging histone modification and immune evasion strategies. C6R-derived protein K7 binding candidate molecule S3I-201 is a priority promising candidate for treating Mpox.

Potent VEGFR-2 inhibitors for resistant breast cancer: a comprehensive 3D-QSAR, ADMET, molecular docking and MMPBSA calculation on triazolopyrazine derivatives.

More people are being diagnosed with resistant breast cancer, increasing the urgency of developing new effective treatments. Several lines of evidence suggest that blocking the kinase activity of VEGFR-2 reduces angiogenesis and slows tumor growth. In this study, we developed novel VEGFR-2 inhibitors based on the triazolopyrazine template by using comparative molecular field analysis (CoMFA) and molecular similarity indices (CoMSIA) models for 3D-QSAR analysis of 23 triazolopyrazine-based compounds against breast cancer cell lines (MCF -7). Both CoMFA (Q2 = 0.575; R 2 = 0.936, Rpred 2 = 0.956) and CoMSIA/SE (Q2 = 0.575; R 2 = 0.936, Rpred 2 = 0.847) results demonstrate the robustness and stability of the constructed model. Six novel compounds with potent inhibitory activity were carefully designed, and screening of ADMET properties revealed their good oral bioavailability and ability to diffuse through various biological barriers. When compared with the most active molecule in the data set and with Foretinib (breast cancer drug), molecular docking revealed that the six designed compounds had strengthened affinity (-8.9 to -10 kcal/mol) to VEGFR-2. Molecular Dynamics Simulations and MMPBSA calculations were applied to the selected compound T01 with the highest predicted inhibitory activity, confirming its stability in the active pocket of VEGFR-2 over 100 ns. The present results provided the basis for the chemical synthesis of new compounds with improved inhibitory properties against the breast cancer cell line (MCF -7).

Novel APOE Mutation in a Moroccan Subject Suffering from Alzheimer Disease: A Case Study and Exploration of Pathogenic Implication.

Alzheimer disease (AD) is a major public health concern worldwide. It is a severe neurodegenerative disease that primarily affects the elderly and causes significant brain cell death. According to the most complete scientific research, the APOE gene, which encodes the APOE protein, maybe the key to identifying the likely cause of delayed AD. The development of plaques and tangles, as well as increased amyloid (amyloid-ß) levels and deposition, have been linked to APOE4. Pathogenic mutations in this gene can impact how beta-amyloid deposits and how they are cleared from the body. In this study, we report a novel pathogenic mutation, Arg160Leu, in APOE that was identified in a Moroccan patient. The magnetic resonance imaging of this 67-year-old woman revealed hippocampal shrinkage, and the results of her cognition testing revealed that she is suffering from severe AD. The current study may increase awareness of the genetic risk factors for AD caused by APOE4 mutations.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities.

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

MegaLTR: a web server and standalone pipeline for detecting and annotating LTR-retrotransposons in plant genomes.

LTR-retrotransposons (LTR-RTs) are a class of RNA-replicating transposon elements (TEs) that can alter genome structure and function by moving positions, repositioning genes, shifting exons, and causing chromosomal rearrangements. LTR-RTs are widespread in many plant genomes and constitute a significant portion of the genome. Their movement and activity in eukaryotic genomes can provide insight into genome evolution and gene function, especially when LTR-RTs are located near or within genes. Building the redundant and non-redundant LTR-RTs libraries and their annotations for species lacking this resource requires extensive bioinformatics pipelines and expensive computing power to analyze large amounts of genomic data. This increases the need for online services that provide computational resources with minimal overhead and maximum efficiency. Here, we present MegaLTR as a web server and standalone pipeline that detects intact LTR-RTs at the whole-genome level and integrates multiple tools for structure-based, homologybased, and de novo identification, classification, annotation, insertion time determination, and LTR-RT gene chimera analysis. MegaLTR also provides statistical analysis and visualization with multiple tools and can be used to accelerate plant species discovery and assist breeding programs in their efforts to improve genomic resources. We hope that the development of online services such as MegaLTR, which can analyze large amounts of genomic data, will become increasingly important for the automated detection and annotation of LTR-RT elements.

Unleashing Nature's potential: a computational approach to discovering novel VEGFR-2 inhibitors from African natural compound using virtual screening, ADMET analysis, molecular dynamics, and MMPBSA calculations.

One of the characteristic features of cancer is angiogenesis, the process by which new, aberrant blood vessels are formed from pre-existing blood vessels. The process of angiogenesis begins when VEGF binds to its receptor, the VEGF receptor (VEGFR). The formation of new blood vessels provides nutrients that can promote the growth of cancer cells. When it comes to new blood vessel formation, VEGFR2 is a critical player. Therefore, inhibiting VEGFR2 is an effective way to target angiogenesis in cancer treatment. The aim of our research was to find new VEGFR-2 inhibitors by performing a virtual screening of 13313 from African natural compounds using different in silico techniques. Using molecular docking calculations and ADMET properties, we identified four compounds that exhibited a binding affinity ranging from -11.0 kcal/mol to -11.5 Kcal/mol when bound to VEGFR-2. These four compounds were further analyzed with 100 ns simulations to determine their stability and binding energy using the MM-PBSA method. After comparing the compounds with Regorafenib, a drug approved for anti-angiogenesis treatment, it was found that all the candidates (EANPDB 252, NANPDB 4577, and NANPDB 4580), with the exception of EANPDB 76, could target VEGFR-2 similarly effectively to Regorafenib. Therefore, we recommend three of these agents for anti-angiogenesis treatment because they are likely to deactivate VEGFR-2 and thus inhibit angiogenesis. However, it should be noted that the safety and suitability of these agents for clinical use needs further investigation, as the computer-assisted study did not include in vitro or in vivo experiments.

Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer.

The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology. This study demonstrates a systematic approach to identifying oncogenic gastric cancer genes linked with different cell types. The raw counts of adjacent normal and gastric cancer samples are subjected to a quality control step. The dimensionality reduction and multidimensional clustering are performed using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. The adjacent normal and gastric cancer sample cell clusters are annotated with the Human Primary Cell Atlas database using the "SingleR." Cellular state transition between the distinct groups is characterized using trajectory analysis. The ligand-receptor interaction between Vascular Endothelial Growth Factor (VEGF) and cell clusters unveils crucial molecular pathways in gastric cancer progression. Chondrocytes, Smooth muscle cells, and fibroblast cell clusters contain genes contributing to poor survival rates based on hazard ratio during survival analysis. The GC-related oncogenic signatures are isolated by comparing the gene set with the DisGeNET database. Twelve gastric cancer biomarkers (SPARC, KLF5, HLA-DRB1, IGFBP3, TIMP3, LGALS1, IGFBP6, COL18A1, F3, COL4A1, PDGFRB, COL5A2) are linked with gastric cancer and further validated through gene set enrichment analysis. Drug-gene interaction found PDGFRB, interacting with various anti-cancer drugs, as a potential inhibitor for gastric cancer. Further investigations on these molecular signatures will assist the development of precision therapeutics, promising longevity among gastric cancer patients.

AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.

Multiple sequence alignment (MSA) is essential for understanding genetic variations controlling phenotypic traits in all living organisms. The post-analysis of MSA results is a difficult step for researchers who do not have programming skills. Especially those working with large scale data and looking for potential variations or variable sample groups. Generating bi-allelic data and the comparison of wild and alternative gene forms are important steps in population genetics. Customising MSA visualisation for a single page view is difficult, making viewing potential indels and variations challenging. There are currently no bioinformatics tools that permit post-MSA analysis, in which data on gene and single nucleotide scales could be combined with gene annotations and used for cluster analysis. We introduce "AlignStatPlot," a new R package and online tool that is well-documented and easy-to use for MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analyses on sequencing data and generates new visualisation methods for MSA results. When compared to currently available tools, AlignStatPlot provides a robust ability to handle and visualise diversity data, while the online version will save time and encourage researchers to focus on explaining their findings. It is a simple tool that can be used in conjunction with population genetics software.

Airway and Oral microbiome profiling of SARS-CoV-2 infected asthma and non-asthma cases revealing alterations-A pulmonary microbial investigation.

New evidence strongly discloses the pathogenesis of host-associated microbiomes in respiratory diseases. The microbiome dysbiosis modulates the lung's behavior and deteriorates the respiratory system's effective functioning. Several exogenous and environmental factors influence the development of asthma and chronic lung disease. The relationship between asthma and microbes is reasonably understood and yet to be investigated for more substantiation. The comorbidities such as SARS-CoV-2 further exacerbate the health condition of the asthma-affected individuals. This study examines the raw 16S rRNA sequencing data collected from the saliva and nasopharyngeal regions of pre-existing asthma (23) and non-asthma patients (82) infected by SARS-CoV-2 acquired from the public database. The experiment is designed in a two-fold pattern, analyzing the associativity between the samples collected from the saliva and nasopharyngeal regions. Later, investigates the microbial pathogenesis, its role in exacerbations of respiratory disease, and deciphering the diagnostic biomarkers of the target condition. LEfSE analysis identified that Actinobacteriota and Pseudomonadota are enriched in the SARS-CoV-2-non-asthma group and SARS-CoV-2 asthma group of the salivary microbiome, respectively. Random forest algorithm is trained with amplicon sequence variants (ASVs) attained better classification accuracy, ROC scores on nasal (84% and 87%) and saliva datasets (93% and 97.5%). Rothia mucilaginosa is less abundant, and Corynebacterium tuberculostearicum showed higher abundance in the SARS-CoV-2 asthma group. The increase in Streptococcus at the genus level in the SARS-CoV-2-asthma group is evidence of discriminating the subgroups.

TEMM: A Curated Data Resource for Transposon Element-Based Molecular Markers in Plants.

Transposon elements (TEs) are mobile genetic elements that can insert themselves into new locations and modify the plant genome. In recent years, they have been used as molecular markers in plant breeding programs. TE-based molecular markers (TE-markers) are divided into two categories depending on the transcription mechanism of the TEs. The first category is retrotransposon-based molecular markers, which include RBIP, IRAP, REMAP, and iPBS. The second group is DNA-based-TE-markers, which include MITE, TE-junction, and CACTA TE-markers. These markers are a good tool for studying genetic diversity and can provide information on plants' phylogenetic and evolutionary history. They can help improve breeding programs to increase agronomic traits and develop new varieties. Overall, TE-markers play an important role in plant genetics and plant breeding and contribute to a better understanding of plant biology. Here, we present TEMM, a curated data resource for TE-markers in plants. Relevant research articles were screened to collect primer sequences and related information. Only articles containing primer sequences are added to the present data resource. TEMM contains 784 primers with their associated PCR reaction programs and their applications in various crops. These include 203 IPBS, 191 RBIP, 140 IRAP, 78 TE-junction, 76 IRAPS, 47 RBIP-IRAP, 16 IRAP-REMAP, 12 REMAP, 12 REMA-IRAP, 6 REMA, and 3 ISBP primers. The data resource is freely available at https://bioinformatics.um6p.ma/TEMM .

CicerSpTEdb2.0: An Upgrade of Cicer Species Transposable Elements Database.

To meet the critical demand of LTR-RTs data-driven research, we updated the CicerSpTEdb database to version 2.0, which includes more accurate intact LTR-RT elements with annotation of internal domains. We also added the ability to BLAST against TEs of Cicer species. As a result, 3701 intact LTR-RTs were detected in the studied genomes, including 2840 Copia and 861 Gypsy elements. Of the 3701 intact LTR-RTs, 588 were in C. arietinum, including 475 Copia and 113 Gypsy. While 1373 were detected in C. reticulatum, including 1041 Copia and 332 Gypsy. Furthermore, 1740 were found in C. echinospermum, including 1324 Copia and 416 Gypsy. Based on LTR-RT clades, the analysis classified the 3701 identified intact LTR-RTs in the studied genomes as Ale (850), SIRE (740), unknown (455), Ikeros (323), Reina (290), Tork (290), Ivana (282), Tekay (197), Athila (128), TAR (99), CRM (31), and Ogre (16) elements. The newly updated CicerSpTEdb2.0 will be a valuable resource for TEs of Cicer species and their comparative genomics.Database URL: http://cicersptedb.easyomics.org/index.php.

A bibliometric analysis of the global impact of metaproteomics research.

Background: Metaproteomics is a subfield in meta-omics that is used to characterize the proteome of a microbial community. Despite its importance and the plethora of publications in different research area, scientists struggle to fully comprehend its functional impact on the study of microbiomes. In this study, bibliometric analyses are used to evaluate the current state of metaproteomic research globally as well as evaluate the specific contribution of Africa to this burgeoning research area. In this study, we use bibliometric analyses to evaluate the current state of metaproteomic research globally, identify research frontiers and hotspots, and further predict future trends in metaproteomics. The specific contribution of Africa to this research area was evaluated. Methods: Relevant documents from 2004 to 2022 were extracted from the Scopus database. The documents were subjected to bibliometric analyses and visualization using VOS viewer and Biblioshiny package in R. Factors such as the trends in publication, country and institutional cooperation networks, leading scientific journals, author's productivity, and keywords analyses were conducted. The African publications were ranked using Field-Weighted Citation Impact (FWCI) scores. Results: A total of 1,138 documents were included and the number of publications increased drastically from 2004 to 2022 with more publications (170) reported in 2021. In terms of publishers, Frontiers in Microbiology had the highest number of total publications (62). The United States of America (USA), Germany, China, and Canada, together with other European countries were the most productive. Institution-wise, the Helmholtz Zentrum für Umweltforschung, Germany had more publications while Max Plank Institute had the highest total collaborative link strength. Jehmlich N. was the most productive author whereas Hettich RL had the highest h-index of 63. Regarding Africa, only 2.2% of the overall publications were from the continent with more publication outputs from South Africa. More than half of the publications from the continent had an FWCI score ≥ 1. Conclusion: The scientific outputs of metaproteomics are rapidly evolving with developed countries leading the way. Although Africa showed prospects for future progress, this could only be accelerated by providing funding, increased collaborations, and mentorship programs.

Multi-combined QSAR, molecular docking, molecular dynamics simulation, and ADMET of Flavonoid derivatives as potent cholinesterase inhibitors.

In searching for a new and efficient therapeutic agent against Alzheimer's disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R2 = 0.801, Q2cv = 0.876, R2test = 0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinestérase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.Communicated by Ramaswamy H. Sarma.

Identification, characterization, and validation of NBS-encoding genes in grass pea.

Grass pea is a promising crop with the potential to provide food and fodder, but its genomics has not been adequately explored. Identifying genes for desirable traits, such as drought tolerance and disease resistance, is critical for improving the plant. Grass pea currently lacks known R-genes, including the nucleotide-binding site-leucine-rich repeat (NBS-LRR) gene family, which plays a key role in protecting the plant from biotic and abiotic stresses. In our study, we used the recently published grass pea genome and available transcriptomic data to identify 274 NBS-LRR genes. The evolutionary relationships between the classified genes on the reported plants and LsNBS revealed that 124 genes have TNL domains, while 150 genes have CNL domains. All genes contained exons, ranging from 1 to 7. Ten conserved motifs with lengths ranging from 16 to 30 amino acids were identified. We found TIR-domain-containing genes in 132 LsNBSs, with 63 TIR-1 and 69 TIR-2, and RX-CCLike in 84 LsNBSs. We also identified several popular motifs, including P-loop, Uup, kinase-GTPase, ABC, ChvD, CDC6, Rnase_H, Smc, CDC48, and SpoVK. According to the gene enrichment analysis, the identified genes undergo several biological processes such as plant defense, innate immunity, hydrolase activity, and DNA binding. In the upstream regions, 103 transcription factors were identified that govern the transcription of nearby genes affecting the plant excretion of salicylic acid, methyl jasmonate, ethylene, and abscisic acid. According to RNA-Seq expression analysis, 85% of the encoded genes have high expression levels. Nine LsNBS genes were selected for qPCR under salt stress conditions. The majority of the genes showed upregulation at 50 and 200 µM NaCl. However, LsNBS-D18, LsNBS-D204, and LsNBS-D180 showed reduced or drastic downregulation compared to their respective expression levels, providing further insights into the potential functions of LsNBSs under salt stress conditions. They provide valuable insights into the potential functions of LsNBSs under salt stress conditions. Our findings also shed light on the evolution and classification of NBS-LRR genes in legumes, highlighting the potential of grass pea. Further research could focus on the functional analysis of these genes, and their potential use in breeding programs to improve the salinity, drought, and disease resistance of this important crop.

A large-scale assessment of the quality of plant genome assemblies using the LTR assembly index.

Recent advances in genome sequencing have led to an increase in the number of sequenced genomes. However, the presence of repetitive sequences complicates the assembly of plant genomes. The LTR assembly index (LAI) has recently been widely used to assess the quality of genome assembly, as a higher LAI is associated with a higher quality of assembly. Here, we assessed the quality of assembled genomes of 1664 plant and algal genomes using LAI and reported the results as data repository called PlantLAI (https://bioinformatics.um6p.ma/PlantLAI). A number of 55 117 586 pseudomolecules/scaffolds with a total length of 988.11 gigabase-pairs were examined using the LAI workflow. A total of 46 583 551 accurate LTR-RTs were discovered, including 2 263 188 Copia, 2 933 052 Gypsy, and 1 387 311 unknown superfamilies. Consequently, only 1136 plant genomes are suitable for LAI calculation, with values ranging from 0 to 31.59. Based on the quality classification system, 476 diploid genomes were classified as draft, 472 as reference, and 135 as gold genomes. We also provide a free webtool to calculate the LAI of newly assembled genomes and the ability to save the result in the repository. The data repository is designed to fill in the gaps in the reported LAI of existing genomes, while the webtool is designed to help researchers calculate the LAI of their newly sequenced genomes.

Evolving strategies and application of proteins and peptide therapeutics in cancer treatment.

Several proteins and peptides have therapeutic potential and can be used for cancer therapy. By binding to cell surface receptors and other indicators uniquely linked with or overexpressed on tumors compared to healthy tissue, protein biologics enhance the active targeting of cancer cells, as opposed to the passive targeting of cells by conventional small-molecule chemotherapeutics. This study focuses on peptide medications that exist to slow or stop tumor growth and the spread of cancer, demonstrating the therapeutic potential of peptides in cancer treatment. As an alternative to standard chemotherapy, peptides that selectively kill cancer cells while sparing healthy tissue are developing. A mountain of clinical evidence supports the efficacy of peptide-based cancer vaccines. Since a single treatment technique may not be sufficient to produce favourable results in the fight against cancer, combination therapy is emerging as an effective option to generate synergistic benefits. One example of this new area is the use of anticancer peptides in combination with nonpeptidic cytotoxic drugs or the combination of immunotherapy with conventional therapies like radiation and chemotherapy. This review focuses on the different natural and synthetic peptides obtained and researched. Discoveries, manufacture, and modifications of peptide drugs, as well as their contemporary applications, are summarized in this review. We also discuss the benefits and difficulties of potential advances in therapeutic peptides.

In silico design of novel CDK2 inhibitors through QSAR, ADMET, molecular docking and molecular dynamics simulation studies.

The present study aims to investigate about the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the DFT method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R2 = 0.725, R2adj = 0.653, MSE = 0.060, R2test = 0.827, Q2cv = 0.536). The energy EHOMO orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Further, new Thiazole derivatives have been designed and their activities and pharmacokinetic properties have been predicted using the validated QSAR model. The designed molecules were then assessed to molecular docking (MD), and molecular dynamic (MDs) simulation accompanied by the calculation of the binding affinity using MMPBSA script according to 100 ns a simulation trajectory, to study both their affinity and their stability towards CDK2 as a target protein for the cancer disease treatment. This research concluded with the identification of four new CDK2 inhibitors which are A1, A3, A5, and A6 showing good pharmacokinetic properties. The MDs results revealed that the newly designed compound A5 remained stable in the active center of the discovered CDK2 protein, indicating its potential as a novel inhibitor for the treatment of hepatocellular carcinoma. The current findings may eventually contribute to the development of robust CDK2 inhibitors in the future.Communicated by Ramaswamy H. Sarma.