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.

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.

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.

Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus.

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure-activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

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.

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.

MGC: a metagenomic gene caller.

BACKGROUND: Computational gene finding algorithms have proven their robustness in identifying genes in complete genomes. However, metagenomic sequencing has presented new challenges due to the incomplete and fragmented nature of the data. During the last few years, attempts have been made to extract complete and incomplete open reading frames (ORFs) directly from short reads and identify the coding ORFs, bypassing other challenging tasks such as the assembly of the metagenome. RESULTS: In this paper we introduce a metagenomics gene caller (MGC) which is an improvement over the state-of-the-art prediction algorithm Orphelia. Orphelia uses a two-stage machine learning approach and computes a model that classifies extracted ORFs from fragmented sequences. We hypothesise and demonstrate evidence that sequences need separate models based on their local GC-content in order to avoid the noise introduced to a single model computed with sequences from the entire GC spectrum. We have also added two amino-acid features based on the benefit of amino-acid usage shown in our previous research. Our algorithm is able to predict genes and translation initiation sites (TIS) more accurately than Orphelia which uses a single model. CONCLUSIONS: Learning separate models for several pre-defined GC-content regions as opposed to a single model approach improves the performance of the neural network as demonstrated by the experimental results presented in this paper. The inclusion of amino-acid usage features also helps improve the overall accuracy of our algorithm. MGC's improvement sets the ground for further investigation into the use of GC-content to separate data for training models in machine learning based gene finders.

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.

Assessing the effect of a series of mutations on the dynamic behavior of phosphite dehydrogenase using molecular docking, molecular dynamics and quantum mechanics/molecular mechanics simulations.

Discovered in Pseudomonas stutzeri, phosphite dehydrogenase (PTDH) is an enzyme that catalyzes the oxidation of phosphite to phosphate while simultaneously reducing NAD+ to NADH. Despite several investigations into the mechanism of reaction and cofactor regeneration, only a few studies have focused on improving the activity and stability of PTDH. In this study, we combine molecular docking, molecular dynamics (MD) simulation, and Quantum Mechanics/Molecular Mechanics (QM/MM) to identify the impact of 30 mutations on the activity and stability of PTDH. Molecular docking results suggest that E266Q, K76A, K76M, K76R, K76C, and R237K can act on the NAD+ binding site through relatively weak bond development due to their high free binding energy. Moreover, Mulliken population analysis and potential energy barrier indicate that T101A, E175A, E175A/A176R, A176R, and E266Q act on phosphite oxidation. The mutants M53N, M53A, K76R, D79N, D79A, T101A, W134A, W134F Y139F, A146S, E175A, F198I, F198M, E266Q, H292K, S295A, R301K, and R301A were found to act on the structural dynamic of PTDH. The remaining mutants cause the loss of the nitrogen atom of R237 and H292, respectively, inactivating the enzyme. This study provides specific explanations of how mutations affect weak interactions of PTDH. The results should allow researchers to conduct experimental studies to improve PTDH activity and stability.Communicated by Ramaswamy H. Sarma.

In silico protein engineering shows that novel mutations affecting NAD+ binding sites may improve phosphite dehydrogenase stability and activity.

Pseudomonas stutzeri phosphite dehydrogenase (PTDH) catalyzes the oxidation of phosphite to phosphate in the presence of NAD, resulting in the formation of NADH. The regeneration of NADH by PTDH is greater than any other enzyme due to the substantial change in the free energy of reaction (G°' = - 63.3 kJ/mol). Presently, improving the stability of PTDH is for a great importance to ensure an economically viable reaction process to produce phosphite as a byproduct for agronomic applications. The binding site of NAD+ with PTDH includes thirty-four residues; eight of which have been previously mutated and characterized for their roles in catalysis. In the present study, the unexplored twenty-six key residues involved in the binding of NAD+ were subjected to in silico mutagenesis based on the physicochemical properties of the amino acids. The effects of these mutations on the structure, stability, activity, and interaction of PTDH with NAD+ were investigated using molecular docking, molecular dynamics simulations, free energy calculations, and secondary structure analysis. We identified seven novel mutations, A155I, G157I, L217I, P235A, V262I, I293A, and I293L, that reduce the compactness of the protein while improving PTDH stability and binding to NAD+.

PlantLTRdb: An interactive database for 195 plant species LTR-retrotransposons.

LTR-retrotransposons (LTR-RTs) are a large group of transposable elements that replicate through an RNA intermediate and alter genome structure. The activities of LTR-RTs in plant genomes provide helpful information about genome evolution and gene function. LTR-RTs near or within genes can directly alter gene function. This work introduces PlantLTRdb, an intact LTR-RT database for 195 plant species. Using homology- and de novo structure-based methods, a total of 150.18 Gbp representing 3,079,469 pseudomolecules/scaffolds were analyzed to identify, characterize, annotate LTR-RTs, estimate insertion ages, detect LTR-RT-gene chimeras, and determine nearby genes. Accordingly, 520,194 intact LTR-RTs were discovered, including 29,462 autonomous and 490,732 nonautonomous LTR-RTs. The autonomous LTR-RTs included 10,286 Gypsy and 19,176 Copia, while the nonautonomous were divided into 224,906 Gypsy, 218,414 Copia, 1,768 BARE-2, 3,147 TR-GAG and 4,2497 unknown. Analysis of the identified LTR-RTs located within genes showed that a total of 36,236 LTR-RTs were LTR-RT-gene chimeras and 11,619 LTR-RTs were within pseudo-genes. In addition, 50,026 genes are within 1 kbp of LTR-RTs, and 250,587 had a distance of 1 to 10 kbp from LTR-RTs. PlantLTRdb allows researchers to search, visualize, BLAST and analyze plant LTR-RTs. PlantLTRdb can contribute to the understanding of structural variations, genome organization, functional genomics, and the development of LTR-RT target markers for molecular plant breeding. PlantLTRdb is available at https://bioinformatics.um6p.ma/PlantLTRdb.

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.

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).

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 .

MegaSSR: a web server for large scale microsatellite identification, classification, and marker development.

Next-generation sequencing technologies have opened new avenues for using genomic data to study and develop molecular markers and improve genetic resources. Simple Sequence Repeats (SSRs) as genetic markers are increasingly used in molecular diversity and molecular breeding programs that require bioinformatics pipelines to analyze the large amounts of data. Therefore, there is an ongoing need for online tools that provide computational resources with minimal effort and maximum efficiency, including automated development of SSR markers. These tools should be flexible, customizable, and able to handle the ever-increasing amount of genomic data. Here we introduce MegaSSR (https://bioinformatics.um6p.ma/MegaSSR), a web server and a standalone pipeline that enables the design of SSR markers in any target genome. MegaSSR allows users to design targeted PCR-based primers for their selected SSR repeats and includes multiple tools that initiate computational pipelines for SSR mining, classification, comparisons, PCR primer design, in silico PCR validation, and statistical visualization. MegaSSR results can be accessed, searched, downloaded, and visualized with user-friendly web-based tools. These tools provide graphs and tables showing various aspects of SSR markers and corresponding PCR primers. MegaSSR will accelerate ongoing research in plant species and assist breeding programs in their efforts to improve current genomic resources.

Transposable elements: multifunctional players in the plant genome.

Transposable elements (TEs) are indispensable components of eukaryotic genomes that play diverse roles in gene regulation, recombination, and environmental adaptation. Their ability to mobilize within the genome leads to gene expression and DNA structure changes. TEs serve as valuable markers for genetic and evolutionary studies and facilitate genetic mapping and phylogenetic analysis. They also provide insight into how organisms adapt to a changing environment by promoting gene rearrangements that lead to new gene combinations. These repetitive sequences significantly impact genome structure, function and evolution. This review takes a comprehensive look at TEs and their applications in biotechnology, particularly in the context of plant biology, where they are now considered "genomic gold" due to their extensive functionalities. The article addresses various aspects of TEs in plant development, including their structure, epigenetic regulation, evolutionary patterns, and their use in gene editing and plant molecular markers. The goal is to systematically understand TEs and shed light on their diverse roles in plant biology.

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.

Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.

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.