Machine learning for synergistic network pharmacology: a comprehensive overview.

Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.

Integrative multiomics profiling of passion fruit reveals the genetic basis for fruit color and aroma.

Passion fruit (Passiflora edulis) possesses a complex aroma and is widely grown in tropical and subtropical areas. Here, we conducted the de novo assembly, annotation, and comparison of PPF (P. edulis Sims) and YPF (P. edulis f. flavicarpa) reference genomes using PacBio, Illumina, and Hi-C technologies. Notably, we discovered evidence of recent whole-genome duplication events in P. edulis genomes. Comparative analysis revealed 7.6∼8.1 million single nucleotide polymorphisms, 1 million insertions/deletions, and over 142 Mb presence/absence variations among different P. edulis genomes. During the ripening of yellow passion fruit, metabolites related to flavor, aroma, and color were substantially accumulated or changed. Through joint analysis of genomic variations, differentially expressed genes, and accumulated metabolites, we explored candidate genes associated with flavor, aroma, and color distinctions. Flavonoid biosynthesis pathways, anthocyanin biosynthesis pathways, and related metabolites are pivotal factors affecting the coloration of passion fruit, and terpenoid metabolites accumulated more in PPF. Finally, by heterologous expression in yeast (Saccharomyces cerevisiae), we functionally characterized 12 terpene synthases. Our findings revealed that certain TPS homologs in both YPF and PPF varieties produce identical terpene products, while others yield distinct compounds or even lose their functionality. These discoveries revealed the genetic and metabolic basis of unique characteristics in aroma and flavor between the 2 passion fruit varieties. This study provides resources for better understanding the genome architecture and accelerating genetic improvement of passion fruits.

Current Progress, Applications and Challenges of Multi-Omics Approaches in Sesame Genetic Improvement.

Sesame is one of the important traditional oil crops in the world, and has high economic and nutritional value. Recently, due to the novel high throughput sequencing techniques and bioinformatical methods, the study of the genomics, methylomics, transcriptomics, proteomics and metabonomics of sesame has developed rapidly. Thus far, the genomes of five sesame accessions have been released, including white and black seed sesame. The genome studies reveal the function and structure of the sesame genome, and facilitate the exploitation of molecular markers, the construction of genetic maps and the study of pan-genomes. Methylomics focus on the study of the molecular level changes under different environmental conditions. Transcriptomics provide a powerful tool to study abiotic/biotic stress, organ development, and noncoding RNAs, and proteomics and metabonomics also provide some support in studying abiotic stress and important traits. In addition, the opportunities and challenges of multi-omics in sesame genetics breeding were also described. This review summarizes the current research status of sesame from the perspectives of multi-omics and hopes to provide help for further in-depth research on sesame.

Network pharmacology based virtual screening of Flavonoids from Dodonea angustifolia and the molecular mechanism against inflammation.

Inflammation is a nonspecific immune response against injury caused by a harmful agent that strives to restore tissue function and homeostasis. Dodonaea angustifolia L.f. (Sapindaceae) is a medium-sized shrub used to treat a variety of diseases in traditional medicine. In the current study, integrated network-pharmacology and molecular docking approaches were used to identify the active constituents, their possible targets, signaling pathways, and anti-inflammatory effects of flavonoids from D.angustifolia. D. angustifolia active ingredients were acquired from the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT), and Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening included the ten most prevalent D. angustifolia components, and the SwissTargetPrediction database was utilized to anticipate the targets of these compounds. Anti-inflammatory genes were found using the GeneCards database. The 175 overlapping genes were discovered as prospective D. angustifolia anti-inflammatory targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the overlapped targets were closely related to the major pathogenic processes linked to inflammation, such as response to organonitrogen compound, protein kinase activity, phosphotransferase activity, pI3k-Akt signaling pathway, metabolic pathways, and chemical carcinogenesis. Compound-target-pathway, and protein-protein interaction networks revealed 6-Methoxykaempferol and 5-Hydroxy-7,8 dimethoxyflavone as key compounds, and AKT1, VEGFA, and EGFR as key targets. Furthermore, molecular docking followed by molecular dynamic (MD) simulation of D. angustifolia active ingredients with core proteins fully complemented the binding affinity of these compounds and indicated stable complexes at the docked site. These findings reveal D. angustifolia 's multi-target, multi-compound, and multi-pathway strategies against inflammation. Our study paved the way for further research into the mechanism for developing D. angustifolia -based natural products as alternative therapies for inflammation.

Target-Specific Machine Learning Scoring Function Improved Structure-Based Virtual Screening Performance for SARS-CoV-2 Drugs Development.

Leveraging machine learning has been shown to improve the accuracy of structure-based virtual screening. Furthermore, a tremendous amount of empirical data is publicly available, which further enhances the performance of the machine learning approach. In this proof-of-concept study, the 3CLpro enzyme of SARS-CoV-2 was used. Structure-based virtual screening relies heavily on scoring functions. It is widely accepted that target-specific scoring functions may perform more effectively than universal scoring functions in real-world drug research and development processes. It would be beneficial to drug discovery to develop a method that can effectively build target-specific scoring functions. In the current study, the bindingDB database was used to retrieve experimental data. Smina was utilized to generate protein-ligand complexes for the extraction of InteractionFingerPrint (IFP) and SimpleInteractionFingerPrint SIFP fingerprints via the open drug discovery tool (oddt). The present study found that randomforestClassifier and randomforestRegressor performed well when used with the above fingerprints along the Molecular ACCess System (MACCS), Extended Connectivity Fingerprint (ECFP4), and ECFP6. It was found that the area under the precision-recall curve was 0.80, which is considered a satisfactory level of accuracy. In addition, our enrichment factor analysis indicated that our trained scoring function ranked molecules correctly compared to smina's generic scoring function. Further molecular dynamics simulations indicated that the top-ranked molecules identified by our developed scoring function were highly stable in the active site, supporting the validity of our developed process. This research may provide a template for developing target-specific scoring functions against specific enzyme targets.

Discovery of Potential Antiviral Compounds against Hendra Virus by Targeting Its Receptor-Binding Protein (G) Using Computational Approaches.

Hendra virus (HeV) belongs to the paramyxoviridae family of viruses which is associated with the respiratory distress, neurological illness, and potential fatality of the affected individuals. So far, no competitive approved therapeutic substance is available for HeV. For that reason, the current research work was conducted to propose some novel compounds, by adopting a Computer Aided Drug Discovery approach, which could be used to combat HeV. The G attachment Glycoprotein (Ggp) of HeV was selected to achieve the primary objective of this study, as this protein makes the entry of HeV possible in the host cells. Briefly, a library of 6000 antiviral compounds was screened for potential drug-like properties, followed by the molecular docking of short-listed compounds with the Protein Data Bank (PDB) structure of Ggp. Docked complexes of top two hits, having maximum binding affinities with the active sites of Ggp, were further considered for molecular dynamic simulations of 200 ns to elucidate the results of molecular docking analysis. MD simulations and Molecular Mechanics Energies combined with the Generalized Born and Surface Area (MMGBSA) or Poisson-Boltzmann and Surface Area (MMPBSA) revealed that both docked complexes are stable in nature. Furthermore, the same methodology was used between lead compounds and HeV Ggp in complex with its functional receptor in human, Ephrin-B2. Surprisingly, no major differences were found in the results, which demonstrates that our identified compounds can also perform their action even when the Ggp is attached to the Ephrin-B2 ligand. Therefore, in light of all of these results, we strongly suggest that compounds (S)-5-(benzylcarbamoyl)-1-(2-(4-methyl-2-phenylpiperazin-1-yl)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide and 5-(cyclohexylcarbamoyl)-1-(2-((2-(3-fluorophenyl)-2-methylpropyl)amino)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide could be considered as potential therapeutic agents against HeV; however, further in vitro and in vivo experiments are required to validate this study.

Molecular and Structural Analysis of Specific Mutations from Saudi Isolates of SARS-CoV-2 RNA-Dependent RNA Polymerase and their Implications on Protein Structure and Drug-Protein Binding.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has stressed the global health system to a significant level, which has not only resulted in high morbidity and mortality but also poses a threat for future pandemics. This situation warrants efforts to develop novel therapeutics to manage SARS-CoV-2 in specific and other emerging viruses in general. This study focuses on SARS-CoV2 RNA-dependent RNA polymerase (RdRp) mutations collected from Saudi Arabia and their impact on protein structure and function. The Saudi SARS-CoV-2 RdRp sequences were compared with the reference Wuhan, China RdRp using a variety of computational and biophysics-based approaches. The results revealed that three mutations-A97V, P323I and Y606C-may affect protein stability, and hence the relationship of protein structure to function. The apo wild RdRp is more dynamically stable with compact secondary structure elements compared to the mutants. Further, the wild type showed stable conformational dynamics and interaction network to remdesivir. The net binding energy of wild-type RdRp with remdesivir is -50.76 kcal/mol, which is more stable than the mutants. The findings of the current study might deliver useful information regarding therapeutic development against the mutant RdRp, which may further furnish our understanding of SARS-CoV-2 biology.

In Silico Analysis and Functional Characterization of Antimicrobial and Insecticidal Vicilin from Moth Bean (Vigna aconitifolia (Jacq.) Marechal) Seeds.

Vicilin has nutraceutical potential and different noteworthy medicative health-promoting biotic diversions, and it is remarkable against pathogenic microorganisms and insects. In this study, Vigna aconitifolia vicilin (VacV) has been identified and characterized from the seed of Vigna aconitifolia (Jacq.) Marechal (Moth beans). LC-MS/MS analysis of VacV provided seven random fragmented sequences comprising 238 residues, showing significant homology with already reported Vigna radiata vicilin (VraV). VacV was purified using ammonium sulfate precipitation (60%) followed by size exclusion chromatography on Hi-Load 16/60 Superdex 200 pg column and anion-exchange chromatography (Hi trap Q FF column). Purified VacV showed a major ~50 kDa band and multiple lower bands on 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) under both reduced and non-reduced conditions. After all, a three-dimensional molecular structure of VacV was predicted, which showed ß-sheeted molecular conformation similar to crystallographic structure of VraV. All Vicilins from V. aconitifolia and other plants were divided into six sub-groups by phylogenetic analysis, and VacV shared a high degree of similarity with vicilins of Vigna radiata, Pisum sativum, Lupinus albus, Cicer arietinum and Glycine max. Additionally, VacV (20 µg) has significant growth inhibition against different pathogenic bacteria along strong antifungal activity (50 µg). Likewise, VacV (3.0 mg) produced significant growth reduction in Rice Weevil Sitophilus oryzae larvae after 9 days compared with control. Furthermore, by using MMT assay, the cytotoxicity effect of VacV on the growth of HepG2 liver cancerous cells was tested. VacV showed cytotoxicity against the HepG-2 line and the acquired value was 180 µg after 48 h. Finally, we performed molecular docking against caspase-3 protein (PDB ID: 3DEI) for VacV bioactive receptor interface residues. Hence, our results reveal that VacV, has nutraceutical potential and moth beans can be used as a rich resource of functional foods.

LC-MS/MS-based metabolomics approach revealed novel phytocompounds from sugarcane rind with promising pharmacological value.

BACKGROUND: Sugarcane provides many secondary metabolites for the pharmacological and cosmetic industries. Secondary metabolites, such as phenolic compounds, flavonoids, and anthocyanins, have been studied, but few reports focus on the identification of alkaloid and non-alkaloid phytocompounds in sugarcane. RESULTS: In this study, we identified 40 compounds in total from the rinds of cultivated sugarcane varieties (including eight alkaloids, 24 non-alkaloids, and eight others) by using the liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach. Among these compounds, 31 were novel and are reported for the first time in sugarcane. Some alkaloids such as 3-indoleacrylic acid, N,N-dimethyl-5-methoxytryptamine, tryptamine, 6-hydroxynicotinic acid, and 6-deoxyfagomine are identified the first time in sugarcane rind. Four alkaloids such as trigonelline, piperidine, 3-indoleacrylic acid, and 6-deoxyfagomine are found abundantly in sugarcane rind and these compounds have promising pharmaceutical value. Some phytocompounds such as choline and acetylcholine (non-alkaloid compounds) were most common in the rind of ROC22 and Yuetang93/159 (YT93/159). Hierarchical cluster analysis and principal component analysis revealed that the ROC22, Taitang172 (F172), and Yuetang71/210 (YT71/210) varieties were quite similar in alkaloid composition when compared with other sugarcane varieties. We have also characterized the biosynthesis pathway of sugarcane alkaloids. The rind of F172, ROC22, and YT71/210 showed the highest total alkaloid content, whereas the rind of ROC16 revealed a minimum level. Interestingly, the rind extract from YT71/210 and F172 showed maximum antioxidant activity, followed by ROC22. CONCLUSION: Our results showed the diversity of alkaloid and non-alkaloid compounds in the rind of six cultivated sugarcanes and highlighted the promising phytocompounds that can be extracted, isolated, and utilized by the pharmacological industry. © 2022 Society of Chemical Industry.

Discovery of anti-MERS-CoV small covalent inhibitors through pharmacophore modeling, covalent docking and molecular dynamics simulation.

Middle east respiratory syndrome coronavirus (MERS-CoV) is a fatal pathogen that poses a serious health risk worldwide and especially in the middle east countries. Targeting the MERS-CoV 3-chymotrypsin-like cysteine protease (3CLpro) with small covalent inhibitors is a significant approach to inhibit replication of the virus. The present work includes generating a pharmacophore model based on the X-ray crystal structures of MERS-CoV 3CLpro in complex with two covalently bound inhibitors. In silico screening of covalent chemical database having 31,642 compounds led to the identification of 378 compounds that fulfils the pharmacophore queries. Lipinski rules of five were then applied to select only compounds with the best physiochemical properties for orally bioavailable drugs. 260 compounds were obtained and subjected to covalent docking-based virtual screening to determine their binding energy scores. The top three candidate compounds, which were shown to adapt similar binding modes as the reported covalent ligands were selected. The mechanism and stability of binding of these compounds were confirmed by 100 ns molecular dynamic simulation followed by MM/PBSA binding free energy calculation. The identified compounds can facilitate the rational design of novel covalent inhibitors of MERS-CoV 3CLpro enzyme as anti-MERS CoV drugs.

ppsPCP: a plant presence/absence variants scanner and pan-genome construction pipeline.

SUMMARY: Since the idea of pan-genomics emerged several tools and pipelines have been introduced for prokaryotic pan-genomics. However, not a single comprehensive pipeline has been reported which could overcome multiple challenges associated with eukaryotic pan-genomics. To aid the eukaryotic pan-genomic studies, here we present ppsPCP pipeline which is designed for eukaryotes especially for plants. It is capable of scanning presence/absence variants (PAVs) and constructing a fully annotated pan-genome. We believe with these unique features of PAV scanning and building a pan-genome together with its annotation, ppsPCP will be useful for plant pan-genomic studies and aid researchers to study genetic/phenotypic variations and genomic diversity. AVAILABILITY AND IMPLEMENTATION: The ppsPCP is freely available at github DOI: https://doi.org/10.5281/zenodo.2567390 and webpage http://cbi.hzau.edu.cn/ppsPCP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conserved Imprinted Genes between Intra-Subspecies and Inter-Subspecies Are Involved in Energy Metabolism and Seed Development in Rice.

Genomic imprinting is an epigenetic phenomenon in which a subset of genes express dependent on the origin of their parents. In plants, it is unclear whether imprinted genes are conserved between subspecies in rice. Here we identified imprinted genes from embryo and endosperm 5-7 days after pollination from three pairs of reciprocal hybrids, including inter-subspecies, japonica intra-subspecies, and indica intra-subspecies reciprocal hybrids. A total of 914 imprinted genes, including 546 in inter-subspecies hybrids, 211 in japonica intra-subspecies hybrids, and 286 in indica intra-subspecies hybrids. In general, the number of maternally expressed genes (MEGs) is more than paternally expressed genes (PEGs). Moreover, imprinted genes tend to be in mini clusters. The number of shared genes by R9N (reciprocal crosses between 9311 and Nipponbare) and R9Z (reciprocal crosses between 9311 and Zhenshan 97), R9N and RZN (reciprocal crosses between Zhonghua11 and Nipponbare), R9Z and RZN was 72, 46, and 16. These genes frequently involved in energy metabolism and seed development. Five imprinted genes (Os01g0151700, Os07g0103100, Os10g0340600, Os11g0679700, and Os12g0632800) are commonly detected in all three pairs of reciprocal hybrids and were validated by RT-PCR sequencing. Gene editing of two imprinted genes revealed that both genes conferred grain filling. Moreover, 15 and 27 imprinted genes with diverse functions in rice were shared with Arabidopsis and maize, respectively. This study provided valuable resources for identification of imprinting genes in rice or even in cereals.

Promising terpenes as SARS-CoV-2 spike receptor-binding domain (RBD) attachment inhibitors to the human ACE2 receptor: Integrated computational approach.

The spike protein receptor binding domain (S-RBD) is a necessary corona-viral protein for binding and entry of coronaviruses (COVs) into the host cells. Hence, it has emerged as an attractive antiviral drug target. Therefore, present study was aimed to target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S-RBD with novel bioactive compounds to retrieve potential candidates that could serve as anti-coronavirus disease 2019 (COVID-19) drugs. In this paper, computational approaches were employed, especially the structure-based virtual screening followed by molecular dynamics (MD) simulation as well as binding energy analysis for the computational identification of specific terpenes from the medicinal plants, which can block SARS-CoV-2 S-RBD binding to Human angiotensin-converting enzyme 2 (H-ACE2) and can act as potent anti-COVID-19 drugs after further advancements. The screening of focused terpenes inhibitors database composed of ~1000 compounds with reported therapeutic potential resulted in the identification of three candidate compounds, NPACT01552, NPACT01557 and NPACT00631. These three compounds established conserved interactions, which were further explored through all-atom MD simulations, free energy calculations, and a residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the hot-spot residues of SARS-CoV-2 S-RBD. Conclusively, the reported SARS-CoV-2 S-RBD specific terpenes could serve as seeds for developing potent anti-COVID-19 drugs. Importantly, the experimentally tested glycyrrhizin (NPACT00631) against SARS-CoV could be used further in the fast-track drug development process to help curb COVID-19.

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations.

Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein-protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.

Comparative analysis of miniature inverted-repeat transposable elements (MITEs) and long terminal repeat (LTR) retrotransposons in six Citrus species.

BACKGROUND: Miniature inverted-repeat transposable elements (MITEs) and long terminal repeat (LTR) retrotransposons are ubiquitous in plants genomes, and highly important in their evolution and diversity. However, their mechanisms of insertion/amplification and roles in Citrus genome's evolution/diversity are still poorly understood. RESULTS: To address this knowledge gap, we developed different computational pipelines to analyze, annotate and classify MITEs and LTR retrotransposons in six different sequenced Citrus species. We identified 62,010 full-length MITEs from 110 distinguished families. We observed MITEs tend to insert in gene related regions and enriched in promoters. We found that DTM63 is possibly an active Mutator-like MITE family in the traceable past and may still be active in Citrus. The insertion of MITEs resulted in massive polymorphisms and played an important role in Citrus genome diversity and gene structure variations. In addition, 6630 complete LTR retrotransposons and 13,371 solo-LTRs were identified. Among them, 12 LTR lineages separated before the differentiation of mono- and dicotyledonous plants. We observed insertion and deletion of LTR retrotransposons was accomplished with a dynamic balance, and their half-life in Citrus was ~ 1.8 million years. CONCLUSIONS: These findings provide insights into MITEs and LTR retrotransposons and their roles in genome diversity in different Citrus genomes.

Epitope-based peptide vaccine design and target site depiction against Middle East Respiratory Syndrome Coronavirus: an immune-informatics study.

BACKGROUND: Middle East Respiratory Syndrome Coronavirus (MERS-COV) is the main cause of lung and kidney infections in developing countries such as Saudi Arabia and South Korea. This infectious single-stranded, positive (+) sense RNA virus enters the host by binding to dipeptidyl-peptide receptors. Since no vaccine is yet available for MERS-COV, rapid case identification, isolation, and infection prevention strategies must be used to combat the spreading of MERS-COV infection. Additionally, there is a desperate need for vaccines and antiviral strategies. METHODS: The present study used immuno-informatics and computational approaches to identify conserved B- and T cell epitopes for the MERS-COV spike (S) protein that may perform a significant role in eliciting the resistance response to MERS-COV infection. RESULTS: Many conserved cytotoxic T-lymphocyte epitopes and discontinuous and linear B-cell epitopes were predicted for the MERS-COV S protein, and their antigenicity and interactions with the human leukocyte antigen (HLA) B7 allele were estimated. Among B-cell epitopes, QLQMGFGITVQYGT displayed the highest antigenicity-score, and was immensely immunogenic. Among T-cell epitopes, MHC class-I peptide YKLQPLTFL and MHC class-II peptide YCILEPRSG were identified as highly antigenic. Furthermore, docking analyses revealed that the predicted peptides engaged in strong bonding with the HLA-B7 allele. CONCLUSION: The present study identified several MERS-COV S protein epitopes that are conserved among various isolates from different countries. The putative antigenic epitopes may prove effective as novel vaccines for eradication and combating of MERS-COV infection.

Identification and Analysis of Micro-Exon Genes in the Rice Genome.

Micro-exons are a kind of exons with lengths no more than 51 nucleotides. They are generally ignored in genome annotation due to the short length, whereas recent studies indicate that they have special splicing properties and important functions. Considering that there has been no genome-wide study of micro-exons in plants up to now, we screened and analyzed genes containing micro-exons in two indica rice varieties in this study. According to the annotation of Zhenshan 97 (ZS97) and Minghui 63 (MH63), ~23% of genes possess micro-exons. We then identified micro-exons from RNA-seq data and found that >65% micro-exons had been annotated and most of novel micro-exons were located in gene regions. About 60% micro-exons were constitutively spliced, and the others were alternatively spliced in different tissues. Besides, we observed that approximately 54% of genes harboring micro-exons tended to be ancient genes, and 13% were Oryza genus-specific. Micro-exon genes were highly conserved in Oryza genus with consistent domains. In particular, the predicted protein structures showed that alternative splicing of in-frame micro-exons led to a local structural recombination, which might affect some core structure of domains, and alternative splicing of frame-shifting micro-exons usually resulted in premature termination of translation by introducing a stop codon or missing functional domains. Overall, our study provided the genome-wide distribution, evolutionary conservation, and potential functions of micro-exons in rice.

Peptide vaccine against chikungunya virus: immuno-informatics combined with molecular docking approach.

BACKGROUND: Chikungunya virus (CHIKV), causes massive outbreaks of chikungunya infection in several regions of Asia, Africa and Central/South America. Being positive sense RNA virus, CHIKV replication within the host resulting in its genome mutation and led to difficulties in creation of vaccine, drugs and treatment strategies. Vector control strategy has been a gold standard to combat spreading of CHIKV infection, but to eradicate a species from the face of earth is not an easy task. Therefore, alongside vector control, there is a dire need to prevent the infection through vaccine as well as through antiviral strategies. METHODS: This study was designed to find out conserved B cell and T cell epitopes of CHIKV structural proteins through immuno-informatics and computational approaches, which may play an important role in evoking the immune responses against CHIKV. RESULTS: Several conserved cytotoxic T-lymphocyte epitopes, linear and conformational B cell epitopes were predicted for CHIKV structural polyprotein and their antigenicity was calculated. Among B-cell epitopes "PPFGAGRPGQFGDI" showed a high antigenicity score and it may be highly immunogenic. In case of T cell epitopes, MHC class I peptides 'TAECKDKNL' and MHC class II peptides 'VRYKCNCGG' were found extremely antigenic. CONCLUSION: The study led to the discovery of various epitopes, conserved among various strains belonging to different countries. The potential antigenic epitopes can be successfully utilized in designing novel vaccines for combating and eradication of CHIKV disease.

Comparative Studies of the Dynamics Effects of BAY60-2770 and BAY58-2667 Binding with Human and Bacterial H-NOX Domains.

Soluble guanylate cyclase (sGC) is a key enzyme implicated in various physiological processes such as vasodilation, thrombosis and platelet aggregation. The enzyme's Heme-Nitric oxide/Oxygen (H-NOX) binding domain is the only sensor of nitric oxide (NO) in humans, which on binding with NO activates sGC to produce the second messenger cGMP. H-NOX is thus a hot target for drug design programs. BAY60-2770 and BAY58-2667 are two widely studied activators of sGC. Here we present comparative molecular dynamics studies to understand the molecular details characterizing the binding of BAY60-2770 and BAY58-2667 with the human H-NOX (hH-NOX) and bacterial H-NOX (bH-NOX) domains. HartreeFock method was used for parametrization of both the activators. A 50 ns molecular dynamics (MD) simulation was run to identify the functionally critical regions of the H-NOX domains. The CPPTRAJ module was used for analysis. BAY60-2770 on binding with bH-NOX, triggered rotational movement in signaling helix F and significant dynamicity in loops α and ß, but in hH-NOX domain the compound showed relatively lesser aforementioned structural fluctuations. Conversely, hH-NOX ligated BAY58-2667 experienced highest transitions in its helix F due to electrostatic interactions with D84, T85 and R88 residues which are not conserved in bH-NOX. These conformational transformations might be essential to communicate with downstream PAS, CC and cyclase domains of sGC. Comparative MD studies revealed that BAY bound bHNOX dynamics varied from that of hH-NOX, plausibly due to some key residues such as R40, F74 and Y112 which are not conserved in bacteria. These findings will help to the design of novel drug leads to cure diseases associated to human sGC.

Network pharmacology and molecular dynamic simulation integrated strategy for the screening of active components and mechanisms of phytochemicals from Datura innoxia on Alzheimer and cognitive decline.

Alzheimer's disease (AD) ranks as the most prevalent neurodegenerative disorder with dementia and it accounts for more than 70% of all cases. Despite extensive reporting on the experimental investigation of Datura innoxia (DI) and its phytochemical components in the treatment of AD, the urgent need for elucidation of the principle of multi-mechanism and multi-level treatment of AD remains. In this research, molecular docking and network pharmacology were used to evaluate active compounds and molecular targets of DI for the treatment of AD. The phytochemical compounds of DI were obtained from the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) as well as the Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening includes the 28 most abundant components of DI and the Swiss Target Prediction database was used to predict targets of these compounds. The GeneCards database was used to collect AD-related genes. Both DI and AD targets were imported into a Venn diagram, and the 28 overlapped genes were identified as potential DI anti-AD targets. The results showed that Dinoxin B, Meteloidine, Scopoline, and Tropic acid had no effect on AD-related genes. Furthermore, the GO enrichment analysis indicates that DI influences molecular functions and biological processes such as learning or memory and modulation of chemical synaptic transmission as well as the membrane raft and membrane microdomain. The KEGG pathway analysis revealed that the key pathways implicated in DI's anti-AD actions include serotonergic synapse, IL-17 signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Based on the STRING and Cytoscape network-analysis platforms, the top ten anti-AD core targets include APP, CASP3, IL6, BACE1, IL1B, ACE, PSEN1, GAPDH, GSK3B and ACHE. The molecular docking and molecular dynamic simulation of the top two molecules against the top three target proteins confirmed the strong binding affinity and stability at the docked site. Overall, our findings pave the path for further research into the development and optimization of potential anti-AD agents from DI.Communicated by Ramaswamy H. Sarma.