Integrated network analysis and experimental verification of the mechanisms employed by Compound Jixuecao Decoction to improve endoplasmic reticulum stress and apoptosis in chronic renal failure.

ETHNOPHARMACOLOGICAL RELEVANCE: Compound Jixuecao Decoction (CJD) is a traditional Chinese herbal medicine prescribed in China to treat chronic renal failure (CRF). Previous studies have shown that CJD affects cell apoptosis and proliferation. However, the mechanism of its renal protective action has not been characterized. AIM OF THE STUDY: To explore the mechanism(s) underlying the effect of CJD on endoplasmic reticulum stress (ERS) and apoptosis in the treatment of CRF using network pharmacology, molecular docking, molecular dynamics simulations, and in vivo studies. MATERIALS AND METHODS: The compounds comprising CJD were extracted from the Traditional Chinese Medicine Systems Pharmacology Database. A Swiss target prediction database and similarity integration approach were employed to identify potential targets of these components. The GeneCards and DisGeNET databases were used to identify targets associated with CRF, apoptosis, and ERS. The STRING database was employed to analyze the protein-protein interactions (PPIs) associated with drug-disease crossover. A chemical composition-shared target network was established, and critical pathways were identified through gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The Protein Data Bank database was used to search key proteins, while molecular docking and dynamics simulations were performed between the top four CJD active ingredients and proteins involved in apoptosis and ERS in CRF. Subsequent in vivo studies using a 5/6 nephrectomy rat model of CRF were performed to verify the findings. RESULTS: The 80 compounds identified in CJD yielded 875 target genes, of which 216 were potentially related to CRF. PPI network analysis revealed key targets via topology filtering. Enrichment analysis, molecular docking, and molecular dynamics simulation results suggested that CJD primarily targets mitofusin-2 (MFN2), B-cell lymphoma-2 (BCL2), BAX, protein kinase RNA-like ER kinase (PERK), and C/EBP homologous protein (CHOP) during CRF treatment. In vivo, CJD significantly increased the abundance of MFN2, BCL2, and significantly reduced the abundance of BAX, PERK, CHOP proteins in kidney tissues, indicating that CJD could improve apoptosis and ERS in CRF rats. CONCLUSIONS: This study provides evidence that CJD effectively delays CFR through modulation of the MFN2 and PERK-eIF2α-ATF4-CHOP signaling pathways.

Network Pharmacology Research Indicates that Wu-Mei-Wan Treats Obesity by Inhibiting Th17 Cell Differentiation and Alleviating Metabolic Inflammation.

BACKGROUND: Wu-Mei-Wan (WMW), a traditional Chinese medicine (TCM) formula, has a good effect on the treatment of obesity and has been proven helpful to promote the metabolism of adipose tissue. However, its underlying mechanism remains to be studied. This study aims to explore the potential pharmacological mechanism of WMW in the treatment of obesity. METHODS: Network pharmacology was used to sort out the relationship between WMW putative targets and obesity-related drug targets or disease targets, which indicated the mechanism of WMW in treating obesity from two aspects of clinical drugs approved by the Food and Drug Administration (FDA) and obesity-related diseases. Databases such as Traditional Chinese Medicine Systems Pharmacology (TCMSP), PubChem, DrugBank, DisGeNET, and Genecards were used to collect information about targets. String platform was used to convert the data into gene symbol of "homo sapiens", and perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. With the Human Protein Reference Database (HPRD) as background data, Cytoscape 3.6.0 software was used to construct a new protein-protein interaction (PPI) network. Mechanism diagrams of key pathways were obtained from the KEGG database. AutoDock Vina software was used to conduct molecular docking verification. RESULTS: The number of targets in the overlap between WMW putative targets and obesity-related drug targets accounted for more than 50% of the latter, and HTR3A, SLC6A4, and CYP3A4 were core targets. In obesity-related disease targets-WMW putative targets PPI network, the Th17 cell differentiation pathway, and the IL-17 signaling pathway were key pathways, and the 1st module and the 7th module were central function modules that were highly associated with immunity and inflammation. Molecular docking verified that STAT3, TGFB1, MMP9, AHR, IL1B, and CCL2 were core targets in the treatment of WMW on obesity. CONCLUSION: WMW has similar effects on lipid and drug metabolism as the current obesity-related drugs, and is likely to treat obesity by inhibiting Th17 cell differentiation and alleviating metabolic inflammation.

Resources for computational prediction of intrinsic disorder in proteins.

With over 40 years of research, researchers in the intrinsic disorder prediction field developed over 100 computational predictors. This review offers a holistic perspective of this field by highlighting accurate and popular disorder predictors and introducing a wide range of practical resources that support collection, interpretation and application of disorder predictions. These resources include meta webservers that expedite collection of multiple disorder predictions, large databases of pre-computed disorder predictions that ease collection of predictions particularly for large datasets of proteins, and modern quality assessment tools. The latter methods facilitate identification of accurate predictions in a specific protein sequence, reducing uncertainty associated to the use of the putative disorder. Altogether, we review eleven predictors, four meta webservers, three databases and two quality assessment tools, all of which are conveniently available online. We also offer a perspective on future developments of the disorder prediction and the quality assessment tools. The availability of this comprehensive toolbox of useful resources should stimulate further growth in the application of the disorder predictions across many areas including rational drug design, systems medicine, structural bioinformatics and structural genomics.

Multiple Profile Models Extract Features from Protein Sequence Data and Resolve Functional Diversity of Very Different Protein Families.

Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyze sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. ProfileView agrees with the large set of functional data collected for these proteins from the literature regarding the organization into functional subgroups and residues that characterize the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer-based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

How Far Are We from the Completion of the Human Protein Interactome Reconstruction?

After more than fifteen years from the first high-throughput experiments for human protein-protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights gained from the holistic investigation of the current network are valid and useful. The unique structure of PICKLE, a meta-database of the human experimentally determined direct PPI network developed by our group, presently covering ~80% of the UniProtKB/Swiss-Prot reviewed human complete proteome, enables the evaluation of the interactome expansion by comparing the successive PICKLE releases since 2013. We observe a gradual overall increase of 39%, 182%, and 67% in protein nodes, PPIs, and supporting references, respectively. Our results indicate that, in recent years, (a) the PPI addition rate has decreased, (b) the new PPIs are largely determined by high-throughput experiments and mainly concern existing protein nodes and (c), as we had predicted earlier, most of the newly added protein nodes have a low degree. These observations, combined with a largely overlapping k-core between PICKLE releases and a network density increase, imply that an almost complete picture of a structurally defined network has been reached. The comparative unsupervised application of two clustering algorithms indicated that exploring the full interactome topology can reveal the protein neighborhoods involved in closely related biological processes as transcriptional regulation, cell signaling and multiprotein complexes such as the connexon complex associated with cancers. A well-reconstructed human protein interactome is a powerful tool in network biology and medicine research forming the basis for multi-omic and dynamic analyses.

Application of Reverse Docking in the Research of Small Molecule Drugs and Traditional Chinese Medicine.

With the development of structural biology and data mining, computer-aided drug design (CADD) has been playing an important role in all aspects of new drug development. Reverse docking, a method of virtual screening based on molecular docking in CADD, is widely used in drug repositioning, drug rescue, and traditional Chinese medicine (TCM) research, for it can search for macromolecular targets that can bind to a given ligand molecule. This review revealed the principle of reverse docking, summarized common target protein databases and docking procedures, and enumerated the applications of reverse docking in drug repositioning, adverse drug reactions, traditional Chinese medicine, and coronavirus disease 2019 (COVID-19) treatment. Hope our work can give some inspiration to researchers engaged in drug development.

Combining Network Pharmacology with Molecular Docking for Mechanistic Research on Thyroid Dysfunction Caused by Polybrominated Diphenyl Ethers and Their Metabolites.

Network pharmacology was used to illuminate the targets and pathways of polybrominated diphenyl ethers (PBDEs) causing thyroid dysfunction. A protein-protein interaction (PPI) network was constructed. Molecular docking was applied to analyze PBDEs and key targets according to the network pharmacology results. A total of 247 targets were found to be related to 16 PBDEs. Ten key targets with direct action were identified, including the top five PIK3R1, MAPK1, SRC, RXRA, and TP53. Gene Ontology (GO) functional enrichment analysis identified 75 biological items. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified 62 pathways mainly related to the regulation of the thyroid hormone signaling pathway, MAPK signaling pathway, PI3K-Akt signaling, pathways in cancer, proteoglycans in cancer, progesterone-mediated oocyte maturation, and others. The molecular docking results showed that BDE-99, BDE-153, 5-OH-BDE47, 5'-OH-BDE99, 5-BDE47 sulfate, and 5'-BDE99 sulfate have a good binding effect with the kernel targets. PBDEs could interfere with the thyroid hormone endocrine through multiple targets and biological pathways, and metabolites demonstrated stronger effects than the prototypes. This research provides a basis for further research on the toxicological effects and molecular mechanisms of PBDEs and their metabolites. Furthermore, the application of network pharmacology to the study of the toxicity mechanisms of environmental pollutants provides a new methodology for environmental toxicology.

Computational hunting of natural active compounds as an alternative for Remdesivir to target RNA-dependent polymerase.

The hunt for potential lead/drug molecules from different resources, especially from natural resources, for possible treatment of COVID-19 is ongoing. Several compounds have already been identified, but only a few are good enough to show potential against the virus. Among the identified druggable target proteins of SARS-CoV-2, this study focuses on non-structural RNA-dependent RNA polymerase protein (RdRp), a well-known enzyme for both viral genome replication and viral mRNA synthesis, and is therefore considered to be the primary target. In this study, the virtual screening followed by an in-depth docking study of the Compounds Library found that natural compound Cyclocurcumin and Silybin B have strong interaction with RdRp and much better than the remdesivir with free binding energy and inhibition constant value as êzŒ-6.29 kcal/mol and 58.39 µMêzŒ, and êzŒ-7.93kcal/mol and 45.3 µMêzŒ, respectively. The finding indicated that the selected hits (Cyclocurcumin and Silybin B) could act as non-nucleotide anti-polymerase agents, and can be further optimized as a potential inhibitor of RdRp by benchwork experiments.

Chalcogen Bonds Involving Selenium in Protein Structures.

Chalcogen bonds are the specific interactions involving group 16 elements as electrophilic sites. The role of chalcogen atoms as sticky sites in biomolecules is underappreciated, and the few available studies have mostly focused on S. Here, we carried out a statistical analysis over 3562 protein structures in the Protein Data Bank (PDB) containing 18 266 selenomethionines and found that Se···O chalcogen bonds are commonplace. These findings may help the future design of functional peptides and contribute to understanding the role of Se in nature.

Charge Assisted S/Se Chalcogen Bonds in SAM Riboswitches: A Combined PDB and ab Initio Study.

In this study, we provide experimental (Protein Data Bank (PDB) inspection) and theoretical (RI-MP2/def2-TZVP level of theory) evidence of the involvement of charge assisted chalcogen bonding (ChB) interactions in the recognition and folding mechanisms of S-adenosylmethionine (SAM) riboswitches. Concretely, an initial PDB search revealed several examples where ChBs between S-adenosyl methionine (SAM)/adenosyl selenomethionine (EEM) molecules and uracil (U) bases belonging to RNA take place. While these interactions are usually described as a merely Coulombic attraction between the positively charged S/Se group and RNA, theoretical calculations indicated that the σ holes of S and Se are involved. Moreover, computational models shed light on the strength and directionality properties of the interaction, which was also further characterized from a charge-density perspective using Bader's "Atoms in Molecules" (AIM) theory, Non-Covalent Interaction plot (NCIplot) visual index, and Natural Bonding Orbital (NBO) analyses. As far as our knowledge extends, this is the first time that ChBs in SAM-RNA complexes have been systematically analyzed, and we believe the results might be useful for scientists working in the field of RNA engineering and chemical biology as well as to increase the visibility of the interaction among the biological community.

Selenium chalcogen bonds are involved in protein-carbohydrate recognition: a combined PDB and theoretical study.

In this manuscript the ability of selenium carbohydrates to undergo chalcogen bonding (ChB) interactions with protein residues has been studied at the RI-MP2/def2-TZVP level of theory. An inspection of the Protein Data Bank (PDB) revealed SeA (A = O, C and S) intermolecular contacts involving Se-pyranose ligands and ASP, TYR, SER and MET residues. Theoretical models were built to analyse the strength and directionality of the interaction together with "Atoms in Molecules" (AIM), Natural Bonding Orbital (NBO) and Non Covalent Interactions plot (NCIplot) analyses, which further assisted in the characterization of the ChBs described herein. We expect that the results from this study will be useful to expand the current knowledge regarding biological ChBs as well as to increase the visibility of the interaction among the carbohydrate chemistry community.

Pharmacological mechanism of mylabris in the treatment of leukemia based on bioinformatics and systematic pharmacology.

Leukemia is a common blood cancer, whose treatment usually necessitates chemo/radiotherapy and bone marrow transplant. Hence, safer and more effective options are urgently needed. Mylabris, the dried body of blister beetles, has been used extensively in traditional Chinese medicine. This study applied bioinformatics and systematic pharmacology to investigate the mechanism of action of mylabris in the treatment of leukemia. Five effective components and 35 corresponding target proteins were identified by screening the TCMSP database; whereas 776 genes related to leukemia were selected using OMIM, GeneCards, and the Therapeutic Target Database. Eight genes common to mylabris and leukemia were identified. Protein-protein interaction network analysis and a component-target-pathway diagram identified TP53 and PTEN as key gene targets of mylabris in the treatment of leukemia. GO enrichment analysis pointed to DNA damage and cell cycle disorder caused by p53 signaling as the most significant processes; whereas KEGG enrichment pointed to the p53 signaling pathway. In summary, mylabris may exert a therapeutic effect on leukemia by triggering DNA damage, inducing apoptosis, as well as inhibiting the growth and proliferation of tumor cells through the regulation of TP53 and PTEN. These findings provide a mechanistic rationale for the treatment of leukemia with traditional Chinese medicine.

Ultrafiltration strategy combined with nanoLC-MS/MS based proteomics for monitoring potential residual proteins in TCMIs.

Traditional Chinese medicine injections (TCMIs) containing complex constituents frequently cause unpredictable adverse reactions. The residual heterologous proteins in TCMIs may be one kind of the sensitized constituents. However, few methods were developed to identify and monitor the residual proteins of TCMIs in industry. Here, we described a method combining the advantages of ultrafiltration and mass spectrometry-based proteomics for monitoring the potential residual proteins in Re Du Ning injection (RDNI) intermediates and preparations. We identified and quantified both de novo peptides and the proteins matched against databases of three raw plants by using PEAKS software. Interesting, we found there was a significant decrease of peptides and proteins in No. 3-5 of RDNI intermediates and some even disappeared. Besides, we found this method could greatly reduce the interference of contaminants in proteomics experiments. The rapid and accurate method proposed in this paper could be used for monitoring potential residual proteins in TCMIs to guarantee their quality and safety.

SARS-CoV-2 Papain-Like Protease Potential Inhibitors-In Silico Quantitative Assessment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes the papain-like protease (PLpro). The protein not only plays an essential role in viral replication but also cleaves ubiquitin and ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from host proteins, making it an important target for developing new antiviral drugs. In this study, we searched for novel, noncovalent potential PLpro inhibitors by employing a multistep in silico screening of a 15 million compound library. The selectivity of the best-scored compounds was evaluated by checking their binding affinity to the human ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), which, as a deubiquitylating enzyme, exhibits structural and functional similarities to the PLpro. As a result, we identified 387 potential, selective PLpro inhibitors, from which we retrieved the 20 best compounds according to their IC50 values toward PLpro estimated by a multiple linear regression model. The selected candidates display potential activity against the protein with IC50 values in the nanomolar range from approximately 159 to 505 nM and mostly adopt a similar binding mode to the known, noncovalent SARS-CoV-2 PLpro inhibitors. We further propose the six most promising compounds for future in vitro evaluation. The results for the top potential PLpro inhibitors are deposited in the database prepared to facilitate research on anti-SARS-CoV-2 drugs.

A Network Pharmacology to Explore the Mechanism of Calculus Bovis in the Treatment of Ischemic Stroke.

BACKGROUND: Calculus Bovis is a valuable Chinese medicine, which is widely used in the clinical treatment of ischemic stroke. The present study is aimed at investigating its target and the mechanism involved in ischemic stroke treatment by network pharmacology. METHODS: Effective compounds of Calculus Bovis were collected using methods of network pharmacology and using the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Potential compound targets were searched in the TCMSP and SwissTargetPrediction databases. Ischemic stroke-related disease targets were searched in the Drugbank, DisGeNet, OMIM, and TTD databases. These two types of targets were uploaded to the STRING database, and a network of their interaction (PPI) was built with its characteristics calculated, aiming to reveal a number of key targets. Hub genes were selected using a plug-in of the Cytoscape software, and Gene Ontology (GO) biological processes and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted using the clusterProfiler package of R language. RESULTS: Among 12 compounds, deoxycorticosterone, methyl cholate, and biliverdin were potentially effective components. A total of 344 Calculus Bovis compound targets and 590 ischemic stroke targets were found with 92 overlapping targets, including hub genes such as TP53, AKT, PIK2CA, MAPK3, MMP9, and MMP2. Biological functions of Calculus Bovis are associated with protein hydrolyzation, phosphorylation of serine/threonine residues of protein substrates, peptide bond hydrolyzation of peptides and proteins, hydrolyzation of intracellular second messengers, antioxidation and reduction, RNA transcription, and other biological processes. CONCLUSION: Calculus Bovis may play a role in ischemic stroke by activating PI3K-AKT and MAPK signaling pathways, which are involved in regulating inflammatory response, cell apoptosis, and proliferation.

Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from an SBVS. In this study, we developed a novel method, using ligand-residue interaction profiles (IPs) to construct machine learning (ML)-based prediction models, to significantly improve the screening performance in SBVSs. Such a kind of the prediction model is called an IP scoring function (IP-SF). We systematically investigated how to improve the performance of IP-SFs from many perspectives, including the sampling methods before interaction energy calculation and different ML algorithms. Using six drug targets with each having hundreds of known ligands, we conducted a critical evaluation on the developed IP-SFs. The IP-SFs employing a gradient boosting decision tree (GBDT) algorithm in conjunction with the MIN + GB simulation protocol achieved the best overall performance. Its scoring power, ranking power and screening power significantly outperformed the Glide SF. First, compared with Glide, the average values of mean absolute error and root mean square error of GBDT/MIN + GB decreased about 38 and 36%, respectively. Second, the mean values of squared correlation coefficient and predictive index increased about 225 and 73%, respectively. Third, more encouragingly, the average value of the areas under the curve of receiver operating characteristic for six targets by GBDT, 0.87, is significantly better than that by Glide, which is only 0.71. Thus, we expected IP-SFs to have broad and promising applications in SBVSs.

ATP-Magnesium Coordination: Protein Structure-Based Force Field Evaluation and Corrections.

In the numerous molecular recognition and catalytic processes across biochemistry involving adenosine triphosphate (ATP), the common bioactive form is its magnesium chelate, ATP·Mg2+. In aqueous solution, two chelation geometries predominate, distinguished by bidentate and tridentate Mg2+-phosphate coordination. These are approximately isoenergetic but separated by a high energy barrier. Force field-based atomistic simulation studies of this complex require an accurate representation of its structure and energetics. Here we focused on the energetics of ATP·Mg2+ coordination. Applying an enhanced sampling scheme to circumvent prohibitively slow sampling of transitions between coordination modes, we observed striking contradictions between Amber and CHARMM force field descriptions, most prominently in opposing predictions of the favored coordination mode. Through further configurational free energy calculations, conducted against a diverse set of ATP·Mg2+-protein complex structures to supplement otherwise limited experimental data, we quantified systematic biases for each force field. The force field calculations were strongly predictive of experimentally observed coordination modes, enabling additive corrections to the coordination free energy that deliver close agreement with experiment. We reassessed the applicability of the thus corrected force field descriptions of ATP·Mg2+ for biomolecular simulation and observed that, while the CHARMM parameters display an erroneous preference for overextended triphosphate configurations that will affect many common biomolecular simulation applications involving ATP, the force field energy landscapes broadly agree with experimental measurements of solution geometry and the distribution of ATP·Mg2+ structures found in the Protein Data Bank. Our force field evaluation and correction approach, based on maximizing consistency with the large and heterogeneous collection of structural information encoded in the PDB, should be broadly applicable to many other systems.

Importance of glutamine 189 flexibility in SARS-CoV-2 main protease: Lesson learned from in silico virtual screening of ChEMBL database and molecular dynamics.

The current global pandemic outbreak of COVID-19, caused by the SARS-CoV-2, strikes an invincible damage to both daily life and the global economy. WHO guidelines for COVID-19 clinical management includes infection control and prevention, social distancing and supportive care using supplemental oxygen and mechanical ventilator support. Currently, evolving researches and clinical reports regarding infected patients with SARS-CoV-2 suggest a potential list of repurposed drugs that may produce appropriate pharmacological therapeutic efficacies in treating COVID-19 infected patients. In this study, we performed virtual screening and evaluated the obtained results of US-FDA approved small molecular database library (302 drug molecule) against two important different protein targets in COVID-19. Best compounds in molecular docking were used as a training set for generation of two different pharmacophores. The obtained pharmacophores were employed for virtual screening of ChEMBL database. The filtered compounds were clustered using Finger print model to obtain two compounds that will be subjected to molecular docking simulations against the two targets. Compounds complexes with SARS-CoV-2 main protease and S-protein were studied using molecular dynamics (MD) simulation. MD simulation studies suggest the potential inhibitory activity of ChEMBL398869 against SARS-CoV-2 main protease and restress the importance of Gln189 flexibility in inhibitors recognition through increasing S2 subsite plasticity.

Kaempferitrin-Treated HepG2 Differentially Expressed Exosomal Markers and Affect Extracellular Vesicle Sizes in the Secretome.

Kaempferitrin is extracted in significantly high quantities from the leaves of Cinnamomum osmophloeum, which belongs to a group of plant species that comes under the genus Cinnamomum, well-known for its established anti-diabetic property in Chinese medicine. Oral administration of kaempferitrin and Cinnamomum osmophloeum extract reduced blood sugar in alloxan-induced diabetic rats and improved the lipid profile in hamsters respectively. In this paper we studied the differential protein expression profile using mass spectrometry approach in the kaempferitrin-treated conditioned medium of liver cancer cell line HepG2. We discovered that 33 genes were up/down-regulated consistently between two biological samples. A slightly different version of the analysis software selected 28 genes, and the final 18 genes that appeared in both lists were selected. Interestingly, 5 proteins out of 18 were either exosomal markers or reported in high frequency of occurrence in exosome/secreted vesicles. We also examined the extracellular particles with atomic force microscopy (AFM), which showed that the conditioned medium of kaempferitrin treated had larger vesicles and fewer small vesicles. Expression of some lipid-regulating genes were also altered. Our data suggested that extracellular vesicle secretions may be regulated by kaempferitrin, and regulation of lipid profile by kampeferitrin involves multiple mechanisms.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.