DOCSLIB.ORG

ARP55242 P050) Data Sheet

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
ARP55242 P050) Data Sheet COG4 antibody - middle region (ARP55242_P050) Data Sheet Product Number ARP55242_P050 Product Name COG4 antibody - middle region (ARP55242_P050) Size 50ug Gene Symbol COG4 Alias Symbols COD1; DKFZp586E1519; CDG2J Nucleotide Accession# NM_015386 Protein Size (# AA) 789 amino acids Molecular Weight 89kDa Subunit 4 Product Format Lyophilized powder NCBI Gene Id 25839 Host Rabbit Clonality Polyclonal Official Gene Full Name Component of oligomeric golgi complex 4 Gene Family COG This is a rabbit polyclonal antibody against COG4. It was validated on Western Blot using a cell lysate as a Description positive control. Aviva Systems Biology strives to provide antibodies covering each member of a whole protein family of your interest. We also use our best efforts to provide you antibodies recognize various epitopes of a target protein. For availability of antibody needed for your experiment, please inquire (). Peptide Sequence Synthetic peptide located within the following region: LFSQGIGGEQAQAKFDSCLSDLAAVSNKFRDLLQEGLTELNSTAIKPQVQ Target Reference Suzuki,Y., (2004) Genome Res. 14 (9), 1711-1718 Multiprotein complexes are key determinants of Golgi apparatus structure and its capacity for intracellular transport and glycoprotein modification. Several complexes have been identified, including the Golgi transport complex (GTC), the LDLC complex, which is involved in glycosylation reactions, and the SEC34 complex, which is involved in vesicular transport. These 3 complexes are identical and have been termed the conserved oligomeric Golgi (COG) complex, which includes COG4.Multiprotein complexes are key determinants of Golgi Description of Target apparatus structure and its capacity for intracellular transport and glycoprotein modification. Several complexes have been identified, including the Golgi transport complex (GTC), the LDLC complex, which is involved in glycosylation reactions, and the SEC34 complex, which is involved in vesicular transport. These 3 complexes are identical and have been termed the conserved oligomeric Golgi (COG) complex, which includes COG4 (Ungar et al., 2002 [PubMed 11980916]).[supplied by OMIM]. PRIMARYREFSEQ_SPAN PRIMARY_IDENTIFIER PRIMARY_SPAN COMP 1-265 AK096557.1 1-265 266-555 BP282697.1 230-519 556-1072 AU125729.1 34-550 1073-2838 AL050101.1 375-2140 Partner Proteins COG1, COG2, COG5, COG7, COG1, COG2, COG3, COG5, COG7 Reconstitution and Add 50 ul of distilled water. Final anti-COG4 antibody concentration is 1 mg/ml in PBS buffer with 2% sucrose. Storage For longer periods of storage, store at -20C. Avoid repeat freeze-thaw cycles. Lead Time Domestic: within 24 hours delivery International: 3-5 business days Blocking Peptide For anti-COG4 antibody is Catalog # AAP55242 (Previous Catalog # AAPP33069) Immunogen The immunogen for anti-COG4 antibody: synthetic peptide directed towards the middle region of human COG4 Swissprot Id Q9H9E3 Protein Name Conserved oligomeric Golgi complex subunit 4 Sample Type Confirmation COG4 is supported by BioGPS gene expression data to be expressed in 721_B Protein Accession # NP_056201 Purification Affinity Purified Species Reactivity Human, Rat, Dog, Horse, Rabbit, Mouse, Guinea pig, Bovine Application WB Predicted Homology Based on Immunogen Dog: 100%; Pig: 100%; Rat: 100%; Horse: 100%; Human: 100%; Mouse: 100%; Bovine: 100%; Rabbit: 100%; Guinea pig: 100% Sequence Human, Mouse Sample Type: 1. Human Cervical Cancer Cell Lysate (15ug) 2. Monkey Fibroblast Cell Lysate (15ug) 3. Human Cervical Cancer Cell transfected with Myc-COG4 (15ug) Primary Dilution: 1:1000 Secondary Antibody: goat anti-Rabbit Image 1 Secondary Dilution: 1:40,000 Image Submitted by: Dr. Jakob Szwedo, Dr. Lupashin's Lab University of Arkansas for Medical Sciences See Customer Feedback tab for detailed information. Human 721_B WB Suggested Anti-COG4 Antibody Titration: 0.2- 1 ug/ml ELISA Titer: 1:312500 Image 2 Positive Control: 721_B cell lysate COG4 is supported by BioGPS gene expression data to be expressed in 721_B __________________________________________________________________________________________________________________________________________________________________ This product is for Research Use Only. Not for diagnostic, human, or veterinary use. Optimal conditions of its use should be determined by end users..
Load more

Recommended publications
  • Analysis of Gene Expression Data for Gene Ontology
    ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.
    [Show full text]
  • Trait Locus Chr Position P-Value Effect Effect SE Ref MA MAF Gene Gene Function FP Rs137787931 14 1880378 4.27E-07
    Table S1. The SNPs which reached significance in single-locus association for fat percentages at suggestive threshold (P < 2.22 × 10-5). Trait Locus Chr Position P-Value Effect Effect SE Ref MA MAF Gene Gene function FP rs137787931 14 1,880,378 4.27E-07 -0.06 0.01 T C 0.42 MROH1 - rs134432442 14 1,736,599 8.69E-07 0.06 0.01 C T 0.49 CPSF1 mRNA polyadenylation FP = fat percentage, Chr = chromosome, Ref = reference allele, MA = minor allele, MAF = minor allele frequency, MROH1 = maestro heat like repeat family member 1, CPSF1 = cleavage and polyadenylation specific factor 1. Table S2. The 1-Mb SNP windows surpass suggestive significance level that is proportion of genetic variance (PVE) at 0.19 % and their window posterior probability of association (WPPA) for percentages of milk fat (FP) and crude protein (CPP), milk urea (MU) and efficiency of crude protein utilization (ECPU). Trait Window Chr Start-end window (Mb) Start SNP End SNP No. of SNP PVE (%) WPPA Gene Gene function FP 1849 18 15-16 15,039,844 15,954,290 21 0.31 0.22 GPT2 Regulation of biosynthesis CPP 323 3 26-27 26,020,004 26,925,312 18 0.36 0.22 TRIM45 Protein ubiquitination 1567 14 62-63 62,081,472 62,960,995 17 0.34 0.12 UBR5 Protein ubiquitination MU 563 5 23-24 23,019,369 23,949,571 19 0.34 0.08 UBE2N Protein ubiquitination 1084 9 75-76 75,026,578 75,935,285 19 0.33 0.14 TNFAIP3 Protein ubiquitination 1677 16 1-2 1,033,239 1,972,109 24 0.32 0.14 ATP2B4, Urinary bladder smooth muscle contraction, REN Kidney development 4 1 3-4 3,079,342 3,987,104 19 0.31 0.18 UBR1 Protein
    [Show full text]
  • Seq2pathway Vignette
    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
    [Show full text]
  • Autism Multiplex Family with 16P11.2P12.2 Microduplication Syndrome in Monozygotic Twins and Distal 16P11.2 Deletion in Their Brother
    European Journal of Human Genetics (2012) 20, 540–546 & 2012 Macmillan Publishers Limited All rights reserved 1018-4813/12 www.nature.com/ejhg ARTICLE Autism multiplex family with 16p11.2p12.2 microduplication syndrome in monozygotic twins and distal 16p11.2 deletion in their brother Anne-Claude Tabet1,2,3,4, Marion Pilorge2,3,4, Richard Delorme5,6,Fre´de´rique Amsellem5,6, Jean-Marc Pinard7, Marion Leboyer6,8,9, Alain Verloes10, Brigitte Benzacken1,11,12 and Catalina Betancur*,2,3,4 The pericentromeric region of chromosome 16p is rich in segmental duplications that predispose to rearrangements through non-allelic homologous recombination. Several recurrent copy number variations have been described recently in chromosome 16p. 16p11.2 rearrangements (29.5–30.1 Mb) are associated with autism, intellectual disability (ID) and other neurodevelopmental disorders. Another recognizable but less common microdeletion syndrome in 16p11.2p12.2 (21.4 to 28.5–30.1 Mb) has been described in six individuals with ID, whereas apparently reciprocal duplications, studied by standard cytogenetic and fluorescence in situ hybridization techniques, have been reported in three patients with autism spectrum disorders. Here, we report a multiplex family with three boys affected with autism, including two monozygotic twins carrying a de novo 16p11.2p12.2 duplication of 8.95 Mb (21.28–30.23 Mb) characterized by single-nucleotide polymorphism array, encompassing both the 16p11.2 and 16p11.2p12.2 regions. The twins exhibited autism, severe ID, and dysmorphic features, including a triangular face, deep-set eyes, large and prominent nasal bridge, and tall, slender build. The eldest brother presented with autism, mild ID, early-onset obesity and normal craniofacial features, and carried a smaller, overlapping 16p11.2 microdeletion of 847 kb (28.40–29.25 Mb), inherited from his apparently healthy father.
    [Show full text]
  • Linkage Analysis in 15 Families, Physical And
    European Journal of Human Genetics (2002) 11, 145 – 154 ª 2002 Nature Publishing Group All rights reserved 1018 – 4813/02 $25.00 www.nature.com/ejhg ARTICLE Familial juvenile hyperuricaemic nephropathy (FJHN): linkage analysis in 15 families, physical and transcriptional characterisation of the FJHN critical region on chromosome 16p11.2 and the analysis of seven candidate genes Blanka Stibu˚ rkova´1, Jacek Majewski2, Katerˇina Hodanˇova´1, Lenka Ondrova´1, Marke´ta Jerˇa´bkova´1, Marie Zika´nova´1, Petr Vylet’al1, Ivan Sˇebesta3, Anthony Marinaki4, Anne Simmonds4, Gert Matthijs5, Jean-Pierre Fryns5, Rosa Torres6, Juan Garcı´a Puig7, Jurg Ott2 and Stanislav Kmoch*,1 1Center for Integrated Genomics, Institute for Inherited Metabolic Disorders, Charles University 1st School of Medicine and General Faculty Hospital Prague, Czech Republic; 2Laboratory of Statistical Genetics, Rockefeller University, New York, NY, USA; 3Department of Clinical Biochemistry, Charles University 1st School of Medicine and General Faculty Hospital Prague, Czech Republic; 4Purine Research Unit, GKT, Guy’s Hospital, London, UK; 5Center for Human Genetics, University of Leuven, Leuven, Belgium; 6Department of Clinical Biochemistry, La Paz Hospital, Madrid, Spain; 7Division of Internal Medicine, La Paz Hospital, Madrid, Spain Familial juvenile hyperuricaemic nephropathy (FJHN) is an autosomal dominant renal disease characterised by juvenile onset of hyperuricaemia, gouty arthritis, and progressive renal failure at an early age. Recent studies in four kindreds showed linkage of a gene for FJHN to the same genomic interval on chromosome 16p11.2, where the gene for the phenotypically similar medullary cystic disease type 2 (MCKD2) has been localised. In this study we performed linkage analysis in additional 15 FJHN families.
    [Show full text]
  • Congenital Disorders of Glycosylation from a Neurological Perspective
    brain sciences Review Congenital Disorders of Glycosylation from a Neurological Perspective Justyna Paprocka 1,* , Aleksandra Jezela-Stanek 2 , Anna Tylki-Szyma´nska 3 and Stephanie Grunewald 4 1 Department of Pediatric Neurology, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland 2 Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland; [email protected] 3 Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, W 04-730 Warsaw, Poland; [email protected] 4 NIHR Biomedical Research Center (BRC), Metabolic Unit, Great Ormond Street Hospital and Institute of Child Health, University College London, London SE1 9RT, UK; [email protected] * Correspondence: [email protected]; Tel.: +48-606-415-888 Abstract: Most plasma proteins, cell membrane proteins and other proteins are glycoproteins with sugar chains attached to the polypeptide-glycans. Glycosylation is the main element of the post- translational transformation of most human proteins. Since glycosylation processes are necessary for many different biological processes, patients present a diverse spectrum of phenotypes and severity of symptoms. The most frequently observed neurological symptoms in congenital disorders of glycosylation (CDG) are: epilepsy, intellectual disability, myopathies, neuropathies and stroke-like episodes. Epilepsy is seen in many CDG subtypes and particularly present in the case of mutations
    [Show full text]
  • Chromosomal Rearrangements Are Commonly Post-Transcriptionally Attenuated in Cancer
    bioRxiv preprint doi: https://doi.org/10.1101/093369; this version posted February 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Chromosomal rearrangements are commonly post-transcriptionally attenuated in cancer 1 3 1 3, 4, 5 Emanuel Gonçalves ,​ Athanassios Fragoulis ,​ Luz Garcia-Alonso ,​ Thorsten Cramer ,​ ​ ​ ​ ​ 1,2# 1# Julio Saez-Rodriguez ,​ Pedro Beltrao ​ ​ 1 ​ European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK 2 ​ RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen 52057, Germany 3 ​ Molecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany 4 ​ NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands 5 ​ ESCAM – European Surgery Center Aachen Maastricht, Germany and The Netherlands # ​ co-last authors: [email protected]; [email protected] ​ ​ ​ Running title: Chromosomal rearrangement attenuation in cancer Keywords: Cancer; Gene dosage; Proteomics; Copy-number variation; Protein complexes 1 bioRxiv preprint doi: https://doi.org/10.1101/093369; this version posted February 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Chromosomal rearrangements, despite being detrimental, are ubiquitous in cancer and often act as driver events.
    [Show full text]
  • Prevalence and Incidence of Rare Diseases: Bibliographic Data
    Number 1 | January 2019 Prevalence and incidence of rare diseases: Bibliographic data Prevalence, incidence or number of published cases listed by diseases (in alphabetical order) www.orpha.net www.orphadata.org If a range of national data is available, the average is Methodology calculated to estimate the worldwide or European prevalence or incidence. When a range of data sources is available, the most Orphanet carries out a systematic survey of literature in recent data source that meets a certain number of quality order to estimate the prevalence and incidence of rare criteria is favoured (registries, meta-analyses, diseases. This study aims to collect new data regarding population-based studies, large cohorts studies). point prevalence, birth prevalence and incidence, and to update already published data according to new For congenital diseases, the prevalence is estimated, so scientific studies or other available data. that: Prevalence = birth prevalence x (patient life This data is presented in the following reports published expectancy/general population life expectancy). biannually: When only incidence data is documented, the prevalence is estimated when possible, so that : • Prevalence, incidence or number of published cases listed by diseases (in alphabetical order); Prevalence = incidence x disease mean duration. • Diseases listed by decreasing prevalence, incidence When neither prevalence nor incidence data is available, or number of published cases; which is the case for very rare diseases, the number of cases or families documented in the medical literature is Data collection provided. A number of different sources are used : Limitations of the study • Registries (RARECARE, EUROCAT, etc) ; The prevalence and incidence data presented in this report are only estimations and cannot be considered to • National/international health institutes and agencies be absolutely correct.
    [Show full text]
  • Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
    Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement.
    [Show full text]
  • MECHANISMS in ENDOCRINOLOGY: Novel Genetic Causes of Short Stature
    J M Wit and others Genetics of short stature 174:4 R145–R173 Review MECHANISMS IN ENDOCRINOLOGY Novel genetic causes of short stature 1 1 2 2 Jan M Wit , Wilma Oostdijk , Monique Losekoot , Hermine A van Duyvenvoorde , Correspondence Claudia A L Ruivenkamp2 and Sarina G Kant2 should be addressed to J M Wit Departments of 1Paediatrics and 2Clinical Genetics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, Email The Netherlands [email protected] Abstract The fast technological development, particularly single nucleotide polymorphism array, array-comparative genomic hybridization, and whole exome sequencing, has led to the discovery of many novel genetic causes of growth failure. In this review we discuss a selection of these, according to a diagnostic classification centred on the epiphyseal growth plate. We successively discuss disorders in hormone signalling, paracrine factors, matrix molecules, intracellular pathways, and fundamental cellular processes, followed by chromosomal aberrations including copy number variants (CNVs) and imprinting disorders associated with short stature. Many novel causes of GH deficiency (GHD) as part of combined pituitary hormone deficiency have been uncovered. The most frequent genetic causes of isolated GHD are GH1 and GHRHR defects, but several novel causes have recently been found, such as GHSR, RNPC3, and IFT172 mutations. Besides well-defined causes of GH insensitivity (GHR, STAT5B, IGFALS, IGF1 defects), disorders of NFkB signalling, STAT3 and IGF2 have recently been discovered. Heterozygous IGF1R defects are a relatively frequent cause of prenatal and postnatal growth retardation. TRHA mutations cause a syndromic form of short stature with elevated T3/T4 ratio. Disorders of signalling of various paracrine factors (FGFs, BMPs, WNTs, PTHrP/IHH, and CNP/NPR2) or genetic defects affecting cartilage extracellular matrix usually cause disproportionate short stature.
    [Show full text]
  • A Compendium of Co-Regulated Protein Complexes in Breast Cancer Reveals Collateral Loss Events
    bioRxiv preprint doi: https://doi.org/10.1101/155333; this version posted June 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. A compendium of co-regulated protein complexes in breast cancer reveals collateral loss events Colm J. Ryan*1, Susan Kennedy1, Ilirjana Bajrami2, David Matallanas1, Christopher J. Lord2 1Systems Biology Ireland, School of Medicine, University College Dublin, Dublin 4, Ireland 2The Breast Cancer Now Toby Robins Breast Cancer Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London, SW3 6JB, United Kingdom. * Correspondence: [email protected] Summary Protein complexes are responsible for the bulk of activities within the cell, but how their behavior and composition varies across tumors remains poorly understood. By combining proteomic profiles of breast tumors with a large-scale protein-protein interaction network, we have identified a set of 258 high-confidence protein complexes whose subunits have highly correlated protein abundance across tumor samples. We used this set to identify complexes that are reproducibly under- or over- expressed in specific breast cancer subtypes. We found that mutation or deletion of one subunit of a complex was often associated with a collateral reduction in protein expression of additional complex members. This collateral loss phenomenon was evident from proteomic, but not transcriptomic, profiles suggesting post- transcriptional control. Mutation of the tumor suppressor E-cadherin (CDH1) was associated with a collateral loss of members of the adherens junction complex, an effect we validated using an engineered model of E-cadherin loss.
    [Show full text]
  • Supplementary Materials
    Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine
    [Show full text]