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Integrating CADD and Herbal Informatics Approach to Explore Potential Drug Candidates Against HPV E6 Associated With Cervical Cancer
Published in Shazia Rashid, Ankur Saxena, Sabia Rashid, Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
Arushi Verma, Jyoti Bala, Navkiran Kaur, Anupama Avasthi
Target structure analysis and preparation: Family and domain studies were performed using Uniprot. Protparam [7] to study physical and chemical composition of the protein and secondary structure analysis was done using SOPMA [8]. PSIPRED [9] was used to predict 2D protein structure and nature of individual amino acid. JPred [10] server was used to obtain secondary structure wiring diagram, to give us a realistic idea on 2D structure. Tertiary structure of the protein was obtained from RCSB PDB database (ID: 4GIZ) [11]. The structure originally obtained was edited in UCSF Chimera interface [12]. Pathway analysis of HPV 16 in human body and its interaction was studied using STRING Viruses database [13]. HPV infection analysis from KEGG database [14] informed us about pathways where E6 protein was involved.
Mammalian allergens
Published in Richard F. Lockey, Dennis K. Ledford, Allergens and Allergen Immunotherapy, 2020
Tuomas Virtanen, Marja Rytkönen-Nissinen
The sequential length of lipocalins is 160–230 amino acids, the average predicted molecular mass being about 20 kDa (without posttranslational modifications) [18]. The overall amino acid identity between lipocalins is low, at the level of 20%–30%. In some cases, the sequential identity among animal species can be much higher (Table 16.1). For example, dog Can f 1 exhibits a 61% identity with human lipocalin-1 (von Ebner gland protein/tear lipocalin), and human epididymal-specific lipocalin-9 exhibits identities about 55% with several nonprimate mammalian proteins (analyzed in UniProt Knowledgebase (UniProtKB), a hub for information on proteins, with the Basic Local Alignment Search Tool (BLAST; https://www.uniprot.org/blast/).
Nomenclature of Food Allergens
Published in Andreas L. Lopata, Food Allergy, 2017
Usually the first three letters of the genus and the first letter of the species are used. In cases of ambiguity, a fourth and second letter, respectively, may be added. For example, in the genus Prunus (stone fruits), allergens from apricot (Prunus armeniaca) and cherry (P. avium) are designated Pru ar and Pru av, respectively. The allergen nomenclature database follows the taxonomic system used in the NCBI (https://www.ncbi.nlm.nih.gov/taxonomy) and UniProt (https://www.uniprot.org/taxonomy/) sequence databases.
Ubiquitin-specific protease 47 is associated with vascular calcification in chronic kidney disease by regulating osteogenic transdifferentiation of vascular smooth muscle cells
Published in Renal Failure, 2022
Qiong Xiao, Yun Tang, Juhua Xia, Haojun Luo, Meidie Yu, Sipei Chen, Wei Wang, Lei Pu, Li Wang, Guisen Li, Yi Li
After each LC–MS/MS test, the raw MS files were processed using MaxQuant (version 1.5.6.0). The protein sequence database was searched using UNIPROT software. This database and its reverse decoy were then searched using MaxQuant software. The analysis was based on the LFQ intensities and standard deviation of this value for all experimental groups. Trypsin was used as a specific enzyme and three missed cleavages were allowed. Carbamidomethyl was used as a fixed modification. Oxidation M and acetyl protein N-terms were considered variable modifications. Both peptide and protein FDR should be less than 1%. Only unmodified and unique peptides were used for quantification. All other parameters were reserved as defaults. The missing values were calculated to replace random numbers selected from a normal distribution using Perseus software (Version 1.4.1.3, Germany). Protein groups with non-missing values less than those of the replicates were discarded. Proteins were defined as possessing significant differences by comparing the mean LFQ intensities with a fold change minimum of ±2 (p < 0.05) at the protein level.
An overview of technologies for MS-based proteomics-centric multi-omics
Published in Expert Review of Proteomics, 2022
Andrew T. Rajczewski, Pratik D. Jagtap, Timothy J. Griffin
When integrating proteomics with transcriptomic, genomic, metabolomic, or other data, there are several challenges that must be considered and addressed. Annotation of corresponding genes and their protein products is one such challenge; for example, unsynchronized annotations of proteomic and transcriptomic data make comparisons between coding regions and their expressed protein products difficult [64]. As a solution, the UniProt database [65] provides a well-curated repository of characterized proteins from diverse organisms. Entries contain annotations for proteins including unique UniProt identifiers cross-referenced with coding gene names, and other identifiers (e.g. RefSeq, Ensembl IDs, etc.) useful for matching proteins to corresponding genomic or transcriptomic sequences. In addition, computational tools, such as biomaRt, can be used to automatically map protein sequences to common genome or transcriptome sequence coordinates [66].
Mass Spectrometry-based Biomarkers for Knee Osteoarthritis: A Systematic Review
Published in Expert Review of Proteomics, 2021
Mirella J.J. Haartmans, Kaj S. Emanuel, Gabrielle J.M. Tuijthof, Ron M. A. Heeren, Pieter J. Emans, Berta Cillero-Pastor
LC-MS, combined with dedicated bioinformatics strategies, is highly specific and well developed over the past years and has become one of the widely used techniques in MS. LC-MS analysis of tissues and fluids give us a detailed overview on the molecular/protein profiles in OA patients. In addition, most LC-MS analyses focus on the detection and identification of proteins in particularly fluids. More specific for synovial fluid; available from different OA patients (TKA surgery) or healthy phenotypes can be purchased. Other tissue types are less available and need multiple processing steps before LC-MS analysis. Different analytical results might be due to differences in sample preparation or type of database used. Protein databases such as UniProt/SwissProt include a high variety of proteins. Still, there can be differences between databases and protein content, resulting in more or less identifications dependent on the database used.