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An Efficient Protein Structure Prediction Using Genetic Algorithm
Published in Abdel-Badeeh M. Salem, Innovative Smart Healthcare and Bio-Medical Systems, 2020
Mohamad Yousef, Tamer Abdelkader, Khaled El-Bahnasy
It is a web-based server for protein homology detection and structure prediction. Input to HHpred can be amino acid sequence or a multiple sequence alignment. It uses a novel approach that conducts a pair-wise alignments of profile hidden Markov models (HMMs). It also uses a variety of databases like SCOP (Structural Classification of Proteins), Pfam, and PDB (Protein Data Bank). HHpred performs fast and well for single domain and for multi-domain query sequences and can be used to predict functional information of a protein from homolog proteins using various sequence-based search tools like BLAST, FASTA, or PSI-BLAST [11, 12, 13].
Allergen nomenclature
Published in Richard F. Lockey, Dennis K. Ledford, Allergens and Allergen Immunotherapy, 2020
Heimo Breiteneder, Rick Goodman, Martin D. Chapman, Anna Pomés
In the allergy literature, it is preferable to use the systematic allergen nomenclature. In other contexts, such as comparisons of biochemical activities or protein structure, it may be appropriate or more useful to use the biochemical names. A selected list of the allergen nomenclature designations and biochemical names of inhalant, food, and venom allergens is shown in Table 4.2. There are 92 three-dimensional allergen structures in the Structural Database of Allergenic Proteins (http://fermi.utmb.edu/SDAP/). Publications in 2014 and 2015 list more than 250 x-ray crystal or nuclear magnetic resonance structures belonging to more than 105 allergenic proteins [60,61]. The Database of Allergen Families, AllFam (http://www.meduniwien.ac.at/allfam/), shows that allergens are found in 216 domains of the currently defined 16,306 protein families in the Pfam protein families database (https://pfam.xfam.org/). Thus, allergens are represented by a 1.3% of the Pfam domains, yet they represent a fair degree of diversity at both the structural and biological levels. Such diversity is likely to preclude the existence of a few common structural features, e.g., amino acid sequence motifs or protein structures, which predispose proteins to act as allergens [27,30].
Biological data: The use of -omics in outcome models
Published in Issam El Naqa, A Guide to Outcome Modeling in Radiotherapy and Oncology, 2018
Issam El Naqa, Sarah L. Kerns, James Coates, Yi Luo, Corey Speers, Randall K. Ten Haken, Catharine M.L. West, Barry S. Rosenstein
There are no dedicated web resources for outcome modeling studies in oncology per se. Nevertheless, oncology biological markers studies can still benefit from existing bioinformatics resources for pharmacogenomic studies that contain databases and tools for genomic, proteomic, and functional analysis as reviewed by Yan [250]. For example, the National Center for Biotechnology Information (NCBI) site hosts databases such as GenBank, dbSNP, Online Mendelian Inheritance in Man (OMIM), and genetic search tools such as BLAST. In addition, the Protein Data Bank (PDB) and the program CPHmodels are useful for protein structure three-dimensional modeling. The Human Genome Variation Database (HGVbase) contains information on physical and functional relationships between sequence variations and neighboring genes. Pattern analysis using PROSITE and Pfam databases can help correlate sequence structures to functional motifs such as phosphorylation [250]. Biological pathways construction and analysis is an emerging field in computational biology that aims to bridge the gap between biomarkers findings in clinical studies with underlying biological processes. Several public databases and tools are being established for annotating and storing known pathways such as KEGG and Reactome projects or commercial ones such as the IPA or MetaCore [251]. Statistical tools are used to properly map data from gene/protein differential experiments into the different pathways such as mixed effect models [252] or enrichment analysis [253].
Presence of a neprilysin on Avicularia juruensis (Mygalomorphae: Theraphosidae) venom
Published in Toxin Reviews, 2022
Soraia Maria do Nascimento, Ursula Castro de Oliveira, Milton Yutaka Nishiyama-Jr, Alexandre Keiji Tashima, Pedro Ismael da Silva Junior
For the identification of potential coding sequences within the assembled transcriptome we used TransDecoder software, version 2.0.1 (https://github.com/TransDecoder/TransDecoder/wiki/), with a minimum protein length of 60. The transcripts containing the translated sequences were aligned by Basic Local Alignment Search Tool (BLASTp) (Altschul et al. 1990) against the database UniProt/Swiss-Prot proteins and non-redundant transcriptome shotgun assembly (TSA-NR) from National Center for Biotechnology Information (NCBI) to assess the protein description with a cutoff e-value of <1e-05 and according to the criterion with longer protein similarity. The predicted proteins were analyzed for Protein families (Pfam) domains with hmmer3 (Wheeler and Eddy 2013), against the Pfam domains database (El-Gebali et al. 2019). TransDecoder may predict more than one coding sequence by transcript, and we selected only the best one based on the priority order of UniProt-KB/Swiss-Prot, Pfam domains database and TSA-NR, all of which databases were used for annotation and selection of the best candidate for each transcript.
Microbial enterotypes beyond genus level: Bacteroides species as a predictive biomarker for weight change upon controlled intervention with arabinoxylan oligosaccharides in overweight subjects
Published in Gut Microbes, 2020
Lars Christensen, Claudia V. Sørensen, Frederikke U. Wøhlk, Louise Kjølbæk, Arne Astrup, Yolanda Sanz, Mads F. Hjorth, Alfonso Benítez-Páez
The functional assessment on the entire genomes using the Pfam annotation system permitted to assess the abundance of more than 3000 Pfam domains. The statistical test to determine the probable enrichment of such protein domains on B. cellulosilyticus genomes recovered 87 domain associations, and 54 of which had reliable functional annotations (Table 4). This analysis corroborated some previous observations during the CAZy gene survey. Thus, several domains associated with different GH and PL enzymes listed in Table 3, and related to xylan and glycosaminoglycan degradation were also retrieved (e.g., GH10, GH79, GH43, PL8) (Table 4). Moreover, we also observed that other domains linked to xylan binding and degradation were enriched in B. cellulosilyticus genomes (e.g., Glyco_hydro_30, CBM_6, Glyco_hydro_3, Bac_rhamnosid). Nevertheless, the glycan metabolism domains enriched in B. cellulosilyticus in comparison with other Bacteroides species is also accompanied by a higher abundance of polysaccharide degradation functions as well as of sensor and kinase subunits of several two-component systems specialized on carbohydrate uptake. Moreover, we detected an enrichment of some peptidase domains (Peptidase C25, Peptidase_M6 and Peptidase_C39), and domains of secreted proteins involved in adhesion (VCBS, fn3, Fn3-like), and flagella- and pili-independent gliding motility (SprA_N and PorP_SprF).
Functional genetic evaluation of DNA house-cleaning enzymes in the malaria parasite: dUTPase and Ap4AH are essential in Plasmodium berghei but ITPase and NDH are dispensable
Published in Expert Opinion on Therapeutic Targets, 2019
Hirdesh Kumar, Jessica Kehrer, Mirko Singer, Miriam Reinig, Jorge M. Santos, Gunnar R. Mair, Friedrich Frischknecht
Nucleotide sanitation enzymes are key to cell survival and promising drug targets. In order to identify such proteins in the malaria model, Plasmodium berghei we performed basic local alignment search tool (BLAST) searches using human DNA sanitation enzymes as query sequences. Our approach returned single homologs for ITPase and dUTPase, as well as two NuDiX domain-containing proteins with similarities to NUDT1/MTH1 and NUDT2/AP4AH (Supplementary figure 1–4); the latter two are annotated as nucleoside diphosphate hydrolase (NDH from here on) and bis(5ʹ-nucleosyl)-tetraphosphatase [asymmetrical] (Ap4AH from here on). Each protein contains a well-supported Pfam protein domain (Table 1 and Figure 2(a)) [44]. A multiple sequence alignment of the orthologs from selected Plasmodium species revealed a high degree of conservation across experimental rodent (P. berghei and P. yoelii) and human malaria parasites (P. falciparum and P. vivax) (Supplementary Figures 5–8). Among the four enzymes, dUTPase is the most conserved with a minimum amino acid sequence identity of 81% between P. vivax and P. berghei dUTPase. P. vivax dUTPase contains an additional loop region, which is absent in other Plasmodium species, and therefore is thus least similar to other Plasmodium dUTPases as well. The other enzymes are slightly less conserved with the lowest sequence identity of 59% occurring between PfITPase and PbITPase (Figure 2(b,c)).