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Structure and function of Human CYP2D6
Published in Shufeng Zhou, Cytochrome P450 2D6, 2018
This consensus sequence shows that the residue corresponding to Arg441 is not always occupied by a positively charged amino acid, but different residues including His, Thr, and Pro are also seen at this position. Among these, the Arg433Trp variant of CYP2C19 (*5 allele) (Ibeanu et al. 1998) and the Arg448His variant of CYP11B1 (Curnow et al. 1993) completely abolished S-mephenytoin and cortisol 11β-hydroxylase activities in vivo, respectively. The Arg448His mutation of CYP11B1 is associated with congenital adrenal hyperplasia and results in a hypertensive form of the disease (Curnow et al. 1993). Another natural variant of human CYP1A2*8 (Arg456His) expresses normal amount of protein but is functionally inactive as a result of missing heme incorporation (Saito et al. 2005). Consistent with the fact that Cys is not part of the CYP19 family or PROSITE pattern, the CYP19 variant Arg435Cys had only residual enzyme activity (1.1% of wild type) (Ito et al. 1993). These findings suggest that Arg441 appears to be essential for heme binding and enzymatic function of CYP2D6 and that the role of this residue in other CYPs appears to be dependent on the sequence context and may also include disturbance of the interaction with cytochrome P450 reductase.
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].
Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design
Published in Pathogens and Global Health, 2023
Chin Peng Lim, Boon Hui Kok, Hui Ting Lim, Candy Chuah, Badarulhisam Abdul Rahman, Abu Bakar Abdul Majeed, Michelle Wykes, Chiuan Herng Leow, Chiuan Yee Leow
GenBank serves as a public database of genetic sequences, focusing on the expansion and dissemination of information. The repository relies on the submissions of sequence data from authors and whole-genome shotgun (WGS) as well as high-throughput data from sequencing centres and issued patents from The U.S. Patent and Trademark Office. GenBank is a partner of the International Nucleotide Sequence Database Collaboration (INSDC) along with European Nucleotide Archive (ENA) and Data Bank of Japan (DDBJ) in which data exchange is done on a daily basis so that a systematic collection of sequence information is accessible worldwide. GenBank also collects and stores amino acid sequences from databases like SWISS-PROT, Protein Research Foundation (PRF) and Protein Data Bank (PDB) [94]. GISAID has gained its reputation as a trustworthy means for international sharing of all influenza virus data including genetic sequence and metadata [95]. In response to the COVID-19 pandemic, related data have also been shared via this public domain recently. InterPro is a unified resource resulting from the integration of protein signature databases including PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The major application is annotation and functional classification of uncharacterized sequences. Based on sequence positions and protein coverage, protein signatures that fall into the same family or functional domain are grouped into single entry with respective annotation and cross-references [96–98].
Neuroprotective ability of TMV coat protein on rat PC-12 cells and it’s in silico study with LRRK2 receptor
Published in Neurological Research, 2018
Yash Sharma, Nidhi Srivastava, Kumud Bala
For docking studies, physicochemical and functional characterization, secondary structure prediction, and model building were studied by standardized method. For physicochemical characterization, protein sequence of TMV coat protein was retrieved from the NCBI Data base. Expasy’s ProtParam server (http://web.expasy.org/protparam/) was used for the analysis of theoretical isoelectric point (pI), molecular weight, total number of positive and negative residues, extinction coefficient [27], instability index [28], aliphatic index [29], and grand average hydropathy (GRAVY) [30,31]. Functional characterization of TMV coat protein was observed by SOSUI server to identify the transmembrane region and disulfide bonds. For the determination of functional linkages, primary structure was studied with the help of tool known as CYS_REC (http://www.softberry.com/cgi-bin/programs/propt/cys_rec.pl) that identify position of cysteins and patterns. Prosite was also used to determine the protein families and domains [32]. To calculate the secondary structural features of the TMV coat protein sequences, SOPMA server was used [33].
Identification of amyloid antibodies for Alzheimer disease – immunotherapy
Published in Archives of Physiology and Biochemistry, 2022
Etimad A. Huwait, Ibtisam M. Baghallab, Charles G. Glabe, Khalid O. Abulnaja, Taha A. Kumosani, Said S. Moselhy
Pratt programme finds sequence patterns within a set of unaligned protein sequences. These patterns match a minimum number of the unaligned sequences which is specified by the user. Pratt allowed for greater ambiguity among partially conserved position of the pattern, also it allowed for limited spacing between variable elements of the pattern. The programme adopts PROSITE notation (Jonassen et al. 2000) to describe its patterns result. PROSITE online website contains around 1000 entries containing a pattern that is diagnostic of a certain family of proteins. An explanation of this notation along with an example is shown in Figure 3 (Karch et al. 2014). The Pratt2.1 programme was modified from the original Pratt programme (Karch and Goate 2015), and it proposed an alternative approach for defining the search for patterns by introducing the concept of a pattern graph which is used to define the set of patterns needed for exploring. The algorithm used in this programme contains pruning strategies which can be optionally employed to avoid exploring a large number of generalisations of the conserved patterns thus economising search time at the expense of exhaustive searching of all patterns. The programme algorithm focuses the search to find the highest scoring conserved patterns that correspond to the least generalised patterns (Kastanenka et al. 2016). Pattern search in Pratt is done in two phases, phase one programme exhaustively searches for patterns as defined by user restrictions and then phase two refines the most significant patterns found in phase one (Kayed et al. 2003). The Pratt 2.1 programme algorithm works by inputting the set of unaligned sequences in either FASTA or Swiss-Prot format and setting the minimum number of sequences to match to a pattern. Then, the programme constructs a pattern graph and starts searching for the highest scoring patterns that matches the minimum number of sequences and pattern constraints chosen by the user. These patterns are the most specialised and least generalised. The programme also constructs block data structure at the same time which searches for all sequences matching each pattern. Then, the highest scoring patterns are subjected to heuristic pattern-refinement phase that clarify the ambiguous components of the pattern from previous phase. However, this refinement phase is optional and could be switched off by the user if not needed. Finally, the most significant patterns which resulted from this search will be output to a text file to the name of the input file (Kayed et al. 2007).