An Analysis of Protein Interaction and Its Methods, Metabolite Pathway and Drug Discovery
Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood in Computational Intelligence and Data Sciences, 2022
Protein sequence alignment is used to know the importance of the homology detection, to predict the various features of a protein and to know the homologous structure. This alignment helps to predict the difference between the structure and the template of the sequence. In sequence alignment, BLAST and FASTA are the basic operations. The operations required to be performed level-wise are sequence identification, searching data in database, detection of homology, alignment of the sequences and updation of the structural information. Figure 13.4 shows the categorization of the sequence alignment. The recent versions of the instrument experiments with the help of NMR and X-ray crystallography are used to store the information of the isolation. These data are input to various algorithms to align the sequences effectively. Three types of alignments are available: single, pairwise and multiple sequence alignments.
Computational Drug Discovery and Development Along With Their Applications in the Treatment of Women-Associated Cancers
Shazia Rashid, Ankur Saxena, Sabia Rashid in Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
In SBDD, the target acts as a prerequisite material and relies on the 3D structure as the drug is binding to 3D surface of macromolecules. Usually, 3D structures of macromolecules are elucidated by various experimental approaches such as NMR or x-ray crystallography and resolved structures are deposited in PDB database [17–18]. If the 3D structure of target protein is not available, then it can be determined by using computational methods such as homology (or comparative), threading (or fold recognition) and ab initio (de novo) modelling. Several computational tools are available for 3D structure prediction (Table 5.1). Homology modelling depends upon the sequence homologs with known structure of protein which is used as a template for generating 3D structure of target protein [19–20]. If the homologs have a low sequence identity (<25–30), then the model is constructed by using a threading method which relies on the secondary structures of proteins [21]. Another method is ab initio, used to predict the structure of target protein if no template is available [22]. Once the model is anticipated, stereochemical and geometrical properties are assessed to optimize the quality of the 3D structure.
Homology of Nonrepeated DNA Sequences in Phylogeny of Fungal Species
S. K. Dutta in DNA Systematics, 2019
Homology, that is structural, physiological, or behavioral similarity, has been used as a criterion in establishing phylogenetic relationships between organisms. Phenotypic homology does not require genotypic homology because some characteristics arise through convergence. Convergence occurs when similar characteristics are acquired independently in two or more phylogenetic lines. The evolutionary relationships can be traced by measuring either the shared characters or divergence. To study the phylogenetic relationships, the ideal conditions would be (1) to compare the structure of the genes themselves, since they carry the evidence of change at the basic information level and (2) to examine their transcriptional and translational products.
Tracing protein and proteome history with chronologies and networks: folding recapitulates evolution
Published in Expert Review of Proteomics, 2021
Gustavo Caetano-Anollés, M. Fayez Aziz, Fizza Mughal, Derek Caetano-Anollés
The cornerstone of these domain hierarchies is common ancestry, i.e. the existence of shared-and-derived features in domain sequence, structure and function. However, the classification of domains does not require structural or functional information nor stringent phylogenetic tests of homology, especially because common ancestry is stronger at lower levels of the classification hierarchy. Most databases benefit from machine learning tools of sequence comparison, including probabilistic hidden Markov model (HMM) methods such as HMMER [78] and HHsearch [79], which conduct HMM-sequence and HMM-HMM alignments, respectively. For example, the Pfam database [80] identifies conserved domain sequences via sequence alignment, which are then used to build HMMs of linear sequence analysis restricting the focus to the sequence level. Conversely, SCOP uses HMMs of structural recognition to recurrently enrich the database [81] in a framework that increases alignment-quality and stability of family and superfamily relationships. Finally, DALI provides structural alignments as either 3-dimensional (3D) or 2-dimensional comparisons by explicitly rotating and translating one domain structure over another or by mapping 3D structure into a matrix of intramolecular distances, respectively [76]. Since structure is far more conserved than sequence, structural similarities can therefore dissect deeper homology relationships than sequences, especially when these are established between domain regions of different sizes.
Molecular radiobiology and the origins of the base excision repair pathway: an historical perspective
Published in International Journal of Radiation Biology, 2023
Susan S. Wallace
In eukaryotes, two Nth homologs were identified in the yeast Saccharomyces cerevisiae (Eide et al. 1996; Augeri et al. 1997). Nth homologues were also characterized from Schizosaccharomyces pombe (Roldan-Arjona et al. 1996; Karahalil et al. 1998). Both bovine (Hilbert et al. 1996) and human (Aspinwall et al. 1997; Hilbert et al. 1997) Nth homologs were also cloned and characterized. The eukaryotic functional homolog of Fpg, Ogg1, recognizes oxidized purines and is in the same structural family as Nth (Bruner et al. 2000). Ogg1 was first isolated from S. cerevisiae (Nash et al. 1996; van der Kemp et al. 1996) and no fewer than seven groups cloned the human counterpart, hOGG1 (Aburatani et al. 1997; Arai et al. 1997; Bjørås et al. 1997; Lu et al. 1997; Radicella et al. 1997; Roldán-Arjona et al. 1997; Rosenquist et al. 1997). OGG enzymes excise 8-oxoG, 8-oxoA, formamidopyrimidines and urea (Bjelland and Seeberg 2003; Dizdaroglu 2003).
Topographical data analysis to identify high-density clusters in stroke patients undergoing post-acute rehabilitation
Published in Topics in Stroke Rehabilitation, 2021
Eliezer Bose, Lisa J. Wood, Qing Mei Wang
We constructed a “continuous” shape on top of the point cloud to identify the underlying topology using a kernel density estimator. Persistent homology is an algebraic method for discerning topological features of data. To address this issue, TDA uses the idea of simplices. Each data point within the point cloud is a zero-dimensional simplex, an edge obtained by joining two points is a one-dimensional simplex, a triangular face is a two-dimensional simplex, and so on for higher-dimensional simplices. TDA algorithms obtain a simplical complex by joining many simplicies together so that the intersection between any two simplicies is also a simplex. Homology refers to the counting of the connected components of a simplical complex. We used TDA algorithms to compute then a way to visualize these features and encode them in a persistence diagram, in the form of bars, each corresponding to the birth and death (time) of a homological feature.
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