China
Africa
Explore chapters and articles related to this topic
Computational Drug Discovery and Development Along With Their Applications in the Treatment of Women-Associated Cancers
Published in Shazia Rashid, Ankur Saxena, Sabia Rashid, Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
Rahul Kumar, Rakesh Kumar, Harsh Goel, Somorjit Singh Ningombam, Pranay Tanwar
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.
An Analysis of Protein Interaction and Its Methods, Metabolite Pathway and Drug Discovery
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, 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.
Molecular Mechanisms Controlling Immunoglobulin E Responses
Published in Thomas F. Kresina, Immune Modulating Agents, 2020
Rachel L. Miller, Paul B. Rothman
It is now recognized that other cytokines share this general model of signal transduction, and that at least seven members of the ST AT family exist (Stats 1, 2, 3, 4, 5a, 5b, and 6). These proteins share significant homology over several functional domains. The greatest homology is observed within the regions of the SH2 and SH3 motifs and the deoxyribonucleic acid (DNA) binding domain (reviewed in Ref. 60) (Figure 2). Nonetheless, different cytokines/ligands are able to activate different STATs with great specificity. Specificity of signaling is achieved at the level of the cytokine receptor/STAT interaction and not regulated by the particular JAKs involved. This specificity results from the interaction between the SH2 domain of the STAT and motifs on the receptor chains that, after activation by ligand, contain phosphorylated tyrosine residues [61]. This interaction allows the recruitment of different STATs into different receptor complexes.
The discovery and development of transmembrane serine protease 2 (TMPRSS2) inhibitors as candidate drugs for the treatment of COVID-19
Published in Expert Opinion on Drug Discovery, 2022
Christiana Mantzourani, Sofia Vasilakaki, Velisaria-Eleni Gerogianni, George Kokotos
In addition to the efforts for repurposing camostat and nafamostat, the design, identification, and development of novel TMPRSS2 inhibitors is of paramount importance. Employment of high-throughput screening approaches may orient our studies, and already a few chemical entities have been identified as novel TMPRSS2 inhibitors by adopting such approaches. A major drawback for the rational design of TMPRSS2 inhibitors is the lack of the crystal structure of TMPRSS2. To overcome this lack, homology models have been already developed, and indeed have helped in identifying novel chemical motifs able to inhibit TMPRSS2. Computational approaches play a predominant role in the identification of new agents inhibiting TMPRSS2, providing thus chemical motifs for the further development of drug candidates against COVID-19. Molecular docking and molecular dynamics simulations may lead to pharmacophore-based virtual screening. Most recently, Hu et al. successfully demonstrated such a strategy [41]. In any case, the determination of the crystal structure of TMPRSS2 may greatly facilitate the rational design of novel TMPRRS2 inhibitors. We encourage researchers to develop homology models and to make them available to the public in order to facilitate drug development.
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.
Common therapeutic advances for Duchenne muscular dystrophy (DMD)
Published in International Journal of Neuroscience, 2021
Arash Salmaninejad, Yousef Jafari Abarghan, Saeed Bozorg Qomi, Hadi Bayat, Meysam Yousefi, Sara Azhdari, Samaneh Talebi, Majid Mojarrad
Utrophin (also known dystrophin-related protein (DRP)) is a protein with 3433 amino acids and its molecular weight is about 395 kDa. Utrophin is produced specifically during embryogenesis. In the absence of dystrophin, utrophin expresses in large amount. Both proteins have very similar structure with approximately 80% homology. Dystrophin and utrophin both contain a rod domain in their structure; however, the sequence of this domain exhibits significant difference between these molecules (Figure 1d). Unlike dystrophin which is expressed throughout the sarcolemma of the muscle fibers, utrophin is only expressed and localized into neuromuscular and myotendinous junctions in muscles [80]. N terminal domain in both dystrophin and utrophin binds to the F-actin in the cytoskeleton and a rod domain which containing spectrin-like repeats [81]. Studies have been shown that cysteine-rich and C terminal domain in utrophin similar to dystrophin bind to a complex of protein, glycoprotein (which is called DAPC) and dystrophin-associated glycoprotein complex [82] and as a result, it can attach to the sarcolemma [83,84].