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Proteins and proteomics
Published in Firdos Alam Khan, Biotechnology Fundamentals, 2018
Knowing a protein’s 3D structure helps us to understand its functionality and provides means for planning experiments and drug design. Experimental methods given by x-ray crystallography and nuclear magnetic resonance spectroscopy to determine protein structure are essential. The Brookhaven Protein Data Bank (PDB) is the repository for those structures. Files including atom coordinates, which are suited for visualization by graphical molecule viewers such as rasmol, can be obtained at this site. PDB is also searchable with a sequence as a query, for example, with the BLAST service located at NCBI with a polypeptide as a query. Nevertheless, experimental methods are technically very difficult and expensive, and the gap in the number between sequenced proteins and known structures increases. Thus, model building of proteins is of great importance. When a protein first is unfolded in vitro and then released again, it folds back to the same 3D structure it had before. Thus, various prediction methods are based on the assumption that the 3D protein structure is determined by its primary structure. Structure prediction methods are coarsely divided into three categories as described in the following section.
Quantification and Elucidation of the Overall Interaction between Nanoparticles
Published in Victor M. Starov, Nanoscience, 2010
W. Richard Bowen, Paul M. Williams
The number of charge-generating groups for the lactoferrin molecule may be determined from the amino acid sequence of the molecule [34] (see Table 4.3). It is well known that most ionizable groups in water-soluble proteins are on the surface of the protein exposed to the solvent [19]. Unlike other proteins, however, a number of ionizable amino acids in lactoferrin lie just inside the protein surface out of contact with the solvent [35], so they are not available for generating a surface charge. The main question now is: how many of each type of the ionizable amino acids are available for charge generation? The answer to this question may be found from the three-dimensional protein structure analysis provided in [34,35]. Together with these papers, the Windows-based program RASMOL, which displays three-dimensional protein structures obtained from the Brookhaven Database, was used to evaluate the number of charge-generating amino acids on the surface of a lactoferrin molecule.
Synthesis, crystal structure, hirshfeld surface analysis, molecular docking and molecular dynamics studies of novel olanzapinium 2,5-dihydroxybenzoate as potential and active antipsychotic compound
Published in Journal of Experimental Nanoscience, 2022
V. Natchimuthu, Mohnad Abdalla, Manasi Yadav, Ishita Chopra, Anushka Bhrdwaj, Khushboo Sharma, S. Ravi, Krishnan Ravikumar, Khalid J. Alzahrani, Tajamul Hussain, Anuraj Nayarisseri
KCNA gene is a comparably conserved region; however, some substitutions in its amino acids have resulted in significant antipsychotic activity, indicating that KCNA docks against antipsychotic compound Olanzapinium 2,5-dihydroxybenzoate. A comprehensive set of the amino acid sequence and atomic coordinates were analyzed from the obtained 3 D structure of the KCNA (PDB ID: 4BGC) [179] using EMBOSS. All proteinogenic amino acids possess common structural features directly proportional to the biochemical properties of the model, which contain 117 aliphatic residues, 66 aromatic residues, 224 polar amino acids, 271 non-polar residues, 65 acidic amino acids, 65 negative residues and 47 positive residues. The X-ray diffraction data of the protein was visualized with RasMol software, which suggests the target protein has two chains: chain A and chain B with 101 groups and total of 913 atoms. There are 952 absolute bonds with four alpha-helices in red colour and four beta-strands in green colour; however, there is no mention of turns or loops in data. The ribbon backbone model of potassium voltage-gated channel obtained from the protein database bank is depicted in Figure 7. These predicted values of the KCNA are proved to be useful in the analysis of protein packing, protein recognition, and ligand design.