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Introduction
Published in Christophe Ley, Thomas Verdebout, Modern Directional Statistics, 2017
Christophe Ley, Thomas Verdebout
Predicting the correct three-dimensional structure of a protein on the basis of its one-dimensional protein sequence is a fundamental problem in life sciences. Solving this holy grail problem would have wide-reaching consequences in drug discovery, biotechnology and evolutionary biology, for instance. Nowadays massive databases of DNA and protein sequences are available, and structural bioinformatics is the domain within bioinformatics concerned with the prediction of the associated three-dimensional structure.
DeepCOVID-19: A model for identification of COVID-19 virus sequences with genomic signal processing and deep learning
Published in Cogent Engineering, 2022
Emmanuel Adetiba, Joshua A. Abolarinwa, Anthony A. Adegoke, Tunmike B. Taiwo, Oluwaseun T. Ajayi, Abdultaofeek Abayomi, Joy N. Adetiba, Joke A. Badejo
The genomic sequences used for this study were extracted from the Virus Pathogen Database and Analysis Resource (ViPR). The ViPR is an integrated repository of information about human pathogenic viruses that integrate genome, gene, and protein sequence information. The database is fully funded by the U.S. National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, USA. Apart from direct deposition of genomic sequences and protein data by researchers on ViPR, other data sources include National Centre for Biotechnology Information (NCBI) Genbank, NCBI RefSeq, Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB), etc (Pickett et al., 2011). The database particularly inspired us because it contains up-to-date complete genomic sequences (from real human subjects) for human pathogenic viruses.
Potency matters: Impacts of embryonic exposure to nAChR agonists thiamethoxam and nicotine on hatching success, growth, and neurobehavior in larval zebrafish
Published in Journal of Toxicology and Environmental Health, Part A, 2022
Shayla Victoria, Megan Hein, Elisabeth Harrahy, Tisha C King-Heiden
In-silico molecular docking models were produced in the software program, Maestro (Schrödinger, LLC, New York, NY, 2020), to evaluate interactions of TM compared to NIC in the nAChR of a vertebrate model (Homo sapiens). Human nAChR was used in the model because fish nAChR protein files are not available. However, the nAChR binding site is thought to be a conserved sequence across most vertebrates and functions as a proxy for pharmacological models (Papke et al. 2012). The three-dimensional structure of the nAChR protein (PDB ID: 6PV7; Gharpure et al. 2019) was obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB; Berman et al. 2000). The chemical structures of TM and NIC were obtained from the ZINC database (Sterling and Irwin 2015). Protein and ligand preparation were conducted in Maestro using Schrödinger’s user guides (Schrödinger, LLC, New York, NY, 2020). Grid generation was performed using the binding site of crystallized NIC as reference. Glide ligand docking was conducted for both compounds. After docking was completed, state and ionization penalties, which quantify how energetically favorable the docked states are, docking and GlideScores, approximations of binding affinity and strength, and ligand interaction diagrams (LID), which display interactions between specific amino acids and ligand were generated through the program.
Battling COVID-19 using machine learning: A review
Published in Cogent Engineering, 2021
Krishnaraj Chadaga, Srikanth Prabhu, Bhat K Vivekananda, S. Niranjana, Shashikiran Umakanth
Medical data is used for this diagnosis and treatment of COVID-19. X-rays and pathology reports can be used for prognosis. There are a few open-source X-ray scans of COVID-19 patients (Wang & Wong, 2020). They can be used to assess and diagnose infections using computer vision (Mery, 2015). There are other X-ray datasets as well, such as (2020; Cohen et al., 2020; Mvd, 2020). These datasets cannot be easily understood by a non-biological student. Thus, we require help from radiologists and clinicians to properly label the data. The datasets are small and deep learning techniques might not be efficient on them. Along with X-rays, CT scans are also used for COVID-19 diagnosis. A lot of CT scan datasets are already available. In (MegSeg, 2020), CT scans of 60 patients were taken. A larger dataset which includes 288 CT scans is present in (J. Zhao et al., 2020). The United Kingdom also provides COVID-19 chest X-rays and CT scans of various patients who were infected (Nhsx, 2020). Leeds University Institute[119], UK NHSx [120] and Ellis Alicante (E. A. Foundation, 2020) also provide images of infected lungs of COVID-19 patients. Genomic sequencing datasets are also available. The datasets RCSB (Research Collaboratory for Structural Bioinformatics) data (Goodsell et al., 2020), And GHDDI (Global Health Drug Discovery Institute) (Zhavoronkov et al., 2020a) analyse the structure of RNA, the structure of spike protein, etc.