Computational Molecular Bioscience

Journal Information
ISSN / EISSN : 2165-3445 / 2165-3453
Current Publisher: Scientific Research Publishing, Inc. (10.4236)
Total articles ≅ 70
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Latest articles in this journal

Abaysew Ayele, Baba Abdissa, Dereje Taye, Bereket Yemane, Rita Singh Majumdar
Computational Molecular Bioscience, Volume 10, pp 95-110; doi:10.4236/cmb.2020.103007

Meanwhile the outbreak of the Covid-19 since December, 2019 in China, it has killed more than a hundred thousand of people of all ages and sex across the globe in a short span of time. On the bases of this study the nearest family member of the virus and its receptor binding domain of S protein including its model structure and function of its active sites were naked through Multiple Sequence Alignment, modelling and molecular docking software accordingly its repository genome databases. The virus was genetically associated and molecular evolutionary related with (RaTG13) and it scores 96.12% homology with 99% query coverage followed by bat-SL-CoVZC45 and bat-SL-CoVZXC21 notch 89.12% and 88.65% respectively. However, SARS and MERS corona type virus those outbreak earlier respectively less likely family members of 2019-nCoV. Though the virus has a close genetic association with those previous SARS coronaviruses, and certainly the spike protein used as a binding receptor to fight against human receptor protein of ACE 2, but on the basis of FRODOC and HDOCK server analysis multi favorable active sites of S protein was discovered such GLN493 shown as a finest key in both model and possessed a unique traits on it resulting unexpected rate of transmission and number of people died while compared to the previous one. TYR500, ASN501, GLN498 and others residues preferably contemplate site also. In particular, the diversity of the virus in the world may be due to the genome structure of the virus and S gene changed over the time, across the world against to host of human genetic diversity, which may be more robust, and may be a new and unique feature. This is because it is characterized close to contact with distance divergence between wild type novel coronavirus which was risen from China against to the genomes from Lebanon, India, Italy, and USA and so on. Thus, the World Health Organization and its researchers should focus on immunologic research and effective drug and vaccine development that will help to address the epidemiology of the virus, which can provide a long-term solution.
Kristine Edgar Danielyan, Tatul Ashot Yeghiazaryan, Samvel Grigoriy Chailyan, Levon Ruben Harutyunyan, Ruben Levon Harutyunyan, Gurgen Sergei Petrosyan
Computational Molecular Bioscience, Volume 10, pp 73-80; doi:10.4236/cmb.2020.103005

We were aiming to delineate, by the utility of the biological data results, in our investigations the link between the purine and pyrimidine metabolism and development of the glioblastoma. We analyzed the sets of the genes, belonging to the purine and pyrimidine metabolism by the utility of GSEA software as well as MSIgnDB application of the GSEA. The GEO database, GEOR2 tools were serving for the visualization of the genes expression profiles of the disease. The Cancer Proteome Atlas as well as the tools of the data sets were also used to collect and analyze the results. We concluded and came to the following consequential results. 1) Neurogenesis and Glioblastoma are sharing some common genes. 2) Purine and pyrimidine metabolism-linked enzymes and genes are responsible for the upregulation of DNA and mRNA synthesis in the settings of the tumor development. 3) EGFR expression responsible genes, mRNA as well as protein is upregulated during the development of the glioblastoma. 4) GMPS genes are more strongly upregulated in the settings of the glioblastoma than ADSL. 5) PRPS1 is strongly synthetized in neurospheres in contrast to the mature tissue during glioblastoma development.
Kassim F. Adebambo
Computational Molecular Bioscience, Volume 10, pp 45-60; doi:10.4236/cmb.2020.102003

Coronavirus (CoVID-19) is a new outbreak of coronavirus disease which started in the Wuhan, China, the spread of this virus has now reached a global stage, urgent need is therefore needed to find new drug molecules which can either be used as a first aid intervention or slow down the multiplication rate of the virus within the system. In order to address this, this research looked into the existing antiviral drugs and screened them for their inhibitory properties towards the CoVID-19 protein. Recently, the crystal structure of the CoVID-19 (6LU7) protein has been established, this gives us the possible drug target site in CoVID-19. The binding affinity of the six compounds was screened using MOE (Molecular Operating Environment) software, four compounds (Zanamivir, Peramivir, Rimantidine, and Oseltamivir) out these six compounds have been approved by the Food Drug and Administration (FDA). The molecular docking calculation, Higher Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) calculation were used to hypothesise the bioactivity of the FDA approved drug against the CoVID-19 protein. The calculation showed that Pimodivir tops the list of the anti influenza drug which can be used as first aid treatment for patient. Apart from Pimodivir, Laninamivir Octanoate is also a very good drug which might be used to inhibit CoVID-19 protein. It was also discovered that based on binding property of Rimantadine, it might be suitable for Fragment Based Drug Design (FBDD) approach which might lead to the discovery of completely new drug entity. Stability of the new protein structure was studied using GROMACS molecular dynamic simulation software. The results showed that the stability of the protein structure was achieved over a range of time, this confirmed that 6LU7 crystal structure might be a suitable protein crystal structure suitable for the development of new drug towards the treatment of CoVID-19. Finally, based on the molecular docking result, Pimodivir and Laninamivir Octanoate might be useful in the treatment of infected patient.
Kubrycht Jaroslav, Sigler Karel, Jaroslav Kubrycht, Karel Sigler
Computational Molecular Bioscience, Volume 10, pp 12-44; doi:10.4236/cmb.2020.101002

The traces of immunoglobulin domain similarities were searched in sequences of higher plants using bioinformatic tools to look for possible early phylogenic structural relationships. 280 thousand sequence IDs, obtained by sixteen types of primary BLAST searches, were differently processed by seventeen selection procedures and an anti-redundant sequence-related approach using JavaScript, PHP, Windows programs and conserved domain searches by means CDD. The resulting seventeen sets of records describing conserved domain similarities of 1323 different sequence IDs yielded a set of next generation (final set) comprising forty-nine records containing superior (“non-refutable”) conserved immunoglobulin domain similarities. The selected sets and their subsets were mapped and subsequently statistically compared with respect to immunoglobulin-related as well as other reciprocal domain linkages. The list of frequently occurring conserved domain similarities concerned first of all domains important for plant and metazoan immunity, e.g. tyrosine kinases accompanying variable immunoglobulin domains in early Metazoa, toll-like receptors, lectin and leucine-rich repeat domains. Detailed description of immunoglobulin domain similarities occurring in the final set was completed by fold analysis of the restricted segments. The data were then discussed with respect to i) immunoglobulin fold evolution, ii) possible structural importance of domains cd14066 (IRAK) and PLN00113 (LRR-associated kinase) for deep evolution of catalytic serine/threonine/tyrosine kinase domains, iii) interatomic, structural and specificity standpoints and iv) traces of antibody-like phosphorylation sites described in our previous paper.
L. Medina-Franco José, Cruz-Lemus Yesenia, Percastre-Cruz Yazmin, José L. Medina-Franco, Yesenia Cruz-Lemus, Yazmin Percastre-Cruz
Computational Molecular Bioscience, Volume 10, pp 1-11; doi:10.4236/cmb.2020.101001

Medicinal Organometallic Chemistry keeps contributing to drug discovery efforts including the development of diagnostic compounds. Despite the limiting issues of metal-based molecules, e.g., such as toxicity, there are drugs approved for clinical use and several others are under clinical and pre-clinical development. Indeed, several research groups continue working on organometallic compounds with potential therapeutic applications. For arguably historical reasons, chemoinformatic methods in drug discovery have been applied thus far mostly to organic compounds. Typically, metal-based molecules are excluded from compound data sets for analysis. Indeed, most software and algorithms for drug discovery applications are focused and parametrized for organic molecules. However, considering the emerging field of material informatics, the objective of this Commentary we emphasize the need to develop cheminformatic applications to further develop metallodrugs. For instance, one of the starting points would be developing a compound database of organometallic molecules annotated with biological activity. It is concluded that chemoinformatic methods can boost the research area of Medicinal Organometallic Chemistry.
Henry J. Hoyhtya, Hanna J. Koster, Maggie M. Christiansen, Ugur Akgun
Computational Molecular Bioscience, Volume 10, pp 81-94; doi:10.4236/cmb.2020.103006

The structural differences between E. coli AmtB and the human RhCG as well as the Rh50 from Nitrosomonas europaea suggest different ammonia conduction mechanisms for the AmtB and the Rh proteins. This study investigates the mechanism differences by using molecular dynamics simulations on RhCG and Rh50NE structures. Unlike AmtB the Rh proteins lack the aromatic cage at the bottom of the periplasmic vestibule. This report establishes the periplasmic Glu residue as the NH+4 binding site for Rh proteins, and the two Phe residues at the entrance of the pore as the NH3 binding site. The NH+4 molecule pushed by another ammonium releases one of its protons on its way to the phenyl gate. This study also discovers that, unlike AmtB, the Rh protein pores allow water molecules, which eventually facilitates the NH3 conduction from periplasm to cytoplasm.
Dipendra C. Sengupta, Matthew D. Hill, Kevin R. Benton, Hirendra N. Banerjee
Computational Molecular Bioscience, Volume 10, pp 61-72; doi:10.4236/cmb.2020.103004

The novel coronavirus (SARS-COV-2) is generally referred to as Covid-19 virus has spread to 213 countries with nearly 7 million confirmed cases and nearly 400,000 deaths. Such major outbreaks demand classification and origin of the virus genomic sequence, for planning, containment, and treatment. Motivated by the above need, we report two alignment-free methods combing with CGR to perform clustering analysis and create a phylogenetic tree based on it. To each DNA sequence we associate a matrix then define distance between two DNA sequences to be the distance between their associated matrix. These methods are being used for phylogenetic analysis of coronavirus sequences. Our approach provides a powerful tool for analyzing and annotating genomes and their phylogenetic relationships. We also compare our tool to ClustalX algorithm which is one of the most popular alignment methods. Our alignment-free methods are shown to be capable of finding closest genetic relatives of coronaviruses.
Hannah Johnson, Hyuk Cho, Madhusudan Choudhary
Computational Molecular Bioscience, Volume 9, pp 1-12; doi:10.4236/cmb.2019.91001

There is a worldwide distribution of heavy metal pollution that can be managed with a bioremediation approach using microorganisms. Several bacterial species belonging to the Proteobacteria have been shown to tolerate heavy metal stress, including toxic salts of noblemetals. Rhodobacter sphaeroides, a model bacterium has previously been utilized for bioremediation studies. A bioinformatics approach was employed here to identify the distribution of genes associated with heavy metal tolerance among the sequenced bacterial genomes currently available on the NCBI database. The distribution of these genes among different groups of bacteria and the Cluster of Orthologous Groups (COGs) were further characterized. A total of 170,000 heavy metal related genes were identified across all bacterial species, with a majority of the genes found in Proteobacteria (46%) and Terrabacteria (39%). Analysis of COGs revealed that the majority of heavy metal related genes belong to metabolism (COG 3), including ionic transport, amino acid biosynthesis, and energy production.
Hassan W. Kayondo, Samuel Mwalili, John M. Mango
Computational Molecular Bioscience, Volume 9, pp 108-131; doi:10.4236/cmb.2019.94009

Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general population. These sub-groups have higher infectivity rates. We came up with a likelihood inference model of multi-type birth-death process that can be used to make inference for HIV epidemic in an African setting. We employ a likelihood inference that incorporates a probability of removal from infectious pool in the model. We have simulated trees and made parameter inference on the simulated trees as well as investigating whether the model distinguishes between heterogeneous and homogeneous dynamics. The model makes fairly good parameter inference. It distinguishes between heterogeneous and homogeneous dynamics well. Parameter estimation was also performed under sparse sampling scenario. We investigated whether trees obtained from a structured population are more balanced than those from a non-structured host population using tree statistics that measure tree balance and imbalance. Trees from non-structured population were more balanced basing on Colless and Sackin indices.
Doh Soro, Lynda Ekou, Bafétigué Ouattara, Mamadou Guy-Richard Kone, Tchirioua Ekou, Nahossé Ziao
Computational Molecular Bioscience, Volume 9, pp 63-80; doi:10.4236/cmb.2019.93006

In this work, we conducted a QSAR study on 18 molecules using descriptors from the Density Functional Theory (DFT) in order to predict the inhibitory activity of hydroxamic acids on histone deacetylase 7. This study is performed using the principal component analysis (PCA) method, the Ascendant Hierarchical Classification (AHC), the linear multiple regression method (LMR) and the nonlinear multiple regression (NLMR). DFT calculations were performed to obtain information on the structure and information on the properties on a series of hydroxamic acids compounds studied. Multivariate statistical analysis yielded two quantitative models (model MLR and model MNLR) with the quantum descriptors: electronic affinity (AE), vibration frequency of the OH bond (ν(OH)) and that of the NH bond (ν(NH)). The LMR model gives statistically significant results and shows a good predictability R2 = 0.9659, S = 0.488, F = 85 and p-value et al.
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