Computational Molecular Bioscience

Journal Information
ISSN / EISSN : 21653445 / 21653453
Current Publisher: Scientific Research Publishing, Inc. (10.4236)
Total articles ≅ 67
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Latest articles in this journal

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

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.
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.
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.
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.
Shaomin Yan, Guang Wu
Computational Molecular Bioscience, Volume 9, pp 27-39; doi:10.4236/cmb.2019.91003

Norma Flores-Holguín, Juan Frau, Daniel Glossman-Mitnik
Computational Molecular Bioscience, Volume 9, pp 95-107; doi:10.4236/cmb.2019.94008

This study involved the assessment of the MNI2SX/Def2TZVP/H2O model chemistry to enhance the understanding of the structural composition of the marine peptide Hemiasterlin and its derivatives A and B used in cancer treatment. The Conceptual Density Functional theory was used in the calculation of molecular properties of the system chemical descriptors during the study. Integration of the active molecular regions into their respective Fukui functions was used in the selection of electrophilic and nucleophilic attacks. Additionally, the proposed correlation between global hardness and the pKa was used as the basis of deriving accurate predictions for the pKa values while a homology technique was used in the prediction of bioactivity and bioavailability scores of the peptides under investigation.
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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