1. Prediction of candidate small non-coding RNAs in Agrobacterium by
computational analysis. J Biomed Res, 2010; 24(1):33-42
Tingting Zhaoa, Ren Zhangb, Mingbo Wangc
a
The Laboratory Center for Basic Medical Sciences, Nanjing Medical University,
Nanjing, Jiangsu 210029, China;
b
Department of Biological Sciences, University of Wollongong, Wollongong, NSW
2522, Australia;
c
CSIRO Plant Industry, Canberra, ACT 2602, Australia.
Abstract: Small non-coding RNAs with important regulatory roles are not confined to
eukaryotes. Recent work has uncovered a growing number of bacterial small RNAs
(sRNAs), some of which have been shown to regulate critical cellular processes.
Computational approaches, in combination with molecular experiments, have played
an important role in the identification of these sRNAs. At present, there is no
information on the presence of small non-coding RNAs and their genes in the
Agrobacterium tumefaciens genome. To identify potential sRNAs in this important
bacterium, deep sequencing of the short RNA populations isolated from
Agrobacterium tumefaciens C58 was carried out. From a data set of more than 10,000
short sequences, 16 candidate sRNAs have been tentatively identified based on
computational analysis. All of these candidates can form stem-loop structures by RNA
folding predictions and the majority of the secondary structures are rich in GC base
pairs. Some are followed by a short stretch of U residues, indicative of a
rho-independent transcription terminator, whereas some of the short RNAs are found
in the stem region of the hairpin, indicative of eukaryotic-like sRNAs. Experimental
strategies will need to be used to verify these candidates. The study of an expanded
list of candidate sRNAs in Agrobacterium will allow a more complete understanding
of the range of roles played by regulatory RNAs in prokaryotes.
http://www.jbr-pub.org/ch/reader/view_abstract.aspx?file_no=jbr100105&flag=1
2. A computational model of the human glucose-insulin regulatory system. J
Biomed Res, 2010; 24(5):347-364
Keh-Dong Shianga,b, Fouad Kandeelc
a
Division of Biostatistics, Department of Information Sciences, City of Hope
National Medical Center, Duarte, CA 91010-3000, USA;
b
Division of Hematopoietic Stem Cell and Leukemia Research, City of Hope
National Medical Center, Duarte, CA 91010-3000, USA;
c
Division of Diabetes, Endocrinology and Metabolism, City of Hope National
Medical Center, Duarte, CA 91010-3000, USA.
Abstract: Objective: A computational model of insulin secretion and glucose
metabolism for assisting the diagnosis of diabetes mellitus in clinical research is
introduced. The proposed method for the estimation of parameters for a system of
ordinary differential equations (ODEs) that represent the time course of plasma
glucose and insulin concentrations during glucose tolerance test (GTT) in
physiological studies is presented. The aim of this study was to explore how to
interpret those laboratory glucose and insulin data as well as enhance the Ackerman
mathematical model. Methods: Parameters estimation for a system of ODEs was
performed by minimizing the sum of squared residuals (SSR) function, which
quantifies the difference between theoretical model predictions and GTT's
experimental observations. Our proposed perturbation search and multiple-shooting
methods were applied during the estimating process. Results: Based on the
Ackerman's published data, we estimated the key parameters by applying R-based
iterative computer programs. As a result, the theoretically simulated curves perfectly
matched the experimental data points. Our model showed that the estimated
parameters, computed frequency and period values, were proven a good indicator of
diabetes. Conclusion: The present paper introduces a computational algorithm to
biomedical problems, particularly to endocrinology and metabolism fields, which
involves two coupled differential equations with four parameters describing the
glucose-insulin regulatory system that Acker-man proposed earlier. The enhanced
approach may provide clinicians in endocrinology and metabolism field in-sight into
the transition nature of human metabolic mechanism from normal to impaired glucose
tolerance.
http://www.jbr-pub.org/ch/reader/view_abstract.aspx?file_no=JBR100502&flag=1
3. Computational interaction analysis of organophosphorus pesticides with
different metabolic proteins in humans. J Biomed Res, 2011; 25(5):335-347
Amit Kumar Sharmaa, Karuna Gaurb, Rajeev Kumar Tiwaria, Mulayam Singh Gaura
a
Pesticides Research & Sensors Laboratory, Department of Physics, Hindustan
College of Science and Technology, Farah, Mathura-281122 (U.P.) India;
b
Department of Bioscience, R. D. Govt. Girls College, Bharatpur, Rajasthan
321001, India.
Abstract: Pesticides have the potential to leave harmful effects on humans, animals,
other living organisms, and the environment. Several human metabolic proteins
inhibited after exposure to organophosphorus pesticides absorbed through the skin,
inhalation, eyes and oral mucosa, are most important targets for this interaction study.
The crystal structure of five different proteins, PDBIDs: 3LII, 3NXU, 4GTU, 2XJ1
and 1YXA in Homo sapiens (H. sapiens), interact with organophosphorus pesticides
at the molecular level. The 3-D structures were found to be of good quality and
validated through PROCHECK, ERRAT and ProSA servers. The results show that the
binding energy is maximum -45.21 relative units of cytochrome P450 protein with
phosmet pesticide. In terms of H-bonding, methyl parathion and parathion with
acetylcholinesterase protein, parathion, methylparathion and phosmet with protein
kinase C show the highest interaction. We conclude that these organophosphorus
pesticides are more toxic and inhibit enzymatic activity by interrupting the metabolic
pathways in H. sapiens.
http://www.jbr-pub.org/ch/reader/view_abstract.aspx?file_no=jbr110505&flag=1
4. Active motif finder - a bio-tool based on mutational structures in DNA
sequences. J Biomed Res, 2011; 25(6):444-448
Mani Udayakumara, Palaniyandi Shanmuga-priyab, Kamalakannan Hemavathic,
Rengasamy Seenivasagamd
a
Department of Bioinformatics; bDepartment of Bioinformatics; cDepartment of
Bioinformatics, School of Chemical and Biotechnology, Shanmugha Arts Science
Technology & Research Academy (SASTRA University), Tanjore, Tamilnadu
613402, India;
d
Division of Drug Discovery and Development, Centre of Molecular and
Computational Biology, Department of Botany, St. Joseph College, Bangalore,
Karnataka 560027, India.
Abstract: Active Motif Finder (AMF) is a novel algorithmic tool, designed based on
mutations in DNA sequences. Tools available at present for finding motifs are based
on matching a given motif in the query sequence. AMF describes a new algorithm
that identifies the occurrences of patterns which possess all kinds of mutations like
insertion, deletion and mismatch. The algorithm is mainly based on the Alignment
Score Matrix (ASM) computation by com-paring input motif with full length
sequence. Much of the effort in bioinformatics is directed to identify these motifs in
the sequences of newly discovered genes. The proposed bio-tool serves as an open
resource for analysis and useful for studying polymorphisms in DNA sequences.
AMF can be searched via a user-friendly interface. This tool is intended to serve the
scientific community working in the areas of chemical and structural biology, and is
freely available to all users, at http://www.sastra.edu/scbt/amf/.
http://www.jbr-pub.org/ch/reader/view_abstract.aspx?file_no=JBR110610&flag=1
5. Three dimensional structure prediction and proton nuclear magnetic
resonance analysis of toxic pesticides in human blood plasma. J Biomed Res,
2012; 26(3):170-184
Amit Kumar Sharma, Rajeev Kumar Tiwari, Mulayam Singh Gaur
Pesticides Research and Sensors Laboratory, Department of Physics, Hindustan
College of Science and Technology, Farah, Mathura-281122 (U.P.), India.
Abstract: The purpose of this study was to investigate the nuclear magnetic
resonance (NMR) assignments of hydrolyzed products extracted from human blood
plasma. The correlations between chemical, functional and structural properties of
highly toxic pesticides were investigated using the PreADME analysis. We observed
that toxic pesticides possessed higher molecular weight and, more hydrogen bond
donors and acceptors when compared with less toxic pesticides. The occurrence of
functional groups and structural properties was analyzed using 1 H-NMR.
The 1H-NMR spectra of the phosphomethoxy class of pesticides were characterized
by methyl resonances at 3.7-3.9 ppm (δ) with the coupling constants of 11-16 Hz
(JP-CH3). In phosphoethoxy pesticides, the methyl resonance was about 1.4 ppm (δ)
with the coupling constant of 10 Hz (JP-CH2) and the methylene resonances was 4.2-4.4
ppm (δ) with the coupling constant of 0.8 Hz (JP-CH3), respectively. Our study shows
that the values of four parameters such as chemical shift, coupling constant,
integration and relaxation time correlated with the concentration of toxic pesticides,
and can be used to characterise the proton groups in the molecular structures of toxic
pesticides.
http://www.jbr-pub.org/ch/reader/view_abstract.aspx?file_no=JBR120305&flag=1