Biochemistry and Bioinformatics

Peptide microarrays are to proteomics as sequencing is to genomics. As microarrays become more content-rich, higher resolution proteomic studies will parallel deep sequencing of nucleic acids. Antigen-antibody interactions can be studied at a much higher resolution using microarrays than was possible only a decade ago. My dissertation focuses on testing the feasibility of using either the Immunosignature platform, based on non-natural peptide sequences, or a pathogen peptide microarray, which uses bioinformatically-selected peptides from pathogens for creating sensitive diagnostics. Both diagnostic applications use relatively little serum from infected individuals, but each approaches diagnosis of disease differently. The first project compares pathogen epitope
peptide (life-space) and non-natural (random-space) peptide microarrays while using them for the early detection of Coccidioidomycosis (Valley Fever). The second project
uses NIAID category A, B and C priority pathogen epitope peptides in a multiplexed microarray platform to assess the feasibility of using epitope peptides to simultaneously diagnose multiple exposures using a single assay. Cross-reactivity is a consistent feature of several antigen-antibody based immunodiagnostics. This work utilizes microarray optimization and bioinformatic approaches to distill the underlying disease specific antibody signature pattern. Circumventing inherent cross-reactivity observed in antibody binding to peptides was crucial to achieve the goal of this work to accurately distinguishing multiple exposures simultaneously.

Structural bioinformatics can be described as an approach that will help decipher biological insights
from protein structures. As an important component of structural biology, this area promises to provide a high
resolution understanding of biology by assisting comprehension and interpretation of a large amount of structural
data. Biological function of protein molecules can be inferred from their three-dimensional structures by comparing
structures, classifying them and transferring function from a related protein or family. It is well known now that
the structure space of protein molecules is more conserved than the sequence space, making it important to seek
functional associations at the structural level. An added advantage of structural bioinformatics over simpler
sequence-based methods is that the former also provides ultimate insights into the mechanisms by which various
biological events take place. A bird’s eye-view of the different aspects of structural bioinformatics is given here
along with various recent advances in the area including how knowledge obtained from structural bioinformatics
can be applied in drug discovery.

The microbial monitoring of environmental and medical-devices’ surface is used to evaluate efficacy of routine cleaning and disinfection practices and to detect the presence of specific Nosocomial Pathogens. The prevalence of Multidrug Resistance organisms in hospital premises projects serious problems in transmitting to susceptible host which is difficult to treat. A cross sectional descriptive research was conducted from December 2016 to June 2017 at the pathology laboratory of Korea Nepal Friendship Hospital (KNFH).  A total 140 samples were considered, that encompasses the medical devices of hospital (n=100), housekeeping surfaces (n=15) and air (n=25). Susceptibility test for bacterial isolates was done by disk diffusion assay. Of the total 140 samples taken and analyzed, 100% showed growth positivity. In most of the swab taken, Coagulase Negative Staphylococci was dominant, followed by Staphylococcus aureus, Streptococcus spp. Micrococcus spp., E coli, Pseudomonas spp., Bacillus spp., Acinetobacter spp., Klebsiella spp., Fungi, and least Proteus spp. The dry surfaces were dominantly contaminated by gram positive bacteria whereas moistened surfaces like wash basin were contaminated by gram negative as well as gram positive bacteria. Total 277 strains were exposed to various class of antibiotics, among the gram positive environmental isolates, Coagulase Negative Staphylococci 16 (34.78%) had highest MDR prevalence followed by  Staphylococcus aureus 8 (29.62%), Streptococcus spp. 4 (12.90%), Micrococcus spp. 4 (9.30%) and no MDR was shown by any Bacillus spp isolates. Whereas, in case of gram negative, Klebsiella spp. 6 (35.29%) had highest MDR prevalence followed by Acinetobacter spp. 6 (31.57%), E. coli 8 (27.58%), Pseudomonas spp. 4 (18.18%), and lastly Proteus spp.  with no MDR at all. The thick dirt covering the cotton swabs and heavy microbial load on them has displayed not only disinfecting practice is lacking but also cleaning practice is missing. Heavy contamination shows possible NIs breakout, it’s important to have routine microbial assessment with standard protocol and find ways to decrease its load.
Key words:  Disk diffusion, Environmental monitoring, MDR, Nosocomial infections, assay

International Journal of Biomedical Engineering and Science (IJBES) is a peer-reviewed, open access which focuses on the latest advances, applications and future trends in the area of Biomedical Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development of Biomedical Engineering and science.

Introduction: Saba  florida (Benth)  Apocynaceae is  one  of  the
African food/medicinal plants that has been appliedtraditionally for
the management of many ill health conditions.
Objectives: As  part  of  efforts  to  authenticate  the  oral  claims
scientifically  the plant’s  leaf  and stem bark  extracts were evaluated
for anthelmintic activity.
Methods: Standard  procedures  were  used  in  the  determination of
anthelmintic  activity  study.  Nigeria  adult  earthworms  (Terrestris
lumbricoides)  were  used  for  the  study.  Albendazole  was  used  as
standard.  All  the  earthworms  were  washed  in  distilled  water  before
they were released into a 20ml of respective formulations including
the  control,  standard  and  extracts  (10,  20,  50,  and 100mg/ml).
Results:All the investigational extracts showed anthelmintic activity
at the dose of 20mg/ml. Time taken for paralysis and death to occur
were  recorded  for  each  sample.  The  aqueous  extract  showed  better
activity against the worms than the chloroform extract. The extracts’
anthelmintic  activity  was  dose-dependent  and  comparable  to  the
standard drug used.
Conclusion: From  this  study  herbal  drug  and  synthetic  ones  are
equally effective in helminth infestations, but aqueous extract of the
leaf  of  Saba  florida showed  higher  anthelmintic  activity  potential
than the chloroform extracts.

Background: Modeling protein–protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein–protein interfaces, which contribute more in stabilizing a protein–protein interface.

Results: We had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate ‘native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning.

Conclusions: PPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein–protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.

The major aim of this study is to introduce bimolecular structures
from  a  bioinformatic  perspective  and  informs  about  the
bioinformatic softwares in this field. Each protein has a specific
chemical or structural function, strongly suggesting that each has a
unique three-dimensional structure. In this study, we investigated
α-amylases  in  Aspergillus  oryzae.  Among  starch  converting
enzymes, the α-amylases (endo-1, 4-α-D-glucan glucanohydrolase
[E.C.3.2.1.1]) are of special importance and extensively studied.
PDB  and  NCBI  databases  and  Chimera,  Predict  Protein  and
Multalign  softwares  used  for  this  research.  By  using  these
softwares, multiple analyses, including determination of residue
composition,  secondary  structures,  conserved  regions,  ligand
binding site done. Starch-hydrolyzing enzymes such as amylases,
pullulanases and glucoamylases play an important role in food,
chemical,  and  pharmaceutical  industries.  In  the  certain
applications like detergent and bakery industries the properties of
α-amylase  needed  is  quite  challenging  Using  of  bioinformatic
softwares  improves  our  understanding  about  proteins  3D
structures and their functions.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

The bioaccumulation and biomagnification of PAHs along successive trophic levels is an important means of exposure to human and animal life. This study evaluated Polycyclic Aromatic Hydrocarbons (PAHs) and heavy metals in fresh fish and fish samples processed/ roasted with smoke/heat/flame generated from firewood, waste tyre and polyethylene materials. PAHs and heavy metals were determined with Gas chromatography with flame ionization detector (GC-FID) and Atomic Absorption Spectrophotometer (AAS) respectively. Significant differences (P<0.05) were observed in the concentrations of PAHs and heavy metals in the heat-processed fish when compared to fresh fish. Appreciable amount of carcinogenic PAHs; benzo(a)pyrene, benz(a)anthracene and dibenz(a,h)anthracene benz(a)anthracene and dibenz(a,h)anthracene and heavy metals such as cadmium (Cd), zinc (Zn) and lead (Pb) were detected in processed/roasted fish samples.

Data mining is defined as the process of extracting or mining knowledge from vast and large database. Data mining is an interdisciplinary field that brings together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large databases. Bioinformatics is defined as the science of organizing and analyzing the biological data. Microarray technology helps biologists for monitoring expression of thousands of genes in a single experiment on a small chip. Microarray is also called as DNA chip, gene chip, or biochip is used to analyze the gene expression profiles. Fuzzy Logic is defined as a multivalued logic that provides the intermediate values to be defined between conventional evaluations like true or false, yes or no, high or low, etc.In this paper, a type 2 fuzzy logic approach is used in microarray gene expression data to convert the numerical values into fuzzy terms. After fuzzification, the fuzzy association patterns are discovered. A framework is proposed to cluster microarray gene data based on fuzzy association patterns. Then the proposed type 2 fuzzy approach is compared with traditional clustering algorithms.

Auricularia mushrooms are bioactive compounds which are mostly polysaccharides. Many studies have shown the use of Auricularia from wild mushroom for medicinal purposes such as antioxidant, antidiabetic, antitumor, hyperlipidemic, immunomodulatory, hepatoprotective and anticoagulant effect. Despite the fact that some studies showed pharmacological functions of polysaccharides extracted from Auricularia Delicata, the efficiency and the chemical process of extraction processes of extraction of polysaccharides from Auricularia were not clear. Therefore, this study proposes a method to purify and characterize the polysaccharide extracted from Auricularia through a method in Yoon et al. (2003). Polysaccharides characterization was done by colorimetric (Phenol-Sulfuric acid method). UV-Vis spectrophotometer was used to take colorimetric analysis of all solutions. The absorbance of the characteristic yellow-orange color was measured at 490 nm for hexose monosaccharide and 480 nm for pentose monosaccharide and uronic acid. The yield of purified polysaccharides from Auricularia was 0.88% with 80.4% purity expressed as glucose/xylose. We successfully obtained the purified polysaccharides from Auricularia with high purity.

β-galactosidase (EC.3.2.1.23) is an important enzyme, mainly used in the preparation of lactose hydrolyzed milk suitable for people with lactose intolerance. It is essential to understand the structural and functional aspects of various β-galactosidase produced from different sources. The present work deals with the use of bioinformatics to describe the physiochemical, functional and structural properties of β-galactosidase enzymes on Bacillus sp. selected from the gene bank of NCBI. The grand average hydropathy (GRAVY) and low range of AFY63015.1 value indicates the possibility of better interaction with water and instability index were computed to characterise YP_004205251.1, ZP_10511829.1, BAL72724.1, AFY63015.1, NP_242888.1 stating that they are stable and disulfide bridges, CYS_REC recognizes the presence of 38 cysteine residues in β-galactosidase sequences and predicted most probable SS bond patterns of pairs in YP_004981461 and 1AFY63015.1. The self-optimized prediction method (SOPM) was used to predict the secondary structure. The SOPM results indicated the presence of alpha helix is more dominated in sequences AFY63015.1 and YP_004981461.1. Overall this represents in silico analysis of sequence, structural and functional information of β-galactosidase of Bacillus species.

The EKG Glove is a wearable glove-based device on which all the necessary electrodes are pre-positioned hence the patient simply inserts his right hand into the glove and properly position the glove his chest to record a standard ECG. It is the first, lead wire free, simultaneous, and fully diagnostic ECG recording system in Pakistan with easy, natural and almost automatic placement of the electrodes, hence eliminating the difficulty of lead wire entanglement and electrode misplacement.

In this work, we evaluated the effects of alpha linoleic acid (ALA), an omega-3 polyunsaturated fatty acid, on amyloid-beta-induced glial-cell-mediated neuroinflammation, amyloidogenesis, and cognitive dysfunction in mice. After an infusion of Aβ1–42 (Aβ1–42, 5 µL/ 5 min/mouse, intracerebroventricular injection (i.c.v), and respective treatments of ALA (60 mg/kg per oral for six weeks), neuroinflammation, apoptotic markers, and synaptic markers were evaluated by Western blot and immunofluorescence analyses. According to our findings, the infusion of Aβ1–42 activated Toll-like receptor 4 (TLR4), glial fibrillary acidic protein (GFAP), and ionized calcium adaptor molecule 1 (Iba-1) in the frontal cortices and hippocampi of the Aβ1–42-injected mice to a greater extent than the Aβ1–42 + ALA-cotreated mice. Similarly, there was an elevated expression of phospho-c-Jun-N-terminal kinase (p-JNK), phospho-nuclear factor-kB p65 (p-NF-kB p65 (Ser536)), and tissue necrosis factor (TNF) in the A...

Bioinformatics is the application of information science and technology to the field of biology to increase the understanding of all biological process. The aim of this journal is to publish all the latest and outstanding research articles in all areas of bioinformatics and Biometrics. Researchers and scientists from the fields of biology, computer science, mathematics, statistics, and physics are invited to share their developments and new techniques in the fields of Biometrics and Bioinformatics.

Mental Retardation (MR) is regarded as a neuronal malfunction characterized by a low Intellectual Quotient (IQ). To date, few genes (GRIK2, TUSC3, TRAPPC9, TECR, ST3GAL3, MED23, MAN1B1, NSUN1 PRSS12, CRBN, CC2D1A) for autosomal-recessive non syndromic MR (NS-ARMR) have been identified and established in various families with MR. The recently reported candidate gene "Tumor Suppressor Candidate Gene-3 (TUSC3)" was selected for computational analysis to explore its potential role in pathology as it is the only gene for MR reported in more than five different familial cases worldwide. TUSC3 gene located at chromosome 8p22 contains 11 exons and encodes a protein involved in the vertebrate plasma membrane magnesium ion transport system. Three dimensional structures of the candidate gene TUSC3 and its mutated structure with Q55X were generated. Template parameters were based on Z-score, E value, resolution and quality. Present study demonstrates the reliability of 3D model which satisfies the overall quality of the model constructed, employing both physiochemical and statistical model evaluation. Previously a homozygous 163C-T transition in exon 2 of the TUSC3 gene, resulting in a gln55-to-ter (Q55X) substitution has been reported. And here we predicted by PHD-SNP that X could be either proline (P) or Aspartic acid (D) causing deleterious effect. As mutation at 55 position was responsible for non-syndromic autosomal recessive mental retardation, so it was assumed that mutation was either by proline or aspartic acid. Abstract Mental Retardation (MR) is regarded as a neuronal malfunction characterized by a low

Biocomputing is a recently growing and highly interdisciplinary field of research that investigates models and computational techniques inspired by biology and related sciences. Here, cyclic behavior (redox cycling) of purified horseradish peroxidase protein among native enzyme and its two electron-oxidized and single electron-oxidized intermediates known as Compounds I and II was algebraically expressed as a cyclic additive group Z 3 = {C 0 , C 2 , C 1 } = {C 0 , 1C 2 , 2C 2 } = {0, 2, 1}, and a cyclic multiplicative group Z * 3 = {C 1 , C 2 } = {C 1 , C 1 2 } = {1, 2}, with C 2 as the common generator. Above algebraically expressed features of the enzyme's redox cycle was applied to help determining the coefficients in polynomials formed after additive and/or multiplicative operations between polynomial rings f (x) and g(x) over a coefficient field derived from Z 3. Similarly, use of a pair of small DNA molecules was proposed for determining the coefficients for additively and multiplicatively obtained polynomials over F , (Z 2 ; +, ×); where Z 2 = {C 0 , C 1 } = {0, 1}. Discussion includes the required designs of two distinct DNA molecules for performing binary logical conjunction (AND) and exclusive disjunc-tion (XOR), upon polymerase-chain reactions.

International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.

Most of the biological processes are governed through specific protein–ligand interactions. Discerning different components that contribute toward a favorable protein–
ligand interaction could contribute significantly toward better understanding protein function, rationalizing drug design and obtaining design principles for protein engineering. The Protein Data Bank (PDB) currently hosts the structure of 68 000 protein–ligand complexes. Although several databases exist that classify proteins according to sequence and structure, a mere handful of them annotate and classify protein–ligand interactions and provide information on different attributes of molecular recognition. In this study, an exhaustive comparison of all the biologically relevant ligand-binding sites
(84 846 sites) has been conducted using PocketMatch: a rapid, parallel, in-house algorithm. PocketMatch quantifies the similarity between binding sites based on structural
descriptors and residue attributes. A similarity network was constructed using binding sites whose PocketMatch scores exceeded a high similarity threshold (0.80). The binding
site similarity network was clustered into discrete sets of similar sites using the Markov clustering (MCL) algorithm. Furthermore, various computational tools have been used to study different attributes of interactions within the individual clusters. The attributes can be roughly divided into (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of atomic probes, (ii) atomic contacts consisting of various types of polar, hydrophobic and aromatic contacts along with binding site water molecules that could play crucial roles in protein–ligand interactions and (iii) binding energetics involved in interactions derived from scoring functions developed for docking. For each ligand-binding site in each protein in the PDB, site similarity information, clusters they belong to and description of site attributes are provided as a relational database—protein–ligand interaction clusters (PLIC).

In the present study of solid media conditions for the refamycin B yield by solid state fermentation was studied and optimized using both classical method and statistical design of experiments). Statistical analysis of the results of Plackett-Burman showed that the lower level of initial moisture , initial pH, barbital, glucose and to solid media, or increase in the concentration of xylose in the range tested, results in significant effect in refamycin B yield of Amycolatopsis rifamycinica MTCC 14 by solid state fermentation. The effect of change in the levels of initial moisture, initial pH, barbital, glucose and xylose on the rfefamycin B yield was studied using central composite design methodology. Statistical analysis of the data showed that all the independent process had significant effect on refamycin B yield. The interaction between initial moisture and initial pH, between initial moisture and barbital, between initial moisture and glucose, between initial moisture and xylose, between initial pH and xylose, between barbital and glucose, between barbital and xylose, and between glucose and xylose were significant when the response was refamycin B.

There are about more than 3000 species of mosquitoes have been recorded in the world. The four
different nucleotide sequences of mosquitoes of culex, anopheles aedes aegypti in mosquito
biodiversity of their sequence similarities and evolutionary relationship were analysed.

In this current study, 16S rDNA (genotypic) identification technique is focused on identification of conventionally unidentifiable isolates those are unevaluated in isolated by employing molecular techniques and Bioinformatics in uploading and retrieving isolate gene sequences which are rapid, reliable and accurate in differentiation of various soils isolates. This study is an automaton of 16Sr DNA gene sequence that allows a queue comparison analysis of published sequences deposited in the microbial genome database was used. Polymerase chain reaction (PCR) amplification of 16SrDNA gene using the consensus bacterial primer and separation of the resulting polymer chain reaction amplicon  by cloning, temperature gradient electrophoresis are major ecological techniques that are used in the description of soil bacteria. The isolated gene was cloned using PTZ57r or T cloning Vector amplified using 16SF and 16SR primer transformed in DH5? Cells resulting PCs 16s Plasmid hybrid. The primer...

International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.

Insulin receptor (IR) and epidermal growth factor receptors (EGFR) are members of the receptor tyrosine kinase super family. The extracellular regions of both IR and EGFR contain two L domains. Many crystal structures of the extracellular regions of the IR and EGFR families have been determined in both the unliganded state and in complexes with ligands. The structures reveal that the L domains consist of four to six leucine rich repeats (LRRs); although, their amino acid sequences are highly variable. The present bioinformatic analysis reveals some features on the LRRs and the structures. We conclude that the LRRs in the L domains belong to a non-canonical motif differing from the known (canonical) motifs; the repeat units consist of two β-strands and the overall shape of the LRRs resembles a prism. To characterize the spatial arrangement of the two L-domains we propose two parameters; the distance between the two L domains (D) and the angle between the two axes showing the direction of the β-sheet stacking of the LRRs in the L domains (Ψ). These two parameters, D and Ψ, describe an essential feature of the structures and ligand induced structural changes.

Chloroquine resistance in P. falciparum is conferred by mutations in the pfcrt gene with K76T mutation being definitive in as far as chloroquine resistance is concerned. The prevalence of point mutations at codons 74, 75 and 76 of P. falciparum of the pfcrt gene was determined by dot�blot/probe hybridization analysis in 121 samples collected in May 2009 twelve years after chloroquine cessation in Kenya. We found out that 80.17% of the field isolates from Mbita harbor the T76 mutation while 19.83% have the wild type allele K76 [ T test, P= 0.108 (2005 versus 2009)]. Majority of the isolates had MNT haplotype at codons 74, 75 and 76. Conclusion: The study showed a significant expansion of the pfcrt 76K allele and thus expansion of parasite populations that are sensitive to chloroquine.

Background / Purpose: This study aimed to obtain recombinant hepatitis C virus (HCV) structural protein as a vaccine candidate; that would produce a CD4+ T and CD8+ T cell immune responses and that could be manufactured on a large scale. In order to obtain a HCV structural protein as a vaccine component, the nucleotide sequence of C, E1, E2 (CE1E2) proteins was constructed. Based on the amino acid sequence consensus of CE1E2 protein, genotype 1b; which was derived from multiple sequence alignment of amino acid sequences retrieved from several websites. Main conclusion: Multiple sequence alignment using both Clustal X software and manual alignment chose a consensus amino acid sequence which had the GenBank accession number ABV46113.1. Consensus sequence of CE1E2 protein could represent amino acid sequences of CE1E2 protein of genotype 1b. Consensus amino acid sequence translation using CHO codon preference gave a nucleotide sequence with a codon adaptation index value, 0.72. CE1E2 pr...

Effects of naturally existing rare-earth metals (REMs; atomic numbers, 39, 57-60, 62-71; Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb and Lu), added as chloride salts, on Ca2+ influx induced by two different stimuli, namely hypoosmotic shock and hydrogen peroxide, were examined in a suspension-cultured transgenic cell line of BY-2 tobacco cells expressing aequorin, a Ca(2+)-sensitive luminescent protein in cytosol. Most REM salts used here showed inhibitory effect against Ca2+ influx. Especially NdCl3, SmCl3, EuCl3, GdCl3 and TbCl3 showed the most robust inhibitory action. In contrast, LuCl3, YbCl3, ErCl3 and YCl3 were shown to be poor inhibitors of Ca2+ influx. Since REMs tested here form a sequential range of ionic radii from 86.1 to 103.2 pm and the optimal range of ionic radii required for blocking the flux of Ca2+ was determined for each stimulus. The hydrogen peroxide-induced Ca2+ influx was optimally blocked by REMs with a broad range of ionic radii (93.8-101 pm) whi...

International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.

Scope International Journal of Artificial Intelligence and Soft Computing (IJAISC) is a open access journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications. Topics of interest include, but are not limited to, the following

Would this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input training data features for supervised learning are still lacking. Therefore, I emphasize not to invest much effort into algorithmic computational optimization before the feature discovery process allows selecting all needed features through variation before realistic predictions can be achieved by enhancing computations and parameters. For me this simple progression from completed feature selection to optimized algorithm is essential for more rapid discoveries. That is why I am still wondering why I have seen so many papers focusing on improving computational predictions without any kind of prior considerations about having properly completed the absolutely essential exhaustive feature selection process without which no subsequent computations can lead to satisfactorily predictions, which are reasonably consistent with our experimental observations and measurements. I am afraid that I am still the only one, to whom the writing above can make sense. I had expected much more enthusiasm, excitement and optimism about very likely accelerating our discovery rate by first focusing on uncovering still hidden objects and features through diverse variations of conditions, procedures, methods, techniques and measurements, followed by the exhaustive selection for all relevant needed features, followed by designing, developing, combining and optimizing the computational steps of our machine learning algorithm until our predictions match our experimentally obtained observations. Once this major machine learning objective has been achieved we have reached its final status beyond which we cannot improve it unless we can generate conditions, which cause our previously perfectly predicting machine learning algorithm to obviously fails because it is an absolute prerequisite for discovering more relevant training reflected by more dimensions. According to my current understanding any newly discovered essential input data feature inevitably causes a rise in the dimensionality of input data components, which must be considered together but never in isolation. For example, if I train with 100 input features our input variable must consist of exactly 100 components or parts, which together form a new level of single measuring points, which tends to be much different in controlling its manipulations and their overall effects from anything, which could possibly get anticipated, when trying to add up the effects of its 100 parts to a new total. This new total tends to consist of much different dimensions and often refers to completely unrelated kind of data than when combining all 100 components consisting of exactly the same input values for every of its 100 variables but by considering all 100 components like a single indivisible unit of measurement points, which often results in completely different kinds of unrelated seeming properties/features, which are not even closely reflecting the results, which we'd obtain if we executed each dimension on its own in isolation and sequential order. For example, stopping translation prematurely by three consecutive tryptophans has a much different impact, i.e. stop, then when translating each of the tryptophan in isolation separated by other amino acids, since this causes the nascent polypeptide chain to grow. Each tRNA charged with tryptophan, which complements mRNA triplets, causes the peptide to grow by a single tryptophan, which gets added to it. So when you try 2 tryptophans the polypeptide grows by two amino acids. But when you try 3 consecutive tryptophans, then-counter-intuitively-instead of the expected growth by 3 amino acids-translation prematurely stops. Stopping translation prematurely is of a much different dimension, level, effect and data kind, then when keep adding more amino acids to the growing peptide chain. If we consider the effect of complementary binding of a tRNA to its mRNA codon our peptide grows by one amino acid any charged tRNA adds another amino acid of one of 20 categorical values. Normally no amino acid can cause the translation to step prematurely, not even two amino acids as a pair. But three amino acids as an indivisible triplet, which must be considered as a new single value requiring all three tryptophans to be sequentially to be present, like a single indivisible data block, which must not be divided only in triplets, because only triplets, but no pair or singlet, can stop translation prematurely. Another example is predicting overall cellular protein composition. It depends on how many mRNA strands coding for a particular protein are in the cytoplasm. There is proportionality between number of cytoplasmic mRNA strands and total protein. Therefore, if the cell needs to double protein abundance it could double transcription and keeps everything else the same. But a much better and less step intensive, more economic way of doubling protein concentration is to double the length of the poly-(A)-tail. Extending the length of the poly-(A)-mRNA-tail may require about 100 additional adenines whereas doubling transcription requires about at least 500-instead of only 100-new nucleotides in addition to all needed transcriptional modification steps with their elaborate synthesis machinery. If the dividing yeast must raise its lipid synthesis by more than 10-fold during the short M-phase, it could increase transcription by a factor of 10, it could make the poly-(A)-mRNA-tail 10 times longer, or it could synthesized 10 times more new ribosomes to increase the ribosomal translation by a factor of 10 simply by reducing the distance of free uncovered mRNA nucleotides between adjacent ribosomes translating running down the same mRNA strand. If more than one ribosome is translating the same mRNA strand simultaneously, it is called a poly-ribosome. Hence, having 10 times more ribosomes binding to the same mRNA strand at the same time increases translation by a factor of 10 without needing any additional transcription. Above I have given three easy examples to get 10 times more proteins. Although all 3 methods have the same final result, i.e. 10 times more proteins, their mode of action, their required essential features, their dimensions and their minimally required parts, which must be considered like a single value, are totally different.

Plants always have to fight against various environmental stress conditions like cold, drought, salinity, submergence, etc. The prime target of recent research in plant biology is to unveil the intricate series of events in responses and adaptation to different stress conditions. Sufficient in-silico computational studies are yet to be done to distinguish the stress related genes from the non-stress related ones. As common mechanisms of stress responses exist among different plants, we sought to identify the general structural and functional features that may be hidden in stress related genes of different plant species. We assumed that these features in stress-related genes might be different from non stress related genes. One hundred and sixty stress-responsive genes from five different plant species were studied. Computational and bioinformatics studies were done to determine several structural properties like length of gene, exon, intron, UTRs as well as to identify overrepresented sequence motif and enrichment of gene ontology (GO) functions. The UTRs of stress related genes were found to be significantly different from non-stress related genes and a “G-C” rich small sequence motif was found to be associated significantly with stress genes. Key biological processes like small GTPase mediated signal transduction, cellular components like thylakoid and molecular functions like oxidoreductase activity are significantly enriched for stress related genes. Further studies are required to identify more stress specific features of plant stress genes which may help to establish a computational model for detecting stress related genes from various gene lists.