Computational Drug Design and Molecular Dynamic Studies-A Review

Drug designing and molecular dynamic studies were an intense, lengthy and an interdisciplinary venture. At present, a new approach towards the use of computational chemistry and molecular modeling for in-silico drug design. Computational in-silico drug design skills are used in bioinformatics, computational biology and molecular biology.

Drug designing using in-silico methods is cost effective in research and development of drugs. Currently, a vast number of software’s used in drug design. In-silico drug designing and molecular dynamic studies can be performed by using different methods namely homology modeling, molecular dynamic studies, energy minimization, docking and QSAR etc. By using in-silico drug designing we can produce an active lead molecule from the preclinical discovery stage to late stage clinical development. The lead molecules which are developed will help us in selection of only potent leads to cure particular diseases. Therefore in-silico methods have been of great importance in target identification and in prediction of novel drugs.

Novel Drug Designing and Target Identification using Computational Bioinformatics

Both drug designing and molecular dynamic studies involve lengthy and extensive efforts that are interdisciplinary in nature. Of late, computational chemistry and molecular modeling are widely applied in the in-silico drug design. Computational in-silico drug design is widely applied in bioinformatics, computational biology and molecular biology. In-silico methods for drug designing has been proved cost effective and in the drug development. In-silico drug designing is helpful in developing active lead molecules from the preclinical discovery stage to late stage clinical development. Vast number of software is used in drug designing and in-silico methods are useful in target identification and the prediction of novel drugs.

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Shikimate Kinase of Yersinia pestis: A Sequence, Structural and Functional Analysis

Yersinia pestis, the causative organism of Plague, is widely recognized as a potential bioterrorism threat. Due to the absence of homologs in human, Shikimate Kinase (SK) is considered as an excellent drug target in several bacterial and protozoan parasites. Ample literature evidences confirm the suitability of this protein as a good target. Therefore, Shikimate Kinase of Shikimate pathway in Yersinia pestis represents an attractive drug target.

In the present study, a clustering approach was undertaken to select the proper representative for Shikimate Kinase sequences belonging to Yersinia pestis for structure determination. Three-dimensional models of the enzyme for KFB61218.1 (SK1), EFA47400.1 (SK2) and WP_016255950.1 (SK3) were generated using a comparative molecular modeling approach where structures were developed using the single specific template as well as multiple closely associated templates. The structures of Shikimate Kinase developed using comparative modeling were evaluated for stereochemical quality using various structural validation tools. Results from structural assessment tools indicated the reasonably good quality of models.

The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer

Breast cancer is a leading fatality cancer for woman. According to epidemiological data, breast cancer accounts for 20-25% of female malignant tumor, with is expected to increase. These facts have driven us to select this deadly cancer as our domain. Breast cancer has four early signs; micro-calcification, mass, architectural distortion and breast asymmetries. However, only data regarding mass will be used as a pilot project to test our system later on. Masses of 2 cm in diameter are palpable with regular breast self-examination while mammogram images can capture it from 5 mm in diameter.

However, these images were to be determined by an expert radiologist who is familiar with breast cancer. Generally, there are 2 types of breast cancer which are in situ and invasive. In situ starts in the milk duct and does not spread to other organs even if it grows. Invasive breast cancer on the contrary, is very aggressive and spreads to other nearby organs and destroys them as well. It is very important to detect the cancerous cell before it spreads to other organs, thus the survival rate for patient will increase to more than 97%. However, time taken from taking mammogram images to biopsy dangerous result varies between 2weeks to a month in average.

The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer

As breast cancer can be very aggressive, only early detection can prevent mortality. The proposed system is to eliminate the unnecessary waiting time as well as reducing human and technical errors in diagnosing breast cancer. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain.

This paper uses the neural networks with an incremental learning algorithm as a tool to classify a mass in the breast (benign and malignant) using selection of the most relevant risk factors and decision making of the breast cancer diagnosis To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). ANN with an incremental learning algorithm performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained classification accuracy of 99.95%, a very promising result compared with previous algorithms already applied and recent classification techniques applied to the same database.

Meta-Analysis of Genomic Data: Between Strengths, Weaknesses and New Perspective

The rapid advances in high-throughput technologies, such as microarrays have revolutionizing the knowledge and understanding of biological systems and genetic signatures of human diseases. This has led to the generation and accumulation of a large amount of genomic data that need to be adequately integrated to obtain more reliable and valid results than those from individual experiments. Meta-analysis of microarray data is one of the most common statistical techniques used for combining multiple data sets. Despite its remarkable successes in discovering molecular subtypes, underlying pathways and biomarkers for the pathological process of interest, this method possesses several limitations.

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A Bayesian Analysis of Copy Number Variations in Array Comparative Genomic Hybridization Data

Array Comparative Genomic Hybridization (CGH) has been widely used for detecting genomic copy number variations (CNVs). The central goal of array CGH data analysis is to accurately detect homogeneous regions of log intensity ratios which represent relative changes in DNA copy number. Various methods have been proposed in recent years. Most methods, however, do not consider correlations of neighboring probe measurements, and are usually designed for analysis at single sample level rather than detecting common or recurrent CNVs among multiple samples. We propose a Bayesian segment-based approach for efficient analysis of array CGH data. The proposed method is based on simple assumptions but is general enough to accommodate various spatial correlations among probe measurements. It also allows for multiple samples with recurrent CNVs, therefore is able to borrow strength across samples.

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A Discriminative Feature Space for Detecting and Recognizing Pathologies of the Vertebral Column

Each year it has become more and more difficult for healthcare providers to determine if a patient has a pathology related to the vertebral column. There is great potential to become more efficient and effective in terms of quality of care provided to patients through the use of automated systems.

However, in many cases automated systems can allow for mis classification and force providers to have to review more causes than necessary. In this study, we analyzed methods to increase the True Positives and lower the False Positives while comparing them against state of- the-art techniques in the biomedical community. We found that by applying the studied techniques of a data-driven model, the benefits to healthcare providers are significant and align with the methodologies and techniques utilized in the current research community.

In silico Study of Bacillus brevis Xylanase - Structure Prediction and Comparative Analysis with Other Bacterial and Fungal Xylanase

The most important building block of hemicelluloses is xylan. It is broken down into xylose oligomer residues by Xylanase - an enzyme, produced by most organisms, to utilize xylose as primary source of carbon. The Xylanase produced are classified into families, viz 5, 8, 10, 11 and 43 - of Glycoside Hydrolases (GH).

Xylanase from family GH 11 are monospecific, they consist solely of Xylanase activity, exclusively active on D-xylose containing substrates.They are inactive on aryl cellobiosidase. The fungal Xylanase are produced in higher concentrations, as compared to bacterial Xylanase, but have limited use in pulp bleaching, as they affect the viscosity and strength of the product. In the present study, we have worked upon the Xylanase of Bacillus brevis, which is fulfilling all the required quality needed to be a commercial Xylanase, and thus is used by many industries. The enzyme, when studied after modelling, provided similar structural configuration with high stability. When compared with other bacterial and fungal Xylanase structures, it provided better potential to ‘activity enhancement’ and ‘in silico handling’.

Bio-Analytical Method Development and Validation for Estimation of Lume fantrine in Human Plasma by Using Lc-Ms/Ms

Lumefantrine and Glimepiride (IS) were extracted from human plasma by Precipitation followed by Solid phase extraction using Orochem (30 mg/1 CC) solid phase extraction cartridge. The chromatographic separation was performed on Hypurity C18 (50 cm×4.6 mm), 5 μ column. The mobile phase consisted of Acetonitrile: 2 mM Ammonium Acetate (pH: 3.5) (90:10, % v/v) was delivered at rate of 0.600 mL/min with Splitter. Detection and quantitation were performed by a triple quadrupole equipped with electro spray ionization and multiple reaction monitoring inpositive ionization mode (API 3000). The most intense [M-H]- transition for Lume fantrine at m/z 528.0→510.0 and for IS at m/z 491.2→352.0 were used for quantification. 

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Role of In-silico methods in the identification of Novel Drugs

Drug designing and the molecular dynamic studies are lengthy, intensified and inter-disciplinary activity. Approaches like computational chemistry and molecular modeling are widely applied in the development of in-silico drug design because it is cost effective.  Currently, a vast number of software is used in drug design. Using in-silico drug designing techniques it is possible to produce active lead molecule right from the preclinical discovery stage to late stage clinical development. The lead molecules will be helpful in the selection of potent leads to cure particular diseases. In-silico methods thus are important in target identification and prediction of novel drugs.

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Sequence Features and Subset Selection Technique for the Prediction of Protein Trafficking Phenomenon in Eukaryotic Non Membrane Proteins

Protein trafficking or protein sorting is the mechanism by which a cell transports proteins to the appropriate position in the cell or outside of it. This targeting is based on the information contained in the protein. Many methods predict the sub cellular location of proteins in eukaryotes from the sequence information. However, most of these methods use a flat structure to perform prediction. In this work, we introduce ensemble methods to predict locations in the eukaryotic protein-sorting non membrane pathway hierarchically.

We used features that were extracted exclusively from full length protein sequences with feature subset selection for classification. Sequence driven features, sequence mapped features and sequence auto correlation features were tested with ensemble learners and classifier performances were compared with and without feature subset selection technique. This study shows the new features extracted from full length eukaryotic protein sequences are effective at capturing biological features among compartments in eukaryotic non membrane pathways at two levels. Feature subset selection techniques helped to reduce the time taken for building the classification model.

Cytotoxic Effects of Aflatoxin B1 Standard in Relation to Aflatoxin Extracts from South African Compound Feeds on Human Lymphocytes

Cytotoxicity testing of aflatoxin (AF) on the viability of cells grown in cultures can be widely used to predict the potential toxic effects of AF in animals. To this end, an in vitro experimental study was conducted to ascertain the toxic effects of AF extracts obtained from compound feeds in South Africa on human lymphocytes in comparison to that of an AFB1 standard. The approach adopted was on the basis of viable cells reducing methyl tetrazolium bromide (MTT) from blue to a purple formazan dye, which was then spectrophotometrically quantified to provide the rate of cytotoxicity. 

Data obtained indicated no cytotoxic response in control cells, as the viability of cells without treatment with AF standard or methanolic extracts of AF extracts [negative control] using methanol as the reconstituting solvent, was 99.9% after 24 hrs. of incubation. However, cell viability significantly (p<0.001) decreased upon exposure to AF extracts especially for poultry feed. This was influenced by both the dose and duration of exposure, which was much more pronounced when the cells were exposed to AFB1 standard than for all the AF extracts tested. This implies that these feeds on exposure to AF can greatly influence animal health with respect to both the contamination dose and exposure time.

Intraductal Carcinoma of the Prostate Diagnosed by Multi-Parametric Prostate Magnetic Resonance Imaging (MRI) and MRI/Ultrasound Fusion-Guided Biopsy

Although, the term “intraductal carcinoma of the prostate” (IDC-P) was first used by Rhamy, McNeak and Yemoto, were the first to delineate IDC-P as a distinct biological entity with definable histological and clinical features. IDC-P is defined as aproliferation of malignant prostate adeno carcinoma cells distending or completely spanning the lumen of pre-existing prostatic ducts and acini, with at least focal preservation of basal cells. Watts et al. estimated the incidence of IDC-P to 2.8% in prostate biopsies.

Histological criteria for the diagnosis of IDC-P include solid; dense cribriform (>50% cellularity of the lumen); trabecular/micropapillary; and loose cribriform intraductal proliferation of malignant cells. The latter two growth patterns share much similarity with HGPIN. In these instances, additional diagnostic criteria, such as marked nuclear pleomorphism (nuclear enlargement > 6x normal nuclei), and nonfocal comedonecrosis (> 1 duct showing comedonecrosis) are criteria needed to differentiate it from HGPIN.

Editorial for International Journal of biomedical Data Mining

This issue of the International Journal of Biomedical Data Mining presents two contributed articles. The first article, entitled Data Inventory for Cancer Patients Receiving Radiotherapy for Outcome Analysis and Modeling, authored by Jason Vickress, Rob Barnett and Slav Yartsev, describes a database created for storing and analyzing patient specific data related to pre-treatment condition, treatment planning, and treatment outcomes, for patients receiving radiotherapy based cancer treatment. 

The proposed database can perform automated analysis regarding quality assurance, dose accumulation for multiple treatments on different machines and can assist physicians in choosing the optimal radiation therapy for new patients. The second article, entitled Likelihood Ratio Test of Hardy-Weinberg Equilibrium Using Uncertain Genotypes for Sibship Data, authored by Qiong Li, Helene Massam and Xin Gao, is concerned with the problem of testing for Hardy-Weinberg equilibrium of genotype frequencies in the area of population genetics.

Likelihood Ratio Test of Hardy-Weinberg Equilibrium Using Uncertain Genotypes for Sibship Data

Testing for Hardy-Weinberg equilibrium of genotype frequencies is a crucial first step in the study of population genetics. In this paper, we develop an Expectation-Maximization algorithm to estimate the genotype frequencies for sibship data with genotype uncertainty. We also develop a likelihood ratio test of Hardy-Weinberg equilibrium for sibships with no parental genotypes available and with possible genotyping errors. Simulations show that our likelihood ratio test maintains valid control of the type I error rate and good statistical power. Finally, the likelihood ratio test is extended across strata when a sample is stratified by multiple ethnic populations with different genotype frequencies.

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Data Inventory for Cancer Patients Receiving Radiotherapy for Outcome Analysis and Modeling

Data collection for cancer patients is recognized as an important task in the USA, where the National Program of Cancer Registries (NPCR) administered by the Centers for Disease Control and Prevention collects data on the occurrence, type, extent, and location of the cancer, and the type of initial treatment. The International Consortium for Health Outcomes Measurements (ICHOM) aims at providing a global resource of in-use outcome measures and risk adjustment factors by medical condition and creating a global standard for measuring results. 

These initiatives will enable public health professionals to understand and address the cancer burden more effectively. We have recently proposed to use the pre-treatment, planning, and treatment outcomes data for cancer patients undergoing radiation therapy to provide guidelines for optimal choice of both radiation modality and planning for new patients. It is important to determine the most influential patient features (or their combinations) that has the strongest correlation with the outcomes. We propose an Overlap Volume Histogram as a valuable representation of size and shape for tumor and organs at risk important for planning.

A Similarity Retrieval Tool for Functional Magnetic Resonance Imaging Statistical Maps

A fundamental goal in functional neuroimaging is to identify areas of activation in the brain relative to a given task. Functional magnetic resonance imaging (fMRI) is one technique used to identify such changes because changes in neuronal activity along a given region of the brain can be captured by a corresponding change in voxel value intensity on the acquired fMRI image. Statistical parametric mapping (SPM) [13] is the current popular technique used to analyze fMRI images. An SPM image contains test statistics determined at each pixel by the ratio between the intensity of the signal and its variance across experimental conditions.

Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images

In this work we introduce the use of penalized logistic regression (PLR) to the problem of classification of MRI images and automatic detection of Alzheimer’s disease. Classification of sMRI is approached as a large scale regularization problem which uses voxels as input features. 

We evaluate how differences in sMRI pre-processing steps such as smoothing, normalization, and template selection affect the performance of high dimensional classification methods. In addition, we compared the relative performance of PLR to a different approach based on support vector machines. To study these questions we used data from the Alzheimer Disease Neuroimaging Initiative (ADNI). 

Enhancing Visual Evoked Potentials Detection with Use of Computational Intelligence Tools

The analysis of evoked potentials (EPs) in the electroencephalogram (EEG) is usually inspected visually and demands subjective interpretation of the results. This paper aims at combining an statistical criterion based on the magnitude square multiple coherence (MSMC) estimate with computational intelligence methods in order to estimate the EPs detection rate (DR) using only portions of the frequency spectrum. 

Thus, networks were used to predict the DR in EEG signals of 15 normal subjects during stroboscopic stimulation. The algorithms were designed to receive the spectral information of two, four or six EEG derivations as the input and DR as the output. Our best result shows that the artificial neural networks can estimate DR with correlation coefficient of 0.97 compared with MSMC, even when a reduced amount of spectral information from the data is available.