Nouman Rasool

A Prediction Model for Membrane Proteins Using Moments Based Features.

The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies.

A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes

Membrane proteins are vital mediating molecules responsible for the interaction of a cell with its surroundings. These proteins are involved in different functionalities such as ferrying of molecules and nutrients across membrane, recognizing foreign bodies, receiving outside signals and translating them into the cell. Membrane proteins play significant role in drug interaction as nearly 50% of the drug targets are membrane proteins. Due to the momentous role of membrane protein in cell activity, computational models able to predict membrane protein with accurate measures bears indispensable importance. The conventional experimental methods used for annotating membrane proteins are time-consuming and costly and in some cases impossible. Computationally intelligent techniques have emerged to be as a useful resource in the automation of prediction and hence the annotation process. In this study, various techniques have been reviewed that are based on different computational intelligence models used for prediction process. These techniques were formulated by different researchers and were further evaluated to provide a comparative analysis. Analysis shows that the usage of support vector machine-based prediction techniques bears more assiduous results.

Prediction of N-linked glycosylation sites using position relative features and statistical moments.

Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen’s recognition, cell-cell communication, expression of genes and protein folding. It is a significant challenge to identify glycosylation sites in protein sequences as experimental methods are time taking and expensive. A reliable computational method is desirable for the identification of glycosylation sites. In this study, a comprehensive technique for the identification of N-linked glycosylation sites has been proposed using machine learning. The proposed predictor was trained using an up-to-date dataset through back propagation algorithm for multilayer neural network. The results of ten-fold cross-validation and other performance measures such as accuracy, sensitivity, specificity and Mathew’s correlation coefficient inferred that the accuracy of proposed tool is far better than the existing systems such as Glyomine, GlycoEP, Ensemble SVM and GPP.

Evaluation of different methods for DNA extraction from human burnt bones and the generation of genetic profiles for identification

Bone exposure to heat in the presence of moisture breaks the phosphodiester bonds of the backbone, leaving sheared DNA in bone cells. This also limits the possibility of generating a complete profile of the victim. With the increasing incidence of fire outbreaks over the past few years, a paradigm shift to establish identity has been observed, from morphological identification of victims to STR profiling. For this study, 10 bone samples were taken from burnt human bodies that were recovered from different fire outbreak scenes. The DNA from these burnt human tissues was isolated using four different extraction methods: the organic extraction method, the total demineralisation method, the Qiagen kit method, and the Chelex extraction method. STR profiles of victims were generated on a genetic analyser using an AmpFlSTR Identifiler® Plus Kit and analysed on Gene Mapper ID-X. DNA isolated from bones using the total demineralisation extraction method and organic extraction method was of the highest quality due to the efficient removal of inhibitors. DNA obtained using these two methods successfully generated the STR profiles of the victims. The quality of isolated DNA obtained through the Qiagen kit was comparatively low, but STR profiles of the victims were successfully generated. The Chelex kit failed to extract good quality DNA of high quantity from the burnt bones, encountering inhibition in all samples at varying degrees. This study concludes that total demineralisation and the Qiagen kit are sophisticated and reliable methods to obtain a good yield of DNA from burnt human bones, which can be used for the identification of victims.

Detection of dry bloodstains on different fabrics after washing with commercially available detergents

Blood is significant evidence that can help an investigator solve a crime. It can link a suspect to a crime and also help in reconstructing the crime scene. Criminals often attempt to eliminate bloodstained evidence at a scene by washing it. These attempts can result in alteration, or partial or complete removal of stained areas. Many presumptive tests are used to detect the bloodstains on clothes. In this study, bloodstained fabrics were washed with commercially available and frequently used detergents. Kastle-Meyer (KM), Leucomalachite green (LMG), Tetramethylbenzidine (TMB) and Hemastix tests were used to detect the presence of blood on these washed fabrics. The Hemastix test was found to be the most sensitive to detecting the washed stains on all cloths. The Leucomalachite green test was found to be the least sensitive. The ability of a fabric to retain blood after washing depends not only upon the chemistry and manufacturing of the fabric but also on the type of detergent. The time of immersion of the fabric with detergent also affects the removal of stains from fabrics. Ariel showed the best results in removing bloodstains from all fabrics. Cotton polyester and khaddar showed the maximum retention of blood after washing with either of the detergents, whereas silk polyester had the minimum ability to hold bloodstains.

Computer-aided analysis of phytochemicals as potential dengue virus inhibitors based on molecular docking, ADMET and DFT studies

Background & objectives: Dengue fever, caused by dengue virus (DENV), has become a serious threat to human lives. Phytochemicals are known to have great potential to eradicate viral, bacterial and fungal-borne diseases in human beings. This study was aimed at in silico drug development against nonstructural protein 4B (NS4B) of dengue virus 4 (DENV4). Methods: A total of 2750 phytochemicals from different medicinal plants were selected for this study. These plants grow naturally in the climate of Pakistan and India and have been used for the treatment of various pathologies in human for long-time. The ADMET studies, molecular docking and density functional theory (DFT) based analysis were carried out to determine the potential inhibitory properties of these phytochemicals. Results: The ADMET analysis and docking results revealed nine phytochemicals, i.e. Silymarin, Flavobion, Derrisin, Isosilybin, Mundulinol, Silydianin, Isopomiferin, Narlumicine and Oxysanguinarine to have potential inhibitory properties against DENV and can be considered for additional in vitro and in vivo studies to assess their inhibitory effects against DENV replication. They exhibited binding affinity ≥−8 kcal/mol against DENV4-NS4B. Furthermore, DFT based analysis revealed high reactivity for these nine phytochemicals in the binding pocket of DENV4-NS4B, based on ELUMO, EHOMO and band energy gap. Interpretation & conclusion: Five out of nine phytochemicals are reported for the first time as novel DENV inhibitors. These included three phytochemicals from Silybum marianum, i.e. Derrisin, Mundulinol, Isopomiferin, and two phytochemicals from Fumaria indica, i.e. Narlumicine and Oxysanguinarine. However, all the nine phytochemicals can be considered for in vitro and in vivo analysis for the development of potential DENV inhibitors.

Computational exploration of antiviral activity of phytochemicals against NS2B/NS3 proteases from dengue virus

Abstract Background: Dengue fever has emerged as a serious threat in Pakistan in the last few years with high morbidity rates and substantial mortality. In the present study, NS2B/NS3 protease from four dengue virus (DENV) serotypes have been targeted using 2350 phytochemicals from various medicinal plants. Material and methods: The phytochemicals were subjected to docking against NS2B/NS3 proteases using AutoDock Vina focusing the binding site, and the binding energies were determined to screen the effectively docked phytochemicals. Pharmacological properties were also analyzed for all the phytochemicals using PreADMET web server. Results: Binding affinities ranged from −4.0 to –9.8 kcal/mol and a threshold of −9.0 kcal/mol was applied for screening compounds. A total of 18 phytochemicals are screened for passing all evaluation criteria of a drug in which three were for DENV1-NS2B/NS3, five for DENV2-NS2B/NS3, six for DENV3-NS2B/NS3 and four for DENV4-NS2B/NS3. Erycristagallin and Osajin from Erythrina variegate, PapraineA from Fumaria indica and Aloe-Emodin from Aloe vera are the most potent inhibitors of NS2B/NS3 protease from DENV1, DENV2, DENV3 and DENV4, having binding affinities of −9.6 kcal/mol, −9.6 kcal/mol, −9.6 kcal/mol and −9.2 kcal/mol, respectively. Conclusion: The effective drug-like properties of all 18 phytochemicals demonstrate the inhibition potential against dengue virus replication in human beings.

iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC

Protein phosphorylation is one of the most fundamental types of post-translational modifications and it plays a vital role in various cellular processes of eukaryotes. Among three types of phosphorylation i.e. serine, threonine and tyrosine phosphorylation, tyrosine phosphorylation is one of the most frequent and it is important for mediation of signal transduction in eukaryotic cells. Site-directed mutagenesis and mass spectrometry help in the experimental determination of cellular signalling networks, however, these techniques are costly, time taking and labour associated. Thus, efficient and accurate prediction of these sites through computational approaches can be beneficial to reduce cost and time. Here, we present a more accurate and efficient sequence-based computational method for prediction of phosphotyrosine (PhosY) sites by incorporation of statistical moments into PseAAC. The study is carried out based on Chou’s 5-step rule, and various position-composition relative features are used to train a neural network for the prediction purpose. Validation of results through Jackknife testing is performed to validate the results of the proposed prediction method. Overall accuracy validated through Jackknife testing was calculated 93.9%. These results suggest that the proposed prediction model can play a fundamental role in the prediction of PhosY sites in an accurate and efficient way.

Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC

For many biological functions membrane proteins (MPs) are considered crucial. Due to this nature of MPs, many pharmaceutical agents have reflected them as attractive targets. It bears indispensable importance that MPs are predicted with accurate measures using effective and efficient computational models (CMs). Annotation of MPs using in vitro analytical techniques is time-consuming and expensive; and in some cases, it can prove to be intractable. Due to this scenario, automated prediction and annotation of MPs through CM based techniques have appeared to be useful. Based on the use of computational intelligence and statistical moments based feature set, an MP prediction framework is proposed. Furthermore, the previously used dataset has been enhanced by incorporating new MPs from the latest release of UniProtKB. Rigorous experimentation proves that the use of statistical moments with a multilayer neural network, trained using back-propagation based prediction techniques allows more thorough results.

Tattooing trend: major cause of HCV transmission among youngsters

Sir,In this journal, recently, a very high prevalence of chronic hepatitis C virus (HCV) in people who inject drugs was reported [1] and the authors called for measures to prevent spread by increas...