I. Arnold Emerson
Bio: I. Arnold Emerson is an academic researcher from VIT University. The author has contributed to research in topic(s): Gene & Protein ligand. The author has an hindex of 1, co-authored 4 publication(s) receiving 5 citation(s).
Topics: Gene, Protein ligand, Docking (molecular), microRNA, Psychosocial
TL;DR: This review outlines the major tools for protein - ligand docking which in turn emphasize the importance of molecular docking in modern drug discovery process.
Abstract: Applications of computer and information technology are indispensable in various fields especially in the field of biology. The use of computer aided tools plays a key role in solving biological problems. The spontaneous process of molecular docking is important for finding potentially strong candidate of drug for various viruses. The binding of protein receptors with ligand molecules is essential in drug discovery process. The aim of molecular docking tools is to predict the interaction between protein and ligand. This review outlines the major tools for protein - ligand docking which in turn emphasize the importance of molecular docking in modern drug discovery process.
TL;DR: A wholistic assessment method was used in understanding problems experienced by an adolescent boy and it was suggested that there was an overall improvement in academic performance, social and communication skills.
Abstract: The present study seeks to outline a wholistic assessment method that was used in understanding problems experienced by an adolescent boy. Quantitative and qualitative assessments were done to identify cognitive and psychosocial problems. Parent, teacher and child’s reports were used in obtaining essential information. We developed intervention strategies using parents as co-therapists. An individualized educational program was designed and assistive techniques were suggested. We reassessed the child after six months to understand the effectiveness of the intervention. Findings suggested that there was an overall improvement in academic performance, social and communication skills. These are important implications for practioners as learning disability can be managed successfully with the help of specially designed individual programs.
TL;DR: This review outlines the roles of oncogenes, the importance of cytochrome P450 (CYP450) in cancer susceptibility, and its impact on drug metabolism, proposing combined approaches to achieve precision therapy.
Abstract: The Human Genome Project has unleashed the power of genomics in clinical practice as a choice of individualized therapy, particularly in cancer treatment. Pharmacogenomics is an interdisciplinary field of genomics that deals with drug response, based on individual genetic makeup. The main genetic events associated with carcinogenesis activate oncogenes or inactivate tumor-suppressor genes. Therefore, drugs should be specific to inactivate or regulate these mutant genes and their protein products for effective cancer treatment. In this review, we summarize how polymedication decisions in cancer treatments based on the evaluation of cytochrome P450 (CYP450) polymorphisms are applied for pharmacogenetic assessment of anticancer therapy outcomes. However, multiple genetic events linked, inactivating a single mutant gene product, may be insufficient to inhibit tumor progress. Thus, genomics and pharmacogenetics directly influence a patient’s response and aid in guiding clinicians to select the safest and most effective combination of medications for a cancer patient from the initial prescription. This review outlines the roles of oncogenes, the importance of cytochrome P450 (CYP450) in cancer susceptibility, and its impact on drug metabolism, proposing combined approaches to achieve precision therapy.
01 Jan 2021-Journal of Genetics
TL;DR: In this article, the authors performed functional enrichment analysis using gene ontology and Elsevier disease pathway collection and constructed a regulatory network of 577 differentially expressed genes (DEGs) where 146 overexpressed and 431 underexpressed with a significant threshold of adjusted P values < 0.05.
Abstract: Human clear cell renal cell carcinoma (ccRCC) is the most common and frequently occurring histological subtype of RCC. Unlike other carcinomas, candidate predictive biomarkers for this type are in need to explore the molecular mechanism of ccRCC and identify candidate target genes for improving disease management. For this, we chose case-control-based studies from the Gene Expression Omnibus and subjected the gene expression microarray data to combined effect size meta-analysis for identifying shared genes signature. Further, we constructed a subnetwork of these gene signatures and evaluated topological parameters during the gene deletion analysis to get to the central hub genes, as they form the backbone of the network and its integrity. Parallelly, we carried out functional enrichment analysis using gene ontology and Elsevier disease pathway collection. We also performed microRNAs target gene analysis and constructed a regulatory network. We identified a total of 577 differentially expressed genes (DEGs), where 146 overexpressed and 431 underexpressed with a significant threshold of adjusted P values <0.05. Enrichment analysis of these DEGs' functions showed a relation to metabolic and cellular pathways like metabolic reprogramming in cancer, proteins with altered expression in cancer metabolic reprogramming, and glycolysis activation in cancer (Warburg effect). Our analysis revealed the potential role of PDHB and ATP5C1 in ccRCC by altering metabolic pathways and amyloid beta precursor protein (APP) role in altering cell-cycle growth for the tumour progression in ccRCC conditions. Identification of these candidate predictive genes paves the way for the development of biomarker-based methods for this carcinoma.
01 May 1951
TL;DR: A novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI is proposed, which has good prediction performance and is a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.
Abstract: Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.
01 Jan 2018-Procedia Computer Science
TL;DR: This paper would discuss large scale data analysis using different implementations on the above mentioned tools and after that it would give a performance analysis of these tools on the given implementation like Cap3, HEP, Cloudburst.
Abstract: Cloud is referred to as a collection of infrastructure services, such as Infrastructure as a service (IaaS) and Platform as a service (PaaS), which are made available to us for utilization by various organizations in which the key factor is virtualization of data as it allow the user to manage, handle and compute a large number of tasks very easily. By referring to Cloud technologies we mean runtime such as Hadoop, Dryad and other Map Reduce frameworks. In this paper we would analyse the above mentioned software’s and techniques for the cloud system by comparing them on the basis of its processing speed, its data handling capacity, the nature of user friendliness. We would discuss large scale data analysis using different implementations on the above mentioned tools and after that we would give a performance analysis of these tools on the given implementation like Cap3, HEP, Cloudburst.
TL;DR: This review will provide an up-to-date and comprehensive review of the methods for detecting NEDDylation activity that will contribute to NEDdylation inhibitor development.
Abstract: NEDDylation is a post-translational modification of a protein, which transfers Ubiquitin like protein NEDD8 (Neuronal Precursor Cell-expressed Developmentally Down-regulated Protein 8) to the lysine residue of the product through a three-stage enzymatic reaction, and widely regulates many biological processes, such as cell cycle signal transduction and immune recognition. In the past ten years, we have witnessed tremendous progress in the study of protein ubiquitination modification, from modification mechanisms to drug development. Which suggests that inhibition of NEDDylation is an effective way to inhibit tumor. A variety of biological detection methods have been developed during the development of the inhibitor. In this review, we briefly introduced the modification process and substrates of NEDDylation, and discussed detection methods of NEDDylation activity in detail. This review will provide an up-to-date and comprehensive review of the methods for detecting NEDDylation activity that will contribute to NEDDylation inhibitor development.
01 Jan 2018-Procedia Computer Science
TL;DR: This work assists to combine various basic low level contexts to provide reliable decision making in the pervasive environment using sensor networks and context aware computing to bring automated decision making along with accuracy information.
Abstract: Context aware applications are seeking for the technologies that provides regularly available data between internal and external environments. Wireless access infrastructure is integrating real time data and heterogeneous devices to establish ubiquitous communication. A wireless sensor network (WSN) is primarily undertaking remote monitoring of any task in any sensing field further to extend for context fetching and refining to collect essential context information for pervasive environment. The applications are well utilized on context observation and interpretation for mobile users. The proposed method assists to combine various basic low level contexts to provide reliable decision making in the pervasive environment using sensor networks and context aware computing. Pervasive computing has powerful and influencing factors to establish dynamic and smart environment with its dominant features in the backend. Pervasiveness provides the regulated smart interactions and interfaces together to avail the information for various applications. Using rich context for event notification than simple position and time, aim to bring automated decision along with accuracy information. The proposed model also facilitate users and group members to create and submit an event. It provides ability to submit an event using a various mode of input.