I. Arnold Emerson

Bio: I. Arnold Emerson is an academic researcher from VIT University. The author has contributed to research in topics: Gene & Protein ligand. The author has an hindex of 1, co-authored 4 publications receiving 5 citations.

A Frame Work for Learning Drug Designing through Molecular Modelling Software Techniques and Biological Databases for Protein-Ligand Interactions

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.

Assessing Adolescents with Learning Disability and Planning A Wholistic Intervention: A Case Analysis

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.

Network-based gene deletion analysis identifies candidate genes and molecular mechanism involved in clear cell renal cell carcinoma

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.

Oncogenomics and CYP450 Implications in Personalized Cancer 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.

Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures

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.

Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model

Abstract: Abstract Alzheimer’s disease (AD) is the most prevalent dementia disorder globally, and there are still no effective interventions for slowing or stopping the underlying pathogenic mechanisms. There is strong evidence implicating neural oxidative stress (OS) and ensuing neuroinflammation in the progressive neurodegeneration observed in the AD brain both during and prior to symptom emergence. Thus, OS-related biomarkers may be valuable for prognosis and provide clues to therapeutic targets during the early presymptomatic phase. In the current study, we gathered brain RNA-seq data of AD patients and matched controls from the Gene Expression Omnibus (GEO) to identify differentially expressed OS-related genes (OSRGs). These OSRGs were analyzed for cellular functions using the Gene Ontology (GO) database and used to construct a weighted gene co-expression network (WGCN) and protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were then constructed to identify network hub genes. A diagnostic model was established based on these hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) and ROC analyses. Immune-related functions were examined by assessing correlations between hub gene expression and immune cell brain infiltration scores. Further, target drugs were predicted using the Drug-Gene Interaction database, while regulatory miRNAs and transcription factors were predicted using miRNet. In total, 156 candidate genes were identified among 11046 differentially expressed genes, 7098 genes in WGCN modules, and 446 OSRGs, and 5 hub genes (MAPK9, FOXO1, BCL2, ETS1, and SP1) were identified by ROC curve analyses. These hub genes were enriched in GO annotations “Alzheimer’s disease pathway,” “Parkinson’s Disease,” “Ribosome,” and “Chronic myeloid leukemia.” In addition, 78 drugs were predicted to target FOXO1, SP1, MAPK9, and BCL2, including fluorouracil, cyclophosphamide, and epirubicin. A hub gene-miRNA regulatory network with 43 miRNAs and hub gene-transcription factor (TF) network with 36 TFs were also generated. These hub genes may serve as biomarkers for AD diagnosis and provide clues to novel potential treatment targets.