Integrating Computational Methods, Molecular Docking, and Molecular Dynamics Simulation Approaches Towards Personalized Medicine in Hematological Disorders

Recent advances and cost reduction in high-throughput genotyping, microarray technology, and next generation sequencing have generated a tremendous amount of human genetic variation data, determining the effects of amino acid substitution will be the next challenge in mutation research. There has been much effort in current epidemiology, medicine, phamarcogenomics studies and personalized medicine in identifying the genetic variations. Among the types of variation, such as Single-nucleotide polymorphisms (SNPs), indels, microsatellites, copy number variants, and epigenetic markers, SNPs were found to be useful and widely applied markers in genetic studies. This flood of data has lead to the creation of bioinformatic databases and software’s which are helpful in discriminating the disease associated variants from neutral ones. Understanding the genotype–phenotype relationship through SNPs is the first and most important step in drug research and development. Building bridges between clinical findings and bioinformatics’ resources will open new possibilities in diagnostic and therapeutic efforts. Several studies have described the application of computational resources, but this section will address the SNP bioinformatics tools, critical databases and their uses, in personalized medicine will tailor treatments to the patients’ specific genotype. The principle aim is to provide a computational pipeline for hematologists conducting cost effective and time consuming SNP-centered research by the application of computational methods, molecular docking, and molecular dynamics simulation approaches in ABL1 gene. This section addresses the powerful and practical applications of bioinformatics in hematological disorders.

Keywords: ABL1, In silico tools, Molecular dynamics, Molecular Docking.