Identification of Conserved Regions in HIV-1 DNA Mutational Patterns Using Multiple Sequence Alignment

Rajesh  Jangade; Yazdani  Hasan; Suryakant  Yadav; Satyakam  Pugla; Mukesh  Chourasia; Jitender  Kumar; Anil  Barnwal

doi:10.5530/ctbp.2025.3s.5

Authors

Rajesh Jangade Center of Computational Biology and Bioinformatics, Amity University, Noida, UP, 201303
Yazdani Hasan Engineering and Technology, Noida International University, Greater Noida, UP, 201310
Suryakant Yadav Department of Computer Science, IIMT Group Of Colleges,. Greater Noida, 201310
Satyakam Pugla Center of Computational Biology and Bioinformatics, Amity University, Noida, UP, 201303
Mukesh Chourasia Center of Computational Biology and Bioinformatics, Amity University, Noida, UP, 201303
Jitender Kumar Center of Computational Biology and Bioinformatics, Amity University, Noida, UP, 201303
Anil Barnwal Center of Computational Biology and Bioinformatics, Amity University, Noida, UP, 201303

DOI:

https://doi.org/10.5530/ctbp.2025.3s.5

Keywords:

HIV-I, Mutational Pattern, Conserved region, Big Data, Multiple Sequence Alignment, Phylogenetic Tree

Abstract

HIV (Human Immunodeficiency Virus) targets and weakens the immune system. If left untreated, it can progress to AIDS (acquired immunodeficiency syndrome), a condition marked by a high viral load and increased infectiousness. Without treatment, individuals with AIDS typically survive about three years. While there is no cure for HIV, it can be managed effectively with proper medical care. This study focuses on identifying conserved regions within the genetic and protein sequences of HIV-1 from large-scale data. By leveraging multiple sequence alignment and machine learning techniques, conserved regions in the mutational patterns of HIV-1 are identified. These conserved regions hold significant potential for the pharmaceutical industry, particularly in development of vaccine.