Description
Title: Machine learning-based methods are used to identify single-stranded DNA binding proteins.
Abstract: Because they guard against ssDNA damage during vital biological processes like DNA replication and gene transcription, single-stranded DNA binding proteins (SSBs) are essential for maintaining genome stability. In case it is mistakenly identified as an anomaly, the single-stranded region of telomeres also needs to be protected by ssDNA binding proteins from being attacked. It has been established that ssDNA and SSB-ssDNA interactions play crucial roles in transcriptional regulation in all three domains of life as well as viruses, in addition to their crucial roles in genome stability and integrity. In this review, we discuss what is currently known about the structure, operation, and structural characteristics of SSBs. We then go over the machine learning-based methods that have been created for predicting SSBs from proteins that bind to double-stranded DNA (dsDNA) (DSBs).
Keywords: single-stranded DNA; ssDNA; single-stranded DNA binding protein; SSB; binding specificity
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Biomolecules
Writer Experience: 20+ Years
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