Application of PolyPRep tools on HIV protease polyproteins using molecular docking.
M F R DiasF L L OliveiraV S PontesManuela L DA SilvaPublished in: Brazilian journal of biology = Revista brasleira de biologia (2021)
In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.
Keyphrases
- molecular docking
- electronic health record
- machine learning
- high throughput
- big data
- molecular dynamics simulations
- antiretroviral therapy
- hiv infected
- data analysis
- hiv positive
- sars cov
- randomized controlled trial
- human immunodeficiency virus
- hiv testing
- artificial intelligence
- hepatitis c virus
- amino acid
- minimally invasive
- binding protein
- quality improvement
- men who have sex with men
- neural network
- south africa
- protein protein