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HIV1-LogRex implements the logistic regression model for HIV-1 protease octapeptide cleavage site prediction and varied algorithms for octapeptide descriptors (amino acid binary profile (AABP), physicochemical properties and bond composition) calculations for various machine/deep learning applications in bioinformatics.

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HIV1-LogRex: Accelerate HIV-1 Protease Inhibitor Discovery

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HIV1-LogRex is a user-friendly webserver that provides powerful tools for predicting HIV-1 protease cleavage sites, a crucial step in HIV-1 replication. Leveraging machine learning algorithms, HIV1-LogRex empowers researchers to optimize the drug discovery process for novel HIV-1 protease inhibitors. It also implements varied algorithms for octapeptide descriptors calculations (amino acid binary profile (AABP), physicochemical properties and bond composition).

Addressing a Global Challenge:

HIV/AIDS remains a significant public health concern, particularly in developing nations. Effective therapeutic strategies are critical to combat this devastating disease. HIV1-LogRex directly addresses this need by facilitating the development of new drugs that target the HIV-1 virus at a fundamental level.

How HIV1-LogRex Works:

The HIV-1 protease plays a vital role in HIV-1 replication. Accurately predicting the cleavage sites of this enzyme is essential for designing effective HIV-1 protease inhibitors. HIV1-LogRex offers:

  • Varied Algorithms: Explore diverse algorithms for octapeptide descriptors calculations - amino acid binary profile (AABP), physicochemical properties and bond composition.
  • Octapeptide Descriptors: Utilize a unique combination of sequence information incorporating bond composition, amino acid properties, and physicochemical features for comprehensive analysis.
  • Logistic Regression Model (HIV1-LogRex): Access a highly accurate model specifically designed for predicting the substrate specificity and cleavage site of HIV-1 protease.

Innovation and Rigorous Evaluation:

HIV1-LogRex distinguishes itself by:

  • Hybrid Feature Set: The webserver employs a novel combination of octapeptide descriptors, leading to potentially superior prediction accuracy.
    • Amino Acid Binary Profile (AABP)
    • Physicochemical properties
    • Bond composition
  • Combined Dataset: Unlike previous studies, HIV1-LogRex leverages a comprehensive dataset for training and evaluation, ensuring robust model performance.
  • Stratified Cross-Validation: This rigorous validation technique guarantees consistent and reliable model performance.

Getting Started:

Visit the HIV1-LogRex at https://hiv-1-logrex.streamlit.app/. The user-friendly interface allows you to effortlessly upload octapeptide sequences for analysis.

Installation:

No installation is required.

License: License: MIT

HIV1-LogRex is freely available under the MIT License, enabling open access and unrestricted use.

Feedback and Contribution:

We welcome your feedback and suggestions for further enhancing HIV1-LogRex. Feel free to reach out and share your thoughts. Additionally, if you're interested in contributing to the codebase, please don't hesitate to get in touch.

Citation: DOI

If you utilize HIV1-LogRex in your research, please cite it as follows:

Onah E. (2024). HIV1-LogRex: Accelerate HIV-1 Protease Inhibitor Discovery (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10851067.

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HIV1-LogRex implements the logistic regression model for HIV-1 protease octapeptide cleavage site prediction and varied algorithms for octapeptide descriptors (amino acid binary profile (AABP), physicochemical properties and bond composition) calculations for various machine/deep learning applications in bioinformatics.

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