ArticleBioinformatics (Oxford, England)2015
State of the art prediction of HIV-1 protease cleavage sites.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 62 citations in OpenAlex.
- HIV-1 protease cleavage sites detection with a quantum convolutional neural network algorithm.Scientific reports · 2026Article
- Evasion of CARD8 activation during HIV-1 assembly.Science advances · 2025Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- A Protocol to Depict the Proteolytic Processes Using a Combination of Metal-Organic Materials (MOMs), Electron Paramagnetic Resonance (EPR), and Mass Spectrometry (MS).Bio-protocol · 2024Article
- An Approach to Identifying and Quantifying Bias in Biomedical Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Calibrated simplex-mapping classification.PloS one · 2023Article
- Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors.BMC bioinformatics · 2022Article
- Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach.BMC bioinformatics · 2022Article
- Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning.Frontiers in genetics · 2021Article
- An automated protocol for modelling peptide substrates to proteases.BMC bioinformatics · 2020Article
- Understanding Selenium and Glutathione as Antiviral Factors in COVID-19: Does the Viral MFrontiers in nutrition · 2020Article
- Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors.Scientific reports · 2017Article
- Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features.BMC bioinformatics · 2016Article
- The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity.Bioengineered · 2016Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationUnderstanding the substrate specificity of human immunodeficiency virus (HIV)-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.
resultsThe linear support vector machine with orthogonal encoding is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor services. It is also found that schemes using physicochemical properties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed. AVAILABILITY AND IMPLEMENTATION: The datasets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available. CONTACT: thorsteinn.rognvaldsson@hh.se.
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