ArticleBMC bioinformatics2016
Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features.
Article in BMC bioinformatics, 2016. 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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Who cites it
14 citing papers in PubMed, 39 citations in OpenAlex.
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- Mechanism and Kinetics of HIV-1 Protease Activation.Viruses · 2024Review
- DiCleave: a deep learning model for predicting human Dicer cleavage sites.BMC bioinformatics · 2024Article
- Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases.Frontiers in microbiology · 2024Review
- 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
- Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms.Virologica Sinica · 2022Article
- SARS-CoV-2 3CLpro whole human proteome cleavage prediction and enrichment/depletion analysis.Computational biology and chemistry · 2022Article
- In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites.Proteins · 2022Article
- ReCGBM: a gradient boosting-based method for predicting human dicer cleavage sites.BMC bioinformatics · 2021Article
- An automated protocol for modelling peptide substrates to proteases.BMC bioinformatics · 2020Article
- TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.Journal of computer-aided molecular design · 2020Article
- Type VII Secretion Substrates of Pathogenic Mycobacteria Are Processed by a Surface Protease.mBio · 2019Article
- iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.Briefings in bioinformatics · 2019Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, thus antagonize AIDS. However, experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Therefore, using computational methods to predict cleavage sites has become highly desirable.
resultsIn this study, we propose a prediction method in which sequence, structural, and physicochemical features are incorporated in various machine learning algorithms. Then, a bidirectional stepwise selection algorithm is incorporated in feature selection to identify discriminative features. Further, only the selected features are calculated by various encoding schemes and used as input for decision trees, logistic regression, and artificial neural networks. Moreover, a more rigorous three-way data split procedure is applied to evaluate the objective performance of cleavage site prediction. Four benchmark datasets collected from previous studies are used to evaluate the predictive performance.
conclusionsExperiment results showed that combinations of sequence, structure, and physicochemical features performed better than single feature type for identification of HIV-1 protease cleavage sites. In addition, incorporation of stepwise feature selection is effective to identify interpretable biological features to depict specificity of the substrates. Moreover, artificial neural networks perform significantly better than the other two classifiers. Finally, the proposed method achieved 80.0% ~ 97.4% in accuracy and 0.815 ~ 0.995 evaluated by independent test sets in a three-way data split procedure.
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