A new feature encoding scheme for HIV-1 protease cleavage site prediction

Gök, Murat; Özcerit, Ahmet Turan

doi:10.1007/s00521-012-0967-5

Cited by 24 publications

(14 citation statements)

References 12 publications

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“…Regarding the linear separability observed in the octamer sequences, the problem of how to encode octapeptides with features distinguishing such separability has attracted much attention recently. Gök and Özcerit ( 2013 ) used the OETMAP coding scheme based on amino acid features and integrated it with a linear classifier. An cross-validation comparison with standard coding schemes was performed and the experiment results showed that the OETMAP coding scheme improved the prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning

Tang

et al. 2021

Front. Genet.

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Understanding the substrate specificity of HIV-1 protease plays an essential role in the prevention of HIV infection. A variety of computational models have thus been developed to predict substrate sites that are cleaved by HIV-1 protease, but most of them normally follow a supervised learning scheme to build classifiers by considering experimentally verified cleavable sites as positive samples and unknown sites as negative samples. However, certain noisy can be contained in the negative set, as false negative samples are possibly existed. Hence, the performance of the classifiers is not as accurate as they could be due to the biased prediction results. In this work, unknown substrate sites are regarded as unlabeled samples instead of negative ones. We propose a novel positive-unlabeled learning algorithm, namely PU-HIV, for an effective prediction of HIV-1 protease cleavage sites. Features used by PU-HIV are encoded from different perspectives of substrate sequences, including amino acid identities, coevolutionary patterns and chemical properties. By adjusting the weights of errors generated by positive and unlabeled samples, a biased support vector machine classifier can be built to complete the prediction task. In comparison with state-of-the-art prediction models, benchmarking experiments using cross-validation and independent tests demonstrated the superior performance of PU-HIV in terms of AUC, PR-AUC, and F-measure. Thus, with PU-HIV, it is possible to identify previously unknown, but physiologically existed substrate sites that are able to be cleaved by HIV-1 protease, thus providing valuable insights into designing novel HIV-1 protease inhibitors for HIV treatment.

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Section: Introductionmentioning

confidence: 99%

Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning

Tang

et al. 2021

Front. Genet.

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“…35 Physicochemical properties are encoded for each amino acid using a binary vector representing membership of 10 respective groups: {small, tiny, proline, charged, negative, positive, hydrophobic, polar, aromatic, aliphatic}, as defined by Taylor et al. 36 Although G€ ok and € Ozcerit found the use of OETMAP encoding to offer an improvement over orthogonal encoding, later evaluations by R€ ognvaldsson et al on a larger dataset found it to in fact be inferior to orthogonal encoding.…”

Section: Literature Reviewmentioning

confidence: 99%

“…30,31,35,46 Given this information, it was decided that each physicochemical encoding should be evaluated in conjunction with orthogonal encoding. Additional evaluations were carried out using combinations of the most promising feature sets: {Orthogonal, Physicochemical, Niu}, {Orthogo-nal, Physicochemical, Niu, z-Scales}, and combining the most promising feature set {Orthogonal, Physicochemical, Niu} with the occurrence percentage, as defined by Jaeger.…”

Section: Individual Classifiersmentioning

confidence: 99%

The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity

Manning

Walsh

2016

Bioengineered

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This paper reviews recent research relating to the application of bioinformatics approaches to determining HIV-1 protease specificity, outlines outstanding issues, and presents a new approach to addressing these issues. Leading machine learning theory for the problem currently suggests that the direct encoding of the physicochemical properties of the amino acid substrates is not required for optimal performance. A number of amino acid encoding approaches which incorporate potentially relevant physicochemical properties of the substrate are identified, and are evaluated using a nonlinear task decomposition based neuroevolution algorithm. The results are evaluated, and compared against a recent benchmark set on a nonlinear classifier using only amino acid sequence and identity information. Ensembles of these nonlinear classifiers using the physicochemical properties of the substrate are demonstrated to consistently outperform the recently published state-of-the-art linear support vector machine based approach in out-of-sample evaluations.

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“…Gök and Özcerit used OETMAP encoding schemes based on amino acid features together with linear classifiers. The encoding schemes improved prediction performance compared to standard amino acid encodings evaluated on two datasets by cross-validation [15]. Song and coworkers developed a protease specificity prediction server to predict unique substrates and their cleavage sites.…”

Section: Introductionmentioning

confidence: 99%

Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features

Singh

2016

BMC Bioinformatics

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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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1337-6) contains supplementary material, which is available to authorized users.

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A new feature encoding scheme for HIV-1 protease cleavage site prediction

Cited by 24 publications

References 12 publications

Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning

Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning

The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity

Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features

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