Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

Dybowski, J. Nikolaj; Riemenschneider, Mona; Hauke, Sascha; Pyka, Martin; Verheyen, Jens; Hoffmann, Daniel; Heider, Dominik

doi:10.1186/1756-0381-4-26

Cited by 21 publications

(15 citation statements)

References 41 publications

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“…There are several studies related to the HIV resistance predictions [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, References [ 13 , 14 , 15 , 16 ] do not provide a prediction of HIV resistance to protease and reverse transcriptase inhibitors; they are aimed either on the prediction of tropism, the usage of a co-receptor, and HIV maturation inhibition [ 13 , 14 , 15 ] or prediction of the HIV inhibitors activity against mutated strains. We compared the performances of prediction using our method with the earlier developed machine learning approaches [ 3 , 4 ], where decision trees were used as the main computational method.…”

Section: Discussionmentioning

confidence: 99%

“…The prediction of HIV resistance to a drug or a combination of drugs is an important issue for the development of new potent and safe antiretroviral drugs. There are several methods aimed at predicting HIV-1 resistance and disease progression based on amino acid / nucleotide sequences of HIV core proteins (reverse transcriptase, protease) [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Some of them have the ultimate goal of predicting HIV-1 resistance to reverse transcriptase (RT) and protease inhibitors (PR) [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] based on amino acid or nucleotide sequences of HIV RT and PR.…”

Section: Introductionmentioning

confidence: 99%

“…There have also been several methods aimed at predicting HIV resistance based on the analysis of tropism [ 14 ] using various physicochemical or structural features of the HIV-1 gp120 protein. The resistance of HIV-1 to Bevirimat, a drug inhibiting the maturation of HIV-1 particles to infectious virions was predicted and analyzed in a study by Dybowski, N. et al [ 15 ]. There are several approaches related to the HIV resistance analyses but not aimed to predict the resistance of HIV variants to a certain drug [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors

et al. 2018

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The high variability of the human immunodeficiency virus (HIV) is an important cause of HIV resistance to reverse transcriptase and protease inhibitors. There are many variants of HIV type 1 (HIV-1) that can be used to model sequence-resistance relationships. Machine learning methods are widely and successfully used in new drug discovery. An emerging body of data regarding the interactions of small drug-like molecules with their protein targets provides the possibility of building models on “structure-property” relationships and analyzing the performance of various machine-learning techniques. In our research, we analyze several different types of descriptors in order to predict the resistance of HIV reverse transcriptase and protease to the marketed antiretroviral drugs using the Random Forest approach. First, we represented amino acid sequences as a set of short peptide fragments, which included several amino acid residues. Second, we represented nucleotide sequences as a set of fragments, which included several nucleotides. We compared these two approaches using open data from the Stanford HIV Drug Resistance Database. We have determined the factors that modulate the performance of prediction: in particular, we observed that the prediction performance was more sensitive to certain drugs than a type of the descriptor used.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors

et al. 2018

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“…by using feature extraction methods 34 could be used to improve prediction performance. Additional information, such as sequence-derived information in combination with structural information 35 36 of the V3 loop could also increase accuracy.…”

Section: Discussionmentioning

confidence: 99%

Genotypic Prediction of Co-receptor Tropism of HIV-1 Subtypes A and C

Riemenschneider

Cashin

Budeus

et al. 2016

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Antiretroviral treatment of Human Immunodeficiency Virus type-1 (HIV-1) infections with CCR5-antagonists requires the co-receptor usage prediction of viral strains. Currently available tools are mostly designed based on subtype B strains and thus are in general not applicable to non-B subtypes. However, HIV-1 infections caused by subtype B only account for approximately 11% of infections worldwide. We evaluated the performance of several sequence-based algorithms for co-receptor usage prediction employed on subtype A V3 sequences including circulating recombinant forms (CRFs) and subtype C strains. We further analysed sequence profiles of gp120 regions of subtype A, B and C to explore functional relationships to entry phenotypes. Our analyses clearly demonstrate that state-of-the-art algorithms are not useful for predicting co-receptor tropism of subtype A and its CRFs. Sequence profile analysis of gp120 revealed molecular variability in subtype A viruses. Especially, the V2 loop region could be associated with co-receptor tropism, which might indicate a unique pattern that determines co-receptor tropism in subtype A strains compared to subtype B and C strains. Thus, our study demonstrates that there is a need for the development of novel algorithms facilitating tropism prediction of HIV-1 subtype A to improve effective antiretroviral treatment in patients.

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“…RFs have been shown to give highly accurate predictions on biological [11–13] and biomedical data [14, 15]. There are different implementations of the RF algorithm in R available, which offer diverse feature selection methods.…”

Section: Methodsmentioning

confidence: 99%

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

et al. 2016

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MotivationBiomarker discovery methods are essential to identify a minimal subset of features (e.g., serum markers in predictive medicine) that are relevant to develop prediction models with high accuracy. By now, there exist diverse feature selection methods, which either are embedded, combined, or independent of predictive learning algorithms. Many preceding studies showed the defectiveness of single feature selection results, which cause difficulties for professionals in a variety of fields (e.g., medical practitioners) to analyze and interpret the obtained feature subsets. Whereas each of these methods is highly biased, an ensemble feature selection has the advantage to alleviate and compensate for such biases. Concerning the reliability, validity, and reproducibility of these methods, we examined eight different feature selection methods for binary classification datasets and developed an ensemble feature selection system.ResultsBy using an ensemble of feature selection methods, a quantification of the importance of the features could be obtained. The prediction models that have been trained on the selected features showed improved prediction performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0114-4) contains supplementary material, which is available to authorized users.

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Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

Cited by 21 publications

References 41 publications

A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors

A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors

Genotypic Prediction of Co-receptor Tropism of HIV-1 Subtypes A and C

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

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