Prediction of Bevi virimat Resist stance in HIV-1 Dr. Dominik - - PowerPoint PPT Presentation

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Prediction of Bevi virimat Resist stance in HIV-1 Dr. Dominik - - PowerPoint PPT Presentation

Nov 16, 2022 308 likes •445 views

Arevir 2009 Prediction of Bevi virimat Resist stance in HIV-1 Dr. Dominik Heider Dept. of Bioinformatics, Center for Medical Biotechnology, April 2009 University of Duisburg-Essen dominik.heider@uni-due.de 2 Bevirimat belongs to the

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  • Dr. Dominik Heider
  • Dept. of Bioinformatics,

Center for Medical Biotechnology, University of Duisburg-Essen dominik.heider@uni-due.de

Prediction of Bevi virimat Resist stance in HIV-1

April 2009

Arevir 2009

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Bevirimat

  • belongs to the class of maturation inhibitors
  • interfere with protease processing of precursor gag
  • gag is cleaved by the protease to produce functionally active proteins
  • unlike the protease inhibitors, Bevirimat binds the gag protein, not protease
  • prevents a critical cleavage at the p24-p2 junction
  • resulting virus particles lack functional capsid protein and have structural defects,

rendering them incapable of infecting other cells.

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Bevirimat

Modified from Salzwedel et al., 2009

HIV-protease cleavage sites

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p2 p24 (part)

HIV-protease cleavage sites

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Classification of Bevirimat resistance

  • data set:
  • 45 susceptible/intermediate resistant sequences (fold change ≤ 10)
  • 110 resistant sequences
  • descriptors
  • hydrophobicity
  • molecular weight
  • solubility
  • HIV-protease cleavage site prediction
  • machine learning approaches
  • artificial neural networks
  • Random forests
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Results

  • NI-Rule
  • position 372 and 376 display a low variance, but different mean values for

hydrophobicitiy

  • sequences with mutations in these positions can be filtered by calculating the

differences of this position specific values and the mean values of either resistant and non-resistant sequences

  • sensitivity: 64%
  • specificity: 91%
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Results

  • filtered sequences are passed to a

neural network

  • best descriptor: hydrophobicity
  • sensitivity: 83.3%
  • Specificity: 100%
  • neural networks outperformed the

Random Forests on filtered sequences

ROC-Plot

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Random Forests neural networks NI-Rule

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ID 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 wild type G H K A R V L A E A M S Q V T N S A T I M 0,68 1,75 0,11 0,64 1,78 0,06 8 S H K A R V L A E A M C Q A

  • N

S T T V M 0,68 1,75 0,85 0,42 0,03 0,17 wild type G H K A R V L A E A M S Q V T N S A T I M H H H H H H H H H H H H H H H H 4 6 8 9 9 9 9 9 9 9 8 8 7 6 6 6 5 4 dQ369 G H K A R V L A E A M S V T N S A T I M H H H H H H H H H H H H H H H H 4 5 7 8 9 9 9 7 5 4 5 5 6 6 6 5 4

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Conclusion

  • beside the QVT motif, the N and the I at position 372 and 376 respectively, play an

important rule by determining the surface characteristics of the p24-p2 region

  • modification of the cleavage sites

may lead to Bevirimat resistance

  • the accuracy of this prediction scheme is

very high (84.8%), and therefore, can be used to preselect sequences for experimental studies

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People being involved...

Daniel Hoffmann and Dominik Heider, Department of Bioinformatics, Center for Medical Biotechnology, University of Duisburg-Essen Jens Verheyen, Institute of Virology, University of Cologne

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Thank you very much for your attention