Towards understanding molecular mechanism of drug resistance: L76V - - PowerPoint PPT Presentation

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Towards understanding molecular mechanism of drug resistance: L76V Daniel.Hoffmann@uni-due.de 2007 c 1 HIV PR complexed with drug (LPV) Daniel.Hoffmann@uni-due.de 2007 c 2 Molecular location of L76V Daniel.Hoffmann@uni-due.de

SLIDE 1

Towards understanding molecular mechanism

f drug resistance: L76V

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HIV PR complexed with drug (LPV)

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SLIDE 3

Molecular location of L76V

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Molecular location of L76V

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Molecular location of L76V

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Possible effects of L76V

No direct interaction of L76V with drugs⇒

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Possible effects of L76V

No direct interaction of L76V with drugs⇒ possible effects somehow “long range”:

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Possible effects of L76V

No direct interaction of L76V with drugs⇒ possible effects somehow “long range”:

◮ electrostatics

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Possible effects of L76V

No direct interaction of L76V with drugs⇒ possible effects somehow “long range”:

◮ electrostatics ◮ conformational rearrangements ⇒

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SLIDE 10

Possible effects of L76V

No direct interaction of L76V with drugs⇒ possible effects somehow “long range”:

◮ electrostatics ◮ conformational rearrangements ⇒indirect effects

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L76V–drug interactions: LPV

"WT"

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L76V–drug interactions: LPV (II)

L76V − "WT" diff.

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L76V–drug interactions: APV

"WT"

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L76V–drug interactions: APV (II)

L76V

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L76V–drug interactions: SQV

"WT"

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L76V–drug interactions: SQV (II)

diff. L76V− "WT"

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Model based estimates of resistance

Estimate of ∆∆G(WT · drug → L76V · drug) based on molecular model:

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Model based estimates of resistance

Estimate of ∆∆G(WT · drug → L76V · drug) based on molecular model: drug estimate of ∆∆G phenotype APV 14.5 resistant LPV 7.8 resistant SQV 9.0 susceptible

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Reasons for discrepancy

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Reasons for discrepancy: global rearrangements

APV LPV SQV 5 10 15 20 25 30 35

% RF>10

WT binding pocket All

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Next steps

◮ More complex model,

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Next steps

◮ More complex model, that is also more compute intensitive

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Next steps

◮ More complex model, that is also more compute intensitive ◮ Test with comprehensive dataset

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Next steps

◮ More complex model, that is also more compute intensitive ◮ Test with comprehensive dataset ◮ Use as predictive tool

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SLIDE 25

Acknowledgments

The whole AREVIR team:

◮ Patrick Braun, Martin Däumer, Rolf Kaiser, Klaus Korn, Hauke

Walter, ...

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SLIDE 26

Acknowledgments

The whole AREVIR team:

◮ Patrick Braun, Martin Däumer, Rolf Kaiser, Klaus Korn, Hauke

Walter, ... and:

◮ Yvonne Schuh ◮ Karen Willbrand

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Daniel.Hoffmann@uni-due.de 2007