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| Funder | European Commission |
|---|---|
| Recipient Organization | Universidad de Vigo |
| Country | Spain |
| Start Date | Sep 01, 2025 |
| End Date | Aug 31, 2027 |
| Duration | 729 days |
| Number of Grantees | 1 |
| Roles | Coordinator |
| Data Source | European Commission |
| Grant ID | 101149811 |
Drug resistant mutations can appear when the selective pressure given by a pharmacological treatment causes the evolution ofpathogen proteins towards variants that become unaffected by the drug.
Currently, to select therapies against pathogens, genotypicresistance analyses and tables of resistance mutations are employed to decide the best treatment for the patients.
However, thesescreenings ignore evolutionary changes that can appear as pathogens adapt, potentially leading to drug resistance.
To address thislimitation, the prediction of which variants are more probable to occur in the pathogen population can be useful in selecting ""a priori""therapies active against those variants before their potential expansion toward reservoirs more inaccessible to drugs.
In this proposedwork, I will apply molecular evolution and computational structural biology techniques to evaluate the evolutionary trajectories ofHIV-1 drug targets proteins that lead to resistance against common antiretroviral treatments.
I will calculate protein fitness landscapesbased on protein folding stability and activity, also considering binding to inhibitors. Next, I will use evolutionary information fromprotein fitness landscapes to improve substitution models of evolution.
The evolutionary trajectories predicted by combiningsubstitution models and fitness landscapes will be validated through comparisons with real data from monitored HIV-1 populationsevolved ""in vitro"" and ""in vivo"". Finally, I will focus on calculating the probability of evolutionary trajectories toward resistancevariants.
This research has the potential to improve the selection of therapies for pathogens by providing predictive tools thatconsider the evolutionary dynamics of these microorganisms.
Furthermore, the results of the project have the potential to be abreakthrough in the field of molecular evolution as this methodology could also be applied to predict the evolution of otherpathogens.
Universidad de Vigo
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