Summary
"Drug resistant mutations can appear when the selective pressure given by a pharmacological treatment causes the evolution of
pathogen proteins towards variants that become unaffected by the drug. Currently, to select therapies against pathogens, genotypic
resistance analyses and tables of resistance mutations are employed to decide the best treatment for the patients. However, these
screenings ignore evolutionary changes that can appear as pathogens adapt, potentially leading to drug resistance. To address this
limitation, 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 proposed
work, I will apply molecular evolution and computational structural biology techniques to evaluate the evolutionary trajectories of
HIV-1 drug targets proteins that lead to resistance against common antiretroviral treatments. I will calculate protein fitness landscapes
based on protein folding stability and activity, also considering binding to inhibitors. Next, I will use evolutionary information from
protein fitness landscapes to improve substitution models of evolution. The evolutionary trajectories predicted by combining
substitution models and fitness landscapes will be validated through comparisons with real data from monitored HIV-1 populations
evolved ""in vitro"" and ""in vivo"". Finally, I will focus on calculating the probability of evolutionary trajectories toward resistance
variants. This research has the potential to improve the selection of therapies for pathogens by providing predictive tools that
consider the evolutionary dynamics of these microorganisms. Furthermore, the results of the project have the potential to be a
breakthrough in the field of molecular evolution as this methodology could also be applied to predict the evolution of other
pathogens."
pathogen proteins towards variants that become unaffected by the drug. Currently, to select therapies against pathogens, genotypic
resistance analyses and tables of resistance mutations are employed to decide the best treatment for the patients. However, these
screenings ignore evolutionary changes that can appear as pathogens adapt, potentially leading to drug resistance. To address this
limitation, 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 proposed
work, I will apply molecular evolution and computational structural biology techniques to evaluate the evolutionary trajectories of
HIV-1 drug targets proteins that lead to resistance against common antiretroviral treatments. I will calculate protein fitness landscapes
based on protein folding stability and activity, also considering binding to inhibitors. Next, I will use evolutionary information from
protein fitness landscapes to improve substitution models of evolution. The evolutionary trajectories predicted by combining
substitution models and fitness landscapes will be validated through comparisons with real data from monitored HIV-1 populations
evolved ""in vitro"" and ""in vivo"". Finally, I will focus on calculating the probability of evolutionary trajectories toward resistance
variants. This research has the potential to improve the selection of therapies for pathogens by providing predictive tools that
consider the evolutionary dynamics of these microorganisms. Furthermore, the results of the project have the potential to be a
breakthrough in the field of molecular evolution as this methodology could also be applied to predict the evolution of other
pathogens."
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101149811 |
| Start date: | 01-09-2025 |
| End date: | 31-08-2027 |
| Total budget - Public funding: | - 181 152,00 Euro |
Cordis data
Original description
"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, genotypic
resistance analyses and tables of resistance mutations are employed to decide the best treatment for the patients. However, these
screenings ignore evolutionary changes that can appear as pathogens adapt, potentially leading to drug resistance. To address this
limitation, 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 proposed
work, I will apply molecular evolution and computational structural biology techniques to evaluate the evolutionary trajectories of
HIV-1 drug targets proteins that lead to resistance against common antiretroviral treatments. I will calculate protein fitness landscapes
based on protein folding stability and activity, also considering binding to inhibitors. Next, I will use evolutionary information from
protein fitness landscapes to improve substitution models of evolution. The evolutionary trajectories predicted by combining
substitution models and fitness landscapes will be validated through comparisons with real data from monitored HIV-1 populations
evolved ""in vitro"" and ""in vivo"". Finally, I will focus on calculating the probability of evolutionary trajectories toward resistance
variants. This research has the potential to improve the selection of therapies for pathogens by providing predictive tools that
consider the evolutionary dynamics of these microorganisms. Furthermore, the results of the project have the potential to be a
breakthrough in the field of molecular evolution as this methodology could also be applied to predict the evolution of other
pathogens."
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
22-11-2024
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