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Predicting protein evolutionary trajectories toward resistance against antiretroviral treatments

Project description

Predicting pathogen evolution

The emergence of drug-resistant mutations poses a formidable challenge in combating pathogens. Selective pressure from pharmacological treatments drives the evolution of pathogen proteins, rendering them impervious to drugs. Current methods, which rely on genotypic resistance analyses, fail to anticipate evolutionary shifts, risking ineffective treatments. Supported by the Marie Skłodowska-Curie Actions (MSCA) programme, the PRERES project pioneers a predictive approach, leveraging molecular evolution and computational biology to forecast the evolution of HIV-1 drug targets and enhance therapy selection. This research offers hope in outmanoeuvring drug resistance, marking a significant leap in molecular evolution studies.

Objective

"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."

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

UNIVERSIDAD DE VIGO
Net EU contribution
€ 181 152,96
Address
LG CAMPUS LAGOAS MARCOSENDE
36310 VIGO PONTEVEDRA
Spain
Region
Noroeste Galicia Pontevedra
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data