ENGEMED · Simulating the dynamics of viral evolution: A computer-aided study toward engineering effective vaccines
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2018-11-01 → 2022-07-03
- EU contribution
- €236,457
- Participants
- 2
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Simulating the dynamics of viral evolution: A computer-aided study toward engineering effective vaccines
Although major advances have been made in understanding pertinent molecular and cellular phenomena, an understanding of the mechanistic principles that govern the emergence of an immune response has proven so far to be elusive. Therapeutic agents that target regions of the viral proteome wherein mutations lead to a large cost in replicative fitness, can be very effective for viral control or aborting the infection. Through ENGEMED's systematic computational means, and clinical/experimental data provided by the partner IMES of MIT, the following objectives were accomplished: (i) A computational method based on statistical mechanics theory that can translate viral sequence databases into quantitative landscapes of intrinsic fitness of viral strains. (ii) A modeling scheme aiming at engineering novel classes of nanoparticles, synthesized at MIT, in order to be used as carrier systems for short interfering RNAs, toward RNA interference–based targeted therapeutics and vaccines. (iii) The development of efficient algorithms based on statistical mechanics, shed light for first time to a DNA-based enantioselective catalytic reaction, conducted at the Johns Hopkins University. (iv) An atomistic simulation study of transcription factors (TF) upon its binding to DNA; TFs act as major regulators of gene expression and cellular differentiation. This last task seeks to address the way the TF mutations modulate the TF-DNA complex dynamics in order to assist novel therapeutic routes that target the transcriptome. The current objective will be continued after the end of the project in collaboration with MIT and a EU group at the Max Plank Inst. in Germany. Our overall objective is the manipulation of the experimental information and computational biology-based methodology, in close synergy with advanced computational tools developed by the NTUA group of the PI of ENGEMED at the Chemical Engineering department of NTUA. Our future goal, through, and after, this MSC project is to contribute, via in silico design, to engineering gene-based pharmaceutics and vaccines, so as computational biomedical engineering to become a systematic scientific and engineering discipline harnessing deep knowledge of the human biological system.
Data: CORDIS, © European Union
Project objective
The adaptive immune system mounts pathogen-specific responses against diverse microbes and establishes memory of past infections thus constituting the basis of vaccination. Although major advances have been made in understanding pertinent molecular and cellular phenomena, an understanding of the mechanistic principles that govern the emergence of an immune response has proven so far to be elusive. An example of a consequence of this missing knowledge is the inability to design a vaccine against HIV. The difficulty in elucidating the mechanistic principles underlying adaptive immune responses are due to the fact that the pertinent processes involve cooperative dynamic events with many participating components that must act collectively for a given phenomenon to emerge. Many groups around the world work on designing vaccines by trying to stimulate good antibodies that will neutralize the virus. In this project we propose an alternative strategy: by means of harnessing the cellular immune response, or the T cell immune response, which controls virus infected cells and controls infections in the body; this strategy might not prevent infection but it can certainly prevent disease. For this, computer-aided approaches will offer the possibility of defining fitness of highly mutable pathogens (and cancers) so that to facilitate rational design of vaccines and therapies. The proposed computer modeling of viral dynamics, can offer an unprecedented means to identify vulnerable regions of the virus through the application of statistical mechanics-based computer simulation of the evolution dynamics of escape mutants in HIV, guiding this way the engineering of efficacious vaccine immunogens for diverse viruses.
Original text from CORDIS.
Participants
- ETHNICON METSOVION POLYTECHNION · ATHINACoordinatorGreece
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY · CambridgeUnited States
Links
Data: CORDIS, © European Union