Interview
Dmitry Korkin: Computational Biology of Coronavirus | Lex Fridman Podcast #90
- Natural viruses are expected to remain the primary near-term concern over engineered pandemics, driven by the observed emergence of new influenza and coronavirus strains from natural processes.
- Modern biotechnology aims to limit access to dangerous historical strains, while the community anticipates understanding viral jumping mechanisms to eventually design universal vaccines effective against all influenza A strains.
- The coronavirus is predicted to evolve into a non-pandemic state due to a biological balance between pathogenicity and contagiousness, similar to MERS, though a second wave remains a distinct possibility requiring continued strict precautions.
- Antiviral drugs are viewed as the primary short-term solution, with existing small molecule binding sites likely unaffected by the new virus, though resistance through mutation remains an open evolutionary question.
- Structural analysis indicates the viral particle is an elongated ellipsoid rather than a sphere, composed of at least 29 proteins, with the spike protein identified as the primary vaccine target for blocking ACE2 receptor interaction.
- Modeling efforts focus on the entire viral virion to enable nanoparticle designs that mimic the virus, leveraging the "interactiveome" and swarm intelligence to understand how minimal information and a limited number of functions create an efficient viral machine.
- Computational methods are expected to accelerate research significantly, allowing computational scientists to achieve in days results that previously took experimental biologists months or years, particularly in protein structure prediction using machine learning approaches like AlphaFold.
- Epidemiological parameters include an R0 between 1.5 and 3, a potentially 50% asymptomatic population, and an incubation period where viral shedding drives transmission, while tears are not considered a contagious vector.
- Masks are expected to work in both directions to prevent asymptomatic spread, with historical precedents like the Spanish flu suggesting a high likelihood of societal adoption, contrasting with the temporary nature of social distancing.
- Scientific publishing is anticipated to shift towards knowledge as the core value rather than journals, fostering a "brand new level of collaborations" where remote work and pre-print cultures allow computational scientists to continue operations despite physical quarantines.
- The "butterfly effect" in viral evolution is expected to be triggered by mutations of just a few atoms or residues, enabling jumps between species such as bats to humans, while the engineering of viruses is deemed dangerous and self-destructive due to a lack of control.
- The scientific community acknowledges being at the very beginning of comprehending cell complexity, with protein folding remaining a computationally expensive combinatorial problem that is increasingly addressed by interactive human participation and global data sharing.
- Future bioinformatics challenges involve processing vast data from next-generation sequencing and transcriptomics, while the Russian scientific community is noted as strong and open despite administrative hurdles for international travel.