For more than a decade, computational scientist Juan R. Perilla of the University of Delaware has been working to digitally reconstruct a very particular structure of the human immunodeficiency virus (HIV). Perilla and his colleagues set out to create an active three-dimensional digital model of the virus shell, or capsid, that researchers could study and probe as if they were working with an actual particle. The processing power required to build the simulation was significant, according to Perilla, because the model needed to track how a change in one area would impact the interactions of all two million atoms in the particle. Perilla and his group succeeded in constructing the model and demonstrating various means of testing the simulation to ensure it behaves as it would in the real world. "You can actually interrogate the simulated particle, pushing and pulling on the capsid as if you were testing the actual physical system," Perilla says.

The research, Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design, published in the journal Science, was conducted by Dyno Therapeutics, a biotechnology company pioneering use of Artificial Intelligence in gene therapy. AAV capsids are presently the most commonly used vector for gene therapy because of their established ability to deliver genetic material to patient organs with a proven safety profile. However, there are only a few naturally occurring AAV capsids, and they are deficient in essential properties for optimal gene therapy, such as targeted delivery, evasion of the immune system, higher levels of viral production, and greater transduction efficiency. Starting at Harvard in 2015, the authors set out to overcome the limitations of current capsids by developing new machine-guided technologies to rapidly and systematically engineer a suite of new, improved capsids for widespread therapeutic use. In the research the authors demonstrate the advance of their unique machine-guided approach to AAV engineering.

Sam Sinai, George Church, Eric Kelsic, and Pierce Ogden are holden small models of the AVVs capsid in their hands. Improved AAV vector capsid for gene therapy engineered with a new machine-guided approach shows, in red, improvements in efficiency of viral production based on the average effect of insertions at all possible amino acid positions, with white showing neutral and blue showing deleterious positions.

Cambridge, MA, November 28, 2019 -- Dyno Therapeutics, a biotechnology company pioneering use of artificial intelligence in gene therapy, today announced a publication in the journal Science that demonstrates the power of a comprehensive machine-guided approach to engineer improved capsids for gene therapy delivery. The research was conducted by Dyno co-founders Eric D. Kelsic, Ph.D. and Sam Sinai, Ph.D., together with colleague Pierce Ogden, Ph.D., at Harvard's Wyss Institute for Biologically Inspired Engineering and the Harvard Medical School laboratory of George M. Church, Ph.D., a Dyno scientific co-founder. AAV capsids are presently the most commonly used vector for gene therapy because of their established ability to deliver genetic material to patient organs with a proven safety profile. However, there are only a few naturally occurring AAV capsids, and they are deficient in essential properties for optimal gene therapy, such as targeted delivery, evasion of the immune system, higher levels of viral production, and greater transduction efficiency. Starting at Harvard in 2015, the authors set out to overcome the limitations of current capsids by developing new machine-guided technologies to rapidly and systematically engineer a suite of new, improved capsids for widespread therapeutic use.

Cambridge, MA, November 28, 2019 -- Dyno Therapeutics, a biotechnology company pioneering use of artificial intelligence in gene therapy, today announced a publication in the journal Science that demonstrates the power of a comprehensive machine-guided approach to engineer improved capsids for gene therapy delivery. The research was conducted by Dyno co-founders Eric D. Kelsic, Ph.D. and Sam Sinai, Ph.D., together with colleague Pierce Ogden, Ph.D., at Harvard's Wyss Institute for Biologically Inspired Engineering and the Harvard Medical School laboratory of George M. Church, Ph.D., a Dyno scientific co-founder. AAV capsids are presently the most commonly used vector for gene therapy because of their established ability to deliver genetic material to patient organs with a proven safety profile. However, there are only a few naturally occurring AAV capsids, and they are deficient in essential properties for optimal gene therapy, such as targeted delivery, evasion of the immune system, higher levels of viral production, and greater transduction efficiency. Starting at Harvard in 2015, the authors set out to overcome the limitations of current capsids by developing new machine-guided technologies to rapidly and systematically engineer a suite of new, improved capsids for widespread therapeutic use.

Two facts about simple viruses have been known for a long time. Firstly, that genetic economy leads to the use of symmetry, such that virus capsids aremostly icosahedral or helical. Secondly, packaging signals, that is secondary structure features in the viral RNA, are often required for encapsidation inviruses with single-stranded genomes. Examples are the origin of assembly sequence in Tobacco Mosaic virus, the psi element in HIV and the TR sequence in MS2. This is an evolutionary advantage, as it ensures vRNA-specific encapsidation and can increase assembly efficiency through a cooperative role of the RNA, which acts as a nucleation site. More recently, it has been shown that taken together, these two facts suggest that there could be more than one packaging signal, with multiple signalsin fact dispersed throughout the genome. This is because the capsid is symmetric, and the packaging signal mechanism functions via interaction betweenviral RNA and the coat protein (CP). In several cases, this RNA-CP interaction leads to a conformational change in the CP, which only then makes it assembly competent (e.g.