I am glad to see you noticed our work on protein design for Covid (https://arxiv.org/abs/2012.01736), that is currently presented at NeurIPS. It has been a lot of hard work, but we think it turned out well.

You are partially right - the coronavirus work we did earlier this year to a large extent influenced DeepChain development, but - despite overlapping quite a bit - both have slightly different focus. The work you refer to was very result driven, aiming at providing the best result possible, with lesser focus on the underlying computational resources. We conducted a lot of experiments, developed a large amount of novel science processes, and protocols. Some of these worked well, others... well, we learned a lot too :-)

DeepChain builds on the coronavirus project, as well as other work performed at InstaDeep and outside. Its focus lies on making quite involved computational biology tasks approachable, usable, and useful to a non-expert user. The AI Designer module does what we did in Covid paper, but does it in an optimised way, delivering actionable results in a reasonable amount of time. It is thought as an exploratory tool, and we are happy to work with you to help you explore your DeepChain findings further.

DeepChain performs also one-click, short, reasonable Molecular Dynamics simulations to validate newly designed proteins, These too are very similar to the validations we did for the Covid paper, but contrary to the paper here it is the user, who gets to decide how involved the simulation needs to be. It is not a platform to run half-a-year worth of simulation in one go, but it works great for quick (and painless) evaluation of multiple poses in a uniform, reproducible manner.

Finally, the Sequence Playground is a way to tap into the fascinating (for me, coming from very structure-oriented background) world of very large AI models of protein sequences, parameterised on pretty much all that we know about proteins (both these annotated and these coming from metagenomic sequencing!). Here, within seconds, one can get an objective estimate of evolutionary pressures onto your protein. As these come from global models, you can (for example) see that a single mutation may make another position more susceptible to adopt a different amino acid, which in turn may potentially turn your entire protein topsy-turvy. What we see is that protein regions being very stable (structurally and evolutionarily) also have a very "tight" probabilities. The Playground allows you to explore any sort of variation in sequence and see how well it works out, given out knowledge on billions of years of evolution. Then you can feed the most intriguing designs into Site-directed mutagenesis tool in AI Designer and see how the mutation works out from the structure and interaction point of view.

As you see, I am rather excited about the platform, so if you want to know anything else, ask away!