Hi Steffen, On 2021-07-18 21:47, Steffen Möller wrote: > Following the references in > > https://www.nature.com/articles/d41586-021-01968-y > > I found this reference > > https://github.com/RosettaCommons/RoseTTAFold/tags > > but I admittedly cannot tell that I'd have fully grasped who is doing > what and publishes what software where, yet.
This would definitely be great to have in Debian. I expect this protein structure predictor heavily depends on pre-trained neural networks. Making them DFSG-compliant might be very difficult (see Unofficial Policy for Debian & Machine Learning [1]). > The context: > > The biochemistry happens in 3D as proteins (and some functional RNA), > while all we easily get are 1D DNA sequence data from which one can > predict the sequences of amino acids that form the protein. To get from > that polymer of amino acids to the 3D structure one typically looks for > patterns of sequences that have been observed in structures that have > already been determined. Or one try computing that structure de novo, > i.e. without a template, and ... wait. > > Once we have the protein structures one can better understand the effect > of mutations, and start other sorts of simulations, like disturbing > protein interactions with compounds, i.e. find new drugs. I am particularly interested in this. However, this field is quite unrepresented in Debian, AFAIK. Some time ago I packaged MacroMoleculeBuilder [2], which does homology modeling. All I have tried is a couple of examples, but it is definitely worth giving a look. Currently I am looking into ProMod3 [3], which seems to be the engine behind the great SWISS-MODEL service [4]. I seem to have figured out the dependencies, will go on to packaging next. [1] https://salsa.debian.org/deeplearning-team/ml-policy [2] https://simtk.org/projects/rnatoolbox [3] https://www.openstructure.org/promod3 [4] https://swissmodel.expasy.org/interactive Best wishes, Andrius
