couteiral

Yes: they predict structures using MSAs, without alignments to known protein structures in a majority of the cases.

Just in case there is a confusion: there is a difference between available sequences (~300 million in standard protein sequence repositories) and structures (~170k structures in the PDB, perhaps about ~120k that are structurally non-redundant). A large amount of CASP14 targets have no available templates; in fact, many of them represented previously unseen topologies. However, all of them had some (in most cases, many) available sequences.

The commonly accepted definition of homology modelling implies using a known structure ("template") as a scaffold to model the protein's topology. Since there are many CASP14 targets without appropriate templates, AlphaFold 2 simply cannot "just do homology modelling".

I do take the point that the correct term is "free modelling" (it does not have, or does not use, any good structure as a template), and not "ab initio modelling" (it uses physics to fold the protein), though. A deep enough MSA is generally a requirement.

I respectfully disagree. AlphaFold 2 demonstrated almost perfect performance for a multitude of proteins for which no meaningful templates were available -- hence, it was not doing homology modelling as it is generally understood, but ab initio protein structure prediction.

What I would support is that AlphaFold 2 does not solve the protein folding problem: how, as opposed to what to, a protein folds.

There is a growing trend to include Docker (or Singularity, which is more compatible with HPC architectures common in bioinformatics) images alongside codes. In particular, AlphaFold 2 does provide a Dockerfile, and they even include a Python "launcher script" hiding all the details of running the code.

Sadly, this is very uncommon in the community. In a bioinformatics meeting, the sentence "I spent X days setting up Y software" will not raise many eyebrows