RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms.

Machine learning applied to protein sequences is an increasingly popular areaof research.

Weaddress these limitations byproposing a Probabilistic Transformer1 (ProbTransformer) that models a hierarchical latent distribution and performs sampling in the latent space.

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines.

A.2 Documentation and intended uses We include a datasheet [1] in Section B. Detailed documentation on the precise structure and content OpenProteinSet is made available under the CC BY 4.0 license. The authors bear all responsibility in case of violation of rights. OpenProteinSet will continue to be hosted on RODA for the foreseeable future. A.7 Alignment tool settings For JackHMMer, we used -N 1 -E 0.0001 -incE 0.0001 -F1 0.0005 -F2 0.00005 -F3 0.0000005 and then capped outputs at depth 5000. B.1 Motivation For what purpose was the dataset created?

Each row of an MSA is a protein sequence.