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ProstT5 is a protein language model (pLM) which can translate between protein sequence and structure. ProstT5 (Protein structure-sequence T5) is based on ProtT5-XL-U50, a T5 model trained on encoding protein sequences using span corruption applied on billions of protein sequences. ProstT5 finetunes ProtT5-XL-U50 on translating between protein sequence and structure using 17M proteins with high-quality 3D structure predictions from the AlphaFoldDB. Protein structure is converted from 3D to 1D using the 3Di-tokens introduced by Foldseek. In a first step, ProstT5 learnt to represent the newly introduced 3Di-tokens by continuing the original span-denoising objective applied on 3Di- and amino acid- (AA) sequences. Only in a second step, ProstT5 was trained on translating between the two modalities. The direction of the translation is indicated by two special tokens ("\ " for translating from 3Di to AAs, “\ ” for translating from AAs to 3Di). To avoid clashes with AA tokens, 3Di-tokens were cast to lower-case (alphabets are identical otherwise). 1. The model can be used for traditional feature extraction. For this, we recommend using only the encoder in half-precision (fp16) together with batching. Examples (currently only for original ProtT5-XL-U50 but replacing repository links and adding prefixes works): script and colab While original ProtT5-XL-U50 could only embed AA sequences, ProstT5 can now also embed 3D structures represented by 3Di tokens. 3Di tokens can either be derived from 3D structures using Foldseek or they can be predicted from AA sequences by ProstT5. 3. "Folding": Translation from sequence (AAs) to structure (3Di). The resulting 3Di strings can be used together with Foldseek for remote homology detection while avoiding to compute 3D structures explicitly. 4. "Inverse Folding": Translation from structure (3Di) to sequence (AA).

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Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids. ProtBert is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents. This means the Next sentence prediction is not used, as each sequence is treated as a complete document. The masking follows the original Bert training with randomly masks 15% of the amino acids in the input.

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