生物序列语言模型

Certainly! Here’s a concise table showing for each model whether it takes a sequence as input and whether it is primarily used to generate text (natural language description) or generate embeddings (for clustering, prediction, etc.).

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Model Name	Input: Sequence	Output: Embedding	Output: Text
ProtT5-XL-UniRef50	✅	✅	❌
ProtT5-XL-BFD	✅	✅	❌
ESM-1b (esm1b_t33_650M_UR50S)	✅	✅	❌
ESM-2 (esm2_t12_35M_UR50D)	✅	✅	❌
ESM-2 (esm2_t33_650M_UR50D)	✅	✅	❌
Prot2Text-Base-v1-1	✅	❌	✅
BiomedNLP-BiomedBERT-base-uncased-abstract	❌	✅*	✅*

* BiomedBERT: Input is text (not protein sequence), so it’s not used for protein sequence tasks. Output is an embedding or generated biomedical text.

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Summary:

• For protein sequence → embedding: use any ESM or ProtT5 model.
• For protein sequence → text: use Prot2Text-Base-v1-1.
• BiomedBERT is only for biomedical language, not protein sequence.

Let me know if you want usage code for a particular model!

esm2 更好

In the MMSite framework (according to the NeurIPS 2024 paper you uploaded), ESM-1b outperforms ESM-2-650M for the active site identification task. This is directly shown in their benchmark results:

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Performance Table (extracted from Table 1 and Table 2 in the paper):

Model	Fmax	AUPRC	MCC	OS	FPR
ESM-1b	0.7052	0.8452	0.7123	0.7211	0.2758
ESM-2-650M	0.6517	0.8230	0.6596	0.6652	0.3240

And after multi-modal enhancement (with text), both improve, but ESM-1b still has better results:

Model	+ MMSite (Fmax)
ESM-1b	0.8250
ESM-2-650M	0.7965

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Conclusion:

In this paper, ESM-1b is consistently better than ESM-2-650M in their framework for active site prediction.

This is consistent across all metrics reported in the tables.

Reason:
The authors do not specifically analyze why, but this may be due to:

• Task-specific compatibility (ESM-1b may better fit their dataset or loss formulation)
• Differences in pretraining or tokenization (ESM-2-650M is newer and sometimes outperforms on other tasks, but not here)

Bottom line:

If you follow MMSite, ESM-1b is the preferred model for sequence embeddings.

Let me know if you need implementation advice or want to know when ESM-2 might be better!