Question 1

How to fine-tune Ankh for custom protein classification?

Accepted Answer

You can use the provided ConvBERT models for binary or multiclass classification by adjusting parameters like input_dim and hidden_dim based on your data, but it requires PyTorch knowledge and manual setup, as shown in the downstream examples. Start by loading a base model and modifying the classification head for your task.

Question 2

Ankh vs ESM2 for secondary structure prediction: which is better?

Accepted Answer

Ankh generally outperforms ESM2 on benchmarks like CASP12 and TS115 for Q3 and Q8 accuracy, with Ankh Large achieving up to 84.40% vs. ESM2-15B's 83.16% on CASP12. However, ESM2 might be preferred for tasks where its larger variants excel, such as certain contact predictions, so check the specific task tables.

Question 3

Can Ankh generate new protein sequences?

Accepted Answer

No, Ankh is primarily designed for understanding and feature extraction from protein sequences, not generation. It focuses on tasks like prediction and classification, though it can compute pseudo-likelihoods for sequence analysis.

Question 4

What hardware do I need to run Ankh3 XL?

Accepted Answer

Running Ankh3 XL requires significant GPU memory due to its large size; a high-end GPU with at least 16-24GB VRAM is recommended for inference, and more for training. The models are optimized but still computationally intensive, as they were trained on TPU-V4.

Question 5

Is Ankh suitable for protein-protein interaction prediction?

Accepted Answer

Not directly; Ankh's current datasets and models focus on single-protein tasks like structure and solubility. For interactions, you'd need to adapt the embeddings or use external tools, as it's not a built-in feature.

Question 6

How to extract and use protein embeddings with Ankh?

Accepted Answer

Use the tokenizer and model to process sequences into embeddings, as shown in the feature extraction example. The embeddings can be used for tasks like similarity search or as input to downstream models, leveraging the pre-trained representations.

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