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Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
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As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model size and the richness of its learned representations is validated, we prioritize accessibility and pursue a path of data-efficient, cost-reduced, and knowledge-guided optimization. Through over twenty experiments ranging from masking, architecture, and pre-training data, we derive insights from protein-specific experimentation into building a model that interprets the language of life, optimally. We present Ankh, the first general-purpose PLM trained on Google's TPU-v4 surpassing the state-of-the-art performance with fewer parameters (<10% for pre-training, <7% for inference, and <30% for the embedding dimension). We provide a representative range of structure and function benchmarks where Ankh excels. We further provide a protein variant generation analysis on High-N and One-N input data scales where Ankh succeeds in learning protein evolutionary conservation-mutation trends and introducing functional diversity while retaining key structural-functional characteristics. We dedicate our work to promoting accessibility to research innovation via attainable resources.
Forward citations
Cited by 6 Pith papers
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Ankh3 shows that combining multiple masking probabilities with sequence completion improves protein language model performance downstream, but the causal role of multi-task pretraining is not proven by a matched ablation.
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A systematic benchmark and architecture comparison shows that hierarchical pooling and pooled cross-attention usually beat concatenation for PLM-based protein-protein binding affinity prediction, although statistical ...
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A Comprehensive Review of Protein Language Models
A survey paper that catalogs protein language models, their architectures, training data, benchmarks, and tools, but lacks a systematic methodology and contains several factual errors.
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