Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP) . These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) . The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs ) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble (  Q2= 89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. The official GitHub repository: URL
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP . These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models ( BERT, Albert, Electra, T5 ) on data from UniRef and BFD containing up to 393 billion amino acids . The LMs were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure ( 3-state accuracy Q3= 81\%-87\%); the second were per-protein predictions of protein sub-cellular localization (ten-state accuracy: Q10= 81\%) and membrane vs. water-soluble ( 2-state accuracy Q2= 91\%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned some of the grammar of the language of life . To facilitate future work, we released our models at URL
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(LMs) taken from Natural Language Processing (NLP)
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language
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Bert, Albert
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(words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia)
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at Oak Ridge National Laboratory (ORNL), using 936 nodes (total
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) and one TPU Pod (V3-512 or V3-1024).
and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences.
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up-scaling LMs to larger models supported by bigger data by predicting
using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein
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3-states:
3-state accuracy
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76-84, 8-states: Q8=65-73),
81\%-87\%); the second were per-protein predictions of protein
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for 10 cellular compartments (
(ten-state accuracy:
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74) and whether a protein is membrane-bound or
81\%) and membrane vs.
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2-state accuracy
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89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning
91\%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned
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realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. The official GitHub repository:
. To facilitate future work, we released our models at