OpenProteinSet: Training data for structural biology at scale
Multiple sequence alignments (MSAs) of proteins encode rich biological
information and have been workhorses in bioinformatic methods for tasks like
protein design and protein structure prediction for decades. Recent
breakthroughs like AlphaFold2 that use transformers to attend directly over
large quantities of raw MSAs have reaffirmed their importance. Generation of
MSAs is highly computationally intensive, however, and no datasets comparable
to those used to train AlphaFold2 have been made available to the research
community, hindering progress in machine learning for proteins. To remedy this
problem, we introduce OpenProteinSet, an open-source corpus of more than 16
million MSAs, associated structural homologs from the Protein Data Bank, and
AlphaFold2 protein structure predictions. We have previously demonstrated the
utility of OpenProteinSet by successfully retraining AlphaFold2 on it. We
expect OpenProteinSet to be broadly useful as training and validation data for
1) diverse tasks focused on protein structure, function, and design and 2)
large-scale multimodal machine learning research.
64d5a027c2eedf9af80773df
Gustaf Ahdritz
claimed_verified
2023-08-13T00:11:30.755Z
64d6bf21bcab729cb4b4b3f1
/avatars/4423dee284ac0ecb8190c6611ed8aa54.svg
Gustaf Ahdritz
user
gahdritz
64d5a027c2eedf9af80773e0
Nazim Bouatta
64d5a027c2eedf9af80773e1
Sachin Kadyan
64d5a027c2eedf9af80773e2
Lukas Jarosch
64d5a027c2eedf9af80773e3
Daniel Berenberg
64d5a027c2eedf9af80773e4
Ian Fisk
64d5a027c2eedf9af80773e5
Andrew M. Watkins
64d5a027c2eedf9af80773e6
Stephen Ra
64d5a027c2eedf9af80773e7
Richard Bonneau
64d5a027c2eedf9af80773e8
Mohammed AlQuraishi
extracted_confirmed
2023-08-11T12:22:16.481Z
62b32ac33a1974c3b0e0bec3
/avatars/52301ef6613e1d52a7dfbdd081628819.svg
Mohammed AlQuraishi
user
alquraishi
OpenProteinSet: Training data for structural biology at scale
Multiple sequence alignments (MSAs) of proteins encode rich biological
information and have been workhorses in bioinformatic methods for tasks like
protein design and protein structure prediction for decades. Recent
breakthroughs like AlphaFold2 that use transformers to attend directly over
large quantities of raw MSAs have reaffirmed their importance. Generation of
MSAs is highly computationally intensive, however, and no datasets comparable
to those used to train AlphaFold2 have been made available to the research
community, hindering progress in machine learning for proteins. To remedy this
problem, we introduce OpenProteinSet, an open-source corpus of more than 16
million MSAs, associated structural homologs from the Protein Data Bank, and
AlphaFold2 protein structure predictions. We have previously demonstrated the
utility of OpenProteinSet by successfully retraining AlphaFold2 on it. We
expect OpenProteinSet to be broadly useful as training and validation data for
1) diverse tasks focused on protein structure, function, and design and 2)
large-scale multimodal machine learning research.
OpenProteinSet provides a large, openly available corpus of MSAs, protein homologs, and AlphaFold2 predictions, facilitating advanced training and validation for protein-focused machine learning tasks.
transformers multiple sequence alignments protein structure prediction AlphaFold2 OpenProteinSet multimodal machine learning