title: OpenProteinSet: Training data for structural biology at scale
summary: 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.
paper_authors._id: 64d5a027c2eedf9af80773df
paper_authors.name: Gustaf Ahdritz
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-13T00:11:30.755Z
paper_authors._id: 64d6bf21bcab729cb4b4b3f1
paper_authors.avatarUrl: /avatars/4423dee284ac0ecb8190c6611ed8aa54.svg
paper_authors.fullname: Gustaf Ahdritz
paper_authors.type: user
paper_authors.user: gahdritz
paper_authors._id: 64d5a027c2eedf9af80773e0
paper_authors.name: Nazim Bouatta
paper_authors._id: 64d5a027c2eedf9af80773e1
paper_authors.name: Sachin Kadyan
paper_authors._id: 64d5a027c2eedf9af80773e2
paper_authors.name: Lukas Jarosch
paper_authors._id: 64d5a027c2eedf9af80773e3
paper_authors.name: Daniel Berenberg
paper_authors._id: 64d5a027c2eedf9af80773e4
paper_authors.name: Ian Fisk
paper_authors._id: 64d5a027c2eedf9af80773e5
paper_authors.name: Andrew M. Watkins
paper_authors._id: 64d5a027c2eedf9af80773e6
paper_authors.name: Stephen Ra
paper_authors._id: 64d5a027c2eedf9af80773e7
paper_authors.name: Richard Bonneau
paper_authors._id: 64d5a027c2eedf9af80773e8
paper_authors.name: Mohammed AlQuraishi
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-08-11T12:22:16.481Z
paper_authors._id: 62b32ac33a1974c3b0e0bec3
paper_authors.avatarUrl: /avatars/52301ef6613e1d52a7dfbdd081628819.svg
paper_authors.fullname: Mohammed AlQuraishi
paper_authors.type: user
paper_authors.user: alquraishi
paper_title: OpenProteinSet: Training data for structural biology at scale
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: transformers multiple sequence alignments protein structure prediction AlphaFold2 OpenProteinSet multimodal machine learning