Timezone: »
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. We describe protein backbone structure as a series of consecutive angles capturing the relative orientation of the constituent amino acid residues, and generate new structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins biologically twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release the first open-source codebase and trained models for protein structure diffusion.
Author Information
Kevin Wu (Stanford University)
Kevin Yang (Microsoft)
Rianne van den Berg (Microsoft Research)
James Zou (Stanford University)
Alex X Lu (Microsoft Research)
I’m a Senior Researcher at Microsoft Research New England, in the BioML group. I’m interested in how machine learning can help us discover new insights from biological data, by finding patterns that are too subtle or large-scale to identify unassisted. I primarily focus on biological images, and my research often designs self-supervised learning methods, as I believe these methods are unbiased by prior knowledge.
Ava Soleimany (Microsoft Research)
More from the Same Authors
-
2021 : FLIP: Benchmark tasks in fitness landscape inference for proteins »
Christian Dallago · Jody Mou · Kadina Johnston · Bruce Wittmann · Nicholas Bhattacharya · Samuel Goldman · Ali Madani · Kevin Yang -
2022 : Masked inverse folding with sequence transfer for protein representation learning »
Kevin Yang · Niccoló Zanichelli · Hugh Yeh -
2022 : Pretrained protein language model transfer learning: is the final layer representation what we want? »
Francesca-Zhoufan Li · Ava Soleimany · Kevin Yang · Alex X Lu -
2022 : Learning from physics-based features improves protein property prediction »
Amy Wang · Ava Soleimany · Alex X Lu · Kevin Yang -
2022 : Predicting Immune Escape with Pretrained Protein Language Model Embeddings »
Kyle Swanson · Howard Chang · James Zou -
2022 : DrML: Diagnosing and Rectifying Vision Models using Language »
Yuhui Zhang · Jeff Z. HaoChen · Shih-Cheng Huang · Kuan-Chieh Wang · James Zou · Serena Yeung -
2022 Workshop: Learning Meaningful Representations of Life »
Elizabeth Wood · Adji Bousso Dieng · Aleksandrina Goeva · Alex X Lu · Anshul Kundaje · Chang Liu · Debora Marks · Ed Boyden · Eli N Weinstein · Lorin Crawford · Mor Nitzan · Rebecca Boiarsky · Romain Lopez · Tamara Broderick · Ray Jones · Wouter Boomsma · Yixin Wang · Stephen Ra -
2022 : Panel »
Guy Van den Broeck · Cassio de Campos · Denis Maua · Kristian Kersting · Rianne van den Berg -
2022 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Gilles Louppe · Siddharth Mishra-Sharma · Benjamin Nachman · Brian Nord · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Lenka Zdeborová · Rianne van den Berg -
2022 : Continuous Time Evidential Distributions for Processing Irregular Time Series »
Taylor Killian · Ava Soleimany -
2021 : Invited Talk #3: Rianne van den Berg »
Rianne van den Berg -
2021 Poster: Structured Denoising Diffusion Models in Discrete State-Spaces »
Jacob Austin · Daniel D. Johnson · Jonathan Ho · Daniel Tarlow · Rianne van den Berg -
2020 Poster: Neuron Shapley: Discovering the Responsible Neurons »
Amirata Ghorbani · James Zou -
2020 Poster: FrugalML: How to use ML Prediction APIs more accurately and cheaply »
Lingjiao Chen · Matei Zaharia · James Zou -
2020 Oral: FrugalML: How to use ML Prediction APIs more accurately and cheaply »
Lingjiao Chen · Matei Zaharia · James Zou -
2020 Social: Computational Biology Speed Networking »
Kevin Yang -
2020 Poster: A Spectral Energy Distance for Parallel Speech Synthesis »
Alexey Gritsenko · Tim Salimans · Rianne van den Berg · Jasper Snoek · Nal Kalchbrenner -
2020 Poster: MOPO: Model-based Offline Policy Optimization »
Tianhe Yu · Garrett Thomas · Lantao Yu · Stefano Ermon · James Zou · Sergey Levine · Chelsea Finn · Tengyu Ma -
2019 : Phenotype »
Nir HaCohen · David Reshef · Matthew Johnson · Sam Morris · Aurel Nagy · Gokcen Eraslan · Meromit Singer · Eliezer Van Allen · Smita Krishnaswamy · Casey Greene · Scott Linderman · Alexander Wiltschko · Dylan Kotliar · James Zou · Brendan Bulik-Sullivan -
2019 Workshop: Graph Representation Learning »
Will Hamilton · Rianne van den Berg · Michael Bronstein · Stefanie Jegelka · Thomas Kipf · Jure Leskovec · Renjie Liao · Yizhou Sun · Petar Veličković -
2019 Poster: Towards Automatic Concept-based Explanations »
Amirata Ghorbani · James Wexler · James Zou · Been Kim -
2019 Poster: Integer Discrete Flows and Lossless Compression »
Emiel Hoogeboom · Jorn Peters · Rianne van den Berg · Max Welling -
2018 Poster: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders »
Abubakar Abid · James Zou