ModelAngelo is an automated cryo-EM atomic model building software. It is free and available for everyone to use on Github. You can find the pre-print on arXiv. If you have an interesting use case, and especially if you have good resolution maps with unknown protein sequences, please reach out to me! In the future, this page will include a fully worked through tutorial along with example output. For now, you can enjoy this picture of ModelAngelo’s output on PHFs.
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modelangelo
portfolio
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publications
ChainGAN: A sequential approach to GANs
Appears on arXiv
DeepConsensus: using the consensus of features from multiple layers to attain robust image classification
Appears on arXiv
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
Published in ICLR 2020
Sparse Fourier Backpropagation in Cryo-EM Reconstruction
Published in NeurIPS 2022
Fast protein structure searching using structure graph embeddings
Published in Machine Learning for Structural Biology Workshop, NeurIPS 2022
A graph neural network approach to automated model building in cryo-EM maps
Published in Machine Learning for Structural Biology Workshop, NeurIPS 2022
A graph neural network approach to automated model building in cryo-EM maps
Published in ICLR 2023
Automated model building and protein identification in cryo-EM maps
Published in Nature
talks
Automated model building in cryo-EM maps using equivariant graph neural networks
Published:
Gave a talk describing my PhD project on using equivariant graph neural networks to do automated model building. This was at the Swiss Equivariant Learning Workshop.
ModelAngelo: Automated atomic model building and protein sequence discovery for cryo-EM maps
Published:
Seminar about ModelAngelo for the Molecular Biophysics Stockholm group at SciLifeLab, Stockholm.
ModelAngelo: Automated atomic model building and protein sequence discovery for cryo-EM maps
Published:
ModelAngelo talk for the SBGrid webinar series. Recording available in the link. This talk included some practical information for running the program as well as the methods details.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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