PT  - JOURNAL ARTICLE
AU  - Billings, Wendy M
AU  - Hedelius, Bryce
AU  - Millecam, Todd
AU  - Wingate, David
AU  - Corte, Dennis Della
TI  - ProSPr: Democratized Implementation of Alphafold Protein Distance Prediction Network
AID  - 10.1101/830273
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 830273
4099  - http://biorxiv.org/content/early/2019/11/04/830273.short
4100  - http://biorxiv.org/content/early/2019/11/04/830273.full
AB  - Deep neural networks have recently enabled spectacular progress in predicting protein structures, as demonstrated by DeepMind’s winning entry with Alphafold at the latest Critical Assessment of Structure Prediction competition (CASP13). The best protein prediction pipeline leverages intermolecular distance predictions to assemble a final protein model, but this distance prediction network has not been published. Here, we make a trained implementation of this network available to the broader scientific community. We also benchmark its predictive power in the related task of contact prediction against the CASP13 contact prediction winner TripletRes. Access to ProSPr will enable other labs to build on best in class protein distance predictions and to engineer superior protein reconstruction methods.