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Title:
Accurate prediction of protein structures and interactions using a 3-track network | bioRxiv
Description:
DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence Summary Accurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function. ### Competing Interest Statement The authors have declared no competing interest.
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Keywords {๐}
biology, view, orcid, profile, structure, network, accurate, medrxiv, subject, reviews, clinical, preprint, protein, track, information, level, enables, rapid, modeling, biorxiv, university, trials, epidemiology, search, results, sergey, adams, pdf, deepmind, predictions, casp, sequence, solution, problems, insights, scientific, community, biological, research, competing, interest, authors, copyright, license, institute, technology, cancer, bioinformatics, context, genetics,
Topics {โ๏ธ}
epidemiology subject categories clinical trials enables rapid solution challenging x-ray crystallography protein-protein complexes genetics microbiology biorxiv subject category structure determination problems enables rapid generation scientific community epidemiology papers reviews preprint accurate prediction author/funder health-related information keyword advanced search 3d coordinate level gyu rie lee tea pavkov-keller speed biological research generous financial support chan zuckerberg initiative imperial college london vrije universiteit amsterdam articles animal behavior 3-track network 1d sequence level accurate models unknown structure protein structures david baker doi competing interest statement context require modeling deepmind competing interest sequence information biological function network results follow frank dimaio jue wang dustin schaeffer claudia millรกn hahnbeom park andy degiovanni van dijk theo sagmeister christoph buhlheller
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