Book Chapter10.1007/978-3-031-30923-6_7
Using a Graph Transformer Network to Predict 3D Coordinates of Proteins via Geometric Algebra Modelling
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TL;DR: In this article , a novel geometric algebra (GA) metric based on the relative orientations of amino acid residues is proposed for protein structure prediction, which is used as an additional input feature to a Graph Transformer (GT) to aid the prediction of the 3D coordinates of a protein.
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Abstract: The state of the art in protein structure prediction (PSP) is currently achieved by complex deep learning pipelines that require several input features. In this paper, we demonstrate the relevance of Geometric Algebra (GA) for modelling protein features in PSP. We do so by proposing a novel GA metric based on the relative orientations of amino acid residues. We then employ this metric as an additional input feature to a Graph Transformer (GT) to aid the prediction of the 3D coordinates of a protein. Adding this GA-based orientational information improves the accuracy of the predicted coordinates even after few learning iterations and on a small dataset.
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Citations
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References
Highly accurate protein structure prediction with AlphaFold
John M. Jumper,Richard O. Evans,Alexander Pritzel,Tim Green,Michael Figurnov,Olaf Ronneberger,Kathryn Tunyasuvunakool,Russell Bates,Augustin Žídek,Anna Potapenko,Alex Bridgland,Clemens Meyer,Simon A. A. Kohl,Andrew J. Ballard,Andrew Cowie,Bernardino Romera-Paredes,Stanislav Nikolov,R. D. Jain,Jonas Adler,Trevor Back,Stig Petersen,David Reiman,Ellen Clancy,Michal Zielinski,Martin Steinegger,Michalina Pacholska,Tamas Berghammer,Sebastian Bodenstein,David L. Silver,Oriol Vinyals,Andrew W. Senior,Koray Kavukcuoglu,Pushmeet Kohli,Demis Hassabis +33 more
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek,Frank DiMaio,Ivan Anishchenko,Justas Dauparas,Sergey Ovchinnikov,Gyu Rie Lee,Jue Wang,Qian Cong,Lisa N. Kinch,R. Dustin Schaeffer,Claudia Millán,Hahnbeom Park,Carson Adams,Caleb R. Glassman,Andy DeGiovanni,Jose Henrique Pereira,Andria V. Rodrigues,Alberdina A. van Dijk,Ana C. Ebrecht,Diederik J. Opperman,Theo Sagmeister,Christoph Buhlheller,Christoph Buhlheller,Tea Pavkov-Keller,Manoj K. Rathinaswamy,Udit Dalwadi,Calvin K. Yip,John E. Burke,K. Christopher Garcia,Nick V. Grishin,Paul D. Adams,Paul D. Adams,Randy J. Read,David Baker +33 more
TL;DR: In this article, a three-track network is proposed to combine information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level.
Improved protein structure prediction using predicted interresidue orientations
TL;DR: A deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints are developed.
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•Book
Geometric Algebra for Physicists
Chris Doran,Anthony Lasenby +1 more
- 07 Jul 2003
TL;DR: Geometric algebra is a powerful mathematical language with applications across a range of subjects in physics and engineering as discussed by the authors, and it can be used as a graduate text for courses on the physical applications of geometric algebra and is also suitable for researchers working in the fields of relativity and quantum theory.
Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive.
Stephen K. Burley,Stephen K. Burley,Helen M. Berman,Gerard J. Kleywegt,John L. Markley,Haruki Nakamura,Sameer Velankar +6 more
TL;DR: The Worldwide Protein Data Bank partners are working closely with experts in related experimental areas to establish a federation of data resources that will support sustainable archiving and validation of 3D structural models and experimental data derived from integrative or hybrid methods.
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