Jin Li1,2, Jinbo Xu1. 1. Toyota Technological Institute at Chicago, USA. 2. Department of Computer Science, University of Chicago, USA.
Abstract
MOTIVATION: Inter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. RESULTS: Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models. AVAILABILITY AND IMPLEMENTATION: https://github.com/j3xugit/RaptorX-3DModeling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Inter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. RESULTS: Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models. AVAILABILITY AND IMPLEMENTATION: https://github.com/j3xugit/RaptorX-3DModeling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Milot Mirdita; Lars von den Driesch; Clovis Galiez; Maria J Martin; Johannes Söding; Martin Steinegger Journal: Nucleic Acids Res Date: 2016-11-28 Impact factor: 16.971
Authors: Andrew W Senior; Richard Evans; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; Chongli Qin; Augustin Žídek; Alexander W R Nelson; Alex Bridgland; Hugo Penedones; Stig Petersen; Karen Simonyan; Steve Crossan; Pushmeet Kohli; David T Jones; David Silver; Koray Kavukcuoglu; Demis Hassabis Journal: Nature Date: 2020-01-15 Impact factor: 49.962