Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13
暂无分享,去创建一个
Yang Zhang | Yang Li | Dong-Jun Yu | Chengxin Zhang | Eric W. Bell | Eric W Bell | Yang Zhang | Dong-Jun Yu | Yang Li | Chengxin Zhang
[1] Markus Gruber,et al. CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations , 2014, Bioinform..
[2] C. Sander,et al. Can three-dimensional contacts in protein structures be predicted by analysis of correlated mutations? , 1994, Protein engineering.
[3] C. Sander,et al. Correlated mutations and residue contacts in proteins , 1994, Proteins.
[4] T. Blundell,et al. Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.
[5] Sean R. Eddy,et al. Accelerated Profile HMM Searches , 2011, PLoS Comput. Biol..
[6] Johannes Söding,et al. Clustering huge protein sequence sets in linear time , 2018 .
[7] A. Biegert,et al. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment , 2011, Nature Methods.
[8] Jun Hu,et al. ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks , 2019, Bioinform..
[9] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[10] Magnus Ekeberg,et al. Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences , 2014, J. Comput. Phys..
[11] David T Jones,et al. Improved protein contact predictions with the MetaPSICOV2 server in CASP12 , 2018, Proteins.
[12] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Milot Mirdita,et al. HH-suite3 for fast remote homology detection and deep protein annotation , 2019, BMC Bioinformatics.
[14] Yang Zhang,et al. Template‐based and free modeling of I‐TASSER and QUARK pipelines using predicted contact maps in CASP12 , 2018, Proteins.
[15] Lisa N Kinch,et al. Evaluation of free modeling targets in CASP11 and ROLL , 2016, Proteins.
[16] Liam J. McGuffin,et al. The PSIPRED protein structure prediction server , 2000, Bioinform..
[17] Yang Zhang,et al. NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers , 2017, Bioinform..
[18] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[19] A. Szilágyi,et al. Improving protein structure prediction using multiple sequence-based contact predictions. , 2011, Structure.
[20] Johannes Söding,et al. Clustering huge protein sequence sets in linear time , 2017, Nature Communications.
[21] Sitao Wu,et al. LOMETS: A local meta-threading-server for protein structure prediction , 2007, Nucleic acids research.
[22] Johannes Söding,et al. HH-suite3 for fast remote homology detection and deep protein annotation , 2019, BMC Bioinformatics.
[23] E. Aurell,et al. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[24] Bonnie Berger,et al. Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks , 2017, Cell systems.
[25] Dong Xu,et al. ThreaDom: extracting protein domain boundary information from multiple threading alignments , 2013, Bioinform..
[26] Nancy Wilkins-Diehr,et al. XSEDE: Accelerating Scientific Discovery , 2014, Computing in Science & Engineering.
[27] David T. Jones,et al. High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features , 2018, Bioinform..
[28] Jie Hou,et al. DNCON2: improved protein contact prediction using two-level deep convolutional neural networks , 2017, bioRxiv.
[29] D. Baker,et al. Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era , 2013, Proceedings of the National Academy of Sciences.
[30] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[31] M. Levitt,et al. Computer simulation of protein folding , 1975, Nature.
[32] Yang Zhang,et al. DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins , 2019, Bioinform..
[33] Massimiliano Pontil,et al. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..
[34] D. Phillips,et al. A possible three-dimensional structure of bovine alpha-lactalbumin based on that of hen's egg-white lysozyme. , 1969, Journal of molecular biology.
[35] C. Sander,et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.
[36] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[37] David E. Kim,et al. Protein structure determination using metagenome sequence data , 2017, Science.
[38] Maria Jesus Martin,et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments , 2016, Nucleic Acids Res..
[39] David T. Jones,et al. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins , 2014, Bioinform..
[40] Zhen Li,et al. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model , 2016, bioRxiv.
[41] Andriy Kryshtafovych,et al. Assessment of hard target modeling in CASP12 reveals an emerging role of alignment‐based contact prediction methods , 2018, Proteins.
[42] David E. Kim,et al. Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta , 2016, Proteins.