Deep Learning in Protein Structural Modeling and Design
Gao, Wenhao, Mahajan, Sai Pooja, Sulam, Jeremias, Gray, Jeffrey J.·2020·Patterns
Cites (273)
Items 201–273 of 273
- Designing Anticancer Peptides by Constructive Machine LearningFrancesca Grisoni, Claudia S. Neuhaus, Gisela Gabernet · 2018 · ChemMedChem
- De Novo Designed Proteins from a Library of Artificial Sequences Function in Escherichia Coli and Enable Cell GrowthMichael A. Fisher, Kara L. McKinley, Luke H. Bradley, Sara R. Viola, Michael H. Hecht · 2011 · PLoS ONE
- Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learningJianzhu Ma, Sheng Wang, Zhiyong Wang, Jinbo Xu · 2015 · Bioinformatics
- Deciphering protein evolution and fitness landscapes with latent space modelsDing, Xinqiang, Zou, Zhengting, Brooks III, Charles L. · 2019 · Nature Communications
- Prediction of interresidue contacts with DeepMetaPSICOV in CASP13Shaun M. Kandathil, Joe G. Greener, David T. Jones · 2019 · Proteins Structure Function and Bioinformatics
- ProFET: Feature engineering captures high-level protein functionsDan Ofer, Michal Linial B · 2015 · Bioinformatics
- Evaluation of residue–residue contact predictions in CASP9Bohdan Monastyrskyy, Krzysztof A. Fidelis, Anna Luna Tramontano, Andriy Kryshtafovych · 2011 · Proteins Structure Function and Bioinformatics
- Deep convolutional networks for quality assessment of protein foldsGeorgy Derevyanko, Sergei Grudinin, Yoshua Bengio, Guillaume Lamoureux · 2018 · Bioinformatics
- DeeplyTough: Learning Structural Comparison of Protein Binding SitesMartin Simonovsky, Joshua Meyers · 2020 · Journal of Chemical Information and Modeling
- Distributed Learning for Stochastic Generalized Nash Equilibrium ProblemsChung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed · 2017 · IEEE Transactions on Signal Processing
- SPIN2: Predicting sequence profiles from protein structures using deep neural networksJames O'Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James Lyons, Kuldip K. Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou · 2018 · Proteins Structure Function and Bioinformatics
- Simulating protein–ligand binding with neural network potentialsShae-Lynn J. Lahey, Christopher N. Rowley · 2020 · Chemical Science
- End-to-end differentiable construction of molecular mechanics force fieldsYuanqing Wang, Josh Fass, Benjamin Kaminow, John E. Herr, Dominic A. Rufa, Ivy Zhang, Iván Pulido, Michael M. Henry, Hannah E. Bruce Macdonald, Kenichiro Takaba et al. · 2022 · Chemical Science
- Protein contact prediction using metagenome sequence data and residual neural networksQi Wu, Zhenling Peng, Ivan V. Anishchenko, Qian Cong, David A. Baker, Jianyi Yang · 2019 · Bioinformatics
- Modeling Event Propagation via Graph Biased Temporal Point ProcessWeichang Wu, Huanxi Liu, Xiaohu Zhang · 2020 · IEEE Transactions on Neural Networks and Learning Systems
- Deep Lagrangian Networks: Using Physics as Model Prior for Deep LearningMichael Lutter, Christian Ritter, Jan Peters · 2019 · arXiv (Cornell University)
- Folding Membrane Proteins by Deep Transfer LearningSheng Wang, Zhen Li, Yizhou Yu, Jinbo Xu · 2017 · Cell Systems
- Correlated substitution analysis and the prediction of amino acid structural contactsDavid Stephen Horner, Walter Pirovano, Graziano Pesole · 2007 · Briefings in Bioinformatics
- Evaluation of residue–residue contact prediction in CASP10Bohdan Monastyrskyy, Daniel D’Andrea, Krzysztof A. Fidelis, Anna Luna Tramontano, Andriy Kryshtafovych · 2013 · Proteins Structure Function and Bioinformatics
- Das Arbeiten mit radioaktiven AtomartenOtto Hahn · 1947 · Angewandte Chemie
- Peptide design by artificial neural networks and computer-based evolutionary searchGisbert Schneider, Wieland Schrödl, Gerd Wallukat, Johannes Müller, Eberhard Nissen, Wolfgang Rönspeck, Paul Wrede, Rudolf Kunze · 1998 · Proceedings of the National Academy of Sciences
- De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial NetworksMostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen · 2020 · Journal of Chemical Information and Modeling
- Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profilesZhixiu Li, Yuedong Yang, Eshel Faraggi, Jian Zhan, Yaoqi Zhou · 2014 · Proteins Structure Function and Bioinformatics
- DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNetYifei Qi, John Z. H. Zhang · 2020 · Journal of Chemical Information and Modeling
- Geometric potentials from deep learning improve prediction of CDR H3 loop structuresJeffrey A. Ruffolo, Carlos Guerra, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray · 2020 · Bioinformatics
- A Review of Deep Learning Methods for AntibodiesJordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati, S. Parish, Brenda P. Medellin, Monica Berrondo · 2020 · Antibodies
- Deep generative models for T cell receptor protein sequencesKristian Davidsen, Branden J Olson, William S. DeWitt, Jean Feng, Elias Harkins, Philip H Bradley, Frederick A. Matsen IV · 2019 · eLife
- Structure-based protein function prediction using graph convolutional networksVladimir Gligorijević, P. Douglas Renfrew, Tomasz Kościółek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, I. Fisk, Hera C. Vlamakis et al. · 2021 · RePEc: Research Papers in Economics
- ProDCoNN: Protein design using a convolutional neural networkYuan Zhang, Yang Chen, Chenran Wang, Chun‐Chao Lo, Xiuwen Liu, Wei Wu, Jinfeng Zhang · 2019 · Proteins Structure Function and Bioinformatics
- Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein FunctionsAnvita Gupta, James Zou · 2018 · arXiv (Cornell University)
- Recent developments in deep learning applied to protein structure predictionShaun M. Kandathil, Joe G. Greener, David T. Jones · 2019 · Proteins Structure Function and Bioinformatics
- ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN frameworkHan Xi, Liheng Zhang, Kang Zhou, Xiaonan Wang · 2019 · Computers & Chemical Engineering
- Characterization of Protein Hubs by Inferring Interacting Motifs from Protein InteractionsRamón Aragüés, Andrej Šali, Jaume Bonet, Marc A. Martı́-Renom, Baldomero Oliva · 2007 · PLoS Computational Biology
- RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learningYujuan Gao, Sheng Wang, Minghua Deng, Jinbo Xu · 2018 · BMC Bioinformatics
- To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance MapSheng Chen, Zhe Sun, Lihua Lin, Zifeng Liu, Xun Liu, Yutian Chong, Yutong Lu, Huiying Zhao, Yuedong Yang · 2019 · Journal of Chemical Information and Modeling
- Model-based reinforcement learning for biological sequence designChristof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin J. Murphy, Lucy J. Colwell · 2020 · International Conference on Learning Representations
- Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein EnergyZhilong Wang, Yanqiang Han, Jinjin Li, Xiao Dong He · 2020 · The Journal of Physical Chemistry B
- Graph neural network based coarse-grained mapping predictionZhiheng Li, Geemi P. Wellawatte, Maghesree Chakraborty, Heta A. Gandhi, Chenliang Xu, Andrew Dickson White · 2020 · Chemical Science
- Protein structure determination by electron cryo-microscopySlavica Jonić, Catherine Vénien‐Bryan · 2009 · Current Opinion in Pharmacology
- Variational auto-encoding of protein sequencesSam Sinai, Eric D. Kelsic, George McDonald Church, Martin A. Nowak · 2017 · arXiv (Cornell University)
- Conditioning by adaptive sampling for robust designDavid H. Brookes, Hahnbeom Park, Jennifer Listgarten · 2019 · arXiv (Cornell University)
- Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein SequencesAshish Ranjan, Md Shah Fahad, David Fernández‐Baca · 2019 · IEEE Transactions on Computational Biology and Bioinformatics
- Fully differentiable full-atom protein backbone generationNamrata Anand, Raphael R. Eguchi, Po‐Ssu Huang · 2019 · International Conference on Learning Representations
- Protein Sequence Design with a Learned PotentialNamrata Anand, Raphael R. Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ Biagio Altman, Po‐Ssu Huang · 2020 · bioRxiv (Cold Spring Harbor Laboratory)
- Protein Design and Variant Prediction Using Autoregressive Generative ModelsJung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch, Conor McMahon, Elana P. Simon, Chris Sander, Aashish Manglik, Andrew C. Kruse, Debora S. Marks · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
- PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide SequencesPayel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cícero Nogueira dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilović · 2018 · arXiv (Cornell University)
- Structural Learning of Proteins Using Graph Convolutional Neural NetworksZamora-Resendiz, Rafael, Crivelli, Silvia · 2019
- Finding the needle in the haystack: towards solving the protein-folding problem computationallyBian Li, Michaela Fooksa, Sten Heinze, Jens Meiler · 2017 · Critical Reviews in Biochemistry and Molecular Biology
- Investigating Active Learning and Meta-Learning for Iterative Peptide DesignRainier Barrett, Andrew D. White · 2020 · Journal of Chemical Information and Modeling
- Structure-Based Protein Function Prediction using Graph Convolutional NetworksVladimir Gligorijević, P. Douglas Renfrew, Tomasz Kościółek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, Ian M. Fisk, Hera C. Vlamakis et al. · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
- Protein sequence design by explicit energy landscape optimizationChristoffer Norn, Basile I. M. Wicky, David Juergens, Sirui Liu, David E. Kim, Brian Koepnick, Ivan V. Anishchenko, Foldit Players, David A. Baker, Sergey Ovchinnikov · 2020 · bioRxiv (Cold Spring Harbor Laboratory)
- Expanding functional protein sequence space using generative adversarial networksDonatas Repecka, Vykintas Jauniškis, Laurynas Karpus et al. · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
- How to Hallucinate Functional ProteinsZak Costello, Héctor García Martín · 2019 · arXiv (Cornell University)
- New Deep Learning Methods for Protein Loop ModelingSon P. Nguyen, Zhaoyu Li, Dong He Xu · 2017 · IEEE Transactions on Computational Biology and Bioinformatics
- Protein Loop Modeling Using Deep Generative Adversarial NetworkZhaoyu Li, Son P. Nguyen, Dong He Xu, Yi Shang · 2017 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
- 3D representations of amino acids—applications to protein sequence comparison and classificationJie Li, Patrice Koehl · 2014 · Computational and Structural Biotechnology Journal
- RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural networkSari Sabban, М.Г. Марковский · 2020 · F1000Research
- Universal approximation of symmetric and anti-symmetric functionsJiequn Han, Yingzhou Li, Lin Lin · 2022 · Communications in Mathematical Sciences
- Fast and flexible design of novel proteins using graph neural networksAlexey V. Strokach, David Becerra, Carles Corbi‐Verge, Albert Perez-Riba, Philip M. Kim · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
- A coarse-grained deep neural network model for liquid waterTarak K Patra, Troy D. Loeffler, Henry Chan · 2019 · Applied Physics Letters
- Structural biology meets data science: does anything change?Cameron Mura, Eli J. Draizen, Philip E. Bourne · 2018 · Current Opinion in Structural Biology
- Prediction of amino acid side chain conformation using a deep neural networkKe Liu, Xiangyan Sun, Jun Ma · 2017 · arXiv (Cornell University)
- Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networksLeonardo Zepeda-Núñez, Yixiao Chen, Zhang, Jiefu · 2019 · arXiv (Cornell University)
- A structure-based deep learning framework for protein engineeringRaghav Shroff, Austin W. Cole, Barrett R. Morrow, Daniel J. Diaz, Isaac Donnell, Jimmy Gollihar, Andrew D. Ellington, Ross T Thyer · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
- Generating protein sequences from antibiotic resistance genes data using Generative Adversarial NetworksPrabal Chhibbar, Joshi, Arpit · 2019 · arXiv (Cornell University)
- Accurate Protein Structure Prediction by Embeddings and Deep Learning RepresentationsIddo Drori, Darshan Thaker, Arjun Srivatsa, Daniel P. Jeong, Yueqi Wang, Linyong Nan, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu et al. · 2019 · arXiv (Cornell University)
- End-to-End Differentiable Molecular Mechanics Force Field ConstructionYuanqing Wang, Josh Fass, Kaminow, Benjamin, Herr, John E., Rufa, Dominic, Zhang, Ivy, Pulido, Iván, Henry, Mike, John D. Chodera · 2020 · arXiv (Cornell University)
- Universal approximation of symmetric and anti-symmetric functionsJiequn Han, Yingzhou Li, Lin Lin · 2019 · arXiv (Cornell University)
- Generalizable Protein Interface Prediction with End-to-End Learning.Raphael J.L. Townshend, Rishi Bedi, Ron O. Dror · 2018 · arXiv (Cornell University)
- Big Data from Sparse Data: Diverse Scientific Benchmarks Reveal Optimization Imperatives for Implicit Membrane Energy FunctionsRebecca F. Alford, Jeffrey J. Gray · 2020 · Biophysical Journal
- Development of a Protein Folding Environment for Reinforcement LearningWenhao Gao · 2020
- Untitled work1999 · 10.1002/(sici)1097-0134(1999)37:3+
- BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingDevlin, Jacob, Chang, Ming-Wei, Lee, Kenton, Toutanova, Kristina · 2018 · arXiv