We recently released a review of machine learning methods in protein engineering, but the field changes so fast and there are so many new papers that any static document will inevitably be missing important work. This format also allows us to broaden the scope beyond engineering-specific applications. We hope that this will be a useful resource for people interested in the field.
To the best of our knowledge, this is the first public, collaborative list of machine learning papers on protein applications. We try to classify papers based on a combination of their applications and model type. If you have suggestions for other papers or categories, please make a pull request or issue!
Within each category, papers are listed in reverse chronological order (newest first). Where possible, a link should be provided.
Reviews
Tools
Machine-learning guided directed evolution
Representation learning
Unsupervised variant prediction
Generative models
Predicting stability
Predicting structure from sequence
Predicting sequence from structure
Classification and annotation
Predicting interactions with other molecules
Other supervised learning
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.
Sebastian Raschka, Benjamin Kaufman.
Preprint, January 2020.
[arXiv]
Machine Learning in Enzyme Engineering.
Stanislav Mazurenko, Zbynek Prokop, Jiri Damborsky.
ACS Catalysis, December 2019.
[doi.org/10.1021/acscatal.9b04321]
Machine learning-guided directed evolution for protein engineering.
Kevin K. Yang, Zachary Wu, Frances H. Arnold.
Nature Methods, July 2019.
[doi.org/10.1038/s41592-019-0496-6]
Preprint available on arxiv.
Evaluating Protein Transfer Learning with TAPE.
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song.
Preprint, June 2019.
[arxiv]
Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes?
Guangyue Li, Yijie Dong, Manfred T. Reetz.
Advanced Synthesis & Catalysis, March 2019.
[10.1002/adsc.201900149]
Population-Based Black-Box Optimization for Biological Sequence Design.
Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley.
ICML, July 2020.
[ICML]
Selene: a PyTorch-based deep learning library for sequence data.
Kathleen M. Chen, Evan M. Cofer, Jian Zhou, Olga G. Troyanskaya.
Nature Methods, March 2019.
[doi.org/10.1038/s41592-019-0360-8]
Learning with uncertainty for biological discovery and design.
Brian Hie, Bryan Bryson, Bonnie Berger.
Preprint, August 2020.
[10.1101/2020.08.11.247072]
De novo protein design by deep network hallucination.
Ivan Anishchenko, Tamuka M. Chidyausiku, Sergey Ovchinnikov, Samuel J. Pellock, David Baker.
Preprint, July 2020.
[10.1101/2020.07.22.211482]
Autofocused oracles for model-based design.
Clara Fannjiang, Jennifer Listgarten.
Preprint, June 2020.
[arxiv]
Domain Extrapolation via Regret Minimization.
Wengong Jin, Regina Barzilay, Tommi Jaakkola.
Preprint, June 2020.
[arxiv]
Fast differentiable DNA and protein sequence optimization for molecular design.
Johannes Linder, Georg Seelig.
Preprint, May 2020.
[arxiv]
A Deep Dive into Machine Learning Models for Protein Engineering.
Yuting Xu, Deeptak Verma, Robert P Sheridan, Andy Liaw, Junshui Ma, Nicholas
Marshall, John McIntosh, Edward C. Sherer, Vladimir Svetnik, Jennifer Johnston.
Journal of Chemical Information and Modeling, April 2020.
[10.1021/acs.jcim.0c00073]
Evolutionary context-integrated deep sequence modeling for protein engineering.
Yunan Luo, Lam Vo, Hantian Ding, Yufeng Su, Yang Liu, Wesley Wei Qian, Huimin Zhao, Jian Peng.
Preprint, January 2020.
[10.1101/2020.01.16.908509]
Biological Sequence Design using Batched Bayesian Optimization.
David Belanger, Suhani Vora, Zelda Mariet, Ramya Deshpande, David Dohan, Christof Angermueller, Kevin Murphy, Olivier Chapelle, Lucy Colwell.
NeurIPS Workshop on Machine Learning and the Physical Sciences, December 2019.
[ML4PS]
Model Inversion Networks for Model-Based Optimization.
Aviral Kumar, Sergey Levine
Preprint, December 2019.
[arxiv]
Interpreting mutational effects predictions, one substitution at a time.
C. K. Sruthi, Meher K. Prakash.
bioRxiv, December 2019
[doi.org/10.1101/867812]
A structure-based deep learning framework for protein engineering.
Raghav Shroff, Austin W. Cole, Barrett R. Morrow, Daniel J. Diaz, Isaac Donnell, Jimmy Gollihar, Andrew D. Ellington, Ross Thyer.
Preprint, November 2019.
[10.1101/833905]
Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design.
Pierce J. Ogden, Eric D. Kelsic, Sam Sinai, George M. Church.
Science, November 2019.
[10.1126/science.aaw2900]
Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics.
Claire N. Bedbrook, Kevin K. Yang, J. Elliott Robinson, Viviana Gradinaru, Frances H Arnold.
Nature Methods, October 2019.
[10.1038/s41592-019-0583-8]
Preprint available on [bioRxiv]
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design.
Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue.
International Conference on Artificial Intelligence and Statistics (AISTATS), April 2019.
[arxiv] [PMLR]
Machine learning-assisted directed protein evolution with combinatorial libraries.
Zachary Wu, S. B. Jennifer Kan, Russell D. Lewis, Bruce J. Wittmann, Frances H. Arnold.
PNAS, April 2019.
[10.1073/pnas.1901979116]
Conditioning by adaptive sampling for robust design.
David H. Brookes, Hahnbeom Park, Jennifer Listgarten.
Preprint, January 2019.
[arxiv]
A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.
Frédéric Cadet, Nicolas Fontaine, Guangyue Li, Joaquin Sanchis, Matthieu Ng Fuk Chong, Rudy Pandjaitan, Iyanar Vetrivel, Bernard Offmann, Manfred T. Reetz.
Scientific Reports, November 2018.
[10.1038/s41598-018-35033-y]
Design by adaptive sampling.
David H. Brookes, Jennifer Listgarten.
Preprint, October 2018.
[arxiv]
Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins.
Yutaka Saito, Misaki Oikawa, Hikaru Nakazawa, Teppei Niide, Tomoshi Kameda, Koji Tsuda, and Mitsuo Umetsu.
ACS Synthetic Biology, August 2018.
[10.1021/acssynbio.8b00155]
Toward machine-guided design of proteins.
Surojit Biswas, Gleb Kuznetsov, Pierce J. Ogden, Nicholas J. Conway, Ryan P. Adams, George M. Church.
Preprint, June 2018.
[10.1101/337154] [bioRxiv]
Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions.
Anvita Gupta, James Zou.
Preprint, April 2018.
[arxiv]
Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization.
Claire N. Bedbrook, Kevin K. Yang, Austin J. Rice, Viviana Gradinaru, Frances H. Arnold.
PLOS Computational Biology, October 2017.
[10.1371/journal.pcbi.1005786]
Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants.
Yaman Musdal, Sridhar Govindarajan, Bengt Mannervik.
Protein Engineering, Design, and Selection, August 2017.
[10.1093%2Fprotein%2Fgzx045]
Navigating the protein fitness landscape with Gaussian processes.
Philip A. Romero, Andreas Krause, Frances H. Arnold.
PNAS, January 2013.
[10.1073/pnas.1215251110]
Engineering proteinase K using machine learning and synthetic genes.
Jun Liao, Manfred K. Warmuth, Sridhar Govindarajan, Jon E. Ness, Rebecca P Wang, Claes Gustafsson, Jeremy Minshull.
BMC Biotechnology, March 2007.
[10.1186/1472-6750-7-16]
Improving catalytic function by ProSAR-driven enzyme evolution.
Richard J. Fox, S. Christopher Davis, Emily C. Mundorff, Lisa M. Newman, Vesna Gavrilovic, Steven K. Ma, Loleta M. Chung, Charlene Ching, Sarena Tam, Sheela Muley, John Grate, John Gruber, John C. Whitman, Roger A. Sheldon, Gjalt W. Huisman.
Nature Biotechnology, February 2007.
[Nature Biotechnology]
Self-Supervised Contrastive Learning of Protein Representations By Mutual Information Maximization.
Amy X. Lu, Haoran Zhang, Marzyeh Ghassemi, Alan Moses.
Preprint, September 2020.
[10.1101/2020.09.04.283929]
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing.
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost.
Preprint, July 2020.
[10.1101/2020.07.12.199554]
Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function.
Amelia Villegas-Morcillo, Stavros Makrodimitris, Roeland van Ham, Angel M. Gomez, Victoria Sanchez, Marcel Reinders.
Preprint, April 2020.
[10.1101/2020.04.07.028373]
Site2Vec: a reference frame invariant algorithm for vector embedding of protein-ligand binding sites.
Arnab Bhadra, Kalidas Y.
Preprint, March 2020.
[arxiv]
Evolutionary context-integrated deep sequence modeling for protein engineering.
Yunan Luo, Lam Vo, Hantian Ding, Yufeng Su, Yang Liu, Wesley Wei Qian, Huimin Zhao, Jian Peng.
Preprint, January 2020.
[10.1101/2020.01.16.908509]
Sequence representations and their utility for predicting protein-protein interactions.
Dhananjay Kimothi, Pravesh Biyani, James M Hogan.
Preprint, December 2019.
[10.1101/2019.12.31.890699]
Language modelling for biological sequences – curated datasets and baselines.
Jose Juan Almagro Armenteros, Alexander Rosenberg Johansen, Ole Winther, Henrik Nielsen.
Preprint, December 2019.
[alrojo.github.io]
Deciphering protein evolution and fitness landscapes with latent space models
Xinqiang Ding, Zhengting Zou, Charles L. Brooks III.
Nature Communications, December 2019.
[doi.org/10.1038/s41467-019-13633-0]
End-to-end multitask learning, from protein language to protein features without alignments.
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Burkhard Rost.
Preprint, December 2019.
[10.1101/864405]
Unified rational protein engineering with sequence-only deep representation learning.
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, George M. Church.
Nature Methods, October 2019
[10.1038/s41592-019-0598-1]
Structure-Based Function Prediction using Graph Convolutional Networks.
Vladimir Gligorijevic, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Kunghyun Cho, Tommi Vatanen, Daniel Berenberg, Bryn Taylor, Ian M. Fisk, Ramnik J. Xavier, Rob Knight, Richard Bonneau.
Preprint, October 2019.
[0.1101/786236]
Modeling the language of life – Deep Learning Protein Sequences.
Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Christian Dallago, Dmitrii Nechaev, Florian Matthes, Burkhard Rost.
Preprint, September 2019.
[10.1101/614313]
Augmenting Protein Network Embeddings with Sequence Information.
Hassan Kane, Mohamed K. Coulibali, Pelkins Ajanoh, Ali Abdallah.
Preprint, August 2019.
[10.1101/730481]
Universal Deep Sequence Models for Protein Classification.
Nils Strodthoff, Patrick Wagner, Markus Wenzel, Wojciech Samek.
Preprint, July 2019.
[10.1101/704874]
DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences.
Ehsaneddin Asgari, Nina Poerner, Alice C. McHardy, Mohammad R.K. Mofrad.
Preprint, July 2019.
[10.1101/705426]
A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence.
Chi-Hua Yu, Zhao Qin, Francisco J. Martin-Martinez, Markus J. Buehler.
ACS Nano, June 2019.
[10.1021/acsnano.9b02180]
Evaluating Protein Transfer Learning with TAPE.
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song.
Preprint, June 2019.
[arxiv]
Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins.
Julius Upmeier zu Belzen, Thore Bürgel, Stefan Holderbach, Felix Bubeck, Lukas Adam, Catharina Gandor, Marita Klein, Jan Mathony, Pauline Pfuderer, Lukas Platz, Moritz Przybilla, Max Schwendemann, Daniel Heid, Mareike Daniela Hoffmann, Michael Jendrusch, Carolin Schmelas, Max Waldhauer, Irina Lehmann, Dominik Niopek, Roland Eils.
Nature Machine Intelligence, May 2019.
[Nature Machine Intelligence]
Modeling the Language of Life – Deep Learning Protein Sequences.
Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Christian Dallago, Dmitrii Nechaev, Florian Matthes, Burkhard Rost.
Preprint, May 2019.
[10.1101/614313] [bioRxiv]
Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences.
Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus.
Preprint, April 2019.
[10.1101/622803] [bioRxiv]
Learning protein constitutive motifs from sequence data.
Jérôme Tubiana, Simona Cocco, Rémi Monasson.
eLife, March 2019.
[10.7554/eLife.39397]
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX).
Ehsaneddin Asgari, Alice C. McHardy, Mohammad R. K. Mofrad.
Scientific Reports, March 2019.
[10.1038/s41598-019-38746-w]
Learning protein sequence embeddings using information from structure.
Tristan Bepler, Bonnie Berger.
International Conference on Learning Representations, February 2019.
[ICLR]
Application of fourier transform and proteochemometrics principles to protein engineering.
Frédéric Cadet, Nicolas Fontaine, Iyanar Vetrivel, Matthieu Ng Fuk Chong, Olivier Savriama, Xavier Cadet, Philippe Charton.
BMC Bioinformatics, October 2018.
[10.1186/s12859-018-2407-8]
Learned protein embeddings for machine learning.
Kevin K Yang, Zachary Wu, Claire N Bedbrook, Frances H Arnold
Bioinformatics, August 2018
[10.1093/bioinformatics/bty178]
Deep Semantic Protein Representation for Annotation, Discovery, and Engineering.
Ariel S Schwartz, Gregory J Hannum, Zach R Dwiel, Michael E Smoot, Ana R Grant, Jason M Knight, Scott A Becker, Jonathan R Eads, Matthew C LaFave, Harini Eavani, Yinyin Liu, Arjun K Bansal, Toby H Richardson
Preprint, July 2018
[10.1101/365965]
Improved Descriptors for the Quantitative Structure–Activity Relationship Modeling of Peptides and Proteins.
Mark H. Barley, Nicholas J. Turner, Royston Goodacre.
Journal of Chemical Information and Modeling, January 2018.
[10.1021/acs.jcim.7b00488]
Variational auto-encoding of protein sequences.
Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak
Preprint, December 2017
[arxiv]
Predicting Protein Binding Affinity With Word Embeddings and Recurrent Neural Networks.
Carlo Mazzaferro.
Preprint, April 2017.
[10.1101/128223] [bioRxiv]
dna2vec: Consistent vector representations of variable-length k-mers.
Patrick Ng
Preprint, January 2017
[arxiv]
Distributed Representations for Biological Sequence Analysis.
Dhananjay Kimothi, Akshay Soni, Pravesh Biyani, James M. Hogan
Preprint, August 2016
[arxiv]
ProFET: Feature engineering captures high-level protein functions.
Dan Ofer, Michal Linial.
Bioinformatics, June 2015.
[10.1093/bioinformatics/btv345]
AAindex: amino acid index database, progress report 2008.
Shuichi Kawashima, Piotr Pokarowski, Maria Pokarowska, Andrzej Kolinski, Toshiaki Katayama, Minoru Kanehisa.
Nucleic Acids Research, January 2008.
[10.1093/nar/gkm998]
Unsupervised inference of protein fitness landscape from deep mutational scan.
Jorge Fernandez-de-Cossio-Diaz, Guido Uguzzoni, Andrea Pagnani.
Preprint, March 2020.
[10.1101/2020.03.18.996595]
Deep generative models of genetic variation capture the effects of mutations.
Adam J. Riesselman, John B. Ingraham, Debora S. Marks
Nature Methods, September 2018
[10.1038/s41592-018-0138-4]
Variational auto-encoding of protein sequences.
Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak
Preprint, December 2017
[arxiv]
Deep learning enables the design of functional de novo antimicrobial proteins.
Javier Caceres-Delpiano, Roberto Ibañez, Patricio Alegre, Cynthia Sanhueza, Romualdo Paz-Fiblas, Simon Correa, Pedro Retamal, Juan Cristóbal Jiménez, Leonardo Álvarez.
Preprint, August 2020.
[10.1101/2020.08.26.266940]
Generative probabilistic biological sequence models that account for mutational variability.
Eli N. Weinstein, Debora S. Marks.
Preprint, August 2020.
[10.1101/2020.07.31.231381]
A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences Johannes Linder, Nicholas Bogard, Alexander B. Rosenberg, Georg Seelig Cell Systems, July 2020 [10.1016/j.cels.2020.05.007]
Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction.
Yuyang Wang, Prakarsh Yadav, Rishikesh Magar, Amir Barati Farimani.
Preprint, June 2020.
[10.1101/2020.06.11.146167]
Generating functional protein variants with variational autoencoders.
Alex Hawkins-Hooker, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen, David Bikard.
Preprint, May 2020.
[10.1101/2020.04.07.029264]
Signal Peptides Generated by Attention-Based Neural Networks.
Zachary Wu, Kevin Kaichuang Yang, Michael Liszka, Alycia Lee, Alina Batzilla, David Wernick, David P Weiner, Frances H Arnold.
ACS Synthetic Biology, July 2020.
[10.1021/acssynbio.0c00219]
Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks.
Tileli Amimeur, Jeremy M. Shaver, Randal R. Ketchem, J. Alex Taylor, Rutilio H. Clark, Josh Smith, Danielle Van Citters, Christine C. Siska, Pauline Smidt, Megan Sprague, Bruce A. Kerwin, Dean Pettit.
Preprint, April 2020.
[10.1101/2020.04.12.024844]
ProGen: Language Modeling for Protein Generation.
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, Richard Socher.
Preprint, March 2020.
[10.1101/2020.03.07.982272]
Expanding functional protein sequence space using generative adversarial networks.
Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Jan Zrimec, Simona Poviloniene, Irmantas Rokaitis, Audrius Laurynenas, Wissam Abuajwa, Otto Savolainen, Rolandas Meskys, Martin K. M. Engqvist, Aleksej Zelezniak.
Preprint, October 2019.
[10.1101/789719]
De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN).
Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen.
Preprint, September 2019.
[10.1101/769919]
Reconstructing continuous distributions of 3D protein structure from cryo-EM images.
Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger.
Preprint, September 2019.
[arXiv]
Accelerating Protein Design Using Autoregressive Generative Models.
Adam Riesselman, Jung-Eun Shin, Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew Kruse, Debora Marks.
Preprint, September 2019.
[10.1101/757252]
Deep generative models for T cell receptor protein sequences.
Kristian Davidsen, Branden J. Olson, William S. DeWitt III, Jean Feng, Elias Harkins, Philip Bradley, Frederick A. Matsen IV.
eLife, September 2019.
[10.7554/eLife.46935.001]
Generative Models for Graph-Based Protein Design.
John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola.
ICLR workshop on Deep Generative Models for Highly Structured Data, May 2019.
[OpenReview]
How to Hallucinate Functional Proteins.
Zak Costello, Hector Garcia Martin
Preprint, March 2019
[arxiv]
Conditioning by adaptive sampling for robust design.
David H. Brookes, Hahnbeom Park, Jennifer Listgarten.
Preprint, January 2019.
[arxiv]
Generative modeling for protein structures.
Namrata Anand, Po-Ssu Huang.
NeurIPS, December 2018.
[NeurIPS]
Design of metalloproteins and novel protein folds using variational autoencoders.
Joe G. Greener, Lewis Moffat, David T Jones.
Scientific Reports, November 2018.
[10.1038/s41598-018-34533-1]
Design by adaptive sampling.
David H. Brookes, Jennifer Listgarten.
Preprint, October 2018.
[arxiv]
Deep generative models of genetic variation capture the effects of mutations.
Adam J Riesselman, John B Ingraham, Debora S. Marks
Nature Methods, September 2018
[10.1038/s41592-018-0138-4]
Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions.
Anvita Gupta, James Zou.
Preprint, April 2018.
[arxiv]
Recurrent Neural Network Model for Constructive Peptide Design.
Alex T. Müller, Jan A. Hiss, and Gisbert Schneider.
Journal of Chemical Information and Modeling, January 2018
[10.1021/acs.jcim.7b00414]
Variational auto-encoding of protein sequences.
Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak
Preprint, December 2017
[arxiv]
Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset. Peishan Huang, Simon K. S. Chu, Henrique N. Frizzo, Morgan P. Connolly, Ryan W. Caster, and Justin B. Siegel [10.1021/acsomega.9b04105]
Predicting changes in protein thermostability upon point mutation with deep 3D convolutional neural networks.
Bian Li, Yucheng T. Yang, John A. Capra, Mark B. Gerstein.
Preprint, February 2020.
[10.1101/2020.02.28.959874]
Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors.
S. Muk, S. Ghosh, S. Achuthan, X. Chen, X. Yao, M. Sandhu, M. C. Griffor, K. F. Fennell, Y. Che, V. Shanmugasundaram, X. Qiu, C. G. Tate, N. Vaidehi.
Preprint, July 2019.
[10.1101/715375]
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion.
Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki.
Bioinformatics, July 2018.
[10.1093/bioinformatics/bty238]
Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.
Lei Jia , Ramya Yarlagadda, Charles C. Reed.
PLOS One, September 2015.
[10.1371/journal.pone.0138022]
NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation.
Manuel Giollo, Alberto J. M. Martin†, Ian Walsh, Carlo Ferrari, Silvio C. E. Tosatto.
BMC Genomics, May 2014.
[10.1186/1471-2164-15-S4-S7]
mCSM: predicting the effects of mutations in proteins using graph-based signatures.
Douglas E. V. Pires, David B. Ascher, Tom L. Blundell.
Bioinformatics, February 2014.
[10.1093/bioinformatics/btt691]
PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes.
Yunqi Li, Jianwen Fang.
PLOS One, October 2012.
[10.1371/journal.pone.0047247]
Predicting changes in protein thermostability brought about by single- or multi-site mutations.
Jian Tian, Ningfeng Wu, Xiaoyu Chu, Yunliu Fan.
BMC Bioinformatics, July 2010.
[10.1186/1471-2105-11-370]
Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0.
Yves Dehouck, Aline Grosfils, Benjamin Folch, Dimitri Gilis, Philippe Bogaerts, Marianne Rooman.
Bioinformatics, October 2009.
[10.1093/bioinformatics/btp445]
Prediction of protein stability changes for single‐site mutations using support vector machines.
Jianlin Cheng, Arlo Randall, Pierre Baldi.
Proteins, December 2005.
[10.1002/prot.20810]
Predicting protein stability changes from sequences using support vector machines.
Emidio Capriotti, Piero Fariselli, Remo Calabrese, Rita Casadio.
Bioinformatics, September 2005.
[10.1093/bioinformatics/bti1109]
I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure.
Emidio Capriotti, Piero Fariselli, Rita Casadio.
Nucleic Acids Research, July 2005.
[10.1093/nar/gki375]
A neural-network-based method for predicting protein stability changes upon single point mutations.
Emidio Capriotti, Piero Fariselli, Rita Casadio.
Bioinformatics, August 2004.
[10.1093/bioinformatics/bth928]
Mismatch string kernels for discriminative protein classification.
Christina S. Leslie, Eleazar Eskin, Adiel Cohen, Jason Weston, William Stafford Noble.
Bioinformatics, March 2004.
[10.1093/bioinformatics/btg431]
Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.
Chen Chen, Tianqi Wu, Zhiye Guo, Jianlin Cheng.
Preprint, September 2020.
[10.1101/2020.09.04.283937]
Phylogenetic correlations have limited effect on coevolution-based contact prediction in proteins.
Edwin Rodriguez Horta, Martin Weigt.
Preprint, August 2020.
[10.1101/2020.08.12.247577]
Template-based prediction of protein structure with deep learning.
Haicang Zhang, Yufeng Shen.
Preprint, June 2020.
[2020.06.02.129270]
Energy-based models for atomic-resolution protein conformations.
Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives.
ICLR, April 2020.
[arXiv]
A fully open-source framework for deep learning protein real-valued distances.
Badri Adhikari.
Preprint, April 2020.
[10.1101/2020.04.26.061820]
PhANNs, a fast and accurate tool and web server to classify phage structural proteins.
Victor Seguritan, Jackson Redfield, David Salamon, Robert A. Edwards, Anca M. Segall.
Preprint, April 2020.
[10.1101/2020.04.03.023523]
Improved protein structure prediction using predicted inter-residue orientations.
Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, David Baker.
PNAS, January 2020.
[10.1073/pnas.1914677117]
Deep learning methods in protein structure prediction.
Mirko Torrisi, Gianluca Pollastri, Quan Lea.
Computational and Structural Biotechnology, January 2020.
[10.1016/j.csbj.2019.12.011]
Improved protein structure prediction using potentials from deep learning.
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.
Nature, January 2020.
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