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]
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]
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]
Interpreting mutational effects predictions, one substitution at a time
C. K. Sruthi, Meher K. Prakash.
bioRxiv, December 2019
[doi.org/10.1101/867812]
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]
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]
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]
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]
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]
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]
Improved protein structure prediction using predicted inter-residue orientations.
Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, David Baker.
Preprint, November 2019.
[10.1101/846279]
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.
Joe G. Greener, Shaun M. Kandathil, David T. Jones.
Nature Communications, September 2019.
[10.1038/s41467-019-11994-0]
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]
End-to-End Differentiable Learning of Protein Structure.
Mohammed AlQuraishi.
Cell Systems, April 2019.
[10.1016/j.cels.2019.03.006]
DESTINI: A deep-learning approach to contact-driven protein structure prediction.
Mu Gao, Hongyi Zhou, Jeffrey Skolnick.
Scientific Reports, March 2019.
[10.1038/s41598-019-40314-1]
Learning protein sequence embeddings using information from structure.
Tristan Bepler, Bonnier Berger.
International Conference on Learning Representations, February 2019.
[ICLR]
Generative modeling for protein structures.
Namrata Anand, Po-Ssu Huang.
NeurIPS, December 2018.
[NeurIPS]
Distance-based Protein Folding Powered by Deep Learning.
Jinbo Xu.
Preprint, November 2018.
[arxiv]
Porter 5: fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes.
Mirko Torrisi, Manaz Kaleel, Gianluca Pollastri.
Preprint, October 2018.
[10.1101/289033] [bioRxiv]
Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method.
Yuming Ma, Yihui Liu, Jinyong Cheng.
Scientific Reports, June 2018.
[10.1038/s41598-018-28084-8]
Protein Secondary Structure Prediction with Long Short Term Memory Networks.
Søren Kaae Sønderby, Ole Winther.
Preprint, December 2014.
[arxiv]
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction.
Jian Zhou, Olga G. Troyanskaya.
Preprint, March 2014.
[arxiv]
Protein Sequence Design with a Learned Potential.
Namrata Anand, Raphael R. Eguchi, Alexander Derry, Russ B. Altman, Po-Ssu Huang.
Preprint, January 2020.
[10.1101/2020.01.06.895466]
Designing real novel proteins using deep graph neural networks.
Alexey Strokach, David Becerra, Carles Corbi, Albert Perez-Riba, Philip M. Kim.
Preprint, December 2019.
[10.1101/868935] [bioRxiv]
RamaNet: Computational De Novo Protein Design using a Long Short-Term Memory Generative Adversarial Neural Network.
Sari Sabban, Mikhail Markovsky.
Preprint, June 2019.
[10.1101/671552] [bioRxiv]
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]
SPIN2: Predicting sequence profiles from protein structures using deep neural networks.
James O'Connell, Zhixiu Li, Jack Hansonm, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou.
Proteins, March 2018.
[10.1002/prot.25489]
Machine Learning Predicts New Anti-CRISPR Proteins.
Gavin J. Knott, Jennifer A. Doudna, Fayyaz ul Amir Afsar Minhas.
Preprint, November 2019.
[10.1101/854950]
Improving protein function prediction with synthetic feature samples created by generative adversarial networks.
Cen Wan, David T. Jones.
Preprint, August 2019.
[10.1101/730143]
Universal Deep Sequence Models for Protein Classification.
Nils Strodthoff, Patrick Wagner, Markus Wenzel, Wojciech Samek.
Preprint, July 2019.
[10.1101/704874]
Critiquing Protein Family Classification Models Using Sufficient Input Subsets.
Brandon Carter, Maxwell L. Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy J. Colwell.
Preprint, June 2019.
[10.1101/674119] [bioRxiv]
A Brief History of Protein Sorting Prediction.
Henrik Nielsen, Konstantinos D. Tsirigos, Søren Brunak, Gunnar von Heijne.
The Protein Journal, May 2019.
[10.1007/s10930-019-09838-3]
DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.
Ahmet Sureyya Rifaioglu, Tunca Dogan, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay.
Scientific Reports, May 2019.
[10.1038/s41598-019-43708-3]
Using Deep Learning to Annotate the Protein Universe.
Maxwell L. Bileschi, David Belanger, Drew Bryant, Theo Sanderson, Brandon Carter, D. Sculley, Mark A. DePristo, Lucy J. Colwell�.
Preprint, May 2019.
[10.1101/626507] [bioRxiv]
ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.
Alperen Dalkiran, Ahmet Sureyya Rifaioglu, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay, Tunca Dogan.
BMC Bioinformatics, September 2018.
[10.1186/s12859-018-2368-y]
DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.
Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf.
Bioinformatics, February 2018.
[10.1093/bioinformatics/btx624]
Near perfect protein multi-label classification with deep neural networks.
Balázs Szalkaia, Vince Grolmuszab.
Methods, January 2018.
[10.1016/j.ymeth.2017.06.034]
Large‐scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants.
Ahmet Sureyya Rifaioglu, Tunca Dogan, Omer Sinan Sarac, Tulin Ersahin, Rabie Saidi, Mehmet Volkan Atalay, Maria Jesus Martin, Rengul Cetin‐Atalay.
Proteins, November 2017.
[10.1002/prot.25416]
ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.
Renzhi Cao, Colton Freitas, Leong Chan, Miao Sun, Haiqing Jiang, Zhangxin Chen.
Molecules, October 2017.
[10.3390/molecules22101732]
Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.
Ehsaneddin Asgari, Mohammad R. K. Mofrad
PLOS One, November 2015.
[10.1371/journal.pone.0141287]
A structural alignment kernel for protein structures.
Jian Qiu, Martial Hue, Asa Ben-Hur, Jean-Philippe Vert, William Stafford Noble
Bioinformatics, January 2007.
[10.1093/bioinformatics/btl642]
The spectrum kernel: A string kernel for SVM protein classification.
Christina S Leslie, Eleazar Eskin, William Stafford Noble.
Pacific Symposium on Biocomputing, January 2002.
[pdf]
Mutation effect estimation on protein-protein interactions using deep contextualized representation learning
Guangyu Zhou, Muhao Chen, Chelsea J.-T. Ju, Zheng Wang, Jyun-Yu Jiang, Wei Wang.
NAR Genomics and Bioinformatics, March 2020
[10.1093/nargab/lqaa015]
Using Single Protein/Ligand Binding Models to Predict Active Ligands for Previously Unseen Proteins.
Vikram Sundar, Lucy Colwell.
NeurIPS Workshop on Machine Learning and the Physical Sciences, December 2019.
[ML4PS]
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.
P. Gainza, F. Sverrisson, F. Monti, E. Rodolà, D. Boscaini, M. M. Bronstein, B. E. Correia.
Nature Methods, December 2019.
[10.1038/s41592-019-0666-6]
End-to-End Learning on 3D Protein Structure for Interface Prediction
Raphael J. L. Townshend, Rishi Bedi, Patricia A. Suriana, Ron O. Dror.
NeurIPS, December 2019.
[arxiv]
USMPep: Universal Sequence Models for Major Histocompatibility Complex Binding Affinity Prediction.
Johanna Vielhaben, Markus Wenzel, Wojciech Samek, Nils Strodthoff.
Preprint, October 2019.
[10.1101/816546]
DeepCLIP: Predicting the effect of mutations on protein-RNA binding with Deep Learning.
Alexander Gulliver Bjørnholt Grønning, Thomas Koed Doktor, Simon Jonas Larsen, Ulrika Simone Spangsberg Petersen, Lise Lolle Holm, Gitte Hoffmann Bruun, Michael Birkerod Hansen, Anne-Mette Hartung, Jan Baumbach, Brage Storstein Andresen.
Preprint, September 2019.
[10.1101/757062]
Multifaceted protein–protein interaction prediction based on Siamese residual RCNN
Muhao Chen, Chelsea J.-T. Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo, Wei Wang.
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