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
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
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]
Machine learning-guided directed evolution for protein engineering.
Kevin K. Yang, Zachary Wu, Frances H. Arnold.
Preprint, April 2019.
[arxiv]
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]
Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics.
Claire N. Bedbrook, Kevin K. Yang, J. Elliott Robinson, Viviana Gradinaru, Frances H .Arnold.
Preprint, March 2019.
[10.1101/565606] [bioRxiv]
Conditioning by adaptive sampling for robust design.
David H. Brookes, Hahnbeom Park, Jennifer Listgarten.
Preprint, January 2019.
[arxiv]
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]
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, Aprili 2019.
[10.1101/622803] [bioRxiv]
Unified rational protein engineering with sequence-only deep representation learning
Ethan C. Alley, Grigory Khimulya, View ORCID ProfileSurojit Biswas, Mohammed AlQuraishi, George M. Church
Preprint, March 2019
[10.1101/589333]
Learning protein sequence embeddings using information from structure.
Tristan Bepler, Bonnier Berger.
International Conference on Learning Representations, February 2019.
[ICLR]
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]
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]
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]
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 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]
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]
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]
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]
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.
Ingoo Lee, Jongsoo Keum, Hojung Nam.
PLOS Computational Biology, June 2019.
[10.1371/journal.pcbi.1007129]
Leveraging binding-site structure for drug discovery with point-cloud methods.
Vincent Mallet, Carlos G. Oliver, Nicolas Moitessier, Jerome Waldispuhl.
Preprint, May 2019.
[arXiV]]
Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides.
Akira Shiraishi, Toshimi Okuda, Natsuko Miyasaka, Tomohiro Osugi, Yasushi Okuno, Jun Inoue, and Honoo Satake.
PNAS, March 2019.
[10.1073/pnas.1816640116]
Simple tricks of convolutional neural network architectures improve DNA–protein binding prediction.
Zhen Cao, Shihua Zhang.
Bioinformatics, October 2018.
[10.1093/bioinformatics/bty893
MHCflurry: Open-Source Class I MHC Binding Affinity Prediction.
Timothy J. O'Donnell, Alex Rubinsteyn, Maria Bonsack, Angelika B. Riemer, Uri Laserson, Jeff Hammerbacher.
Cell Systems, June 2018.
[10.1016/j.cels.2018.05.014]
DeepMHC: Deep Convolutional Neural Networks for High-performance peptide-MHC Binding Affinity Prediction.
Jianjun Hu, Zhonghao Liu.
Preprint, December 2017.
[10.1101/239236] [bioRxiv]
DeepSite: protein-binding site predictor using 3D-convolutional neural networks.
J, Jiménez. S. Doerr, G. Martínez-Rosell, A. S. Rose, G. De Fabritiis.
Bioinformatics, October 2017.
[10.1093/bioinformatics/btx350]
Predicting Protein Binding Affinity With Word Embeddings and Recurrent Neural Networks.
Carlo Mazzaferro.
Preprint, April 2017.
[10.1101/128223] [bioRxiv]
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity.
Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg, Vijay S. Pande.
Preprint, March 2017.
[arxiv]
Convolutional neural network architectures for predicting DNA–protein binding.
Haoyang Zeng, Matthew D. Edwards. Ge Liu, David K. Gifford.
Bioinformatics, 15 June 2016.
[10.1093/bioinformatics/btw255]
A deep learning framework for modeling structural features of RNA-binding protein targets.
Sai Zhang, Jingtian Zhou, Hailin Hu, Haipeng Gong, Ligong Chen, Chao Cheng, Jianyang Zeng.
Nucleic Acids Research, October 2015.
[10.1093/nar/gkv1025]
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
Babak Alipanahi, Andrew Delong, Matthew T. Weirauch, Brendan J. Frey.
Nature Biotechnology, July 2015.
[10.1038/nbt.3300]
Using machine learning to predict organismal growth temperatures from protein primary sequences.
David B. Sauer, Da-Neng Wang.
Preprint, June 2019.
[10.1101/677328] [bioRxiv]
Minimum epistasis interpolation for sequence-function relationships.
Juannan Zhou, David M. McCandlish.
Preprint, June 2019.
[10.1101/657841] [bioRxiv]
SolXplain: An Explainable Sequence-Based Protein Solubility Predictor.
Raghvendra Mall.
Preprint, May 2019.
[10.1101/651067] [bioRxiv]
High precision protein functional site detection using 3D convolutional neural networks.
Wen Torng, Russ B Altman.
Bioinformatics, May 2019.
[10.1093/bioinformatics/bty813]
DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction.
Abdurrahman Elbasir, Balasubramanian Moovarkumudalvan, Khalid Kunji, Prasanna R Kolatkar, Raghvendra Mall, Halima Bensmail.
Bioinformatics, November 2018.
[10.1093/bioinformatics/bty953]
DeepSol: a deep learning framework for sequence-based protein solubility prediction.
Sameer Khurana, Reda Rawi, Khalid Kunji, Gwo-Yu Chuang, Halima Bensmail, Raghvendra Mall.
Bioinformatics, March 2018.
[10.1093/bioinformatics/bty166]
A statistical model for improved membrane protein expression using sequence-derived features.
Shyam M. Saladi, Nauman Javed, Axel Müller, William M. Clemons, Jr.
Journal of Biological Chemistry, March 2018.
[10.1074/jbc.RA117.001052]
Learning epistatic interactions from sequence-activity data to predict enantioselectivity.
Julian Zaugg, Yosephine Gumulya, Alpeshkumar K. Malde, Mikael Bodén.
Journal of Computer Aided Molecular Design, December 2017.
[10.1007/s10822-017-0090-x]
Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.
Vanessa E. Gray, Ronald J. Hause, Jens Luebeck, Jay Shendure, Douglas M. Fowler.
Cell Systems, December 2017.
[10.1016/j.cels.2017.11.003]
DeepLoc: prediction of protein subcellular localization using deep learning.
Jose Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther.
Bioinformatics, September 2017.
[10.1093/bioinformatics/btx548]
Semisupervised Gaussian Process for Automated Enzyme Search.
Joseph Mellor, Ioana Grigoras, Pablo Carbonell, and Jean-Loup Faulon.
ACS Synthetic Biology, March 2016.
[10.1021/acssynbio.5b00294]
High Precision Prediction of Functional Sites in Protein Structures.
Ljubomir Buturovic, Mike Wong, Grace W. Tang, Russ B. Altman, Dragutin Petkovic.
PLOS One, March 2014.
[10.1371/journal.pone.0091240]
Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein-Protein Interaction..
Aalt D. J. van Dijk, Giuseppa Morabito, Martijn Fiers, Roeland C. H. J. van Ham, Gerco C. Angenent, Richard G. H. Immink.
PLOS Computational Biology, November 2010.
[10.1371/journal.pcbi.1001017]
Predicting and understanding transcription factor interactions based on sequence level determinants of combinatorial control.
A.D.J. van Dijk, C.J.F. ter Braak, R.G. Immink, G.C. Angenent, R.C.H.J. van Ham.
Bioinformatics, January 2008.
[10.1093/bioinformatics/btm539]