Deep learning methods for 3D structural proteome and interactome modeling

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Advances in Neural Information Processing Systems 14

(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,184 · see citing works

Accurate prediction of protein structures and interactions using a 3-track network

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works

Towards a proteome-scale map of the human protein–protein interaction network

Rual, J.-F. M., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., Ayivi-Guedehoussou, N., et al. (2005) · Nature · doi:10.1038/nature04209 · Cited by 2,904 · see citing works

ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers

Pierce, B. G., Wiehe, K. J., Hwang, H., Kim, B., Vreven, T., & Weng, Z. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu097 · Cited by 1,767 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1994) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1994.323798 · Cited by 1,490 · see citing works

A reference map of the human binary protein interactome

Luck, K., Kim, D.-K., Lambourne, L., Spirohn, K., Begg, B. E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F. J., Charloteaux, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2188-x · Cited by 1,197 · see citing works

Unified rational protein engineering with sequence-based deep representation learning

Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · cited 1× · see citing works

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

Gaínza, P., Sverrisson, F., Monti, F., Rodolà, E., Boscaini, D., Bronstein, M. M., & Correia, B. E. (2019) · Nature Methods · doi:10.1038/s41592-019-0666-6 · Cited by 811 · see citing works

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works

Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era

Kamisetty, H., Ovchinnikov, S., & Baker, D. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314045110 · Cited by 672 · see citing works

Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models

Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661 · see citing works

Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information

Ovchinnikov, S., Kamisetty, H., & Baker, D. (2014) · eLife · doi:10.7554/elife.02030 · Cited by 629 · see citing works

Interactome3D: adding structural details to protein networks

Mosca, R., Céol, A., & Aloy, P. (2013) · Nature Methods · doi:10.1038/nmeth.2289 · Cited by 479 · see citing works

Single-sequence protein structure prediction using a language model and deep learning

Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works

Single-sequence protein structure prediction using language models from deep learning

Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works

CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations

Seemayer, S., Gruber, M., & Söding, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu500 · Cited by 416 · see citing works

Distance-based protein folding powered by deep learning

Xu, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821309116 · Cited by 401 · see citing works

End-to-End Differentiable Learning of Protein Structure

AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371 · see citing works

MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins

Jones, D. T., Singh, T., Kościółek, T., & Tetchner, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu791 · Cited by 345 · see citing works

Deep architectures for protein contact map prediction

Lena, P. D., Nagata, K., & Baldi, P. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts475 · Cited by 273 · see citing works

Improved protein structure prediction by deep learning irrespective of co-evolution information

(2021) · doi:10.1038/s42256-021-00348-5 · Cited by 256 · see citing works

Fast and Flexible Protein Design Using Deep Graph Neural Networks

Strokach, A. V., Becerra, D., Corbi‐Verge, C., Perez‐Riba, A., & Kim, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.016 · Cited by 218 · see citing works

Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences

Ekeberg, M., Hartonen, T., & Aurell, E. M. (2014) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.07.024 · Cited by 184 · see citing works

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features

Jones, D. T., & Kandathil, S. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty341 · Cited by 184 · see citing works

Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks

Hanson, J., Paliwal, K. K., Litfin, T., Yang, Y., & Zhou, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty481 · Cited by 179 · see citing works

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks

Adhikari, B., Hou, J., & Cheng, J. J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx781 · Cited by 168 · see citing works

DELPHI: accurate deep ensemble model for protein interaction sites prediction

Li, Y., Golding, G. B., & Ilie, L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa750 · Cited by 164 · see citing works

Predicting protein residue–residue contacts using deep networks and boosting

Eickholt, J., & Cheng, J. J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts598 · Cited by 161 · see citing works

ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks

Li, Y., Hu, J., Zhang, C., Yu, D.-J., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz291 · Cited by 158 · see citing works

FreeContact: fast and free software for protein contact prediction from residue co-evolution

Kaján, L., Hopf, T. A., Kalaš, M., Marks, D. S., & Rost, B. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-85 · Cited by 152 · see citing works

Interactome INSIDER: a structural interactome browser for genomic studies

Meyer, M. J., Beltrán, J. F., Liang, S., Fragoza, R., Rumack, A., Liang, J. H., Wei, X., & Yu, H. (2018) · Nature Methods · doi:10.1038/nmeth.4540 · Cited by 152 · see citing works

NNcon: improved protein contact map prediction using 2D-recursive neural networks

Tegge, A. N., Wang, Z., Eickholt, J., & Cheng, J. J. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp305 · Cited by 148 · see citing works

ComplexContact: a web server for inter-protein contact prediction using deep learning

Zeng, H., Wang, S., Zhou, T., Zhao, F., Li, X., Wu, Q., & Xu, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky420 · Cited by 138 · see citing works

DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network

Pu, L., Govindaraj, R. G., Lemoine, J. M., Wu, H.-C., & Brylinski, M. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006718 · Cited by 133 · see citing works

Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network

Zhang, B., Li, J., Quan, L., Chen, Y., & Lü, Q. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.013 · Cited by 129 · see citing works

Deep Learning-Based Advances in Protein Structure Prediction

Pakhrin, S. C., Shrestha, B., Adhikari, B., & KC, D. B. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22115553 · Cited by 117 · see citing works

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J. (2018) · Cell Systems · doi:10.1016/j.cels.2017.11.014 · Cited by 114 · see citing works

Protein interaction interface region prediction by geometric deep learning

Dai, B., & Bailey‐Kellogg, C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab154 · Cited by 100 · see citing works

Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13

Li, Y., Zhang, C., Bell, E. W., Yu, D., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25798 · Cited by 94 · see citing works

BIPSPI: a method for the prediction of partner-specific protein–protein interfaces

Sánchez-García, R., Sorzano, C. Ó. S., Carazo, J. M., & Segura, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty647 · Cited by 93 · see citing works

Protein contact prediction using metagenome sequence data and residual neural networks

Wu, Q., Peng, Z., Anishchenko, I. V., Cong, Q., Baker, D. A., & Yang, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz477 · Cited by 86 · see citing works

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

(2021) · doi:10.1371/journal.pcbi.1008865 · Cited by 85 · see citing works

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks

Škalič, M., Varela‐Rial, A., Jiménez-Luna, J., Martínez-Rosell, G., & Fabritiis, G. D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty583 · Cited by 63 · see citing works

Structural proteomics: developments in structure-to-function predictions

Norin, M., & Sundström, M. (2002) · Trends in biotechnology · doi:10.1016/s0167-7799(01)01884-4 · Cited by 62 · see citing works

Predicting residue–residue contacts using random forest models

Li, Y., Fang, Y., & Fang, J. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr579 · Cited by 57 · see citing works

DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout

Adhikari, B. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz593 · Cited by 49 · see citing works

Deep Learning of High-Order Interactions for Protein Interface Prediction

Liu, Y., Yuan, H., Cai, L., & Ji, S. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403110 · Cited by 48 · see citing works

DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment

Fukuda, H., & Tomii, K. (2020) · BMC Bioinformatics · doi:10.1186/s12859-019-3190-x · Cited by 44 · see citing works

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Chen, C., Wu, T., Guo, Z., & Cheng, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26052 · Cited by 44 · see citing works

Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts

Björkholm, P., Daniluk, P., Kryshtafovych, A., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp149 · Cited by 42 · see citing works

A deep learning framework for improving long-range residue–residue contact prediction using a hierarchical strategy

Xiong, D., Zeng, J., & Gong, H. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx296 · Cited by 40 · see citing works

GANcon: Protein Contact Map Prediction With Deep Generative Adversarial Network

Yang, H. X., Wang, M., Yu, Z., Zhao, X., & Li, A. (2020) · IEEE Access · doi:10.1109/access.2020.2991605 · Cited by 32 · see citing works

When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14 —( tFold for CASP14 contact prediction)

Shen, T., Wu, J., Lan, H., Zheng, L., Pei, J., Wang, S., Liu, W., & Huang, J. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26232 · Cited by 32 · see citing works

R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter

Yang, J., Jin, Q., Zhang, B., & Shen, H. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw181 · Cited by 28 · see citing works

DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts

Ding, W., Mao, W., Shao, D., Zhang, W., & Gong, H. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.10.009 · Cited by 27 · see citing works

Protein inter‐residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14

Li, Y., Zhang, C., Zheng, W. H., Zhou, X., Bell, E. W., Yu, D., & Zhang, Y. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26211 · Cited by 23 · see citing works

Protein contact map refinement for improving structure prediction using generative adversarial networks

Subramaniya, S. R. M. V., Terashi, G., Jain, A., Kagaya, Y., & Kihara, D. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab220 · Cited by 21 · see citing works

Protein Structure Prediction Using a Maximum Likelihood Formulation of a Recurrent Geometric NetworkPreprint

Qi, G., Tollefson, M. R., Gogal, R. A., Smith, R. J., AlQuraishi, M., & Schnieders, M. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.03.458873 · Cited by 5 · see citing works

AlphaFold, Artificial Intelligence (AI), and Allostery

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works

Plant Protein Interactomes

Falter‐Braun, P., Aubourg, S., Leene, J. V., Jaeger, G. D., & Claire Lurin, A. (2013) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-050312-120140 · Cited by 126 · see citing works

Defining Viral Protein Interactomes Using the Yeast Two-Hybrid Assay

Diefenbach, E., Cunningham, A. L., & Diefenbach, R. J. (2005) · Current Proteomics · doi:10.2174/157016405774641138 · Cited by 1 · see citing works

Two‐Hybrid Systems to Measure Protein–Protein Interactions

Finley, R. L., & Mairiang, D. (2018) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0005980.pub3 · Cited by 3 · see citing works

Two‐Hybrid Systems to Measure Protein–Protein Interactions

Finley, R. L., & Mairiang, D. (2014) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0005980.pub2 · Cited by 1 · see citing works

Peptide array–based interactomics

Pérez-Hernández, D., & Dittmar, G. (2021) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-021-03367-8 · Cited by 9 · see citing works