Artificial intelligence approaches to human-microbiome protein–protein interactions

Attention Is All You Need

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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

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

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Structure, function and diversity of the healthy human microbiome

The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726 · see citing works

A human gut microbial gene catalogue established by metagenomic sequencing

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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpacePreprint

Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 9,834 · see citing works

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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Dynamic Graph CNN for Learning on Point Clouds

Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199 · 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

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works

Proceedings of the 21st International Conference on Neural Information Processing Systems

Koller, D., Schuurmans, D., Bengio, Y., & Bottou, L. (2008) · Cited by 4,461 · see citing works

VoxNet: A 3D Convolutional Neural Network for real-time object recognition

Maturana, D., & Scherer, S. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,674 · see citing works

Geometric Deep Learning: Going beyond Euclidean data

Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · see citing works

The human microbiome: at the interface of health and disease

Cho, I., & Blaser, M. J. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3182 · Cited by 3,449 · see citing works

Human Protein Reference Database--2009 update

Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,205 · see citing works

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works

A framework for human microbiome research

The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11209 · Cited by 2,650 · see citing works

Deep Learning for 3D Point Clouds: A Survey

Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., & Bennamoun, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3005434 · Cited by 2,445 · see citing works

Rotation Forest: A New Classifier Ensemble Method

Rodríguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.211 · Cited by 1,897 · see citing works

Graph convolutional networks: a comprehensive review

Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888 · see citing works

The <scp>BioGRID</scp> database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions

Oughtred, R., Rust, J., Chang, C., Breitkreutz, B., Stark, C., Willems, A., Boucher, L., Leung, G., Kolas, N., Zhang, F., et al. (2021) · Protein Science · doi:10.1002/pro.3978 · Cited by 1,858 · see citing works

The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases

Orchard, S. E., Ammari, M. G., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F. C., Campbell, N. H., Chavali, G. B., Chen, C. C. L., del‐Toro, N., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1115 · Cited by 1,835 · see citing works

Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., & Bronstein, M. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.576 · Cited by 1,820 · see citing works

DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions

Xénarios, I. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.303 · Cited by 1,746 · see citing works

MINT, the molecular interaction database: 2012 update

Licata, L., Briganti, L., Peluso, D., Perfetto, L., Iannuccelli, M., Galeota, E., Sacco, F., Palma, A., Nardozza, A. P., Santonico, E., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr930 · Cited by 1,062 · see citing works

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2014)

Wada, T. (2015) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.33.100 · Cited by 960 · see citing works

The gut microbiome: Relationships with disease and opportunities for therapy

Durack, J., & Lynch, S. V. (2018) · The Journal of Experimental Medicine · doi:10.1084/jem.20180448 · Cited by 955 · see citing works

The PDBbind database: methodologies and updates

Wang, R., Fang, X., Lu, Y., Yang, C.-Y., & Wang, S. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm048957q · Cited by 933 · see citing works

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities

Davis, J. J., Wattam, A. R., Aziz, R. K., Brettin, T. S., Butler, R. M., Butler, R., Chlenski, P., Conrad, N. R., Dickerman, A. W., Dietrich, E. M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz943 · Cited by 882 · see citing works

American Gut: an Open Platform for Citizen-Science Microbiome Research

McDonalda, D., Hyde, E., Debelius, J. W., Morton, J. T., Gonzalez, A., Ackermann, G., Aksenov, A. A., Behsaz, B., Brennan, C., Chen, Y., et al. (2018) · doi:10.1101/277970 · Cited by 878 · 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

The human microbiome: Our second genome

Grice, E. A., & Segre, J. A. (2012) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090711-163814 · Cited by 759 · see citing works

SAbDab: the structural antibody database

Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1043 · Cited by 674 · see citing works

Gut microbiota associations with common diseases and prescription medications in a population-based cohort

Jackson, M. A., Verdi, S., Maxan, M.-E., Shin, C. M., Zierer, J., Bowyer, R. C. E., Martin, T., Williams, F. M. K., Menni, C., Bell, J. T., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05184-7 · Cited by 616 · see citing works

Protein interface prediction using graph convolutional networks

Fout, A., Ben‐Hur, A., Anderson, C., Chitsaz, H., & Zhou, W. (2017) · Digital Collections of Colorado (Colorado State University) · doi:10.25675/3.021826 · Cited by 606 · 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

Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2

Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424 · see citing works

Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation

Lim, J., Ryu, S., Park, K., Choe, Y. J., Ham, J., & Kim, W. Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00387 · Cited by 403 · see citing works

Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

Chen, M., Ju, C. J. ‐., Zhou, G., Chen, X., Zhang, T., Chang, K.-W., Zaniolo, C., & Wang, W. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz328 · Cited by 375 · see citing works

Gut microbiome and its role in obesity and insulin resistance

Lee, C. J., Sears, C. L., & Maruthur, N. M. (2019) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14107 · Cited by 352 · see citing works

Protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences

Xiao, N., Cao, D.-S., Zhu, M.-F., & Xu, Q.-S. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv042 · Cited by 334 · see citing works

Protein–protein interaction site prediction through combining local and global features with deep neural networks

Zeng, M., Zhang, F., Wu, F., Li, Y., Wang, J., & Li, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz699 · Cited by 281 · see citing works

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM

Tunçbağ, N., Gürsoy, A., Nussinov, R., & Keskin, Ö. (2011) · Nature Protocols · doi:10.1038/nprot.2011.367 · Cited by 264 · see citing works

VirHostNet 2.0: surfing on the web of virus/host molecular interactions data

Guirimand, T., Delmotte, S., & Navratil, V. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1121 · Cited by 237 · see citing works

Introduction to host microbiome symbiosis in health and disease

Malard, F., Dore, J. M., Gaugler, B., & Mohty, M. (2020) · Mucosal Immunology · doi:10.1038/s41385-020-00365-4 · Cited by 211 · see citing works

The Human Microbiome: From Symbiosis to Pathogenesis

Eloe‐Fadrosh, E. A., & Rasko, D. A. (2013) · Annual Review of Medicine · doi:10.1146/annurev-med-010312-133513 · Cited by 210 · see citing works

PRISM: protein interactions by structural matching

Ogmen, U., Keskin, Ö., Aytuna, A. S., Nussinov, R., & Gürsoy, A. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki585 · Cited by 207 · see citing works

PRISM: a web server and repository for prediction of protein–protein interactions and modeling their 3D complexes

Baspinar, A., Cukuroglu, E., Nussinov, R., Keskin, Ö., & Gürsoy, A. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku397 · Cited by 201 · see citing works

DeepMicro: Deep representation learning for disease prediction based on microbiome data

Oh, M., & Zhang, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-63159-5 · Cited by 190 · see citing works

PHISTO: pathogen-host interaction search tool

Tekir, S. D., Çakır, T., Ardiç, E., Sayilirbas, A. S., Konuk, G., Konuk, M., Sariyer, H., Ugurlu, A., Karadeniz, I., Özgür, A., et al. (2013) · Bioinform. · doi:10.1093/bioinformatics/btt137 · Cited by 175 · see citing works

Improving B-cell epitope prediction and its application to global antibody–antigen docking

Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu190 · Cited by 174 · see citing works

Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method

Yang, X., Yang, S., Li, Q., Wuchty, S., & Zhang, Z. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.005 · Cited by 163 · see citing works

VirusMentha: a new resource for virus-host protein interactions

Alberto, C., Licata, L., & Cesareni, G. (2014) · Nucleic Acids Res. · doi:10.1093/nar/gku830 · Cited by 161 · 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

APID database: redefining protein–protein interaction experimental evidences and binary interactomes

Alonso-López, D., Campos-Laborie, F., Gutiérrez, M. A., Lambourne, L., Calderwood, M., Vidal, M., & Rivas, J. D. L. (2019) · Database J. Biol. Databases Curation · doi:10.1093/database/baz005 · Cited by 152 · see citing works

Fast end-to-end learning on protein surfaces

Sverrisson, F., Feydy, J., Correia, B. E., & Bronstein, M. M. (2021) · 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/cvpr46437.2021.01502 · Cited by 150 · see citing works

INstruct: a database of high-quality 3D structurally resolved protein interactome networks

Meyer, M. J., Das, J., Wang, X., & Yu, H. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt181 · Cited by 144 · see citing works

Learning context-aware structural representations to predict antigen and antibody binding interfaces

Pittala, S., & Bailey‐Kellogg, C. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa263 · Cited by 138 · see citing works

Structural principles within the human-virus protein-protein interaction network

Franzosa, E. A., & Xia, Y. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1101440108 · Cited by 132 · see citing works

PAIRpred: Partner‐specific prediction of interacting residues from sequence and structure

Minhas, F., Geiss, B. J., & Ben‐Hur, A. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24479 · Cited by 125 · see citing works

Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins

Dey, L., Chakraborty, S., & Mukhopadhyay, A. (2020) · Biomedical Journal · doi:10.1016/j.bj.2020.08.003 · Cited by 121 · see citing works

The role of the gut microbiome in the development of schizophrenia

Kelly, J. R., Minuto, C., Cryan, J. F., Clarke, G. A., & Dinan, T. G. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.02.010 · Cited by 119 · see citing works

Computational approaches for prediction of pathogen-host protein-protein interactions

Nourani, E., Khunjush, F., & Durmuş, S. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00094 · Cited by 115 · see citing works

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

Moreno‐Indias, I., Lahti, L., Nedyalkova, M., Elbere, I., Roshchupkin, G. V., Adilović, M., Aydemir, Ö., Bakır-Güngör, B., Pau, E. C. D. S., D’Elia, D., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.635781 · Cited by 112 · see citing works

Protein docking model evaluation by 3D deep convolutional neural networks

Wang, X., Terashi, G., Christoffer, C. W., Zhu, M., & Kihara, D. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz870 · Cited by 111 · see citing works

Interplay between host and pathogen: immune defense and beyond

Jo, E. (2019) · Experimental & Molecular Medicine · doi:10.1038/s12276-019-0281-8 · Cited by 105 · see citing works

HVIDB: a comprehensive database for human–virus protein–protein interactions

Yang, X., Lian, X., Fu, C., Wuchty, S., Yang, S., & Zhang, Z. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa425 · Cited by 100 · 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

Protein Docking Model Evaluation by Graph Neural Networks

Wang, X., Flannery, S. T., & Kihara, D. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.647915 · Cited by 100 · 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

HCVpro: Hepatitis C virus protein interaction database

Kwofie, S. K., Schaefer, U., Sundararajan, V. S., Bajić, V. B., & Christoffels, A. G. (2011) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2011.09.001 · Cited by 89 · see citing works

MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks

Lo, C., & Mărculescu, R. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2833-2 · Cited by 79 · see citing works

Non-Redundant Unique Interface Structures as Templates for Modeling Protein Interactions

Cukuroglu, E., Gürsoy, A., Nussinov, R., & Keskin, Ö. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086738 · Cited by 76 · see citing works

DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes

Liu-Wei, W., Kafkas, Ş., Chen, J., Dimonaco, N. J., Tegner, J. N., & Hoehndorf, R. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab147 · Cited by 76 · see citing works

TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction

Sharma, D., Paterson, A. D., & Xu, W. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa542 · Cited by 74 · see citing works

Welcome to CAPRI: A Critical Assessment of PRedicted Interactions

Janin, J. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10111 · Cited by 71 · see citing works

A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications

Gezawa, A. S., Zhang, Y., Wang, Q., & Lei, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2982196 · Cited by 71 · see citing works

Pathogen mimicry of host protein-protein interfaces modulates immunity

Guven‐Maiorov, E., Tsai, C., & Nussinov, R. (2016) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2016.06.004 · Cited by 68 · see citing works

Machine-Learning-Based Predictor of Human–Bacteria Protein–Protein Interactions by Incorporating Comprehensive Host-Network Properties

Lian, X., Yang, S., Li, H., Fu, C., & Zhang, Z. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00074 · Cited by 61 · see citing works

Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions

Chen, H., Li, F., Wang, L., Jin, Y., Chi, C., Kurgan, L., Song, J., & Shen, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa068 · Cited by 60 · see citing works

Fast end-to-end learning on protein surfacesPreprint

Sverrisson, F., Feydy, J., Correia, B. E., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.28.424589 · Cited by 58 · see citing works

Computational analysis of interactomes: Current and future perspectives for bioinformatics approaches to model the host–pathogen interaction space

Arnold, R. R., Boonen, K., Sun, M., & Kim, P. M. (2012) · Methods · doi:10.1016/j.ymeth.2012.06.011 · Cited by 55 · see citing works

Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry

Guven‐Maiorov, E., Tsai, C., & Nussinov, R. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.01236 · Cited by 55 · see citing works

Structural host-microbiota interaction networks

Guven‐Maiorov, E., Tsai, C., & Nussinov, R. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005579 · Cited by 52 · see citing works

Energy‐based graph convolutional networks for scoring protein docking models

Cao, Y., & Shen, Y. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25888 · Cited by 50 · see citing works

Exploiting Conformational Ensembles in Modeling Protein–Protein Interactions on the Proteome Scale

Kuzu, G., Gursoy, A., Nussinov, R., & Keskin, O. (2013) · Journal of Proteome Research · doi:10.1021/pr400006k · Cited by 47 · see citing works

A review on recommender systems

Mansur, F., Patel, V. D., & Patel, M. (2017) · International Conference on Innovations in Information, Embedded and Communication Systems · doi:10.1109/iciiecs.2017.8276182 · Cited by 47 · see citing works

The diverse roles of RIP kinases in host-pathogen interactions

Eng, V. V., Wemyss, M. A., & Pearson, J. S. (2020) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2020.08.005 · Cited by 46 · see citing works

Targeting protein-protein interaction interfaces in COVID-19 drug discovery

Chang, C., Lin, S., Satange, R., Lin, S., Sun, S.-C., Wu, H., Kehn‐Hall, K., & Hou, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.003 · Cited by 46 · see citing works

3D Imaging, Analysis and Applications

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Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

Goncearenco, A., Li, M., Simonetti, F. L., Shoemaker, B. A., & Panchenko, A. R. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7201-2_15 · Cited by 41 · see citing works

Prediction of human-Bacillus anthracis protein–protein interactions using multi-layer neural network

Ibrahim, A., Witbooi, P. J., & Christoffels, A. G. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty504 · Cited by 38 · see citing works

Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions

Wang, L., You, Z., Yan, X., Xia, S., Liu, F., Li, L., Zhang, W., & Zhou, Y. Z. (2018) · Scientific Reports · doi:10.1038/s41598-018-30694-1 · Cited by 38 · see citing works

HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry

Guven‐Maiorov, E., Hakouz, A., Valjevac, S., Keskin, Ö., Tsai, C., Gürsoy, A., & Nussinov, R. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.01.025 · Cited by 38 · see citing works

A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions

Bandyopadhyay, S., Ray, S., Mukhopadhyay, A., & Maulik, U. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu041 · Cited by 34 · see citing works

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Khatun, M. S., Shoombuatong, W., Hasan, M. M., & Kurata, H. (2020) · Current Genomics · doi:10.2174/1389202921999200625103936 · Cited by 34 · see citing works

Comprehensive host-pathogen protein-protein interaction network analysis

Khorsand, B., Savadi, A., & Naghibzadeh, M. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03706-z · Cited by 33 · see citing works

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions

Hayashi, T., Matsuzaki, Y., Yanagisawa, K., Ohue, M., & Akiyama, Y. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2073-x · Cited by 31 · see citing works

Prediction of Host–Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer

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Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

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Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins

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Graph Neural Networks for the Prediction of Protein-Protein Interfaces.

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Performance of rotation forest ensemble classifier and feature extractor in predicting protein interactions using amino acid sequences

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Microbiome in Colorectal Cancer: How to Get from Meta-omics to Mechanism?

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In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks

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IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data

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A framework towards data analytics on host–pathogen protein–protein interactions

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Embedding Alternative Conformations of Proteins in Protein–Protein Interaction Networks

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DeepInterface: Protein-protein interface validation using 3D Convolutional Neural NetworksPreprint

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APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

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Protein-Protein Interaction Networks

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Drug-microbiota interactions: an emerging priority for precision medicine

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AlphaFold, Artificial Intelligence (AI), and Allostery

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The Role of AI in Drug Discovery

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Microbiome as an immune regulator in health, disease, and therapeutics

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Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study

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Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function

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Artificial intelligence-based approaches for traditional fermented alcoholic beverages’ development: review and prospect

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Human–gut bacterial protein–protein interactions: understudied but impactful to human health

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Artificial intelligence for microbiology and microbiome research

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microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

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Computational methods for modeling protein–protein interactions in the AI era: Current status and future directions

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Interaction of microbiota, mucosal malignancies, and immunotherapy—Mechanistic insights

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An Approach for Predicting Protein-Protein Interactions using Supervised Autoencoders

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Editorial overview: Artificial intelligence (AI) methodologies in structural biology

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Modulating drug response through the gut microbiome: pathways to precision therapy

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Bioinformatic Resources for Exploring Human–virus Protein–protein Interactions Based on Binding Modes

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Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions

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Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Özdemir, E. S., & Nussinov, R. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1061595 · Cited by 3 · see citing works

Role of Computational Biology in Microbiome Research

Noor, F., Khurshid, M., Aslam, B., & Ashfaq, U. A. (2024) · Springer eBooks · doi:10.1007/978-981-97-3790-1_4 · Cited by 3 · see citing works

Impact of Structural Relaxation on Protein–Protein Docking in Large Macromolecular Complexes

Rosa, R. S. D. L., Bulla, A. C. S., Bernardi, R. C., & da Silva, M. L. (2025) · Applied Biosciences · doi:10.3390/applbiosci4040048 · Cited by 3 · see citing works

Current Trend and Performance Evaluation of Machine Learning Methods for Predicting Host-Pathogen Protein-Protein Interactions

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From Extreme Environments in Nature to Molecular and Cellular Adaptation and Functional Regulation

Berezovsky, I. N., & Nussinov, R. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169639 · Cited by 1 · see citing works

Molecular-level host–microbe interactions: mechanisms, molecules, and modeling toward precision probiotics

Zhang, H., Zhang, S., & Zhu, J. (2026) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2026.2671689 · Cited by 1 · see citing works

Bioinformatics roadmap for characterizing the gut microbiome to study its interactions and associations with the gut mucosal immune system

Anwar, M. Z., Prieto, M. D., & Hsiao, W. (2026) · Mucosal Immunology · doi:10.1016/j.mucimm.2026.100375 · Cited by 1 · see citing works

Postbiotics and Para-Probiotics: The Future of Gut Health Beyond Probiotics

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Artificial intelligence driven diet‐intestinal microbiota‐host health integration: A four‐dimensional paradigm for advancing host wellness research

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Host-pathogen interactions: databases and approaches for data generation

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Comparative Analysis of Structural Alignment Algorithms for Protein-Protein Interfaces in Template-Based Docking StudiesPreprint

Cankara, F., Tunçbağ, N., Gürsoy, A., & Keskin, Ö. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.03.587755 · Cited by 1 · see citing works

Deciphering the Soybean Rust Arsenal: Effectorome Studies and Host-Pathogen Interaction (HPI) network in Phakopsora pachyrhizi and Glycine max PathosystemPreprint

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Semipres: an Integrated Deep Learning Method of Sequence and 3d Object Detection Models for Host-microbe Interactions by Identification of Protein-protein Contact ResiduesPreprint

Lim, H., Keskin, Ö., Nussinov, R., & Gürsoy, A. (2022) · Research Square · doi:10.21203/rs.3.rs-2035249/v1

Analisis Bibliometric : Satu Dekade Penelitian Kecerdasan Buatan Data Anggrek

Putra, A. S., Febriani, O. M., & Kurniawan, D. (2023) · Explore Jurnal Sistem informasi dan telematika · doi:10.36448/jsit.v14i2.3324

Prediction of Gut Microbiome-Host Interaction

Singh, B., Mal, G., Kalra, R. S., & Marotta, F. (2024) · Springer eBooks · doi:10.1007/978-3-031-65459-6_31

Revolutionizing Pharma: How AI is Shaping Pharmaceutical Sector

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IMPLEMENTING ARTIFICIAL INTELLIGENCE IN DRUG RESEARCH DEVELOPMENT

Obbalareddy, S., Shahi, S., & Dubey, A. (2025) · BULLETIN OF STOMATOLOGY AND MAXILLOFACIAL SURGERY · doi:10.58240/1829006x-2025.21.6-361

Integrating graph neural networks for predictive modeling in complex biological systems

Bansal, A., Anand, A., Verma, N., Singh, N., & Kumar, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27608-8.00010-2

Unveiling Ebola–Human Protein Links Through Network Embedding and Unsupervised Machine Learning

Chatterjee, S., & Mandal, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07735-6_6

Significance and challenges in dissecting cancer-bacteriome interactions

Alshareedah, I., Brunner, J. D., Chain, P., & Kumar, A. (2026) · BJC Reports · doi:10.1038/s44276-026-00229-7

Modulating the human gut microbiome-host system: a new drug discovery paradigm

Colmenarejo, G. (2026) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2026.1898583

Gut bacterial-derived beneficial proteins in intestinal disease.

Zhou, S.-Y., Tian, Q.-C., Ullah, S., Su, D., Geng, J.-J., & Zhang, M.-M. (2026) · Microbiology Research · doi:10.1016/j.micres.2026.128794

MHIPM: Accurate Prediction of Microbe-Host Interactions Using Multiview Features from a Heterogeneous Microbial Network

Pan, J., Zhang, G., Yang, Y., Yang, W., Mao, N., You, Z., Feng, J., Wang, S., & Sun, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01296 · Cited by 4 · see citing works

HMI-PRED 2.0: a biologist-oriented web application for prediction of host–microbe protein–protein interaction by interface mimicry

Lim, H., Tsai, C., Keskin, Ö., Nussinov, R., & Gürsoy, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac633 · Cited by 6 · see citing works

HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry

Guven‐Maiorov, E., Hakouz, A., Valjevac, S., Keskin, Ö., Tsai, C., Gürsoy, A., & Nussinov, R. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.01.025 · Cited by 38 · see citing works

Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions

Kiouri, D. P., Batsis, G. C., & Chasapis, C. T. (2025) · Proteomes · doi:10.3390/proteomes13010010 · Cited by 3 · see citing works

Recent progress and future challenges in structure-based protein-protein interaction prediction

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