Predicting Protein–Protein Interactions from the Molecular to the Proteome Level

Cytoscape: A software Environment for integrated models of biomolecular interaction networks

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066

STRING v10: protein–protein interaction networks, integrated over the tree of life

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436

Inference of Macromolecular Assemblies from Crystalline State

Krissinel, E., & Henrick, K. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.05.022 · Cited by 10,007

Alternative isoform regulation in human tissue transcriptomes

Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181

A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706

STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398

A comprehensive two-hybrid analysis to explore the yeast protein interactome

Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., & Sakaki, Y. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.061034498 · Cited by 3,566

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 3,252

Potentiating Tweezer Affinity to a Protein Interface with Sequence-Defined Macromolecules on Nanoparticles

Seiler, T., Lennartz, A., Klein, K., Hommel, K., Bietti, A. F., Hadrović, I., Kollenda, S., Sager, J., Beuck, C., Chlosta, E., et al. (2023) · Biomacromolecules · doi:10.1021/acs.biomac.3c00393 · Cited by 6

Molecular Modeling Method Applications: Probing the Mechanism of Endocrine Disruptor Action

Yang, X., Liu, H., & Kusko, R. L. (2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-16443-0_16 · Cited by 6

Prediction of Protein-Protein Interactions: Looking Through the Kaleidoscope

Laddach, A., Chung, S. S., & Fraternali, F. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20470-6 · Cited by 6

AligNet: Alignment of Protein-Protein Interaction NetworksPreprint

Alberich, R., Alcalá, A., Llabrés, M., Rosselló, F., & Valiente, G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/551242 · Cited by 6

SSPPI: Cross-Modality Enhanced Protein–Protein Interaction Prediction From Sequence and Structure Perspectives

Bi, X.-P., Ma, W.-J., Jiang, H.-S., Lu, W.-G., Wei, Z.-Q., & Zhang, S.-G. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3599927 · Cited by 5

Valuable Contributions and Lessons Learned from Proteomics and Metabolomics Studies of COVID-19

Chatterjee, B., & Thakur, S. S. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00340 · Cited by 5

A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study

Wang, M., Withers, J. B., Ricchiuto, P., Voitalov, I., McAnally, M. O., Sanchez, H. N., Saleh, A., Akmaev, V. R., & Ghiassian, S. D. (2021) · Life Science Alliance · doi:10.26508/lsa.202000904 · Cited by 6

In Vivo Detection of Metabolic Fluctuations in Real Time Using the NanoBiT Technology Based on PII Signalling Protein Interactions

Rozbeh, R., & Forchhammer, K. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25063409 · Cited by 5

Specifically targeted antimicrobial cyclic peptide to staphylococcal protein A for the treatment of S. aureus infection

Lei, X., Li, Y., Cheng, X., Zhuang, J., Zhang, F., Xie, X., Tan, L., Fan, J., Zhao, Y., Wu, C.-L., et al. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.162243 · Cited by 5

Making the Right Choice: Critical Parameters of the Y2H Systems

Mehla, J., Caufield, J. H., & Uetz, P. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7871-7_2 · Cited by 5

Epitope‐specific affinity maturation improved stability of potent protease inhibitory antibodies

Lopez, T., Chen, C., Ramirez, A., Chen, K. E., Lorenson, M. Y., Benitez, C., Mustafa, Z., Pham, H., Sanchez, R., Walker, A. M., et al. (2018) · Biotechnology and Bioengineering · doi:10.1002/bit.26814 · Cited by 5

Phosphatase POPX2 interferes with cell cycle by interacting with Chk1

Kim, P. R., Koon, Y. L., Lee, R. T. C., Azizan, F., Koh, D. H. Z., Chiam, K., & Koh, C.-G. (2020) · Cell Cycle · doi:10.1080/15384101.2020.1711577 · Cited by 5

Computational techniques for studying protein-protein interactions

Al-Khafaji, K., & Taşkın‐Tok, T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90264-9.00009-x · Cited by 5

Malignant peritoneal mesothelioma interactome with 417 novel protein–protein interactions

Karunakaran, K. B., & Ganapathiraju, M. K. (2024) · BJC Reports · doi:10.1038/s44276-024-00062-w · Cited by 4

Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites

Ataş, H., Tunçbağ, N., & Doğan, T. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7756-7_4 · Cited by 5

ITRAQ-based proteomic analysis reveals possible target-related proteins in human adrenocortical adenomas

Ma, H., Li, R., Di, X., Jin, X., Wang, Y., Lai, B., Shi, C., Ji, M., Zhu, X., & Wang, K. (2019) · BMC Genomics · doi:10.1186/s12864-019-6030-5 · Cited by 5

Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of Protein Dimers

Hadi‐Alijanvand, H. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00594 · Cited by 5

Prediction, Analysis, Visualization, and Storage of Protein–Protein Interactions Using Computational Approaches

Poluri, K. M., Gulati, K., & Sarkar, S. (2021) · Springer eBooks · doi:10.1007/978-981-16-1594-8_6 · Cited by 5

Protein-Protein Interactions: An Overview

Folador, E. L., Tiwari, S., Barbosa, C. E. D. P., Jamal, S. B., Schulze, M. D. C., Barh, D., & Azevedo, V. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20292-6 · Cited by 5

Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration

Fang, T., Szklarczyk, D., Hachilif, R., & von Mering, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-55655-9 · Cited by 5

Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics

Netsey, E. K., Naandam, S. M., Asante, J., Kuukua, A. E., Yadem, A. C., Owusu, G., Shaffer, J. G., Srivastav, S. K., Doumbia, S., Owusu‐Dabo, E., et al. (2025) · JMIR Bioinformatics and Biotechnology · doi:10.2196/73637 · Cited by 7

Side‐Chain Engineering Guides the Design of β‐Sheet Inhibitor to Anchor Protein‐Protein Interaction Interfaces

Zhang, L., Wang, M., Wang, B., Zhao, J., Sun, B., Yu, Y., Han, K., Wang, X., Zhang, J., Wei, M., et al. (2026) · Angewandte Chemie International Edition · doi:10.1002/anie.202521473 · Cited by 4

Quantitative proteomic and phosphoproteomic analysis of chicken skeletal muscle during embryonic development

Cui, C., Yin, H., Han, S., Zhang, Y., Zhang, Y., & Zhu, Q. (2021) · Animal Biotechnology · doi:10.1080/10495398.2021.1941071 · Cited by 4

Graph-based machine learning model for weight prediction in protein–protein networks

Akid, H., Chennen, K., Frey, G., Thompson, J., Ayed, M. B., & Lachiche, N. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05973-6 · Cited by 4

The computational prediction of protein assemblies

Tramontano, A. L. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.006 · Cited by 4

Partner-Specific Prediction of Protein-Dimer Stability from Unbound Structure of Monomer

Hadi‐Alijanvand, H., & Rouhani, M. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00606 · Cited by 4

Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?

Irle, S., Vuong, V., Elayyan, M., Talipov, M. R., & Abel, S. M. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0282-9_10 · Cited by 4

Modulation of interaction of BRCA1-RAD51 and BRCA1-AURKA protein complexes by natural metabolites using as possible therapeutic intervention toward cardiotoxic effects of cancer drugs: an in-silico approach

Tiwari, S., Pandey, V. P., Yadav, K., & Dwivedi, U. N. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1976278 · Cited by 4

Ligand‐assisted imprinting‐probe‐labeling strategy reveals Siglec‐7 ‐ glycoprotein interactions

Li, P., Kawade, S. K., Adak, A. K., Shen, Y., Fan, C., Hsieh, Y., Angata, T., Chen, Y., & Lin, C. (2022) · Journal of the Chinese Chemical Society · doi:10.1002/jccs.202200127 · Cited by 4

Prediction of transient and permanent protein interactions using AI methods

Rathore, R. S. (2023) · Bioinformation · doi:10.6026/97320630019749 · Cited by 4

The potential impact of recent developments in three-dimensional quantitative interaction proteomics on structural biology

Tunçbağ, N., Gürsoy, A., Keskin, Ö., & Nussinov, R. (2016) · Expert Review of Proteomics · doi:10.1080/14789450.2016.1182023 · Cited by 4

Fast and scalable protein motif sequence clustering based on Hadoop framework

Maleki, E. F., Ghadiri, N., Asadi, M., Nikbakht, M. A., & Pitre, S. (2017) · International Conference on Web Research (ICWR) · doi:10.1109/icwr.2017.7959300 · Cited by 4

Proteome profiling of formalin‑fixed, paraffin‑embedded lung adenocarcinoma tissues using a tandem mass tag‑based quantitative proteomics approach

Xie, Q., Wang, D., Luo, X., Li, Z., Hu, A., Yang, H., Tang, J., Gao, P., Sun, T., & Kong, L. (2021) · Oncology Letters · doi:10.3892/ol.2021.12967 · Cited by 4

Selective Disruption of Survivin's Protein‐Protein Interactions: A Supramolecular Approach Based on Guanidiniocarbonylpyrrole

Aschmann, D., Vallet, C., Tripathi, S. K., Ruiz‐Blanco, Y. B., Brabender, M., Schmuck, C., Sánchez‐García, E., Knauer, S. K., & Giese, M. (2022) · ChemBioChem · doi:10.1002/cbic.202100618 · Cited by 4

Monoclonal Antibodies

Klein, C. (2018) · MDPI eBooks · doi:10.3390/books978-3-03842-876-3 · Cited by 4

Next-generation graph computing with electric current-based and quantum-inspired approaches

Jang, Y. H., Han, J., Lee, S. H., & Hwang, C. S. (2025) · Nature Communications · doi:10.1038/s41467-025-63494-z · Cited by 4

One Primer Pair for All: A Standardized Vector Toolbox for Protein‐Protein Interactions and Protein Localization in Plants

Luo, C., Wang, L., Wu, Y., Cai, J., Peng, B., Chen, J., Yu, F., & Wu, D. (2025) · Plant Cell & Environment · doi:10.1111/pce.70219 · Cited by 4

Computationally identified peptides immobilized to hydrogel delivery vehicles enable affinity-controlled release of proteins.

Yao, Y., Hong, C., Teal, C. J., Bahsoun, N. E., Lu, S. P., Fernandes, I., Keller, G. M., & Shoichet, M. (2025) · Acta Biomaterialia · doi:10.1016/j.actbio.2025.12.034 · Cited by 2

Targeting protein–protein interactions with reversible covalent modalities: Non‐cysteine chemistries

Basu, R., & Fletcher, S. (2026) · British Journal of Pharmacology · doi:10.1111/bph.70377 · Cited by 3

Cell line-specific features of 3D chromatin organization in hepatocellular carcinoma

Kim, Y., Yang, H.-J., & Lee, D. (2023) · Genomics & Informatics · doi:10.5808/gi.23015 · Cited by 4

Analysis and prediction of interactions between transmembrane and non-transmembrane proteins

Lu, C. F., Jiang, J., Chen, Q., Liu, H., Ju, X., & Wang, H. (2024) · BMC Genomics · doi:10.1186/s12864-024-10251-z · Cited by 3

Comparative interactomics build the bridges from micromolecules to biological behaviour and morphology

Chen, J. (2024) · Crop Design · doi:10.1016/j.cropd.2024.100078 · Cited by 3

In-silico screening, pharmacokinetic evaluation, DFT study, and molecular dynamic simulation as approaches for the identification of acetamide and acetic acid derivatives as potential antisickling agents targeting Hba protein

Ejeh, S. P., Ajala, A., Adawara, S. N., Olasupo, S. B., Ibrahim, Z. Y., & Abdulfatai, U. (2025) · Discover Chemistry. · doi:10.1007/s44371-025-00136-9 · Cited by 3

Structural Aspects of Protein–Protein Interactions

Poluri, K. M., Gulati, K., & Sarkar, S. (2021) · Springer eBooks · doi:10.1007/978-981-16-1594-8_2 · Cited by 3

ProSPs: Protein Sites Prediction Based on Sequence Fragments

Quadrini, M., Cavallin, M., Daberdaku, S., & Ferrari, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95467-3_41 · Cited by 3

The association between genetic variations and morphology‐based brain networks changes in Alzheimer's disease

Xiong, W., Cai, J., Sun, B., Lin, H., Wei, C., Huang, C., Zhu, X., Tan, H., & Initiative, A. D. N. (2023) · Journal of Neurochemistry · doi:10.1111/jnc.15761 · Cited by 3

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 4

Programming interchangeable and reversible heterooligomeric protein self-assembly using a bifunctional ligand

Son, S.-Y., & Song, W. J. (2024) · Chemical Science · doi:10.1039/d3sc05448a · Cited by 3

Interaction strength of osmolytes with the anion of a salt-bridge determines its stability

Tiwari, M. K., & Murarka, R. K. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp05378c · Cited by 3

Prediction and analysis of hot region in protein-protein interactions

Lin, X., & Zhang, X. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822758 · Cited by 3

Construction and Analysis of Protein-Protein Interaction Network

Dasagrandhi, D., Ravindran, A. S. K., Anusuyadevi, M., & S., J. K. (2018) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-7326-5.ch009 · Cited by 3

Proteoinformatics and Agricultural Biotechnology Research: Applications and Challenges

Al‐Obaidi, J. R. (2019) · Springer eBooks · doi:10.1007/978-3-030-19318-8_1 · Cited by 3

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Vora, D. S., Kalakoti, Y., & Sundar, D. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2617-7_15 · Cited by 3

BraneMF: integration of biological networks for functional analysis of proteins

Jagtap, S., Çelikkanat, A., Pirayre, A., Bidard, F., Duval, L. C., & Malliaros, F. D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac691 · Cited by 3

Pathogenic impact of transcript isoform switching in 1209 cancer samples covering 27 cancer types using an isoform-specific interaction networkPreprint

Kahraman, A., Karakulak, T., Szklarczyk, D., & von Mering, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/742379 · Cited by 3

Accelerated Transfer Learning for Protein-Protein Interaction Prediction

Barnes, B. (2018) · doi:10.22215/etd/2018-12707 · Cited by 3

Assessment of community efforts to advance computational prediction of protein-protein interactionsPreprint

Wang, X., Madeddu, L., Spirohn, K., Martini, L., Fazzone, A., Becchetti, L., Wytock, T. P., Kovács, I. A., Balogh, O. M., Benczik, B., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.22.461292 · Cited by 3

Biorthogonal masked acylating agents for proximity-dependent RNA labelingPreprint

Pani, S., Qiu, T., Kentala, K., Azizi, S., & Dickinson, B. C. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-vx3hm · Cited by 3

A Review of Protein-Protein Interaction Databases

Acharya, K. K., Davuluri, S., & Karimindla, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00076-2 · Cited by 3

PPILS: Protein-protein interaction prediction with language of biological coding

Howladar, N., Kabir, M. W. U., Hoque, F., Katebi, A., & Hoque, M. T. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109678 · Cited by 2

Decoding Molecular Interactions: Unraveling the Crosstalk between the Wnt Pathway and Key Signaling Networks by miRNA in Colorectal Cancer Progression

Pasdar, A., Rahmani, F., & Amerizadeh, F. (2025) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2025.26.7.2511 · Cited by 2

Graph based link prediction for epilepsy drug discovery

Shi, X., Sundareswaran, R., Shanmugapriya, M., Jayaguptha, N., & Khan, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-14550-7 · Cited by 2

Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes

Zhang, Y., Liu, Y., Zhang, Y., Wang, Z., Lu, X., Ge, S., & Min, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf645 · Cited by 2

Hierarchical Network Organization and Dynamic Perturbation Propagation in Autism Spectrum Disorder: An Integrative Machine Learning and Hypergraph Analysis Reveals Super-Hub Genes and Therapeutic Targets

Batrancea, L. M., Akgüller, Ö., Balcı, M. A., & Gãban, L. (2026) · Biomedicines · doi:10.3390/biomedicines14010137 · Cited by 3

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Balkenhol, J., Bencúrová, E., Gupta, S. K., Schmidt, H., Heinekamp, T., Brakhage, A. A., Pottikkadavath, A., & Dandekar, T. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.07.050 · Cited by 2

Fascin − F-actin interaction studied by molecular dynamics simulation and protein network analysis

Wu, X., Li, D., Chen, Y., Wang, L., Xu, L., Li, E., & Dong, G. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2199083 · Cited by 2

A ubiquitination-mediated degradation system to target 14-3-3-binding phosphoproteins

Li, Z., Huang, X., Li, M., Chen, Y. E., Wang, Z., & Liu, L. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16318 · Cited by 2

Current progress on the computational methods for prediction of host-pathogen protein-protein interaction in the Ganoderma boninense-oil palm pathosystem

Khairi, M. H. F., Muhammad, N. A. N., Bunawan, H., Daud, K. M., Sulaiman, S., Mohamed‐Hussein, Z., Wong, M., & Ramzi, A. B. (2023) · Physiological and Molecular Plant Pathology · doi:10.1016/j.pmpp.2023.102201 · Cited by 2

Dock2D: Synthetic Data for the Molecular Recognition Problem

Bhadra-Lobo, S., Derevyanko, G., & Lamoureux, G. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3407477 · Cited by 3

Fluorescence Resonance Energy Transfer: A Technique to Unleash Biomolecular Structure and Dynamics

Subadini, S., Behera, D. P., Hota, P. R., Prusty, L., & Sahoo, H. (2025) · Springer eBooks · doi:10.1007/978-981-96-2088-3_10 · Cited by 2

Synthesis of Gold Nanoparticles to Capture Lifelike Proteins: Application on the Multichannel Sensor Array Design

Leng, Y., Ma, C., Zhang, W., Liu, K., & Lu, Z. (2018) · Journal of Nanomaterials · doi:10.1155/2018/8540172 · Cited by 2

Thrombopoietin receptor‐based protein–protein interaction screening (THROPPIS)

Horikawa, M., Kakiuchi, Y., Kashima, D., Ogawa, K., & Kawahara, M. (2021) · Biotechnology and Bioengineering · doi:10.1002/bit.27975 · Cited by 2

Quantification of Protein Expression by Proximity Ligation Assay in the Nonhuman Primate in Response to Estrogen

Bonagura, T. W., Babischkin, J. S., Pepe, G. J., & Albrecht, E. D. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1920-9_6 · Cited by 2

Conformationally Programmable Chiral Foldamers with Compact and Extended Domains Controlled by Monomer Structure

Lockhart, Z., & Knipe, P. C. (2018) · Angewandte Chemie · doi:10.1002/ange.201802822 · Cited by 2

Computer aided analysis of disease linked protein networks

Sabetian, S., & Shamsir, M. S. (2019) · Bioinformation · doi:10.6026/97320630015513 · Cited by 2

A genetically encoded photo-proximity labeling approach for mapping protein territoriesPreprint

Hananya, N., Ye, X., Koren, S., & Muir, T. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.30.502153 · Cited by 2

Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights from Predictive Modeling and Analytics (Preprint)Preprint

Netsey, E. K., Naandam, S. M., Asante, J., Kuukua, A. E., Yadem, A. C., Owusu, G., Shaffer, J. G., Srivastav, S. K., Doumbia, S., Owusu‐Dabo, E., et al. (2025) · doi:10.2196/preprints.73637 · Cited by 2

Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner

Choi, J. (2023) · iScience · doi:10.1016/j.isci.2023.106911 · Cited by 1

An engineered ubiquitin binding coiled coil peptide

Vosbein, P., Vergara, P. P., Huang, D. T. C., & Thomson, A. R. (2024) · Chemical Science · doi:10.1039/d4sc04204b · Cited by 1

Construction of a proximity labeling vector to identify protein-protein interactions in human stem cells

Gomes-Júnior, R., Moreira, C. M. D. N., & Dallagiovanna, B. (2025) · PLoS ONE · doi:10.1371/journal.pone.0324779 · Cited by 1

Exploring effector-host interactions in Pyricularia oryzae infections: A molecular and structural approach to rice blast resistance

Amin, Z., Mohiddin, F. A., Ali, A., Shikari, A. B., Nabi, S. U., Bhat, T. A., Gupta, V., Wani, F. J., Parveen, S., Mir, M. S., et al. (2025) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2025.102092 · Cited by 1

Photocatalytic Proximity Labeling in Biological System

Singh, P. P., Sinha, S., Gupta, N., & Srivastava, V. (2025) · ChemistrySelect · doi:10.1002/slct.202502605 · Cited by 1

CanGRNNA: Ensembling Message Passing and Temporal Dynamics with Attention for Prediction of Structural Cancer Protein–Protein Interactions

Jan, R., Hussain, A. B., Assad, A., & Bhat, B. A. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10498-y · Cited by 1

Sensitive and specific affinity purification-mass spectrometry assisted by PafA-mediated proximity labeling

Luo, S., Xie, L., Yang, L., Hu, Z., Wang, L., Wang, Y., Li, Q., Guo, S., Tao, S., & Jiang, H. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101166 · Cited by 1

DynaBench: Dynamic data for the docking benchmark

Barlas, A. B., Laurent, B., Karaca, E., Prévost, C., & Sacquin‐Mora, S. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169650 · Cited by 1

Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers

Zhilkin, A., Medaparambath, M., & Mendels, D. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00418 · Cited by 1

Toward autonomous science with agentic artificial intelligence

Guo, L. Y., Ting, D. S. J., Su, X., Aliper, A., Zhavoronkov, A. A., & Ting, D. S. W. (2026) · Cell · doi:10.1016/j.cell.2026.08.052 · Cited by 2

Approaches for Preparation and Biophysical Characterization of Transmembrane β-Barrels

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