Nussinov, R., Jang, H., Nir, G., Tsai, C. J. & Cheng, F. Open structural data in precision medicine. Annu. Rev. Biomed. Data Sci. 5, 95–117 (2022).
Braberg, H., Echeverria, I., Kaake, R. M., Sali, A. & Krogan, N. J. From systems to structure—using genetic data to model protein structures. Nat. Rev. Genet. 23, 342–354 (2022).
Vidal, M., Cusick, M. E. & Barabási, A.-L. Interactome networks and human disease. Cell 144, 986–998 (2011).
Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
Meyer, M. J. et al. Interactome INSIDER: a structural interactome browser for genomic studies. Nat. Methods 15, 107–114 (2018).
Wang, X. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol. 30, 159–164 (2012).
Cheng, F. et al. Comprehensive characterization of protein–protein interactions perturbed by disease mutations. Nat. Genet. 53, 342–353 (2021).
Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015).
Wierbowski, S. D. et al. A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations. Nat. Methods 18, 1477–1488 (2021).
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022).
Gao, M., Nakajima An, D., Parks, J. M. & Skolnick, J. AF2Complex predicts direct physical interactions in multimeric proteins with deep learning. Nat. Commun. 13, 1744 (2022).
Burke, D. F. et al. Towards a structurally resolved human protein interaction network. Nat. Struct. Mol. Biol. 30, 216–225 (2023).
Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).
Bianchi, F. M., Grattarola, D., Livi, L. & Alippi, C. Graph neural networks with convolutional ARMA filters. IEEE Trans. Pattern Anal. Mach. Intell. 44, 3496–3507 (2022).
Cho, K. et al. Learning phrase representations using rnn encoder–decoder for statistical machine translation. In Proc. of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1724–1734 (Association for Computational Linguistics, 2014).
Zhuang, F. et al. A comprehensive survey on transfer learning. Proc. of the IEEE 109, 43–76 (2021).
Krapp, L. F., Abriata, L. A., Cortes Rodriguez, F. & Dal Peraro, M. PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces. Nat. Commun. 14, 2175 (2023).
Tubiana, J., Schneidman-Duhovny, D. & Wolfson, H. J. ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction. Nat. Methods 19, 730–739 (2022).
Gainza, P. et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17, 184–192 (2020).
Sanchez-Garcia, R., Macias, J. R., Sorzano, C. O. S., Carazo, J. M. & Segura, J. BIPSPI+: mining type-specific datasets of protein complexes to improve protein binding site prediction. J. Mol. Biol. 434, 167556 (2022).
Zeng, M. et al. Protein–protein interaction site prediction through combining local and global features with deep neural networks. Bioinformatics 36, 1114–1120 (2020).
Townshend, R. J. L., Bedi, R., Suriana, P. A. & Dror, R. O. End-to-end learning on 3D protein structure for interface prediction. 33rd Conference on Neural Information Processing Systems. https://proceedings.neurips.cc/paper_files/paper/2019/file/6c7de1f27f7de61a6daddfffbe05c058-Paper.pdf(NeurIPS, 2019).
Fout, A., Byrd, J., Shariat, B. & Ben-Hur, A. Protein interface prediction using graph convolutional networks. Advances in Neural Information Processing Systems 30. https://papers.nips.cc/paper_files/paper/2017/file/f507783927f2ec2737ba40afbd17efb5-Paper.pdf (NIPS, 2017).
Lensink, M. F. & Wodak, S. J. Score_set: a CAPRI benchmark for scoring protein complexes. Proteins 82, 3163–3169 (2014).
UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).
Das, J. & Yu, H. HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol. 6, 92 (2012).
Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47, D529–D541 (2019).
Salwinski, L. et al. The database of interacting proteins: 2004 update. Nucleic Acids Res. 32, D449–D451 (2004).
Orchard, S. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358–D363 (2014).
Licata, L. et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 40, D857–D861 (2012).
Turner, B. et al. iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database 2010, baq023 (2010).
Keshava Prasad, T. S. et al. Human protein reference database—2009 update. Nucleic Acids Res. 37, D767–D772 (2009).
Mewes, H. W. et al. MIPS: curated databases and comprehensive secondary data resources in 2010. Nucleic Acids Res. 39, D220–D224 (2011).
Nelson, L. & Cox, M. Lehninger Principles of Biochemistry 7th edn (W.H. Freeman, 2017).
Rose, G. D., Geselowitz, A. R., Lesser, G. J., Lee, R. H. & Zehfus, M. H. Hydrophobicity of amino acid residues in globular proteins. Science 229, 834–838 (1985).
Aftabuddin, M. & Kundu, S. Hydrophobic, hydrophilic, and charged amino acid networks within protein. Biophys. J. 93, 225–231 (2007).
Tsai, C. J., Lin, S. L., Wolfson, H. J. & Nussinov, R. Studies of protein–protein interfaces: a statistical analysis of the hydrophobic effect. Protein Sci. 6, 53–64 (1997).
Ansari, S. & Helms, V. Statistical analysis of predominantly transient protein–protein interfaces. Proteins 61, 344–355 (2005).
Burley, S. K. et al. RCSB Protein Data Bank: celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Sci. 31, 187–208 (2022).
Wei, X. et al. A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations. PLoS Genet. 10, e1004819 (2014).
Xiong, D., Lee, D., Li, L., Zhao, Q. & Yu, H. Implications of disease-related mutations at protein–protein interfaces. Curr. Opin. Struct. Biol. 72, 219–225 (2022).
Stenson, P. D. et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum. Genet. 136, 665–677 (2017).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).
Zhou, Y. et al. A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19. PLoS Biol. 18, e3000970 (2020).
Plasilova, M. et al. Homozygous missense mutation in the lamin A/C gene causes autosomal recessive Hutchinson–Gilford progeria syndrome. J. Med. Genet. 41, 609–614 (2004).
Favretto, F. et al. The molecular basis of the interaction of cyclophilin A with α-synuclein. Angew. Chem. Int. Ed. 59, 5643–5646 (2020).
Liu, Q. et al. HIF2A germline–mutation-induced polycythemia in a patient with VHL-associated renal-cell carcinoma. Cancer Biol. Ther. 18, 944–947 (2017).
Tarade, D., Robinson, C. M., Lee, J. E. & Ohh, M. HIF-2α-pVHL complex reveals broad genotype-phenotype correlations in HIF-2α-driven disease. Nat. Commun. 9, 3359 (2018).
V, F. R. L. et al. Three novel EPAS1/HIF2A somatic and germline mutations associated with polycythemia and pheochromocytoma/paraganglioma. Blood 120, 2080 (2012).
Chang, K. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
Nguyen, B. et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 185, 563–575 (2022).
Rabara, D. et al. KRAS G13D sensitivity to neurofibromin-mediated GTP hydrolysis. Proc. Natl Acad. Sci. USA 116, 22122–22131 (2019).
Wang, Z. et al. The diverse roles of SPOP in prostate cancer and kidney cancer. Nat. Rev. Urol. 17, 339–350 (2020).
Song, Y. et al. The emerging role of SPOP protein in tumorigenesis and cancer therapy. Mol. Cancer 19, 2 (2020).
Xu, J. & Lin, D. I. Oncogenic c-terminal cyclin D1 (CCND1) mutations are enriched in endometrioid endometrial adenocarcinomas. PLoS ONE 13, e0199688 (2018).
Ryu, D. et al. Alterations in the transcriptional programs of myeloma cells and the microenvironment during extramedullary progression affect proliferation and immune evasion. Clin. Cancer Res. 26, 935–944 (2020).
Zhang, M. et al. CanProVar 2.0: an updated database of human cancer proteome variation. J. Proteome Res. 16, 421–432 (2017).
Mészáros, B., Kumar, M., Gibson, T. J., Uyar, B. & Dosztányi, Z. Degrons in cancer. Sci. Signal. 10, eaak9982 (2017).
Yang, Q., Zhao, J., Chen, D. & Wang, Y. E3 ubiquitin ligases: styles, structures and functions. Mol. Biomed. 2, 23 (2021).
Senft, D., Qi, J. & Ronai, Z. E. A. Ubiquitin ligases in oncogenic transformation and cancer therapy. Nat. Rev. Cancer 18, 69–88 (2018).
Han, Y., Lee, H., Park, J. C. & Yi, G. S. E3Net: a system for exploring E3-mediated regulatory networks of cellular functions. Mol. Cell. Proteomics 11, O111.014076 (2012).
Li, Z. et al. UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase–substrate interactions. Database 2021, baab010 (2021).
Mena, E. L. et al. Dimerization quality control ensures neuronal development and survival. Science 362, eaap8236 (2018).
Wang, Q. et al. Alterations of anaphase-promoting complex genes in human colon cancer cells. Oncogene 22, 1486–1490 (2003).
Yin, Q., Wyatt, C. J., Han, T., Smalley, K. S. M. & Wan, L. ITCH as a potential therapeutic target in human cancers. Semin. Cancer Biol. 67, 117–130 (2020).
Li, L. et al. CHIP mediates degradation of Smad proteins and potentially regulates Smad-induced transcription. Mol. Cell. Biol. 24, 856–864 (2004).
Tsai, W.-W. et al. TRIM24 links a non-canonical histone signature to breast cancer. Nature 468, 927–932 (2010).
Lv, D. et al. TRIM24 is an oncogenic transcriptional co-activator of STAT3 in glioblastoma. Nat. Commun. 8, 1454 (2017).
Cuadrado, A. et al. Therapeutic targeting of the NRF2 and KEAP1 partnership in chronic diseases. Nat. Rev. Drug Discov. 18, 295–317 (2019).
Furukawa, M. & Xiong, Y. BTB protein Keap1 targets antioxidant transcription factor Nrf2 for ubiquitination by the Cullin 3-Roc1 ligase. Mol. Cell. Biol. 25, 162–171 (2005).
Fukutomi, T., Takagi, K., Mizushima, T., Ohuchi, N. & Yamamoto, M. Kinetic, thermodynamic, and structural characterizations of the association between Nrf2-DLGex Degron and Keap1. Mol. Cell. Biol. 34, 832–846 (2014).
Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).
Vasaikar, S. et al. Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177, 1035–1049.e19 (2019).
Abi-Habib, R. J. et al. BRAF status and mitogen-activated protein/extracellular signal-regulated kinase kinase 1/2 activity indicate sensitivity of melanoma cells to anthrax lethal toxin. Mol. Cancer Ther. 4, 1303–1310 (2005).
Roberts, P. J. & Der, C. J. Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene 26, 3291–3310 (2007).
Endres, N. F. et al. Conformational coupling across the plasma membrane in activation of the EGF receptor. Cell 152, 543–556 (2013).
Lu, C. F. et al. Structural evidence for loose linkage between ligand binding and kinase activation in the epidermal growth factor receptor. Mol. Cell. Biol. 30, 5432–5443 (2010).
Liang, S. I. et al. Phosphorylated EGFR dimers are not sufficient to activate ras. Cell Rep. 22, 2593–2600 (2018).
Bishayee, A., Beguinot, L. & Bishayee, S. Phosphorylation of tyrosine 992, 1068, and 1086 is required for conformational change of the human epidermal growth factor receptor C-terminal tail. Mol. Biol. Cell. 10, 525–536 (1999).
Siegelin, M. D. & Borczuk, A. C. Epidermal growth factor receptor mutations in lung adenocarcinoma. Lab Invest. 94, 129–137 (2014).
Hillig, R. C. et al. Discovery of potent SOS1 inhibitors that block RAS activation via disruption of the RAS–SOS1 interaction. Proc. Natl Acad. Sci. USA 116, 2551–2560 (2019).
You, X. et al. Unique dependence on Sos1 in KrasG12D-induced leukemogenesis. Blood 132, 2575–2579 (2018).
Hofmann, M. H. et al. Trial in process: phase 1 studies of BI 1701963, a SOS1::KRAS inhibitor, in combination with MEK inhibitors, irreversible KRASG12C inhibitors or irinotecan. Cancer Res. 81, CT210 (2021).
Huijberts, S. C. F. A. et al. Phase I study of lapatinib plus trametinib in patients with KRAS-mutant colorectal, non-small cell lung, and pancreatic cancer. Cancer Chemother. Pharmacol. 85, 917–930 (2020).
Cho, M. et al. A phase I clinical trial of binimetinib in combination with FOLFOX in patients with advanced metastatic colorectal cancer who failed prior standard therapy. Oncotarget 8, 79750–79760 (2017).
Hofmann, M. H. et al. BI-3406, a potent and selective SOS1–KRAS interaction inhibitor, is effective in KRAS-driven cancers through combined MEK inhibition. Cancer Discov. 11, 142–157 (2021).
Liu, F., Yang, X., Geng, M. & Huang, M. Targeting ERK, an Achilles’ Heel of the MAPK pathway, in cancer therapy. Acta Pharm. Sin. B 8, 552–562 (2018).
Tran, T. H. et al. KRAS interaction with RAF1 RAS-binding domain and cysteine-rich domain provides insights into RAS-mediated RAF activation. Nat. Commun. 12, 1176 (2021).
Patelli, G. et al. Strategies to tackle RAS-mutated metastatic colorectal cancer. ESMO Open 6, 100156 (2021).
Li, Z.-N., Zhao, L., Yu, L.-F. & Wei, M.-J. BRAF and KRAS mutations in metastatic colorectal cancer: future perspectives for personalized therapy. Gastroenterol. Rep. 8, 192–205 (2020).
Corcoran, R. B. et al. Combined BRAF, EGFR, and MEK inhibition in patients with BRAFV600E-mutant colorectal cancer. Cancer Discov. 8, 428–443 (2018).
Lin, Q. et al. The association between BRAF mutation class and clinical features in BRAF-mutant Chinese non-small cell lung cancer patients. J. Transl. Med. 17, 298 (2019).
Caunt, C. J., Sale, M. J., Smith, P. D. & Cook, S. J. MEK1 and MEK2 inhibitors and cancer therapy: the long and winding road. Nat. Rev. Cancer 15, 577–592 (2015).
Huang, K. L. et al. Regulated phosphosignaling associated with breast cancer subtypes and druggability. Mol. Cell. Proteomics 18, 1630–1650 (2019).
Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).
Luck, K. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020).
Huttlin, E. L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425–440 (2015).
Cho, N. H. et al. OpenCell: endogenous tagging for the cartography of human cellular organization. Science 375, eabi6983 (2022).
Petrey, D., Zhao, H., Trudeau, S. J., Murray, D. & Honig, B. PrePPI: a structure informed proteome-wide database of protein–protein interactions. J. Mol. Biol. 435, 168052 (2023).
Gao, Z. et al. Hierarchical graph learning for protein–protein interaction. Nat. Commun. 14, 1093 (2023).
Pieper, U. et al. ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 42, D336–D346 (2014).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Su, J. et al. SaProt: protein language modeling with structure-aware vocabulary. The Twelfth International Conference on Learning Representations. https://openreview.net/pdf?id=6MRm3G4NiU (ICLR, 2023).
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
Gary, W. B. et al. The Alzheimer’s disease sequencing project: study design and sample selection. Neurol. Genet. 3, e194 (2017).
Mosca, R., Céol, A. & Aloy, P. Interactome3D: adding structural details to protein networks. Nat. Methods 10, 47–53 (2013).
Velankar, S. et al. SIFTS: structure integration with function, taxonomy and sequences resource. Nucleic Acids Res. 41, D483–D489 (2013).
Lee, B. & Richards, F. M. The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol. 55, 379–400 (1971).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Sievers, F. & Higgins, D. G. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 27, 135–145 (2018).
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
Pierce, B. G. et al. ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30, 1771–1773 (2014).
Scardapane, S., Van Vaerenbergh, S., Totaro, S. & Uncini, A. Kafnets: kernel-based non-parametric activation functions for neural networks. Neural Netw. 110, 19–32 (2019).
Li, Y., Golding, G. B. & Ilie, L. DELPHI: accurate deep ensemble model for protein interaction sites prediction. Bioinformatics 37, 896–904 (2021).
Zhang, J. & Kurgan, L. SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences. Bioinformatics 35, i343–i353 (2019).
Zhang, B., Li, J., Quan, L., Chen, Y. & Lü, Q. Sequence-based prediction of protein–protein interaction sites by simplified long short-term memory network. Neurocomputing 357, 86–100 (2019).
Fu, W. et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).
Walhout, A. J. M. & Vidal, M. High-throughput yeast two-hybrid assays for large-scale protein interaction mapping. Methods 24, 297–306 (2001).
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
Dominguez, C., Boelens, R. & Bonvin, A. M. J. J. HADDOCK: a protein−protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125, 1731–1737 (2003).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Wu, E. L. et al. CHARMM-GUI Membrane Builder toward realistic biological membrane simulations. J. Comput. Chem. 35, 1997–2004 (2014).
Xiong, D., Lee, D. & Liang, S. GitHub code repository for PIONEER. https://github.com/hyulab/PIONEER (2024).
