Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
27 Sep 2024
27 Sep 2024
Historique:
received:
28
12
2023
accepted:
12
09
2024
medline:
28
9
2024
pubmed:
28
9
2024
entrez:
27
9
2024
Statut:
epublish
Résumé
Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To address this issue, we introduce phase separation, which is tightly linked to IDRs, into the investigation of missense variants. Phase separation is vital for multiple physiological processes. By leveraging missense variants that alter phase separation propensity, we develop a machine learning approach named PSMutPred to predict the impact of missense mutations on phase separation. PSMutPred demonstrates robust performance in predicting missense variants that affect natural phase separation. In vitro experiments further underscore its validity. By applying PSMutPred on over 522,000 ClinVar missense variants, it significantly contributes to decoding the pathogenesis of disease variants, especially those in IDRs. Our work provides insights into the understanding of a vast number of VUSs in IDRs, expediting clinical interpretation and diagnosis.
Identifiants
pubmed: 39333476
doi: 10.1038/s41467-024-52580-3
pii: 10.1038/s41467-024-52580-3
doi:
Substances chimiques
Intrinsically Disordered Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8279Informations de copyright
© 2024. The Author(s).
Références
Vacic, V. & Iakoucheva, L. M. Disease mutations in disordered regions–exception to the rule? Mol. Biosyst. 8, 27–32 (2012).
pubmed: 22080206
doi: 10.1039/C1MB05251A
Colak, R. et al. Distinct types of disorder in the human proteome: functional implications for alternative splicing. PLoS Comput. Biol. 9, e1003030 (2013).
pubmed: 23633940
pmcid: 3635989
doi: 10.1371/journal.pcbi.1003030
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
pubmed: 34265844
pmcid: 8371605
doi: 10.1038/s41586-021-03819-2
Alderson, T. R., Pritisanac, I., Kolaric, D., Moses, A. M. & Forman-Kay, J. D. Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2. Proc. Natl Acad. Sci. USA 120, e2304302120 (2023).
pubmed: 37878721
pmcid: 10622901
doi: 10.1073/pnas.2304302120
Alberti, S. Phase separation in biology. Curr. Biol. 27, R1097–R1102 (2017).
pubmed: 29065286
doi: 10.1016/j.cub.2017.08.069
Gao, Y., Li, X., Li, P. & Lin, Y. A brief guideline for studies of phase-separated biomolecular condensates. Nat. Chem. Biol. 18, 1307–1318 (2022).
pubmed: 36400991
doi: 10.1038/s41589-022-01204-2
Tsang, B., Pritišanac, I., Scherer, S. W., Moses, A. M. & Forman-Kay, J. D. Phase separation as a missing mechanism for interpretation of disease mutations. Cell 183, 1742–1756 (2020).
pubmed: 33357399
doi: 10.1016/j.cell.2020.11.050
Gomes, E. & Shorter, J. The molecular language of membraneless organelles. J. Biol. Chem. 294, 7115–7127 (2019).
pubmed: 30045872
doi: 10.1074/jbc.TM118.001192
Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).
pubmed: 28225081
pmcid: 7434221
doi: 10.1038/nrm.2017.7
Wang, H. et al. Temporal and spatial assembly of inner ear hair cell ankle link condensate through phase separation. Nat. Commun. 14, 1657 (2023).
pubmed: 36964137
pmcid: 10039067
doi: 10.1038/s41467-023-37267-5
Lin, L. et al. Phase separation-mediated condensation of Whirlin-Myo15-Eps8 stereocilia tip complex. Cell Rep. 34, 108770 (2021).
pubmed: 33626355
doi: 10.1016/j.celrep.2021.108770
He, Y., Li, J. & Zhang, M. Myosin VII, USH1C, and ANKS4B or USH1G together form condensed molecular assembly via liquid-liquid phase separation. Cell Rep. 29, 974–986.e974 (2019).
pubmed: 31644917
doi: 10.1016/j.celrep.2019.09.027
Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).
pubmed: 26406374
pmcid: 5149108
doi: 10.1016/j.cell.2015.09.015
Murakami, T. et al. ALS/FTD mutation-induced phase transition of FUS liquid droplets and reversible hydrogels into irreversible hydrogels impairs RNP granule function. Neuron 88, 678–690 (2015).
pubmed: 26526393
pmcid: 4660210
doi: 10.1016/j.neuron.2015.10.030
Gopal, P. P., Nirschl, J. J., Klinman, E. & Holzbaur, E. L. Amyotrophic lateral sclerosis-linked mutations increase the viscosity of liquid-like TDP-43 RNP granules in neurons. Proc. Natl Acad. Sci. USA 114, E2466–e2475 (2017).
pubmed: 28265061
pmcid: 5373408
doi: 10.1073/pnas.1614462114
Kim, G. H. & Kwon, I. Distinct roles of hnRNPH1 low-complexity domains in splicing and transcription. Proc. Natl Acad. Sci. USA 118, e2109668118 (2021).
pubmed: 34873036
pmcid: 8685725
doi: 10.1073/pnas.2109668118
Wong, L. E., Kim, T. H., Muhandiram, D. R., Forman-Kay, J. D. & Kay, L. E. NMR experiments for studies of dilute and condensed protein phases: application to the phase-separating protein CAPRIN1. J. Am. Chem. Soc. 142, 2471–2489 (2020).
pubmed: 31898464
doi: 10.1021/jacs.9b12208
Kim, T. H. et al. Interaction hot spots for phase separation revealed by NMR studies of a CAPRIN1 condensed phase. Proc. Natl Acad. Sci. USA 118, e2104897118 (2021).
pubmed: 34074792
pmcid: 8201762
doi: 10.1073/pnas.2104897118
Bierma, J. C. et al. Controlling liquid-liquid phase separation of cold-adapted crystallin proteins from the antarctic toothfish. J. Mol. Biol. 430, 5151–5168 (2018).
pubmed: 30414964
doi: 10.1016/j.jmb.2018.10.023
Gui, X. et al. Structural basis for reversible amyloids of hnRNPA1 elucidates their role in stress granule assembly. Nat. Commun. 10, 2006 (2019).
pubmed: 31043593
pmcid: 6494871
doi: 10.1038/s41467-019-09902-7
Zhou, X. et al. Mutations linked to neurological disease enhance self-association of low-complexity protein sequences. Science 377, eabn5582 (2022).
pubmed: 35771920
pmcid: 9610444
doi: 10.1126/science.abn5582
Niaki, A. G. et al. Loss of dynamic RNA interaction and aberrant phase separation induced by two distinct types of ALS/FTD-linked FUS mutations. Mol. Cell 77, 82–94.e84 (2020).
pubmed: 31630970
doi: 10.1016/j.molcel.2019.09.022
Hofweber, M. et al. Phase separation of FUS is suppressed by its nuclear import receptor and arginine methylation. Cell 173, 706–719.e713 (2018).
pubmed: 29677514
doi: 10.1016/j.cell.2018.03.004
Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).
pubmed: 26317470
doi: 10.1016/j.cell.2015.07.047
Silva, J. L. et al. Targeting biomolecular condensation and protein aggregation against cancer. Chem. Rev. 123, 9094–9138 (2023).
pubmed: 37379327
doi: 10.1021/acs.chemrev.3c00131
Xiang, J. et al. Development of an α-synuclein positron emission tomography tracer for imaging synucleinopathies. Cell 186, 3350–3367.e3319 (2023).
pubmed: 37421950
pmcid: 10527432
doi: 10.1016/j.cell.2023.06.004
Fan, Y. et al. Generic amyloid fibrillation of TMEM106B in patient with Parkinson’s disease dementia and normal elders. Cell Res. 32, 585–588 (2022).
pubmed: 35477998
pmcid: 9160068
doi: 10.1038/s41422-022-00665-3
Raimondi, D. et al. DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Nucleic Acids Res. 45, W201–w206 (2017).
pubmed: 28498993
pmcid: 5570203
doi: 10.1093/nar/gkx390
Fariselli, P., Martelli, P. L., Savojardo, C. & Casadio, R. INPS: predicting the impact of non-synonymous variations on protein stability from sequence. Bioinformatics 31, 2816–2821 (2015).
pubmed: 25957347
doi: 10.1093/bioinformatics/btv291
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–d894 (2019).
pubmed: 30371827
doi: 10.1093/nar/gky1016
Frazer, J. et al. Disease variant prediction with deep generative models of evolutionary data. Nature 599, 91–95 (2021).
pubmed: 34707284
doi: 10.1038/s41586-021-04043-8
Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).
pubmed: 37733863
doi: 10.1126/science.adg7492
Lancaster, A. K., Nutter-Upham, A., Lindquist, S. & King, O. D. PLAAC: a web and command-line application to identify proteins with prion-like amino acid composition. Bioinformatics 30, 2501–2502 (2014).
pubmed: 24825614
pmcid: 4147883
doi: 10.1093/bioinformatics/btu310
Chong, P. A., Vernon, R. M. & Forman-Kay, J. D. RGG/RG motif regions in RNA binding and phase separation. J. Mol. Biol. 430, 4650–4665 (2018).
pubmed: 29913160
doi: 10.1016/j.jmb.2018.06.014
Vernon, R. M. et al. Pi-Pi contacts are an overlooked protein feature relevant to phase separation. Elife 7, e31486 (2018).
pubmed: 29424691
pmcid: 5847340
doi: 10.7554/eLife.31486
Martin, E. W. et al. Valence and patterning of aromatic residues determine the phase behavior of prion-like domains. Science 367, 694–699 (2020).
pubmed: 32029630
pmcid: 7297187
doi: 10.1126/science.aaw8653
Wang, J. et al. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174, 688–699.e616 (2018).
pubmed: 29961577
pmcid: 6063760
doi: 10.1016/j.cell.2018.06.006
Bolognesi, B. et al. A concentration-dependent liquid phase separation can cause toxicity upon increased protein expression. Cell Rep. 16, 222–231 (2016).
pubmed: 27320918
pmcid: 4929146
doi: 10.1016/j.celrep.2016.05.076
Saar, K. L. et al. Learning the molecular grammar of protein condensates from sequence determinants and embeddings. Proc. Natl Acad. Sci. USA 118, e2019053118 (2021).
pubmed: 33827920
pmcid: 8053968
doi: 10.1073/pnas.2019053118
Dignon, G. L., Best, R. B. & Mittal, J. Biomolecular phase separation: from molecular driving forces to macroscopic properties. Annu Rev. Phys. Chem. 71, 53–75 (2020).
pubmed: 32312191
pmcid: 7469089
doi: 10.1146/annurev-physchem-071819-113553
Brangwynne, CliffordP., Tompa, P. & Pappu, RohitV. Polymer physics of intracellular phase transitions. Nat. Phys. 11, 899–904 (2015).
doi: 10.1038/nphys3532
Martin, E. W. & Mittag, T. Relationship of sequence and phase separation in protein low-complexity regions. Biochemistry 57, 2478–2487 (2018).
pubmed: 29517898
doi: 10.1021/acs.biochem.8b00008
Chen, Z. et al. Screening membraneless organelle participants with machine-learning models that integrate multimodal features. Proc. Natl Acad. Sci. USA 119, e2115369119 (2022).
pubmed: 35687670
pmcid: 9214545
doi: 10.1073/pnas.2115369119
Shen, B. et al. Computational screening of phase-separating proteins. Genom. Proteom. Bioinform. 19, 13–24 (2021).
doi: 10.1016/j.gpb.2020.11.003
Monahan, Z. et al. Phosphorylation of the FUS low-complexity domain disrupts phase separation, aggregation, and toxicity. EMBO J. 36, 2951–2967 (2017).
pubmed: 28790177
pmcid: 5641905
doi: 10.15252/embj.201696394
Brandes, N., Goldman, G., Wang, C. H., Ye, C. J. & Ntranos, V. Genome-wide prediction of disease variant effects with a deep protein language model. Nat. Genet. 55, 1512–1522 (2023).
pubmed: 37563329
pmcid: 10484790
doi: 10.1038/s41588-023-01465-0
You, K. et al. PhaSepDB: a database of liquid-liquid phase separation related proteins. Nucleic Acids Res. 48, D354–d359 (2020).
pubmed: 31584089
doi: 10.1093/nar/gkz847
Li, Q. et al. LLPSDB: a database of proteins undergoing liquid-liquid phase separation in vitro. Nucleic Acids Res. 48, D320–d327 (2020).
pubmed: 31906602
doi: 10.1093/nar/gkz778
Wang, X. et al. LLPSDB v2.0: an updated database of proteins undergoing liquid-liquid phase separation in vitro. Bioinformatics 38, 2010–2014 (2022).
pubmed: 35025997
pmcid: 8963276
doi: 10.1093/bioinformatics/btac026
Yamasaki, A. et al. Liquidity is a critical determinant for selective autophagy of protein condensates. Mol. Cell 77, 1163–1175.e1169 (2020).
pubmed: 31995729
doi: 10.1016/j.molcel.2019.12.026
Koehler, L. C. et al. TDP-43 Oligomerization and phase separation properties are necessary for autoregulation. Front. Neurosci. 16, 818655 (2022).
pubmed: 35495061
pmcid: 9048411
doi: 10.3389/fnins.2022.818655
Li, G., Panday, S. K. & Alexov, E. SAAFEC-SEQ: a sequence-based method for predicting the effect of single point mutations on protein thermodynamic stability. Int. J. Mol. Sci. 22, 606 (2021).
pubmed: 33435356
pmcid: 7827184
doi: 10.3390/ijms22020606
Quan, L., Lv, Q. & Zhang, Y. STRUM: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics 32, 2936–2946 (2016).
pubmed: 27318206
pmcid: 5039926
doi: 10.1093/bioinformatics/btw361
Geng, C., Vangone, A., Folkers, G. E., Xue, L. C. & Bonvin, A. iSEE: interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins 87, 110–119 (2019).
pubmed: 30417935
doi: 10.1002/prot.25630
Iqbal, S. et al. Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations. Brief. Bioinform. 22, bbab184 (2021).
pubmed: 34058752
doi: 10.1093/bib/bbab184
Fisher, R. S. & Elbaum-Garfinkle, S. Tunable multiphase dynamics of arginine and lysine liquid condensates. Nat. Commun. 11, 4628 (2020).
pubmed: 32934220
pmcid: 7492283
doi: 10.1038/s41467-020-18224-y
Ukmar-Godec, T. et al. Lysine/RNA-interactions drive and regulate biomolecular condensation. Nat. Commun. 10, 2909 (2019).
pubmed: 31266957
pmcid: 6606616
doi: 10.1038/s41467-019-10792-y
Qin, Z. et al. Deactylation by SIRT1 enables liquid-liquid phase separation of IRF3/IRF7 in innate antiviral immunity. Nat. Immunol. 23, 1193–1207 (2022).
pubmed: 35879450
doi: 10.1038/s41590-022-01269-0
Erdős, G., Pajkos, M. & Dosztányi, Z. IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation. Nucleic Acids Res. 49, W297–w303 (2021).
pubmed: 34048569
pmcid: 8262696
doi: 10.1093/nar/gkab408
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
pubmed: 20354512
pmcid: 2855889
doi: 10.1038/nmeth0410-248
van Mierlo, G. et al. Predicting protein condensate formation using machine learning. Cell Rep. 34, 108705 (2021).
pubmed: 33535034
doi: 10.1016/j.celrep.2021.108705
Hardenberg, M., Horvath, A., Ambrus, V., Fuxreiter, M. & Vendruscolo, M. Widespread occurrence of the droplet state of proteins in the human proteome. Proc. Natl Acad. Sci. USA 117, 33254–33262 (2020).
pubmed: 33318217
pmcid: 7777240
doi: 10.1073/pnas.2007670117
Landrum, M. J. & Kattman, B. L. ClinVar at five years: delivering on the promise. Hum. Mutat. 39, 1623–1630 (2018).
pubmed: 30311387
doi: 10.1002/humu.23641
Landrum, M. J. et al. ClinVar: improvements to accessing data. Nucleic Acids Res. 48, D835–d844 (2020).
pubmed: 31777943
doi: 10.1093/nar/gkz972
Manor, U. et al. Regulation of stereocilia length by myosin XVa and whirlin depends on the actin-regulatory protein Eps8. Curr. Biol. 21, 167–172 (2011).
pubmed: 21236676
pmcid: 3040242
doi: 10.1016/j.cub.2010.12.046
Frittoli, E. et al. The signaling adaptor Eps8 is an essential actin capping protein for dendritic cell migration. Immunity 35, 388–399 (2011).
pubmed: 21835647
pmcid: 3424277
doi: 10.1016/j.immuni.2011.07.007
Yap, L. F. et al. Upregulation of Eps8 in oral squamous cell carcinoma promotes cell migration and invasion through integrin-dependent Rac1 activation. Oncogene 28, 2524–2534 (2009).
pubmed: 19448673
doi: 10.1038/onc.2009.105
Menna, E. et al. Eps8 regulates axonal filopodia in hippocampal neurons in response to brain-derived neurotrophic factor (BDNF). PLoS Biol. 7, e1000138 (2009).
pubmed: 19564905
pmcid: 2696597
doi: 10.1371/journal.pbio.1000138
Hertzog, M. et al. Molecular basis for the dual function of Eps8 on actin dynamics: bundling and capping. PLoS Biol. 8, e1000387 (2010).
pubmed: 20532239
pmcid: 2879411
doi: 10.1371/journal.pbio.1000387
Disanza, A. et al. Regulation of cell shape by Cdc42 is mediated by the synergic actin-bundling activity of the Eps8-IRSp53 complex. Nat. Cell Biol. 8, 1337–1347 (2006).
pubmed: 17115031
doi: 10.1038/ncb1502
Disanza, A. et al. Eps8 controls actin-based motility by capping the barbed ends of actin filaments. Nat. Cell Biol. 6, 1180–1188 (2004).
pubmed: 15558031
doi: 10.1038/ncb1199
Shi, Y., Lin, L., Wang, C. & Zhu, J. Promotion of row 1-specific tip complex condensates by Gpsm2-Gαi provides insights into row identity of the tallest stereocilia. Sci. Adv. 8, eabn4556 (2022).
pubmed: 35687681
pmcid: 9187228
doi: 10.1126/sciadv.abn4556
Brangwynne, C. P. et al. Germline P granules are liquid droplets that localize by controlled dissolution/condensation. Science 324, 1729–1732 (2009).
pubmed: 19460965
doi: 10.1126/science.1172046
Chen, X., Wu, X., Wu, H. & Zhang, M. Phase separation at the synapse. Nat. Neurosci. 23, 301–310 (2020).
pubmed: 32015539
doi: 10.1038/s41593-019-0579-9
Shin, Y. et al. Spatiotemporal control of intracellular phase transitions using light-activated optodroplets. Cell 168, 159–171.e114 (2017).
pubmed: 28041848
doi: 10.1016/j.cell.2016.11.054
Bhat, P., Honson, D. & Guttman, M. Nuclear compartmentalization as a mechanism of quantitative control of gene expression. Nat. Rev. Mol. Cell Biol. 22, 653–670 (2021).
pubmed: 34341548
doi: 10.1038/s41580-021-00387-1
Ong, J. Y. & Torres, J. Z. Phase separation in cell division. Mol. Cell 80, 9–20 (2020).
pubmed: 32860741
pmcid: 7541545
doi: 10.1016/j.molcel.2020.08.007
Wu, X. et al. Vesicle tethering on the surface of phase-separated active zone condensates. Mol. Cell 81, 13–24.e17 (2021).
pubmed: 33202250
doi: 10.1016/j.molcel.2020.10.029
Wu, X., Cai, Q., Feng, Z. & Zhang, M. Liquid-liquid phase separation in neuronal development and synaptic signaling. Dev. Cell 55, 18–29 (2020).
pubmed: 32726576
doi: 10.1016/j.devcel.2020.06.012
Xiao, Q., McAtee, C. K. & Su, X. Phase separation in immune signalling. Nat. Rev. Immunol. 22, 188–199 (2022).
pubmed: 34230650
doi: 10.1038/s41577-021-00572-5
Noda, N. N., Wang, Z. & Zhang, H. Liquid-liquid phase separation in autophagy. J. Cell Biol. 219, e202004062 (2020).
pubmed: 32603410
pmcid: 7401820
doi: 10.1083/jcb.202004062
Su, Q., Mehta, S. & Zhang, J. Liquid-liquid phase separation: orchestrating cell signaling through time and space. Mol. Cell 81, 4137–4146 (2021).
pubmed: 34619090
pmcid: 8541918
doi: 10.1016/j.molcel.2021.09.010
Sanders, D. W. et al. Competing Protein-RNA Interaction Networks Control Multiphase Intracellular Organization. Cell 181, 306–324.e328 (2020).
pubmed: 32302570
pmcid: 7816278
doi: 10.1016/j.cell.2020.03.050
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput Biol. 7, e1002195 (2011).
pubmed: 22039361
pmcid: 3197634
doi: 10.1371/journal.pcbi.1002195
Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412–d419 (2021).
pubmed: 33125078
doi: 10.1093/nar/gkaa913
Zbinden, A., Pérez-Berlanga, M., De Rossi, P. & Polymenidou, M. Phase separation and neurodegenerative diseases: a disturbance in the force. Dev. Cell 55, 45–68 (2020).
pubmed: 33049211
doi: 10.1016/j.devcel.2020.09.014
Liu, Y., Zhang, T., You, N., Wu, S. & Shen, N. MAGPIE: accurate pathogenic prediction for multiple variant types using machine learning approach. Genome Med. 16, 3 (2024).
pubmed: 38185709
pmcid: 10773112
doi: 10.1186/s13073-023-01274-4
Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).
pubmed: 33876751
pmcid: 8053943
doi: 10.1073/pnas.2016239118
Guillen-Boixet, J. et al. RNA-induced conformational switching and clustering of G3BP drive stress granule assembly by condensation. Cell 181, 346–361.e317 (2020).
pubmed: 32302572
pmcid: 7181197
doi: 10.1016/j.cell.2020.03.049
Saito, M. et al. Acetylation of intrinsically disordered regions regulates phase separation. Nat. Chem. Biol. 15, 51–61 (2019).
pubmed: 30531905
doi: 10.1038/s41589-018-0180-7
Stenson, P. D. et al. The Human Gene Mutation Database (HGMD(®)): optimizing its use in a clinical diagnostic or research setting. Hum. Genet. 139, 1197–1207 (2020).
pubmed: 32596782
pmcid: 7497289
doi: 10.1007/s00439-020-02199-3
Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B. & Xavier, R. J. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
pubmed: 32461654
pmcid: 7334197
doi: 10.1038/s41586-020-2308-7
Tanford, C. Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J. Am. Chem. Soc. 84, 4240–4247 (1962).
doi: 10.1021/ja00881a009
Zimmerman, J. M., Eliezer, N. & Simha, R. The characterization of amino acid sequences in proteins by statistical methods. J. Theor. Biol. 21, 170–201 (1968).
pubmed: 5700434
doi: 10.1016/0022-5193(68)90069-6
Nelson D. L., Cox M. M. Lehninger Principles of Biochemistry. (W.H. Freeman and Company, New York, 2005).
Guo, Y., Yu, L., Wen, Z. & Li, M. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res. 36, 3025–3030 (2008).
pubmed: 18390576
pmcid: 2396404
doi: 10.1093/nar/gkn159
Li, X. et al. SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction. BMC Genomics 23, 474 (2022).
pubmed: 35761175
pmcid: 9235110
doi: 10.1186/s12864-022-08687-2
Guo, Z. et al. 3D genome assisted protein–protein interaction prediction. Future Gener. Comput. Syst. 137, 87–96 (2022).
doi: 10.1016/j.future.2022.07.005
Zhu-Hong et al. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinform. 14, S10 (2013). (Suppl 8).
doi: 10.1186/1471-2105-14-S8-S10
Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185, 862–864 (1974).
pubmed: 4843792
doi: 10.1126/science.185.4154.862
Pontius, J., Richelle, J. & Wodak, S. J. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J. Mol. Biol. 264, 121–136 (1996).
pubmed: 8950272
doi: 10.1006/jmbi.1996.0628
Lee, B. K. & Richards, F. M. interpretation of protein structures: estimation of static accessibility. J. Mol. Biol. 55, 379,IN373–400,IN374 (1971).
doi: 10.1016/0022-2836(71)90324-X
Zhou, P., Tian, F. F., Li, B., Wu, S. R. & Li, Z. L. Genetic algorithm-based virtual screening of combinative mode for peptide/protein. Acta Chim. Sin. 64, 691–697 (2006).
Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).
pubmed: 34293799
pmcid: 8387240
doi: 10.1038/s41586-021-03828-1
Piñero, J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48, D845–d855 (2020).
pubmed: 31680165