Decoding Missense Variants by Incorporating Phase Separation Via Machine Learning

Overview

Journal Nat Commun

Specialty Biology

Date 2024 Sep 27

PMID 39333476

Authors

Mofan Feng

Xiaoxi Wei

Xi Zheng

Liangjie Liu

Lin Lin

Manying Xia

Guang He

Yi Shi

Qing Lu

Affiliations

Soon will be listed here.

Abstract

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.

References

Erdos G, Pajkos M, Dosztanyi Z . IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation. Nucleic Acids Res. 2021; 49(W1):W297-W303. PMC: 8262696. DOI: 10.1093/nar/gkab408. View

Vacic V, Iakoucheva L . Disease mutations in disordered regions--exception to the rule?. Mol Biosyst. 2011; 8(1):27-32. PMC: 3307532. DOI: 10.1039/c1mb05251a. View

Karczewski K, Francioli L, Tiao G, Cummings B, Alfoldi J, Wang Q . The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020; 581(7809):434-443. PMC: 7334197. DOI: 10.1038/s41586-020-2308-7. View

Bhat P, Honson D, Guttman M . Nuclear compartmentalization as a mechanism of quantitative control of gene expression. Nat Rev Mol Cell Biol. 2021; 22(10):653-670. DOI: 10.1038/s41580-021-00387-1. View

Martin E, Holehouse A, Peran I, Farag M, Incicco J, Bremer A . Valence and patterning of aromatic residues determine the phase behavior of prion-like domains. Science. 2020; 367(6478):694-699. PMC: 7297187. DOI: 10.1126/science.aaw8653. View

Alberti S . Phase separation in biology. Curr Biol. 2017; 27(20):R1097-R1102. DOI: 10.1016/j.cub.2017.08.069. View

Bolognesi B, Gotor N, Dhar R, Cirillo D, Baldrighi M, Tartaglia G . A Concentration-Dependent Liquid Phase Separation Can Cause Toxicity upon Increased Protein Expression. Cell Rep. 2016; 16(1):222-231. PMC: 4929146. DOI: 10.1016/j.celrep.2016.05.076. View

Li X, Han P, Wang G, Chen W, Wang S, Song T . SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction. BMC Genomics. 2022; 23(1):474. PMC: 9235110. DOI: 10.1186/s12864-022-08687-2. View

Lee B, Richards F . The interpretation of protein structures: estimation of static accessibility. J Mol Biol. 1971; 55(3):379-400. DOI: 10.1016/0022-2836(71)90324-x. View

10.

Quan L, Lv Q, Zhang Y . STRUM: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics. 2016; 32(19):2936-46. PMC: 5039926. DOI: 10.1093/bioinformatics/btw361. View

11.

Patel A, Lee H, Jawerth L, Maharana S, Jahnel M, Hein M . A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation. Cell. 2015; 162(5):1066-77. DOI: 10.1016/j.cell.2015.07.047. View

12.

Silva J, Foguel D, Ferreira V, Vieira T, Marques M, Ferretti G . Targeting Biomolecular Condensation and Protein Aggregation against Cancer. Chem Rev. 2023; 123(14):9094-9138. DOI: 10.1021/acs.chemrev.3c00131. View

13.

Xiang J, Tao Y, Xia Y, Luo S, Zhao Q, Li B . Development of an α-synuclein positron emission tomography tracer for imaging synucleinopathies. Cell. 2023; 186(16):3350-3367.e19. PMC: 10527432. DOI: 10.1016/j.cell.2023.06.004. View

14.

Fariselli P, Martelli P, Savojardo C, Casadio R . INPS: predicting the impact of non-synonymous variations on protein stability from sequence. Bioinformatics. 2015; 31(17):2816-21. DOI: 10.1093/bioinformatics/btv291. View

15.

Martin E, Mittag T . Relationship of Sequence and Phase Separation in Protein Low-Complexity Regions. Biochemistry. 2018; 57(17):2478-2487. PMC: 6476794. DOI: 10.1021/acs.biochem.8b00008. View

16.

Lin L, Shi Y, Wang M, Wang C, Lu Q, Zhu J . Phase separation-mediated condensation of Whirlin-Myo15-Eps8 stereocilia tip complex. Cell Rep. 2021; 34(8):108770. DOI: 10.1016/j.celrep.2021.108770. View

17.

Su Q, Mehta S, Zhang J . Liquid-liquid phase separation: Orchestrating cell signaling through time and space. Mol Cell. 2021; 81(20):4137-4146. PMC: 8541918. DOI: 10.1016/j.molcel.2021.09.010. View

18.

Saito M, Hess D, Eglinger J, Fritsch A, Kreysing M, Weinert B . Acetylation of intrinsically disordered regions regulates phase separation. Nat Chem Biol. 2018; 15(1):51-61. DOI: 10.1038/s41589-018-0180-7. View

19.

Gui X, Luo F, Li Y, Zhou H, Qin Z, Liu Z . Structural basis for reversible amyloids of hnRNPA1 elucidates their role in stress granule assembly. Nat Commun. 2019; 10(1):2006. PMC: 6494871. DOI: 10.1038/s41467-019-09902-7. View

20.

Landrum M, Kattman B . ClinVar at five years: Delivering on the promise. Hum Mutat. 2018; 39(11):1623-1630. PMC: 11567647. DOI: 10.1002/humu.23641. View