Adzhubei I, Jordan DM, Sunyaev SR (2013) Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protocol Human Gen, Chap 7(Unit7):20. https://doi.org/10.1002/0471142905.hg0720s76

Article  Google Scholar 

Agarwal N, Becker A, Jost KL, Haase S, Thakur BK, Brero A, Hardt T, Kudo S, Leonhardt H, Cardoso MC (2011) MeCP2 rett mutations affect large scale chromatin organization. Hum Mol Genet 20(21):4187–4195. https://doi.org/10.1093/hmg/ddr346

Article  CAS  PubMed  Google Scholar 

Alirezaie N, Kernohan KD, Hartley T, Majewski J, Hocking TD (2018) Clinpred: prediction tool to identify disease-relevant nonsynonymous single-nucleotide variants. Am J Hum Genet 103(4):474–483. https://doi.org/10.1016/j.ajhg.2018.08.005

Article  CAS  PubMed  PubMed Central  Google Scholar 

Amir RE, Van den Veyver IB, Wan M, Tran CQ, Francke U, Zoghbi HY (1999) Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat Genet 23(2):185–188. https://doi.org/10.1038/13810

Article  CAS  PubMed  Google Scholar 

Ballestar E, Yusufzai TM, Wolffe AP (2000) Effects of Rett syndrome mutations of the methyl-CpG binding domain of the transcriptional repressor MeCP2 on selectivity for association with methylated DNA. Biochemistry 39(24):7100–7106. https://doi.org/10.1021/bi0001271

Article  CAS  PubMed  Google Scholar 

Brown K, Selfridge J, Lagger S, Connelly J, De Sousa D, Kerr A, Webb S, Guy J, Merusi C, Koerner MV, Bird A (2016) The molecular basis of variable phenotypic severity among common missense mutations causing Rett syndrome. Hum Mol Genet 25(3):558–570. https://doi.org/10.1093/hmg/ddv496

Article  CAS  PubMed  Google Scholar 

Buschdorf JP, Strätling WH (2004) A WW domain binding region in methyl-CpG-binding protein MeCP2: impact on Rett syndrome. J Mol Med (Berl) 82(2):135–143. https://doi.org/10.1007/s00109-003-0497-9

Article  CAS  PubMed  Google Scholar 

Capriotti E, Altman RB (2011) Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics 12(Suppl 4):S3. https://doi.org/10.1186/1471-2105-12-S4-S3

Article  CAS  PubMed  PubMed Central  Google Scholar 

Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R (2013) WS-SNPs&go: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics 14(3(Suppl 3)):S6. https://doi.org/10.1186/1471-2164-14-S3-S6

Article  PubMed  PubMed Central  Google Scholar 

Carter H, Douville C, Stenson PD, Cooper DN, Karchin R (2013) Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 14(Suppl 3):S3. https://doi.org/10.1186/1471-2164-14-S3-S3

Article  PubMed  PubMed Central  Google Scholar 

Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, Pritzel A, Wong LH, Zielinski M, Sargeant T, Schneider RG, Senior AW, Jumper J, Hassabis D, Kohli P, Avsec Ž (2023) Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (New York, ny) 381(6664):eadg7492. https://doi.org/10.1126/science.adg7492

Article  CAS  Google Scholar 

Chicco D, Jurman G (2023) The matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Min 16(1):4. https://doi.org/10.1186/s13040-023-00322-4

Article  PubMed  PubMed Central  Google Scholar 

Choi Y, Chan AP (2015) Provean web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics (Oxford, England) 31(16):2745–2747. https://doi.org/10.1093/bioinformatics/btv195

Article  CAS  PubMed  Google Scholar 

Crockett DK, Ridge PG, Wilson AR, Lyon E, Williams MS, Narus SP, Facelli JC, Mitchell JA (2012) Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting. Genome Med 4(5):48. https://doi.org/10.1186/gm347

Article  PubMed  PubMed Central  Google Scholar 

D’Annessa I, Gandaglia A, Brivio E, Stefanelli G, Frasca A, Landsberger N, Di Marino D (2018) Tyr120Asp mutation alters domain flexibility and dynamics of MeCP2 DNA binding domain leading to impaired DNA interaction: atomistic characterization of a Rett syndrome causing mutation. Biochim Biophys Acta 1862(5):1180–1189. https://doi.org/10.1016/j.bbagen.2018.02.005

Article  CAS  Google Scholar 

Del Conte A, Camagni GF, Clementel D, Minervini G, Monzon AM, Ferrari C, Piovesan D, Tosatto SCE (2024) RING 4.0: faster residue interaction networks with novel interaction types across over 35,000 different chemical structures. Nucleic Acid Res 52(W1):W306–W312. https://doi.org/10.1093/nar/gkae337

Article  PubMed  PubMed Central  Google Scholar 

Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, Liu X (2015) Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet 24(8):2125–2137. https://doi.org/10.1093/hmg/ddu733

Article  CAS  PubMed  Google Scholar 

Dunham AS, Beltrao P, AlQuraishi M (2023) High-throughput deep learning variant effect prediction with sequence UNET. Genome Biol 24(1):110. https://doi.org/10.1186/s13059-023-02948-3

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ehrhart F, Jacobsen A, Rigau M, Bosio M, Kaliyaperumal R, Laros JFJ, Willighagen EL, Valencia A, Roos M, Capella-Gutierrez S, Curfs LMG, Evelo CT (2021) A catalogue of 863 Rett-syndrome-causing MECP2 mutations and lessons learned from data integration. Sci Data 8(1):10. https://doi.org/10.1038/s41597-020-00794-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, Gal Y, Marks DS (2021) Disease variant prediction with deep generative models of evolutionary data. Nature 599(7883):91–95. https://doi.org/10.1038/s41586-021-04043-8

Article  CAS  PubMed  Google Scholar 

Free A, Wakefield RI, Smith BO, Dryden DT, Barlow PN, Bird AP (2001) DNA recognition by the methyl-CpG binding domain of MeCP2. J Biol Chem 276(5):3353–3360. https://doi.org/10.1074/jbc.M007224200

Article  CAS  PubMed  Google Scholar 

Ganakammal SR, Alexov E (2019) Evaluation of performance of leading algorithms for variant pathogenicity predictions and designing a combinatory predictor method: application to Rett syndrome variants. PeerJ 7:e8106. https://doi.org/10.7717/peerj.8106

Article  PubMed  PubMed Central  Google Scholar 

Gandaglia A, Brivio E, Carli S, Palmieri M, Bedogni F, Stefanelli G, Bergo A, Leva B, Cattaneo C, Pizzamiglio L, Cicerone M, Bianchi V, Kilstrup-Nielsen C, D’Annessa I, Di Marino D, D’Adamo P, Antonucci F, Frasca A, Landsberger N (2019) A novel Mecp2Y120D knock-in model displays similar behavioral traits but distinct molecular features compared to the Mecp2-null mouse implying precision medicine for the treatment of Rett syndrome. Mol Neurobiol 56(7):4838–4854. https://doi.org/10.1007/s12035-018-1412-2

Article  CAS  PubMed  Google Scholar 

Ghosh RP, Horowitz-Scherer RA, Nikitina T, Gierasch LM, Woodcock CL (2008) Rett syndrome-causing mutations in human MeCP2 result in diverse structural changes that impact folding and DNA interactions. J Biol Chem 283(29):20523–20534. https://doi.org/10.1074/jbc.M803021200

Article  CAS  PubMed  PubMed Central  Google Scholar 

Good KV, Vincent JB, Ausió J (2021) MeCP2: the genetic driver of Rett syndrome epigenetics. Front Genet 12:620859. https://doi.org/10.3389/fgene.2021.620859

Article  CAS  PubMed  PubMed Central  Google Scholar 

Guy J, Alexander-Howden B, FitzPatrick L, DeSousa D, Koerner MV, Selfridge J, Bird A (2018) A mutation-led search for novel functional domains in MeCP2. Hum Mol Genet 27(14):2531–2545. https://doi.org/10.1093/hmg/ddy159

Article  CAS  PubMed  PubMed Central  Google Scholar 

Heckman LD, Chahrour MH, Zoghbi HY (2014) Rett-causing mutations reveal two domains critical for MeCP2 function and for toxicity in MECP2 duplication syndrome mice. Elife 3:e02676. https://doi.org/10.7554/eLife.02676