Mutations in the LMNA gene cause diseases called laminopathies. LMNA encodes lamins A and C, intermediate filaments with multiple roles at the nuclear envelope. LMNA mutations are frequently single base changes that cause diverse disease phenotypes affecting muscles, nerves, and fat. Disease-associated amino acid substitutions were mapped in silico onto three-dimensional structures of lamin A/C, revealing no apparent genotype–phenotype connections. In silico analyses revealed that seven of nine predicted partner protein binding pockets in the Ig-like fold domain correspond to sites of disease-associated amino acid substitutions. Different amino acid substitutions at the same position within lamin A/C cause distinct diseases, raising the question of whether the nature of the amino acid replacement or genetic background differences contribute to disease phenotypes. Substitutions at R249 in the rod domain cause muscular dystrophies with varying severity. To address this variability, we modeled R249Q and R249W in Drosophila Lamin C, an orthologue of LMNA. Larval body wall muscles expressing mutant Lamin C caused abnormal nuclear morphology and premature death. When expressed in indirect flight muscles, R249W caused a greater number of adults with wing posturing defects than R249Q, consistent with observations that R249W and R249Q cause distinct muscular dystrophies, with R249W more severe. In this case, the nature of the amino acid replacement appears to dictate muscle disease severity. Together, our findings illustrate the utility of Drosophila for predicting muscle disease severity and pathogenicity of variants of unknown significance.

The majority (85%) of nonsyndromic cleft lip with or without cleft palate (nsCL/P) cases occur sporadically, suggesting a role for de novo mutations (DNMs) in the etiology of nsCL/P. To identify high impact protein-altering DNMs that contribute to the risk of nsCL/P, we conducted whole-genome sequencing (WGS) analyses in 130 African case-parent trios (affected probands and unaffected parents). We identified 162 high confidence protein-altering DNMs some of which are based on available evidence, contribute to the risk of nsCL/P. These include novel protein-truncating DNMs in the ACTL6A, ARHGAP10, MINK1, TMEM5 and TTN genes; as well as missense variants in ACAN, DHRS3, DLX6, EPHB2, FKBP10, KMT2D, RECQL4, SEMA3C, SEMA4D, SHH, TP63, and TULP4. Many of these protein-altering DNMs were predicted to be pathogenic. Analysis using mouse transcriptomics data showed that some of these genes are expressed during the development of primary and secondary palate. Gene-set enrichment analysis of the protein-altering DNMs identified palatal development and neural crest migration among the few processes that were significantly enriched. These processes are directly involved in the etiopathogenesis of clefting. The analysis of the coding sequence in the WGS data provides more evidence of the opportunity for novel findings in the African genome.

No abstract

Only ~40% of the human proteome has structural coordinates available from experiment (i.e., X-ray crystallography, NMR spectroscopy, or cryo-EM) or homology modeling with quality templates (i.e., 30% sequence identity or greater), leaving most of the proteome structurally unsolved. Deep learning (DL) methods for predicting protein structure can help close knowledge gaps where experimental and homology models are difficult to obtain. Recent advances in these DL methods have shown promising results in expanding structural coverage to the scale of the entire human proteome, providing researchers with more complete protein structural information. Here, we improve upon an existing DL algorithm for protein structure prediction, the Recurrent Geometric Network (RGN). We first expand the training dataset to include experimental uncertainty data in the form of atomic displacement parameters, then derive a maximum likelihood loss function that incorporates this uncertainty data into model training. Compared to the original RGN, our novel maximum likelihood model improves the rate of convergence of initial model training and ultimately results in more accurate structure prediction according to the root mean square deviation (RMSD) of backbone atoms, the Global Distance Test (GDT), the Global Distance Test High Accuracy (GDT-HA), and the Template-Modeling Score (TM-Score). Our model also predicts structures with more favorable backbone torsions, which provide more accurate starting coordinates for downstream physics-based simulations. Based on these results, our maximum likelihood reformulation provides a framework for improving existing or future machine learning algorithms for protein structure prediction. The augmented dataset, data collection scripts, reformulated RGN source code, and a series of trained models are publicly available at https://github.com/SchniedersLab/likelihood-rgn.

The majority (85%) of nonsyndromic cleft lip with or without cleft palate (nsCL/P) cases occur sporadically, suggesting a role for de novo mutations (DNMs) in the etiology of nsCL/P. To identify high impact DNMs that contribute to the risk of nsCL/P, we conducted whole genome sequencing (WGS) analyses in 130 African case-parent trios (affected probands and unaffected parents). We identified 162 high confidence protein-altering DNMs that contribute to the risk of nsCL/P. These include novel loss-of-function DNMs in the ACTL6A, ARHGAP10, MINK1, TMEM5 and TTN genes; as well as missense variants in ACAN, DHRS3, DLX6, EPHB2, FKBP10, KMT2D, RECQL4, SEMA3C, SEMA4D, SHH, TP63, and TULP4. Experimental evidence showed that ACAN, DHRS3, DLX6, EPHB2, FKBP10, KMT2D, MINK1, RECQL4, SEMA3C, SEMA4D, SHH, TP63, and TTN genes contribute to facial development and mutations in these genes could contribute to CL/P. Association studies have identified TULP4 as a potential cleft candidate gene, while ARHGAP10 interacts with CTNNB1 to control WNT signaling. DLX6, EPHB2, SEMA3C and SEMA4D harbor novel damaging DNMs that may affect their role in neural crest migration and palatal development. This discovery of pathogenic DNMs also confirms the power of WGS analysis of trios in the discovery of potential pathogenic variants.