Abstract:The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and provides insight into the functional importance of protein regions and the potential pathogenicity of protein alterations. Here, we comprehensively evaluate the 3D spatial patterns of human germline and somatic variation in 6,604 experimentally derived protein structures and 33,144 computationally derived homology models covering 77% of all human proteins. Using a systematic approach, we quantify differences … Show more
“…Our data also suggests the possible existence of an as-yet unidentified interaction of Menin, as evidenced by the cluster of pathogenic variants lying on the protein surface opposite the JunD binding pocket. Notably, MEN1 has recently been identified as one of the genes exhibiting significant spatial clustering of pathogenic variants (47); our analysis suggests that this clustering is likely to apply both to regions of structural importance, which are buried in the interior of the protein, and to surface regions which form essential interactions with binding partners.…”
Previous studies have shown that thermodynamic analysis of protein structure in silico can discriminate between groups of benign and pathogenic missense variants. However, although structures exist for many human disease-associated proteins, such analysis remains largely unexploited in clinical laboratories. Here, we analysed the predicted effect of 338 known missense variants on the structure of Menin, the MEN1 gene product. Results provided strong discrimination between pathogenic and benign variants, with a threshold of >4 kcal/mol for the predicted change in stability providing a strong indicator of pathogenicity. Subsequent analysis of 7 novel missense variants identified during clinical testing of MEN1 patients showed that all 7 were predicted to destabilise Menin by >4 kcal/mol. We conclude that structural analysis provides a useful tool in understanding the impact of missense variants in MEN1, and that integration of proteomic with genomic data could potentially contribute to the classification of novel variants in this disease. collagenomas and meningiomas (11), resulting in a range of clinical symptoms which may overlap with other diseases of different genetic etiology (12)(13)(14). This overlap presents one of the key problems in assessing genetic variants in cases of MEN1. While a large number of pathogenic variants in MEN1 have been reported, genetic testing continues to uncover novel missense substitutions which require assessment of their potential pathogenicity. A further confounding issue is the often later onset of disease, with reported age-related penetrance of 10-43% at 20 years and 81-94% by 50 years (10, 15), which may lead to apparent non-segregation of a variant with disease within a family pedigree.
“…Our data also suggests the possible existence of an as-yet unidentified interaction of Menin, as evidenced by the cluster of pathogenic variants lying on the protein surface opposite the JunD binding pocket. Notably, MEN1 has recently been identified as one of the genes exhibiting significant spatial clustering of pathogenic variants (47); our analysis suggests that this clustering is likely to apply both to regions of structural importance, which are buried in the interior of the protein, and to surface regions which form essential interactions with binding partners.…”
Previous studies have shown that thermodynamic analysis of protein structure in silico can discriminate between groups of benign and pathogenic missense variants. However, although structures exist for many human disease-associated proteins, such analysis remains largely unexploited in clinical laboratories. Here, we analysed the predicted effect of 338 known missense variants on the structure of Menin, the MEN1 gene product. Results provided strong discrimination between pathogenic and benign variants, with a threshold of >4 kcal/mol for the predicted change in stability providing a strong indicator of pathogenicity. Subsequent analysis of 7 novel missense variants identified during clinical testing of MEN1 patients showed that all 7 were predicted to destabilise Menin by >4 kcal/mol. We conclude that structural analysis provides a useful tool in understanding the impact of missense variants in MEN1, and that integration of proteomic with genomic data could potentially contribute to the classification of novel variants in this disease. collagenomas and meningiomas (11), resulting in a range of clinical symptoms which may overlap with other diseases of different genetic etiology (12)(13)(14). This overlap presents one of the key problems in assessing genetic variants in cases of MEN1. While a large number of pathogenic variants in MEN1 have been reported, genetic testing continues to uncover novel missense substitutions which require assessment of their potential pathogenicity. A further confounding issue is the often later onset of disease, with reported age-related penetrance of 10-43% at 20 years and 81-94% by 50 years (10, 15), which may lead to apparent non-segregation of a variant with disease within a family pedigree.
“…This improvement in classification ability for LOF variants in SCN5A when adding functional density for peak current (0.69 without vs. 0.78 with, p = .01) suggests structure-based features contribute information not contained in other predictive features (Fig. S8) an observation gaining appreciation elsewhere [ [42] , [43] ].…”
Rare variants in the cardiac potassium channel K
V
7.1 (
KCNQ1
) and sodium channel Na
V
1.5 (
SCN5A
) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of Na
V
1.5 (
n
= 356) and K
V
7.1 (
n
= 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported Na
V
1.5 and K
V
7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting Na
V
1.5 peak current (adj. R
2
= 0.27) and K
V
7.1 + KCNE1 half-maximal voltage of activation (adj. R
2
= 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of
SCN5A
variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test
p
= .01). These results suggest structural data can inform predictions of the effect of uncharacterized
SCN5A
and
KCNQ1
variants to provide a deeper understanding of their burden on carriers.
“…Our previous work 34,35 has shown that evaluating the Euclidean distance in 3D space of uncharacterized variants relative to pathogenic and benign variants can aid in variant prioritization. Sivley et al 34,35 developed a program called PathProx that evaluates the relative 3D proximity of a variant to known pathogenic and benign variants.…”
Section: Resultsmentioning
confidence: 99%
“…Our previous work 34,35 has shown that evaluating the Euclidean distance in 3D space of uncharacterized variants relative to pathogenic and benign variants can aid in variant prioritization. Sivley et al 34,35 developed a program called PathProx that evaluates the relative 3D proximity of a variant to known pathogenic and benign variants. To explore the spatial distribution of variants in AP‐4, we mapped onto the AP‐4 homology model four reported pathogenic variants (curated from the literature) 13–16,36 and six variants annotated as likely pathogenic in ClinVar (Table 3).…”
Section: Resultsmentioning
confidence: 99%
“…We quantified the spatial proximity of each residue in the AP‐4 model to annotated pathogenic variants and genetic variants observed in individuals without severe disease using the previously described PathProx score 34,35 . The data set of reported pathogenic variants was derived from clinical cases and variants annotated from ClinVar as either pathogenic or likely pathogenic 32 .…”
Genetic variation in the membrane trafficking adapter protein complex 4 (AP-4) can result in pathogenic neurological phenotypes including microencephaly, spastic paraplegias, epilepsy, and other developmental defects. We lack molecular mechanisms responsible for impaired AP-4 function arising from genetic variation, because AP-4 remains poorly understood structurally. Here, we analyze patterns of AP-4 genetic evolution and conservation to identify regions that are likely important for function and thus more susceptible to pathogenic variation.We map known variants onto an AP-4 homology model and predict the likelihood of pathogenic variation at a given location on the structure of AP-4. We find significant clustering of likely pathogenic variants located at the interface between the β4 and N-μ4 subunits, as well as throughout the C-μ4 subunit. Our work offers an integrated perspective on how genetic and evolutionary forces affect AP-4 structure and function. As more individuals with uncharacterized AP-4 variants are identified, our work provides a foundation upon which their functional effects and disease relevance can be interpreted.
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