Gene‐specific features enhance interpretation of mutational impact on acid α‐glucosidase enzyme activity

Abstract

We present a computational model for predicting mutational impact on enzymatic activity of human acid α‐glucosidase (GAA), an enzyme associated with Pompe disease. Using a model that combines features specific to GAA with other general evolutionary and physiochemical features, we made blind predictions of enzymatic activity relative to wildtype human GAA for >300 GAA mutants, as part of the Critical Assessment of Genome Interpretation 5 GAA challenge. We found that gene‐specific features can improve the performance of existing impact prediction tools that mostly rely on general features for pathogenicity prediction. Majority of the poorly predicted mutants that lower wildtype GAA enzyme activity occurred on the surface of the GAA protein. We also found that gene‐specific features were uncorrelated with existing methods and provided orthogonal information for interpreting the origin of pathogenicity, particular in variants that are poorly predicted by existing general methods. Specific variants in GAA, when investigated in the context of its protein structure, suggested gene‐specific information like the disruption of local backbone torsional geometry and disruption of particular sidechain‐sidechain hydrogen bonds as some potential sources for pathogenicity.

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

“…Over all CAGI editions, the plurality of challenges have been on the interpretation of isolated missense variants, and CAGI5 continues that trend. There are assessment, data provider, and participant papers for the prediction of the destabilizing effect of missense mutations in a cancer‐relevant protein (Frataxin, with biophysical measurements of protein stability; Petrosino et al, ; Savojardo, Petrosino et al, ; Strokach, Corbi‐Verge, & Kim, ); on the effect of missense changes in a human calmodulin, assayed using a high‐throughput yeast complementation assay (Zhang et al, ); the effect of missense mutations related to schizophrenia in human Pericentriolar Material 1 ( PCM1 ), using a zebrafish development model (Miller, Wang, & Bromberg, ; Monzon et al, ); the effect of missense mutations in two cancer‐related proteins, PTEN and TPMT , on intracellular protein levels, measured in a high‐throughput assay (Pejaver et al, ); and the effect of missense changes in a monogenic disease related protein, acid alpha‐glucosidase ( GAA ), with measurements of total intracellular enzyme activity (Adhikari, ). Three participant papers describe results on all the missense challenges (Garg & Pal, ; Katsonis & Lichtarge, ; Savojardo, Babbi et al, ).…”

Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency

“…Multiple metrics were calculated to evaluate the performance of our classification models as well as other published models, including sensitivity or true positive rate, specificity or true negative rate, precision or positive predictive value (PPV), negative predictive value (NPV), accuracy, AUC, and Matthew’s correlation coefficient (MCC), all of which have been defined previously ( 20 , 23 ).…”

“…Further, highly cited gene-agnostic prediction models perform worse for membrane proteins (such as OCTN2) compared to soluble proteins ( 19 ). Recently, a number of protein-specific variant effect predictors have been successful in outperforming gene-agnostic models ( 20 – 24 ), though none have resolved issues of genomic inclusion.…”

Computational approaches for predicting variant impact: An overview from resources, principles to applications

“…The phenotype-targeting predictors range widely from common cardiac ( Zhang et al, 2021 ), cancer ( Kaminker et al, 2007 ), and neurodegenerative disease ( Ahmed et al, 2015 ), to rare diseases, such as methylmalonic acidemia ( Peng et al, 2019 ), X-linked incomplete Congenital Stationary Night Blindness ( Sallah et al, 2020 ) and Pompe disease ( Adhikari, 2019 ). More details are presented in Table 2 .…”