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1. Assessing variants of uncertain significance implicated in hearing loss using a comprehensive deafness proteome

   https://doi.org/10.1007/s00439-023-02559-9  

   Tollefson, Mallory R.; Gogal, Rose A.; Weaver, A. Monique; Schaefer, Amanda M.; Marini, Robert J.; Azaiez, Hela; Kolbe, Diana L.; Wang, Donghong; Weaver, Amy E.; Casavant, Thomas L.; et al (April 2023, Human Genetics)

   Abstract Hearing loss is the leading sensory deficit, affecting ~ 5% of the population. It exhibits remarkable heterogeneity across 223 genes with 6328 pathogenic missense variants, making deafness-specific expertise a prerequisite for ascribing phenotypic consequences to genetic variants. Deafness-implicated variants are curated in the Deafness Variation Database (DVD) after classification by a genetic hearing loss expert panel and thorough informatics pipeline. However, seventy percent of the 128,167 missense variants in the DVD are “variants of uncertain significance” (VUS) due to insufficient evidence for classification. Here, we use the deep learning protein prediction algorithm, AlphaFold2, to curate structures for all DVD genes. We refine these structures with global optimization and the AMOEBA force field and use DDGun3D to predict folding free energy differences (∆∆G Fold ) for all DVD missense variants. We find that 5772 VUSs have a large, destabilizing ∆∆G Fold that is consistent with pathogenic variants. When also filtered for CADD scores (> 25.7), we determine 3456 VUSs are likely pathogenic at a probability of 99.0%. Of the 224 genes in the DVD, 166 genes (74%) exhibit one or more missense variants predicted to cause a pathogenic change in protein folding stability. The VUSs prioritized here affect 119 patients (~ 3% of cases) sequenced by the OtoSCOPE targeted panel. Approximately half of these patients previously received an inconclusive report, and reclassification of these VUSs as pathogenic provides a new genetic diagnosis for six patients. 

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2. A Computational Approach: The Functional Effects of Thyroid Peroxidase Variants in Thyroid Cancer and Genetic Disorders

   https://doi.org/10.1200/CCI.23.00140  

   Sobitan, Adebiyi; Gebremedhin, Brhan; Yao, Qiaobin; Xie, Guiqin; Gu, Xinbin; Li, Jiang; Teng, Shaolei (March 2024, JCO Clinical Cancer Informatics)

   PURPOSEThyroid peroxidase (TPO) is essential for the synthesis of thyroid hormones. However, specific mutations render TPO antigenic and prone to autoimmune attacks leading to thyroid cancer, TPO deficiency, and congenital hypothyroidism (CH). Despite technological advancement, most experimental procedures cannot quickly identify the genetic causes of CH nor detect thyroid cancer in the early stages. METHODSWe performed saturated computational mutagenesis to calculate the folding energy changes (∆∆G) caused by missense mutations and analyzed the mutations involved in post-translational modifications (PTMs). RESULTSOur results showed that the functional important missense mutations occurred in the heme peroxidase domain. Through computational saturation mutagenesis, we identified the TPO mutations in G393 and G348 affecting protein stability and PTMs. Our folding energy calculations revealed that seven of nine somatic thyroid cancer mutations destabilized TPO. CONCLUSIONThese findings highlight the impact of these specific mutations on TPO stability, linking them to thyroid cancer and other genetic thyroid-related disorders. Our results show that computational mutagenesis of proteins provides a quick insight into rare mutations causing Mendelian disorders and cancers in humans. 

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3. Structural Analysis of Missense Mutations on the Stability of APOE3 and APOE4

   https://doi.org/10.3390/genes16121509  

   Anthony, Malcolm; Xie, Yixin; O’Neil, Jahn N; Teng, Shaolei (December 2025, Genes)

   Background/Objectives: Apolipoprotein E (APOE) plays a central role in lipid transport and neuronal cholesterol metabolism. Among its three major isoforms (APOE2, APOE3, and APOE4), the APOE4 variant is the strongest genetic risk factor for late-onset Alzheimer’s disease (AD). However, the structural consequences of specific APOE mutations on protein stability remain poorly understood. Methods: Here, we performed computational saturation mutagenesis and molecular dynamics simulations on the non-lipidated N-terminal fragments of APOE3 and APOE4 to examine how missense mutations affect their structural stability. Results: Based on the folding energy (ΔΔG) calculations, mutations G165W and L155W were particularly destabilizing in APOE4. Molecular dynamics analyses showed that these mutations altered local flexibility and compactness, particularly within the helix 4 region, a key structural element for maintaining APOE’s structural integrity. Conclusions: Our findings, which are state dependent and hypothesis generating, highlight isoform-dependent differences in protein stability and identify regions of structural vulnerability within APOE. These insights enhance our understanding of APOE’s conformational dynamics and may inform future studies on its role in neurodegenerative disease mechanisms. 

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4. Prediction of the Effects of Missense Mutations on Human Myeloperoxidase Protein Stability Using In Silico Saturation Mutagenesis

   https://doi.org/10.3390/genes13081412  

   Sobitan, Adebiyi; Edwards, William; Jalal, Md Shah; Kolawole, Ayanfeoluwa; Ullah, Hemayet; Duttaroy, Atanu; Li, Jiang; Teng, Shaolei (August 2022, Genes)

   Myeloperoxidase (MPO) is a heme peroxidase with microbicidal properties. MPO plays a role in the host’s innate immunity by producing reactive oxygen species inside the cell against foreign organisms. However, there is little functional evidence linking missense mutations to human diseases. We utilized in silico saturation mutagenesis to generate and analyze the effects of 10,811 potential missense mutations on MPO stability. Our results showed that ~71% of the potential missense mutations destabilize MPO, and ~8% stabilize the MPO protein. We showed that G402W, G402Y, G361W, G402F, and G655Y would have the highest destabilizing effect on MPO. Meanwhile, D264L, G501M, D264H, D264M, and G501L have the highest stabilization effect on the MPO protein. Our computational tool prediction showed the destabilizing effects in 13 out of 14 MPO missense mutations that cause diseases in humans. We also analyzed putative post-translational modification (PTM) sites on the MPO protein and mapped the PTM sites to disease-associated missense mutations for further analysis. Our analysis showed that R327H associated with frontotemporal dementia and R548W causing generalized pustular psoriasis are near these PTM sites. Our results will aid further research into MPO as a biomarker for human complex diseases and a candidate for drug target discovery. 

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5. Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

   https://doi.org/10.1371/journal.pcbi.1008291  

   Li, Bian; Yang, Yucheng T.; Capra, John A.; Gerstein, Mark B. (November 2020, PLOS Computational Biology)

   Fariselli, Piero (Ed.)

   Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used S sym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between S sym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms. 

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