Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classification

Staklinski, Stephen J.; Scheben, Armin; Siepel, Adam; Kilberg, Michael S.

doi:10.1101/2023.10.30.564808

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…This result could potentially be attributed to AlphaMissense's ability to pinpoint functionally crucial sites (instead of simply evaluating the overall evolutionary conservation of a protein) [24]. It is noted that a few recent studies have shown that AlphaMissense can reliably classify subsets of variants that are known to affect molecular function [47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

Andhika,

Biswas,

Hardcastle

et al. 2024

Eur J Hum Genet

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The PAX6 gene encodes a highly-conserved transcription factor involved in eye development. Heterozygous loss-of-function variants in PAX6 can cause a range of ophthalmic disorders including aniridia. A key molecular diagnostic challenge is that many PAX6 missense changes are presently classified as variants of uncertain significance. While computational tools can be used to assess the effect of genetic alterations, the accuracy of their predictions varies. Here, we evaluated and optimised the performance of computational prediction tools in relation to PAX6 missense variants. Through inspection of publicly available resources (including HGMD, ClinVar, LOVD and gnomAD), we identified 241 PAX6 missense variants that were used for model training and evaluation. The performance of ten commonly used computational tools was assessed and a threshold optimization approach was utilized to determine optimal cut-off values. Validation studies were subsequently undertaken using PAX6 variants from a local database. AlphaMissense, SIFT4G and REVEL emerged as the best-performing predictors; the optimized thresholds of these tools were 0.967, 0.025, and 0.772, respectively. Combining the prediction from these top-three tools resulted in lower performance compared to using AlphaMissense alone. Tailoring the use of computational tools by employing optimized thresholds specific to PAX6 can enhance algorithmic performance. Our findings have implications for PAX6 variant interpretation in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

Andhika,

Biswas,

Hardcastle

et al. 2024

Eur J Hum Genet

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show abstract

“…In particular, a pivotal concern arises from the specificities of its missense mutation predictions and the limited accessibility to its dataset. Whereas there are initiatives to make the data accessible through R and Python tools (12)(13)(14)(15)(16), these require a certain level of computational skills, thus significantly restricting the user base. Addressing these voids, we assessed AlphaMissense performance on different datasets using ClinVar data and developed a web resource that notably facilitates streamlined data extraction for research purposes and also offers a visual representation of these predictions within a structural context.…”

Section: Introductionmentioning

confidence: 99%

Lightway access to AlphaMissense data that demonstrates a balanced performance of this missense mutation predictor

Tordai,

Torres,

Csepi

et al. 2023

Preprint

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Single amino acid substitutions can profoundly affect protein folding, dynamics, and function, leading to potential pathological consequences. The ability to discern between benign and pathogenic substitutions is pivotal for therapeutic interventions and research directions. Given the limitations in experimental examination of these variants, AlphaMissense has emerged as a promising predictor of the pathogenicity of single nucleotide polymorphism variants. In our study, we assessed the efficacy of AlphaMissense across several protein groups, such as mitochondrial, housekeeping, transmembrane proteins, and specific proteins like CFTR, using ClinVar data for validation. Our comprehensive evaluation showed that AlphaMissense delivers outstanding performance, with MCC scores predominantly between 0.6 and 0.74. We observed low performance on the CFTR and disordered, membrane-interacting MemMoRF datasets. However, an enhanced performance with CFTR was shown when benchmarked against the CFTR2 database. Our results also emphasize that quality of AlphaFold’s predictions can seriously influence AlphaMissense predictions. Most importantly, AlphaMissense’s consistent capability in predicting pathogenicity across diverse protein groups, spanning both transmembrane and soluble domains was found. Moreover, the prediction of likely-pathogenic labels for IBS and CFTR coupling helix residues emphasizes AlphaMissense’s potential as a tool for pinpointing functionally significant sites. Additionally, to make AlphaMissense predictions more accessible, we have introduced a user-friendly web resource (https://alphamissense.hegelab.org) to enhance the utility of this valuable tool. Our insights into AlphaMissense’s capability, along with this online resource, underscore its potential to significantly aid both research and clinical applications.

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Using computational approaches to enhance the interpretation of missense variants in thePAX6gene

Andhika,

Biswas,

Hardcastle

et al. 2023

Preprint

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PurposeThePAX6gene encodes a highly-conserved transcription factor involved in eye development. Heterozygous loss-of-function variants inPAX6can cause a range of ophthalmic disorders including aniridia. A key molecular diagnostic challenge is that manyPAX6missense changes are presently classified as variants of uncertain significance. While computational tools can be used to assess the effect of genetic alterations, the accuracy of their predictions varies. Here, we evaluated and optimised the performance of computational prediction tools in relation toPAX6missense variants.MethodsThrough inspection of publicly available resources (including HGMD, ClinVar, LOVD and gnomAD), we identified 241PAX6missense variants that were used for model training and evaluation. The performance of ten commonly-used computational tools was assessed and a threshold optimization approach was utilized to determine optimal cut-off values. Validation studies were subsequently undertaken usingPAX6variants from a local database.ResultsAlphaMissense, SIFT4G and REVEL emerged as the best-performing predictors; the optimized thresholds of these tools were 0.967, 0.025, and 0.772, respectively. Combining the prediction from these top-three tools resulted in lower performance compared to using AlphaMissense alone.ConclusionTailoring the use of computational tools by employing optimized thresholds specific toPAX6can enhance algorithmic performance. Our findings have implications forPAX6variant interpretation in clinical settings.

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Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classification

Cited by 5 publications

References 51 publications

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

Lightway access to AlphaMissense data that demonstrates a balanced performance of this missense mutation predictor

Using computational approaches to enhance the interpretation of missense variants in thePAX6gene

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