A Saturation Mutagenesis Approach to Understanding PTEN Lipid Phosphatase Activity and Genotype-Phenotype Relationships

Mighell, Taylor L.; Evans-Dutson, Sara; O’Roak, Brian J.

doi:10.1016/j.ajhg.2018.03.018

Cited by 161 publications

(201 citation statements)

References 81 publications

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“…As a source for pathogenic PTEN variants, we extracted 87 pathogenic missense variants from ClinVar 27 (review criteria provided and no conflicting annotations; accessed August 2018). We turned to gnomAD 26 to examine whether there are variants that are sufficiently common in the population to make it likely that they do not cause disease 21,26,28 . Of the 80 PTEN variants we analyse 29 after removing those absent from the experimental and computational data ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Classifying disease-associated variants using measures of protein activity and stability

Jepsen¹,

Fowler

Hartmann‐Petersen³

et al. 2019

Preprint

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Decreased cost of human exome and genome sequencing provides new opportunities for diagnosing genetic disorders, but we need better and more robust methods for interpreting sequencing results including determining whether and by which mechanism a specific missense variants may be pathogenic. Using the protein PTEN (phosphatase and tensin homolog) as an example, we show how recent developments in both experiments and computational modelling can be used to determine whether a missense variant is likely to be pathogenic. One approach relies on multiplexed experiments that enable determination of the effect of all possible individual missense variants in a cellular assay. Another approach is to use computational methods to predict variant effects. We compare two different multiplexed experiments and two computational methods to classify variant effects in PTEN. We distinguish between methods that focus on effects on protein stability and proteinspecific methods that are more directly related to enzyme activity. Our results on PTEN suggest that ~60% of pathogenic variants cause loss of function because they destabilise the folded protein which is subsequently degraded. Methods that quantify a broader range of effects on PTEN activity perform better at predicting variant effects. Either experimental method performs better than the corresponding computational predictions.

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Section: Resultsmentioning

confidence: 99%

Classifying disease-associated variants using measures of protein activity and stability

Jepsen¹,

Fowler

Hartmann‐Petersen³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…We compare the length-normalized probabilities (bits-per-residue) calculated by the autoregressive model to experimental assays for the fitness of mutated biomolecules, using rank correlation (ρ) and area under the receiver-operator curve (AUC), identifying the two groups with a two-component Gaussian mixture model. The model is able to capture the effects of single amino acid deletions on PTEN phosphatase (ρ=0.67, AUC=0.83, N=340; Figure 2b; Mighell et al, 2018), multiple amino acid insertions and deletions in imidazoleglycerol-phosphate (IGP) dehydratase (ρ=0.68, AUC=0.92, N=6102; Figure 2c; Pokusaeva et al, 2018), and insertions and deletions in yeast snoRNA (ρ=0.51, AUC=0.76, N=14736; Puchta et al, 2016).…”

Section: The Generative Model Predicts Experimental Mutation Effectsmentioning

confidence: 99%

Protein Design and Variant Prediction Using Autoregressive Generative Models

Shin

Riesselman

Kollasch

et al. 2019

Preprint

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A major biomedical challenge is the interpretation of genetic variation and the ability to design functional novel sequences. Since the space of all possible genetic variation is enormous, there is a concerted effort to develop reliable methods that can capture genotype to phenotype maps. State-of-art computational methods rely on models that leverage evolutionary information and capture complex interactions between residues. However, current methods are not suitable for a large number of important applications because they depend on robust protein or RNA alignments. Such applications include genetic variants with insertions and deletions, disordered proteins, and functional antibodies. Ideally, we need models that do not rely on assumptions made by multiple sequence alignments. Here we borrow from recent advances in natural language processing and speech synthesis to develop a generative deep neural network-powered autoregressive model for biological sequences that captures functional constraints without relying on an explicit alignment structure. Application to unseen experimental measurements of 42 deep mutational scans predicts the effect of insertions and deletions while matching state-of-art missense mutation prediction accuracies. We then test the model on single domain antibodies, or nanobodies, a complex target for alignment-based models due to the highly variable complementarity determining regions. We fit the model to a naïve llama immune repertoire and generate a diverse, optimized library of 10 5 nanobody sequences for experimental validation. Our results demonstrate the power of the 'alignment-free' autoregressive model in mutation effect prediction and design of traditionally challenging sequence families.

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“…These included well-known oncogenic alleles (KRAS:p.G12D, SMAD4:p.D351G) and PTEN:p.R173H. A literature search confirmed that PTEN:p.R173H was identified as functionally damaging in saturation mutagenesis assays 17 .…”

Section: Case Study 2: Identifying Driver Missense Mutations Among Mementioning

confidence: 99%

OpenCRAVAT, an open source collaborative platform for the annotation of human genetic variation

Pagel

Kim

Moad

et al. 2019

Preprint

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PURPOSEThe modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, and in silico variant effect prediction tools. Because variant prioritization is a multi-factorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, web dependencies that expose protected data, or limited scalability. METHODSWe present OpenCRAVAT, a new open source, scalable decision support system for variant and gene prioritization. We have designed the resource catalog to be open and modular to maximize community and developer involvement, and as a result the catalog is being actively developed and growing every month. Resources made available via the store are well-suited for analysis of cancer, as well as Mendelian and complex diseases. RESULTS High-throughput annotation of genetic variation with OpenCRAVAT 2OpenCRAVAT offers both command line utility and dynamic GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines, and explore results in a richly detailed viewing environment. We present several case studies to illustrate the design of custom workflows to prioritize genes and variants. CONCLUSION OpenCRAVAT is distinguished from similar tools by its capabilities to access and integrate an unprecedented amount of diverse data resources and computational prediction methods, which span germline, somatic, common, rare, coding and noncoding variants. OpenCRAVAT is freely available at https://opencravat.org

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A Saturation Mutagenesis Approach to Understanding PTEN Lipid Phosphatase Activity and Genotype-Phenotype Relationships

Cited by 161 publications

References 81 publications

Classifying disease-associated variants using measures of protein activity and stability

Classifying disease-associated variants using measures of protein activity and stability

Protein Design and Variant Prediction Using Autoregressive Generative Models

OpenCRAVAT, an open source collaborative platform for the annotation of human genetic variation

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