Distinguishing between PTEN clinical phenotypes through mutation analysis

Portelli, Stephanie; Barr, Lucy; Sá, Alex Guimarães Cardoso de; Pires, Douglas E. V.; Ascher, David B.

doi:10.1016/j.csbj.2021.05.028

Cited by 23 publications

(16 citation statements)

References 91 publications

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“…We have previously shown that computational tools can be used to understand the consequences of missense mutations on protein structure, providing insight into molecular mechanisms of disease13 14 and further predicting disease outcome 15 16. Towards achieving the same aim in ALS, we initially curated a set (n = 1343) of clinically observed missense mutations across 111 genes from ALSoD8 and the literature (online supplemental table 1).…”

Section: Introductionmentioning

confidence: 99%

Identifying the molecular drivers of ALS-implicated missense mutations

et al. 2022

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BackgroundAmyotrophic lateral sclerosis (ALS) is a progressively fatal, neurodegenerative disease associated with both motor and non-motor symptoms, including frontotemporal dementia. Approximately 10% of cases are genetically inherited (familial ALS), while the majority are sporadic. Mutations across a wide range of genes have been associated; however, the underlying molecular effects of these mutations and their relation to phenotypes remain poorly explored.MethodsWe initially curated an extensive list (n=1343) of missense mutations identified in the clinical literature, which spanned across 111 unique genes. Of these, mutations in genes SOD1, FUS and TDP43 were analysed using in silico biophysical tools, which characterised changes in protein stability, interactions, localisation and function. The effects of pathogenic and non-pathogenic mutations within these genes were statistically compared to highlight underlying molecular drivers.ResultsCompared with previous ALS-dedicated databases, we have curated the most extensive missense mutation database to date and observed a twofold increase in unique implicated genes, and almost a threefold increase in the number of mutations. Our gene-specific analysis identified distinct molecular drivers across the different proteins, where SOD1 mutations primarily reduced protein stability and dimer formation, and those in FUS and TDP-43 were present within disordered regions, suggesting different mechanisms of aggregate formation.ConclusionUsing our three genes as case studies, we identified distinct insights which can drive further research to better understand ALS. The information curated in our database can serve as a resource for similar gene-specific analyses, further improving the current understanding of disease, crucial for the development of treatment strategies.

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Section: Introductionmentioning

confidence: 99%

Identifying the molecular drivers of ALS-implicated missense mutations

et al. 2022

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“…[37][38][39] Furthermore, biophysical simulations combined with structure-based modeling of residue interaction networks have also been used to reveal the functional role of mutation hotspots in molecular communication in some tumor suppressor proteins, [40] regulatory complexes including HSp90 [41], and SARS-CoV-2 Spike Protein, [42] as well as classify PTEN missense variants corresponding to cancer or autism spectrum disorder. [43][44][45] Our recent works have highlighted that biophysics-based and data-driven approaches, (which was not certified by peer review) is the author/funder. All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

Dissecting Mutational Allosteric Effects in Alkaline Phosphatases Associated with Different Hypophosphatasia Phenotypes: An Integrative Computational Investigation

Xiao¹,

Zhou²,

Song³

et al. 2022

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Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization, is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach for large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs is proposed. Allosteric molecular signatures of ALPL mutations were determined using an integrated pipeline of synergistic approaches including sequence and energetic-based analysis, structural topology network modeling and elastic network models. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control and mild and severe HPP phenotypes, and the developed machine learning model suggested that the topological network parameters could serve as is a robust indicator for severe mutations. Molecular dynamics simulations coupled with protein structure network was employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and we found that severe disease-associated mutations have a significantly greater effect on allosteric communications. The results of this study suggested that ALPL mutations associated with mild and severe HPPs tend to have different effects on protein stability on local scale and long-range communications caused by network rewiring. By linking disease phenotypes with allosteric molecular signatures, the proposed integrative computational approach elucidates the complex sequence-structure-allostery relationships of ALPL mutations and dissects the role of allosteric effects in the pathogenesis of HPPs.

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“…Previous studies have explored the relationship of MTR analysis to specific proteins, particularly with respect to the use of segmental intolerance analysis to predict or illuminate the linkage of proteins to human disease 2,3,11–15 . This highlights the fact that proteins containing intolerant segments are sometimes subjected to known disease mutations in other parts of the protein and, more rarely, even within the intolerant segment.…”

Section: Introductionmentioning

confidence: 99%

“…11 Previous studies have explored the relationship of MTR analysis to specific proteins, particularly with respect to the use of segmental intolerance analysis to predict or illuminate the linkage of proteins to human disease. 2,3,[11][12][13][14][15] This highlights the fact that proteins containing intolerant segments are sometimes subjected to known disease mutations in other parts of the protein and, more rarely, even within the intolerant segment. The latter instance occurs when a disease mutation is observed in a patient-derived database such as ClinVar that is too rare to be seen in the sample of the global (mostly healthy) human population represented by the current gnomAD collection of sequences.…”

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Compendium of proteins containing segments that exhibit zero‐tolerance to amino acid variation in humans

et al. 2022

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Genetic missense tolerance ratio (MTR) analysis systematically evaluates all possible segments in a given protein‐encoding transcript found in the human population. This method scores each segment for the number of observed missense variants versus the number of silent mutations in that same segment. An MTR score of 0 indicates that no missense mutations are observed within a given segment. This is indicative of evolutionary purifying selection, which excludes mutations in that segment from the general human population. Here, we conducted MTR analysis on each of the roughly 20,000 protein‐encoding human genes. It was seen that there are 257 genes with at least one 31‐residue encoding segment with MTR = 0 (1.3% of all human genes). The proteins encoded by these 257 genes were tabulated along with information regarding the sequence location of each intolerant segment, the likely function of the protein, and so forth. The most functionally‐enriched family among these proteins is a collection of several dozen proteins that are directly involved in RNA splicing. Some of the other proteins with zero‐tolerance segments have thus far escaped significant characterization. Indeed, while a number of these proteins have previously been genetically linked to human disorders, many have not. We hypothesize that this compendium of human proteins with zero‐tolerance segments can be used to complement disease mutation data as a pointer to genes and proteins that are associated with interesting and underexplored human biology.

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Distinguishing between PTEN clinical phenotypes through mutation analysis

Abstract: Graphical abstract

Cited by 23 publications

References 91 publications

Identifying the molecular drivers of ALS-implicated missense mutations

Identifying the molecular drivers of ALS-implicated missense mutations

Dissecting Mutational Allosteric Effects in Alkaline Phosphatases Associated with Different Hypophosphatasia Phenotypes: An Integrative Computational Investigation

Compendium of proteins containing segments that exhibit zero‐tolerance to amino acid variation in humans

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