Biology subjects

Livesey, B. J.

Publications and source records attributed to Livesey, B. J..

11 recordsLinked to original sources

bioRxiv2025

Why variant effect predictors and multiplexed assays agree and disagree

Computational variant effect predictors (VEPs) and multiplexed assays of variant effect (MAVEs) are two key tools for assessing the functional consequences of genetic variants. While their outputs are often concordant, there are also many differences. Here, we analyse missense MAVE data from 37 different human proteins, comparing them to five state-of-the-art VEPs in order to quantify and explain their points of agreement and disagreement. We find that discordance is not random but reflects fundamental differences in how each method infers functional impact. VEPs, which rely heavily on sequence conservation and basic structural features, tend to overcall pathogenicity at buried and hydrophobic residues, while underestimating impact in disordered regions and on charged surface residues. MAVEs, by contrast, capture context-specific mechanisms more accurately, but can miss pathogenic variants when the assay fails to reflect disease biology, or be subject to high levels of experimental noise. By comparing both global patterns and specific clinically relevant variants, we show how protein features, assay design, and variant type shape predictive discordance. Our findings provide a framework for interpreting when and why VEPs and MAVEs diverge and point toward strategies for improving variant interpretation through integrated, mechanism-aware approaches.

bioinformatics↗

bioRxiv2025

Disease-associated genetic variants can cause mutations in tissue-specific protein isoforms

Genetic variants can cause protein-coding mutations that result in disease. Variants are typically interpreted using the reference transcript for a gene. However, most human multi-exon genes encode alternative isoforms. Here, we show that coding exons in alternative isoforms harbour more population variants than exons of reference isoforms, consistent with their reduced evolutionary constraint, and that these variants are more likely to cause nonsynonymous coding mutations. Common and rare disease-associated variants mapping to alternative transcripts can lead to amino acid substitutions predicted to be structurally damaging in the corresponding protein isoform. The alternative transcripts to which disease-associated variants map demonstrate high tissue-specific expression, with many unannotated in reference human genomes, revealed only by long-read RNA-sequencing. As an example, we report an unannotated alternative transcript of the inflammasome regulator DPP9 that is lung epithelium-specific and which harbours a common genetic variant associated with severe COVID-19 and lung fibrosis. The variant causes a p.Leu8Pro missense mutation in an alternative first exon, predicted to disrupt the encoded alpha helix. These findings highlight the importance of considering alternative isoforms, their tissue-specific expression, and full-length transcripts in variant interpretation, with implications for uncovering underappreciated mechanisms of both common and rare disease.

genomics↗

bioRxiv2024

Pervasive ancestry bias in variant effect predictors

Variant effect predictors (VEPs) - computational tools that assess the potential impact of genetic variants - have become increasingly vital for clinical variant interpretation. Currently, most VEPs used in variant prioritisation have been trained on datasets of clinically curated or population-derived variants. These datasets, however, disproportionately represent individuals of European descent. We hypothesised that this bias may lead to unequal VEP performance across different populations. To test this, we evaluated the scoring patterns of 52 VEPs for missense variants across 14 ancestry groups. We observe striking disparities: some VEPs predict a markedly higher proportion of damaging variants in underrepresented populations, such as those of Malay descent, compared to individuals of European ancestry. In contrast, VEPs that do not rely on clinical or population data predict more consistent pathogenicity burdens across ancestry groups. Moreover, we could closely link these discrepancies across methods to biases in training data. Our findings underscore the urgent need to adopt tools that minimise ancestry bias to ensure fairer and more accurate variant effect prediction and genetic diagnoses for all populations.

bioinformatics↗

bioRxiv2024

Variant effect predictor correlation with functional assays is reflective of clinical classification performance

BackgroundUnderstanding the relationship between protein sequence and function is crucial for accurate genetic variant classification. Variant effect predictors (VEPs) play a vital role in deciphering this complex relationship, yet evaluating their performance remains challenging for several reasons including data circularity, where the same or related data is used for training and assessment. High-throughput experimental strategies like deep mutational scanning (DMS) offer a promising solution. ResultsIn this study, we extend upon our previous benchmarking approach, assessing the performance of 97 different VEPs using DMS experiments from 36 different human proteins. In addition, a new pairwise, VEP-centric ranking method reduces the impact of missing predictions on the overall ranking. We observe a remarkably high correspondence between VEP performance in DMS-based benchmarks and clinical variant classification, especially for predictors that have not been directly trained on human clinical variants. ConclusionsOur results suggest that comparing VEP performance against diverse functional assays represents a reliable strategy for assessing their relative performance in clinical variant classification. However, major challenges in clinical interpretation of VEP scores persist, highlighting the need for further research to fully leverage computational predictors for genetic diagnosis. We also address practical considerations for end users in terms of choice of methodology.

bioinformatics↗

bioRxiv2023

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2,700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were particularly detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused molecular gain-of-function effects, including variants in position Ile71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting to moderate evidence to suggest that 1,306 variants are likely benign, and 977, likely pathogenic. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

genetics↗

bioRxiv2023

Correspondence between functional scores from deep mutational scans and predicted effects on protein stability

Many methodologically diverse computational methods have been applied to the growing challenge of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach is to use protein structural information to model the physical impacts of variants and predict their likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of stability predictors in reproducing thermodynamically accurate values and evaluated their ability to distinguish between known pathogenic and benign mutations. Here, we take an alternate approach, and explore how well stability predictor scores correlate with functional impacts derived from deep mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein stability-based tools against mutant protein fitness values from 45 independent DMS datasets, covering 161,441 unique single amino acid variants. We find that FoldX and Rosetta show the strongest correlations with DMS-based functional scores, similar to their previous top performance in distinguishing between pathogenic and benign variants. For both methods, performance is considerably improved when considering intermolecular interactions from protein complex structures, when available. Finally, we also highlight that predicted stability effects show consistently higher correlations with certain DMS experimental phenotypes, particularly those based upon protein abundance, and, in certain cases, can be competitive with other sequence-based variant effect prediction methodologies for predicting functional scores from DMS experiments.

bioinformatics↗

bioRxiv2022

Updated benchmarking of variant effect predictors using deep mutational scanning

Variant effect predictors (VEPs) provide a potential solution to the influx of variants of uncertain clinical significance produced by genome sequencing studies. However, the assessment of VEP performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimum data circularity. The top VEPs are dominated by unsupervised methods including EVE, DeepSequence and ESM-1v, a new protein language model that ranked first overall. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, with EVE, DeepSequence and ESM-1v performing best, further supporting the utility of DMS as an independent benchmark.

bioinformatics↗

bioRxiv2021

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we have investigated the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors underperform on non-LOF mutations, even those based solely on sequence conservation. However, we do find that non-LOF mutations could potentially be identified by their tendency to cluster in three-dimensional space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mutations are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.

bioinformatics↗

bioRxiv2021

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in 3D space, also show a significant disease enrichment. Finally, we show that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

bioinformatics↗

bioRxiv2021

Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects

The prevalence of clonal haematopoiesis of indeterminate potential (CHIP) in healthy individuals increases rapidly from age 60 onwards and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in stem cells that are also drivers of myeloid malignancies. Since mutations in stem cells often drive leukaemia, we hypothesised that stem cell fitness substantially contributes to transformation from CHIP to leukaemia. Stem cell fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. It is currently unknown whether mutations in different CHIP genes lead to distinct fitness advantages that could form the basis for patient stratification. We set out to quantify the fitness effects of CHIP drivers over a 12 year timespan in older age, using longitudinal error-corrected sequencing data. We developed a new method based on drift-induced fluctuation (DIF) filtering to extract fitness effects from longitudinal data, and thus quantify the growth potential of variants within each individual. Our approach discriminates naturally drifting populations of cells and faster growing clones, while taking into account individual mutational context. We show that gene-specific fitness differences can outweigh inter-individual variation and therefore could form the basis for personalised clinical management.

cell biology↗

bioRxiv2019

Using deep mutational scanning data to benchmark computational phenotype predictors and identify pathogenic missense mutations

To deal with the huge number of novel protein-coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant datasets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top-ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO and REVEL based upon their performance in these analyses.

bioinformatics↗