Biology subjects

Rubin, A. F.

Publications and source records attributed to Rubin, A. F..

At least 19 recordsLinked to original sources

bioRxiv2026

Massively scaled variant effect mapping across the MAPK pathway reveals the importance of molecular and cellular context

Variation alters inherent protein properties like thermodynamic stability and catalytic activity, and these intrinsic effects are shaped by the complex cellular environment. Signaling cascades typify this complexity, with tens or hundreds of proteins acting in concert through regulated states and interactions to transduce signals. Here, we measure the effect of 186,676 variants across 17 proteins in the RAS-MAPK pathway on protein abundance and pathway activity in human cells under diverse conditions, for a total of 503,099 variant effect measurements. We discovered 14,412 dominant negative variants across 12 proteins, nearly all previously unknown, that disrupt specific domains or interaction interfaces while leaving other functions intact and that are depleted from the human population. Measuring the abundance of 106,642 variants across 10 proteins under HSP90 inhibition showed that HSP90 shapes the effect of 67% of missense variants across the pathway, buffering destabilizing variants in both strong and weak clients. Our variant effect data, collected in the context of an intact proteome, enabled us to assess the functional importance of 395 experimentally and computationally modeled MAPK protein interactions. Using additional mutational scans, we resolved how the promiscuous switch II helix binding surface of KRAS accommodates overlapping interactions with LZTR1 and SOS1, revealing distinct allosteric networks that support each interaction. Thus, we establish a framework for measuring variant effects at unprecedented scale in human cells, illuminating fundamental MAPK pathway signaling mechanisms and demonstrating that cellular context profoundly shapes the effect of most variants.

genomics↗

bioRxiv2026

Tree-aware conditional language modeling recovers mutational patterns of viral evolution

The evolutionary accessibility of a protein mutation depends on the sequence background in which it arises and its lineage history, yet most protein language models estimate sequence plausibility without explicitly considering the ordered sequence changes through which descendants arise. We developed evoPLM-Tree, a tree-aware conditional autoregressive language model that predicts descendant protein sequences from ancestral sequences together with phylogenetically derived evolutionary features. We demonstrated our approach using SARS-CoV-2 spike protein, pairing sequences from early Omicron lineages according to their positions on a mutation-annotated phylogeny, and evaluating model performance on sequence pairs from later lineages. Prompt-masking experiments showed that incorporating phylogenetic context substantially increased reliance on the supplied input information compared with a sequence-only model. Generated descendant sequences accurately reproduced the positional distribution of mutations observed during viral evolution, with strong correlations between predicted and observed mutation-frequency profiles for both the receptor-binding domain (Spearman's {rho} = 0.823) and the full spike protein ({rho} = 0.736). Although prediction accuracy for individual substitutions decreased with increasing evolutionary distance, the model consistently captured aggregate mutational patterns across the spike protein. Model-assigned mutation probabilities were also enriched among substitutions experimentally tolerated in deep mutational scanning assays of Omicron BA.2 receptor-binding domain expression (1.19-fold enrichment) and ACE2 binding (1.04-fold enrichment), despite the model being trained solely on observed ancestor-descendant sequence pairs and associated phylogenetic context features. These results demonstrate that explicitly providing protein language models with phylogenetic context during sequence generation can recover lineage-specific mutational patterns and yields probabilistic predictions consistent with experimentally measured functional constraints. evoPLM-Tree provides a framework for modeling protein evolution along phylogenetic lineages and prioritizing plausible future mutations from genomic surveillance data.

bioinformatics↗

bioRxiv2026

CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis

Deep Mutational Scanning (DMS) experiments generate large volumes of sequencing data that must be processed through multi-step computational pipelines to yield interpretable variant scores. At least twelve dedicated tools have been published for this purpose, yet the diversity of experimental designs, scoring strategies, and software implementations has produced a fragmented landscape in which no single tool accommodates the full range of workflows encountered in practice. Here we present CountESS (Count-based Experiment Scoring and Statistics), an open-source pipeline tool that provides a modular, graphical interface for constructing flexible DMS analysis workflows. CountESS supports a wide range of input formats, barcode translation, HGVS variant calling, and user-defined scoring functions, enabling it to accommodate diverse experimental designs including selection assays, time-series experiments, and bin-based assays such as VAMP-seq. Implemented in Python with DuckDB as a computational backend, the software provides high-performance, memory-efficient processing suitable for large datasets. CountESS is freely available at https://github.com/CountESS-Project/CountESS under the 3-Clause BSD Licence. Supplementary data, including demonstration pipelines and example datasets, are available at https://github.com/CountESS-Project/countess-demo.

bioinformatics↗

bioRxiv2025

Shared and distinct sequence-function signatures define different modes of human TpoR activation

The human thrombopoietin receptor (hTpoR) exists primarily as JAK2-associated monomers that become activated when converted to dimeric forms that support JAK trans-phosphorylation. This can be achieved by several different modes of stimuli, including the natural ligand Tpo, biologic agonists that bind the same site as Tpo, small-molecule drugs that bind the transmembrane (TM) domain, oncogenic mutations in and near the TM domain, and by association with constitutively active JAK V617F or a mutant form of the chaperone protein calreticulin. It is unclear how the dimeric structures induced by synthetic agonists and mutations relate to one another, and whether any of these induce the same active structure as the native ligand Tpo, yet this has important implications both for fundamental cytokine receptor biology and for development of targeted interventions for hTpoR-driven myeloproliferative diseases. Here we used deep mutational scanning (DMS) across the TM and juxtamembrane (JM) regions of hTpoR to extract feature-rich sequence-function signatures across a variety of different activating contexts. While each displayed some unique features, synthetic agonists and activating mutations all exhibited strong dependence on a common TM interface that is consistent with previous models of a left-handed, near-parallel helix dimer with H499 facing lipid. In contrast, Tpo-mediated activation was broadly insensitive to TM-JM substitutions, indicating that it does not rely on the same interface. Modeling with AlphaFold 3 (AF3) consistently yielded a right-handed, "splayed" helix dimer that is close at the extracellular face, contains H499 in the interface and diverges toward the cytosolic face, resting on an intracellular amphipathic JM helix that lies parallel to the membrane, which is also observed in a DMS/AF3 analysis of human erythropoietin receptor. This splayed Tpo-bound dimer could be stably inserted into a lipid bilayer with associated JAK2 using molecular dynamics and is supported by experiments showing that most or all of the TM domain can be replaced by poly-valine, with little effect on Tpo-driven activation but catastrophic effects on responses to synthetic ligands. Our data support at least two different structural modes of hTpoR activation that reconcile prior biochemical models, rationalize patient variants, and inform mechanism-based agonist and antagonist design.

biochemistry↗

bioRxiv2025

Functional evidence for G6PD variant classification from mutational scanning

G6PD deficiency is one of the most common enzyme deficiencies worldwide, and increases the likelihood of adverse reactions to certain drugs and foods. Identifying people at risk is challenging, since most are asymptomatic until they encounter a trigger. This is further complicated since over 60% of 1,559 known genetic variants in G6PD are variants of uncertain significance and thus cannot guide drug prescribing and dosing. To resolve which variants are clinically meaningful and avoid harm from adverse drug reactions, we conducted two high-throughput functional assays: one for G6PD activity, and one for abundance. We measured the function of 9,527 missense, nonsense, and synonymous G6PD variants. The patterns of variant effect on activity and abundance confirmed the importance of structural NADP+ for G6PD activity and abundance, and G6PD dimerization for G6PD activity. Based on the ability of our functional assay scores to accurately classify G6PD variants of known clinical effect, we generated evidence that 4,870 missense variants contribute to G6PD deficiency and 2,245 are unlikely to contribute to G6PD deficiency. Our data can be used to deepen our understanding of G6PD as a protein, and to close the gap in classification for variants of uncertain significance to improve implementation of genetic medicine for G6PD deficiency.

genetics↗

bioRxiv2025

Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects

Genetic variants often produce complex phenotypic effects that confound current assays and predictive models. We developed Variant in situ sequencing (VIS-seq), a pooled, image-based method that measures variant effects on molecular and cellular phenotypes in diverse cell types. Applying VIS-seq to [~]3,000 LMNA and PTEN variants yielded high-dimensional morphological profiles that captured variant-driven changes in protein abundance, localization, activity and cell architecture. We identified gain-of-function LMNA variants that reshape the nucleus and autism-associated PTEN variants that mislocalize. Morphological profiles predicted variant pathogenicity with near-perfect accuracy and distinguished autism-linked from tumor syndrome-linked PTEN variants. Most variants impacted a multidimensional continuum of phenotypes not recapitulated by any single functional readout. By linking protein variation to cell images at scale, we illuminate how variant effects cascade from molecular to subcellular to cell morphological phenotypes, providing a framework for resolving the complexity of variant function.

genomics↗

bioRxiv2025

VEFill: a model for accurate and generalizable deep mutational scanning score imputation across protein domains

BackgroundDeep Mutational Scanning (DMS) assays can systematically assess the effects of amino acid substitutions on protein function. While DMS datasets have been generated for many targets, they often suffer from incomplete variant coverage due to technical constraints, limiting their utility in variant interpretation and downstream analyses. ResultsWe developed VEFill, a gradient boosting model for imputing missing DMS scores across protein domains. VEFill is trained on the Human Domainome 1 dataset, a large, standardized set of DMS experiments using a uniform stability-based assay, and integrates a broad set of additional biologically informative features including ESM-1v sequence embeddings, evolutionary conservation (EVE scores), amino acid substitution matrices, and physicochemical descriptors. The model achieved robust predictive performance (R2 = 0.64, Pearson r = 0.80). It also demonstrated reliable generalization to unseen proteins in other stability-based datasets, while showing weaker performance on activity-based assays. Per-protein models further confirmed VEFills effectiveness under limited-data conditions. A reduced two-feature version using only ESM-1v embeddings and mean DMS scores performed comparably to the full model, suggesting a computationally efficient alternative. However, true zeroshot prediction without positional context remains a challenge, particularly for functionally complex proteins. ConclusionsVEFill offers an interpretable, scalable framework for DMS score imputation, especially effective in stability-focused and sparse-data settings. It enables systematic mutation prioritization and may support the design of efficient experimental libraries for variant effect studies.

bioinformatics↗

bioRxiv2025

Gene-based calibration of high-throughput functional assays for clinical variant classification

High-throughput assays measure a broad range of variant effects on gene function and hold promise for supporting genomic medicine. Current clinical guidelines for rare Mendelian diseases rely on establishing gene-specific score thresholds for each assay that separate pathogenic from benign variants. This introduces inconsistencies and subjectivity, ultimately lacking the rigor of calibration; i.e., mapping a variant score to a probability of pathogenicity. To address this problem, we introduce a semi-supervised framework for calibrating experimental assay data and propose Experimental score CALIBRator (ExCALIBR), a method that jointly models pathogenic, benign, synonymous, and population variants using skew normal mixtures to produce variant-specific probabilities of pathogenicity. Evaluated across 80 datasets from 39 genes, all meeting fit quality criteria, ExCALIBR substantially outperformed existing field standards and was further validated on the All of Us biobank data. Our results demonstrate that calibrated experimental assays generate indispensable evidence that will dramatically reduce variants of uncertain significance.

bioinformatics↗

bioRxiv2024

Generation of a PARPi-sensitive homozygous BRCA1-methylated OVCAR8 cell line using targeted CRISPR gene editing

Up to 17% of high grade serous ovarian carcinomas (HGSOC) harbour BRCA1 promoter methylation (meBRCA1), making them susceptible to treatment with targeted PARP inhibitor (PARPi) therapy. Unfortunately, meBRCA1 loss can be acquired following PARPi or platinum chemotherapy, resulting in BRCA1 re-expression and PARPi resistance. Our understanding of meBRCA1 stability in HGSOC is currently limited, in part due to a paucity of pre-clinical models with homozygous meBRCA1. Herein, we describe the generation of a several OVCAR8 cell line derivatives containing landing pad constructs, for future functional studies, and representing various BRCA1 states, including a homozygous meBRCA1 variant. Our PARPi resistant OVCAR8 has two methylated BRCA1 copies and one unmethylated copy, enabling BRCA1 expression. CRISPR-Cas9 gene editing was used to delete copies of the BRCA1 gene in landing pad-containing clones of this cell line (A6 and H4). We produced one variant with deletion of all BRCA1 copies (H4-53), and another with two copies deleted and only a single methylated gene copy remaining (A6-30 - validated further using nanopore long-read sequencing). These both lacked BRCA1 gene expression and were sensitive to PARPi treatment. The A6-30 line was transplanted into immunocompromised mice to generate a xenograft model that retained homozygous meBRCA1 and demonstrated some response to PARPi in vivo. Thus, using CRISPR gene editing we have created several novel isogenic HGSOC cell line models, including one with homozygous meBRCA1, that will support future studies of meBRCA1 stability and PARPi resistance.

cancer biology↗

bioRxiv2024

Proinsulin C-peptide is a major source of HLA-DQ8 restricted HIPs recognized by human Islet-Infiltrating CD4+ T cells

Type 1 diabetes (T1D) is an autoimmune disease that develops when T cells destroy the pancreatic insulin-producing beta cells that reside in the pancreatic islets. Immune cells, including T cells infiltrate the islets and gradually destroy the beta cells. Human islet-infiltrating CD4+ T cells recognize peptide epitopes derived from proinsulin, particularly C-peptide. Hybrid Insulin peptides (HIPs) are neoepitopes formed by the fusion of two peptides derived from beta-cell granule proteins and are known to be the targets of pathogenic CD4+ T cells in the NOD mouse and human islet-infiltrating CD4+ T cells. Proinsulin is widely recognized as a central antigen in T1D, but its role in forming HIPs is unclear. We developed a method to functionally screen TCRs derived from human islet-infiltrating CD4+ T cells and applied this to the identification of new proinsulin-derived HIPs. We generated a library of 4,488 candidate HIPs formed by fusion of proinsulin fragments and predicted to bind to HLA-DQ8. This library was screened against 109 islet-infiltrating CD4+ T-cell TCRs isolated from four organ donors who had T1D. We identified 13 unique HIPs recognized by 9 different TCRs from two organ donors. HIP specific T-cell avatars responded specifically to a peptide extract from human islets. These new HIPs predominantly stimulated CD4+ T-cell proliferation in PBMCs from people with T1D in contrast to HLA-matched controls. This is the first unbiased functional, islet-infiltrating T-cell based, screen to identify proinsulin derived HIPs. It has revealed many new HIPs and a central role of proinsulin C-peptide in their formation. SUMMARYType 1 diabetes is an autoimmune disease caused by T cells destroying the pancreatic insulin-producing beta cells. The antigens/epitopes seen by disease promoting CD4+ T cells are poorly understood. Hybrid insulin peptides (HIPs) are a new class of CD4+ antigen recognized by pathogenic NOD mouse CD4+ T cells. In humans very few HIPs recognized by human islet-infiltrating CD4+ T cells are known. We show that proinsulin HIPs are recognized by human islet-infiltrating CD4+ T cells from T1D donors and describe 13 new HIPs formed by fusion of proinsulin peptides. This work shows that proinsulin, particularly C-peptide, is a major contributor to the pool HIPs recognized by human islet-infiltrating CD4+ T cells and are therefore central to autoimmunity in T1D.

immunology↗

bioRxiv2024

Multiplex, multimodal mapping of variant effects in secreted proteins

Despite widespread advances in DNA sequencing, the functional consequences of most genetic variants remain poorly understood. Multiplexed Assays of Variant Effect (MAVEs) can measure the function of variants at scale, and are beginning to address this problem. However, MAVEs cannot readily be applied to the [~]10% of human genes encoding secreted proteins. We developed a flexible, scalable human cell surface display method, Multiplexed Surface Tethering of Extracellular Proteins (MultiSTEP), to measure secreted protein variant effects. We used MultiSTEP to study the consequences of missense variation in coagulation factor IX (FIX), a serine protease where genetic variation can cause hemophilia B. We combined MultiSTEP with a panel of antibodies to detect FIX secretion and post-translational modification, measuring a total of 44,816 effects for 436 synonymous variants and 8,528 of the 8,759 possible missense variants. 49.6% of possible F9 missense variants impacted secretion, post-translational modification, or both. We also identified functional constraints on secretion within the signal peptide and for nearly all variants that caused gain or loss of cysteine. Secretion scores correlated strongly with FIX levels in hemophilia B and revealed that loss of secretion variants are particularly likely to cause severe disease. Integration of the secretion and post-translational modification scores enabled reclassification of 63.1% of F9 variants of uncertain significance in the My Life, Our Future hemophilia genotyping project. Lastly, we showed that MultiSTEP can be applied to a wide variety of secreted proteins. Thus, MultiSTEP is a multiplexed, multimodal, and generalizable method for systematically assessing variant effects in secreted proteins at scale.

genomics↗

bioRxiv2024

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

bioinformatics↗

bioRxiv2023

Deep mutational scanning of CYP2C19 reveals a substrate specificity-abundance tradeoff

Cytochrome P450s (CYPs) are a family of enzymes responsible for metabolizing nearly 80% of small molecule drugs. Variants in CYPs can substantially alter drug metabolism, which may result in improper dosing and severe adverse drug reactions. CYPs have low sequence conservation, making it difficult to anticipate whether variant effects measured in one CYP may extend to others based on sequence alone. Even closely related CYPs, like CYP2C9 and its closest homolog CYP2C19, have distinct phenotypic properties despite sharing 92% amino acid sequence identity. Thus, we used Variant Abundance by Massively Parallel sequencing (VAMP-seq) to measure the steady-state protein abundance, a proxy for protein stability, of 7,660 missense variants in CYP2C19 expressed in cultured human cells. Our results confirmed positions and structural features critical for CYP function and revealed how variants at positions conserved across all eukaryotic CYPs influence abundance. We jointly analyzed 4,670 variants whose abundance was measured in both CYP2C19 and CYP2C9, finding that the homologs have different variant abundances in substrate recognition sites within the hydrophobic core, and that substitutions in some regions reduced abundance in CYP2C19 but not CYP2C9. We also measured the abundance of all single and some multiple WT amino acid exchanges between CYP2C19 and CYP2C9. While most exchanges had no effect, substitutions in substrate recognition site 4 (SRS4) reduced abundance in CYP2C19. When nearby amino acids were exchanged in double and triple mutants, we found distinct interactions between the sites in CYP2C19 and CYP2C9, revealing a region that is partially responsible for the difference in thermodynamic stability between the two homologs. Since these positions are also important for determining substrate specificity, there may be an evolutionary tradeoff between stability and altered enzymatic function. Finally, we used our data to analyze 368 previously unannotated human variants, finding that 43% had decreased abundance. Thus, by comparing variant effects between two closely related and important human genes, we have uncovered regions underlying their functional differences and paved the way for a more complete understanding of one of the most versatile families of enzymes.

genetics↗

bioRxiv2023

Mapping MAVE data for use in human genomics applications

The large-scale experimental measures of variant functional assays submitted to MaveDB have the potential to provide key information for resolving variants of uncertain significance, but the reporting of results relative to assayed sequence hinders their downstream utility. The Atlas of Variant Effects Alliance mapped multiplexed assays of variant effect data to human reference sequences, creating a robust set of machine-readable homology mappings. This method processed approximately 2.5 million protein and genomic variants in MaveDB, successfully mapping 98.61% of examined variants and disseminating data to resources such as the UCSC Genome Browser and Ensembl Variant Effect Predictor.

bioinformatics↗

bioRxiv2022

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely-used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

bioinformatics↗

bioRxiv2022

Robust differential composition and variability analysis for multisample cell omics

Cell omics such as single-cell genomics, proteomics and microbiomics allow the characterisation of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression such as cancer and pathogen infection. For cell omic data, no method for differential variability analysis exists, and methods for differential composition analysis only take a few fundamental data properties into account. Here we introduce sccomp, a generalised method for differential composition and variability analyses able to jointly model data count distribution, compositionality, group-specific variability and proportion mean-variability association, with awareness against outliers. Sccomp is an extensive analysis framework that allows realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies showing the inadequacy of the very popular Dirichlet-multinomial modelling and provide mandatory principles for differential variability analysis. We show that sccomp accurately fits experimental data, with a 50% incremental improvement over state-of-the-art algorithms. Using sccomp, we identified novel differential constraints and composition in the microenvironment of primary breast cancer. Significance statementDetermining the composition of cell populations is made possible by technologies like single-cell transcriptomics, CyTOF and microbiome sequencing. Such analyses are now widespread across fields (~800 publications/month, Scopus). However, existing methods for differential abundance do not model all data features, and cell-type/taxa specific differential variability is not yet possible. Increase in the variability of tissue composition and microbial communities is a well-known indicator of loss of homeostasis and disease. A suitable statistical method would enable new types of analyses to identify component-specific loss of homeostasis for the first time. This and other innovations are now possible through our discovery of the mean-variability association for compositional data. Based on this fundamental observation, we have developed a new statistical model, sccomp, that enables differential variability analysis for composition data, improved differential abundance analyses, with cross-sample information borrowing, outlier identification and exclusion, realistic data simulation, based on experimental datasets, cross-study knowledge transfer.

bioinformatics↗

bioRxiv2021

MaveDB v2: a curated community database with over three million variant effects from multiplexed functional assays

A central problem in genomics is understanding the effect of individual DNA variants. Multiplexed Assays of Variant Effect (MAVEs) can help address this challenge by measuring all possible single nucleotide variant effects in a gene or regulatory sequence simultaneously. Here we describe MaveDB v2, which has become the database of record for MAVEs. MaveDB now contains a large fraction of published studies, comprising over two hundred datasets and three million variant effect measurements. We created tools and APIs to streamline data submission and access, transforming MaveDB into a hub for the analysis and dissemination of these impactful datasets.

genomics↗

bioRxiv2020

MaveRegistry: a collaboration platform for multiplexed assays of variant effect

SummaryMultiplexed assays of variant effect (MAVEs) are capable of experimentally testing all possible single nucleotide or amino acid variants in selected genomic regions, generating variant effect maps, which provide biochemical insight and functional evidence to enable more rapid and accurate clinical interpretation of human variation. Because the international community applying MAVE approaches is growing rapidly, we developed the online MaveRegistry platform to catalyze collaboration, reduce redundant efforts, allow stakeholders to nominate targets, and enable tracking and sharing of progress on ongoing MAVE projects. Availability and implementationhttps://registry.varianteffect.org Contactfritz.roth@utoronto.ca

bioinformatics↗