Biology subjects

Gerasimavicius, L.

Publications and source records attributed to Gerasimavicius, L..

7 recordsLinked to original sources

bioRxiv2025

ER-liquid condensate contacts sequester FAM134B/C and RhoA to govern cell morphology

ER-phagy receptors have elusive physiological functions beyond ER remodelling. Here, we use proximity biotinylation to identify their cytoplasmic interactomes. Secondary CRISPR/Cas9 screening reveals regulators of the prototypical FAM134B/C receptors, which include PRKAR1A, canonically known as a subunit of PKA. PRKAR1A directly binds an amphipathic helix in the otherwise disordered cytoplasmic domain of FAM134B. Super-resolution, FRAP and CLEM imaging reveal novel interorganellar contacts between liquid-like condensates of cAMP-bound PRKAR1A and the ER. Condensates promote clustering of ER-embedded FAM134B/C with LC3B, independently of regulation of PKA by PRKAR1A. Proteomics reveal that cytoplasmic RhoA interacts with FAM134B/C clusters and that sequestration of both these molecules occurs within lysosomes embedded within the proximal condensates. This results in reduced actomyosin contractility and ER-condensate interactions thusly determine cell morphology and cancer cell invasion modality. In summary, ER-condensate contacts mediated by FAM134B/C are novel cellular degradation hubs that coordinate ER and cellular remodelling.

cell biology↗

bioRxiv2025

A knowledge-based distance metric highlights underperformance of variant effect predictors on gain-of-function missense variants

Most current variant effect predictors (VEPs) are less effective at identifying non-loss-of-function (non-LOF) disease variants, which arise due to a gain of function (GOF) or dominant-negative (DN) mechanism. Previously, we showed that Extent of Disease Clustering (EDC), a structure-derived metric, can be used to prioritize putative non-LOF genes due to the tendency of their pathogenic missense variants to cluster. We also introduced a structure-derived spatial distance metric, Spatial Proximity to Disease Variants (SPDV), which proved useful for scoring RyR1 variants. Here, we apply EDC and SPDV across thousands of human disease genes. In comparison with 72 variant effect predictors (VEPs), SPDV exceeds or is at least as effective as half of the tested models at identifying dominant disease variants. When applied to GOF variants, SPDV performs similarly to top VEPs. We show that SPDV performance improves with increasing target EDC values and identify an EDC threshold after which SPDV consistently surpasses all other predictors. Its use as an analytical metric enables the identification of a subset of putative GOF disease genes where current VEP methods underperform relative to SPDV, indicating they may be improved by incorporating spatial distance information.

bioinformatics↗

bioRxiv2025

A structure-guided approach to non-coding variant evaluation for transcription factor binding using AlphaFold 3

Non-coding single-nucleotide variants (SNVs) that alter transcription factor (TF) binding can affect gene expression and contribute to disease. Sequence-based methods can excel at predicting TF binding, but rely on training data and can exhibit TF-specific biases. Here we propose a structure-guided approach for non-coding SNVs, using AlphaFold 3 (AF3) to model TF-DNA complexes and FoldX for downstream physics-based assessment. Benchmarked against SNP-SELEX data for six TFs (SPIB, ELK3, ETV4, SF-1, PAX5 and MEIS2), the FoldX-based strategy showed good agreement with experimental allele preferences. Interestingly, differences in AF3s interface predicted template modelling (ipTM) score aligned even more closely with SNP-SELEX results, generally surpassing energy-based metrics. Application to known disease-associated variants recapitulated most reported effects for TFs including NKX2-5, GATA3 and USF2A-USF1. In these examples, considering both {Delta}ipTM and FoldX energies proved more reliable than either metric alone. While less accurate than state-of-the-art sequence-based methods, this work demonstrates that structural modelling can yield interpretable insights into how non-coding variants influence TF binding. By highlighting both the promise and limitations of AF3 in this context, our study provides a framework for complementary structural evaluation of regulatory variants.

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↗

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↗

bioRxiv2020

Identification of pathogenic missense mutations using protein stability predictors

Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 12 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, considerably outperforms all others in the identification of disease variants. Moreover, we demonstrate that employing absolute energy change scores improves performance of nearly all predictors. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all are inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

bioinformatics↗