Biology subjects

Gureghian, V.

Publications and source records attributed to Gureghian, V..

3 recordsLinked to original sources

bioRxiv2025

Generalizable prediction of liquid-liquid phase separation from protein sequence

Liquid-liquid phase separation (LLPS) is emerging as a fundamental process supporting multiple facets of biological systems. This phenomenon enables the dynamic compartmentalization of biomolecules contributing to a wide range of cellular functions, though in many instances its precise role and evolution remain unclear. Protein phase separation naturally occurs within cells and is prevalent across all species. Despite a recent surge in protein LLPS discovery, current predictive models lack generalizability and fail to identify the full spectrum of phase-separating proteins. To address this shortcoming, we developed Phaseek, a hybrid model integrating contextual sequence encoding with statistical graph representations to score LLPS propensity of amino acid sequences. Phaseek accurately identifies phase-separating proteins across diverse biological contexts, predicting key functional regions and the effects of point mutations. Proteome-wide predictions for 18 species highlight important physicochemical features. Gene Ontology enrichments recapitulate known processes (e.g., nucleic acid binding, nuclear localization, chromatin organization) and suggest novel areas of investigation. Phylogenetic analysis of orthologs further suggests that LLPS is evolutionarily conserved beyond sequence similarity. In addition, we used Phaseek to design de novo phase-separating peptides and achieved a 70% success rate in vivo. Provided with a user-friendly implementation, Phaseek serves as a multipurpose LLPS predictor for advancing both fundamental and applied LLPS research.

molecular biology↗

bioRxiv2024

CodonTransformer: a multispecies codon optimizer using context-aware neural networks

The genetic code is degenerate allowing a multitude of possible DNA sequences to encode the same protein. This degeneracy impacts the efficiency of heterologous protein production due to the codon usage preferences of each organism. The process of tailoring organism-specific synonymous codons, known as codon optimization, must respect local sequence patterns that go beyond global codon preferences. As a result, the search space faces a combinatorial explosion that makes exhaustive exploration impossible. Nevertheless, throughout the diverse life on Earth, natural selection has already optimized the sequences, thereby providing a rich source of data allowing machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all kingdoms of life. The model demonstrates context-awareness thanks to the attention mechanism and bidirectionality of the Transformers we used, and to a novel sequence representation that combines organism, amino acid, and codon encodings. CodonTransformer generates host-specific DNA sequences with natural-like codon distribution profiles and with negative cis-regulatory elements. This work introduces a novel strategy of Shared Token Representation and Encoding with Aligned Multi-masking (STREAM) and provides a state-of-the-art codon optimization framework with a customizable open-access model and a user-friendly interface.

synthetic biology↗

bioRxiv2023

A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Together with targeted therapeutics, senolytics, specifically targeting senescent cancer cells, constitute a promising avenue for improved cancer treatments. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCELL-based integration of bulk and single-cell RNA-seq data identified biological processes perturbed during senescence, and predicts new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

cancer biology↗