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Sorokin, O.

Publications and source records attributed to Sorokin, O..

2 recordsLinked to original sources

Hyperbolic stratification of protein intrinsic disorder and structure-mediated interactions in the human protein interactome

Classical models of protein-protein interactions (PPIs) focus on stable, structure-driven interfaces between folded domains, yet recent work highlights the central role of intrinsic disorder and phase separation in shaping dynamic, multivalent associations. How these interaction modes are reflected in the large-scale organization of PPI networks remains unclear. Here, we map the human interactome onto a hyperbolic representation, integrating sequence- and structure-derived features to test whether network organization reflects distinct molecular interaction strategies. Radial position defines a continuum: central proteins are enriched in folded domains, structural complexity, and post-translational modifications, whereas peripheral proteins show increased intrinsic disorder and liquid-liquid phase separation (LLPS) propensity. Angular organization further reveals communities structured by characteristic domain architectures or disorder-linked motifs. Combined analysis of intrinsic disorder, LLPS propensity, and binding-mode diversity uncovers interaction patterns associated with distinct molecular functions and motif repertoires. Condensate-associated proteins span multiple communities while retaining shared short linear motif signatures. Together, these results show that the hyperbolic map links sequence composition, structural organization, and network topology, providing a framework to interpret protein interaction behavior and to guide functional analysis within the human interactome.

systems biology↗

CENTRA: Knowledge-Based Gene Contexuality Graphs Reveal Functional Master Regulators by Centrality and Fractality

Deciphering gene function via context-aware approaches is limited by various means. Especially static gene sets used in enrichment analyses and the lack of single-gene resolution in such analyses restrains the flexible association of genes with specific context. Here, we introduce CENTRA (Centrality-based Exploration of Network Topologies from Regulatory Assemblies), a framework that models gene contextuality through topic-specific gene co-occurrence networks derived from curated gene sets and associated literature. Using Latent Dirichlet Allocation on 12,045 abstracts linked to MSigDB C2 gene sets, we uncovered 27 biological topics and constructed corresponding topic-specific networks that reflect distinct biological states, perturbation conditions, and disease-related regulatory programs. Graph-topological metrics, including centrality, local fractality, and perturbation sensitivity, were computed for each gene to capture structural relevance within these topic-specific contexts. We demonstrate that topological profiles distinguish well-characterized regulators, identify emerging functional candidates, and reveal context-specific roles. Thereby, our framework enables the prioritization of understudied genes by assessing the robustness of their topological signatures across topic-specific networks. To support exploration of these results, we developed a publicly accessible interactive browser application, CENTRA, which enables dynamic navigation of networks and their functional annotations. CENTRA provides an interpretable, scalable framework for investigating context-dependent gene function and hypothesis generation, offering a novel entry point beyond traditional enrichment approaches. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/662180v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@10e4eeorg.highwire.dtl.DTLVardef@125d5b4org.highwire.dtl.DTLVardef@f137aeorg.highwire.dtl.DTLVardef@7ea4e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗