Biology subjects

Glavina, J.

Publications and source records attributed to Glavina, J..

4 recordsLinked to original sources

bioRxiv2026

Electrostatic buffering of non-native interactions in the transition state ensemble for binding of intrinsically disordered proteins

Intrinsically disordered protein regions (IDRs) establish highly specific protein-protein interactions that play key roles in cell signaling. Many IDRs adopt an ordered structure upon binding to folded protein domains, but the energy landscape for coupled folding and binding (CFB) is poorly understood. Here, we elucidate the energy landscape for CFB using the LxCxE motif from the human papillomavirus E7 protein (LxCxEWT) as a minimal model system. Kinetic and structural analysis uncovers strong compensatory energetics whereby stabilizing electrostatic interactions counterbalance energetic frustration in the rate-limiting step for CFB. This mechanism, which we refer to as electrostatic compensation, enables a dynamic search for native contacts while preventing complex dissociation. A global analysis reveals energetic frustration in earliest steps of CFB for many IDRs. Electrostatic compensation may be a widespread mechanism evolved to allow fine-tuning of the affinity and specificity of IDR interactions, which dictates functional selection for charge content within IDRs.

biophysics↗

bioRxiv2025

LINKER-Pred: A Public Web Server for Accurate Prediction of Disordered Flexible Linkers in Proteins

Disordered Flexible Linkers (DFLs) are unstructured regions that play critical roles in inter-domain communication and multivalent protein interactions. Despite their biological significance, the accurate identification of DFLs remains challenging due to limited experimental annotations and sparsity of dedicated prediction tools. Here we introduce LINKER-Pred, a publicly available web server featuring two convolutional neural network-based predictors trained on a novel large-scale dataset of linkers connecting folded domains (DLD dataset) and DisProt linkers. LINKER-Pred2 combines ProtTrans and MSA-Transformer embeddings within an ensemble CNN framework, achieving state-of-the-art performance on CAID2 and CAID3 benchmarks. LINKER-Pred-Lite excludes MSA-based features, improving speed while maintaining competitive predictive accuracy. LINKER-Pred predictors offer robust residue-level DFL predictions directly from sequence, providing a scalable solution for DFL annotation across proteomes. The LINKER-Pred web server and associated resources are freely available at https://linkerpred.chemeslab.org/, offering the research community an accessible tool for studying protein disorder and modularity.

bioinformatics↗

bioRxiv2025

Expanding the Landscape of Disordered Flexible Linkers: A Structural and Computational Framework for DLD dataset assembly

Disordered flexible linkers (DFLs) are functional elements found within intrinsically disordered regions that carry out key functions by connecting domains and/or short linear motifs. Understanding the features of DFLs is limited by the lack of comprehensive datasets and accurate predictive models. In this study, we propose a classification for DFLs that includes linkers joining two domains (DLD), a domain and a motif or two short linear motifs. We developed a workflow that allows the systematic identification of DLD-type linkers from protein structures and created a comprehensive dataset known as the DLD dataset. The DLD dataset includes 1640 independent domain linkers (IDLs) which expands currently available linker datasets and annotates related regions such as dependent-domain linkers, intra-domain loops, and termini. Our data collection process integrates missing residue completion and smoothing of short secondary structure stretches enabling to capture a higher number of longer IDLs. We assessed the features of IDLs using t-SNE analysis and protein language model embedding with a CNN-based classifier as well as PCA analysis. IDLs can be distinguished from other disordered and folded protein regions, and their features highly overlap with DisProt Linkers, considered the gold standard for linker annotation. The DLD dataset offers a valuable resource for researchers seeking to investigate the features of disordered flexible linkers and to improve the accuracy and generalizability of DFL predictive models. The DLD dataset is available via an interactive web server at https://dld.chemeslab.org/ where linkers are annotated with sequence and structural features and can be visualized using a structure viewer.

bioinformatics↗

bioRxiv2021

Conformational buffering underlies functional selection in intrinsically disordered protein regions

Many disordered proteins conserve essential functions in the face of extensive sequence variation. This makes it challenging to identify the forces responsible for functional selection. Viruses are robust model systems to investigate functional selection and they take advantage of protein disorder to acquire novel traits. Here, we combine structural and computational biophysics with evolutionary analysis to determine the molecular basis for functional selection in the intrinsically disordered adenovirus early gene 1A (E1A) protein. E1A competes with host factors to bind the retinoblastoma (Rb) protein, triggering early S-phase entry and disrupting normal cellular proliferation. We show that the ability to outcompete host factors depends on the picomolar binding affinity of E1A for Rb, which is driven by two binding motifs tethered by a hypervariable disordered linker. Binding affinity is determined by the spatial dimensions of the linker, which constrain the relative position of the two binding motifs. Despite substantial sequence variation across evolution, the linker dimensions are finely optimized through compensatory changes in amino acid sequence and sequence length, leading to conserved linker dimensions and maximal affinity. We refer to the mechanism that conserves spatial dimensions despite large-scale variations in sequence as conformational buffering. Conformational buffering explains how variable disordered proteins encode functions and could be a general mechanism for functional selection within disordered protein regions.

biophysics↗