Giulio Tesei

α‐Synuclein (aSyn) fibrils in Parkinson's disease are generally represented by their ordered cross‐β cores, although nearly half of the protein remains disordered in the fibrillar state. The unresolved N‐ and C‐terminal segments form a fuzzy coat that is expected to shape the surface encountered by molecular partners. Here, we reconstructed full‐length, brain‐derived aSyn Lewy‐fold fibrils by extending a patient‐derived cryo‐EM core spanning residues 31–100, with disordered termini, and sampling the resulting assemblies with the CALVADOS coarse‐grained force field. The acidic C‐terminal tails established recurrent, transient contacts with solvent‐exposed core motifs, especially β5/P2 and β9/P3, generating a dynamic surface in which aggregation‐prone ladders are intermittently shielded rather than permanently buried. Simulations with two experimentally validated aSyn‐binding peptides, PSMα3 and LL‐37, show that peptide recognition follows a two‐step process. Cationic peptides are first retained by the anionic fuzzy coat and subsequently engage exposed hydrophobic/electrostatic hotspots on the structured core. PSMα3 variants and LL‐37 controls support the importance of positive charge for capture, peptide flexibility for multivalent engagement, and the full‐length fibril architecture for persistent binding. These results present a mechanistic model in which selectivity for aggregated aSyn arises from an emergent mesoscale interface combining a polyelectrolyte brush with recurrent core hotspots. Together, these results establish full‐length, brain‐derived fibril ensembles as a practical framework for understanding ligand recognition at amyloid surfaces.

Many membrane proteins contain intrinsically disordered regions (IDRs) that play key biological roles by providing structural plasticity, harboring sites for post-translational modifications, and mediating protein clustering and phase separation. Residue-level molecular models parameterized against experimental data have provided insights into how IDR sequence controls conformational properties and phase behavior in soluble proteins. Here, we extend this modeling framework to membrane-associated IDRs. We adapt a coarse-grained lipid model, iSoLFv2, for four phospholipids and combine it with CALVADOS, a residue-level model for IDRs and multi-domain proteins. Protein–lipid cross-interaction parameters are calibrated to reproduce predicted insertion and orientation of transmembrane proteins with diverse architectures. We validate the resulting model against Wimley–White free energies of transfer of hydrophobic peptides and against an experimentally refined conformational ensemble of a flexible membrane receptor. Finally, we show the applicability of the model to a membrane-associated assembly of signaling proteins. The model provides a computationally efficient framework for studying conformational ensembles and assembly of proteins at bilayer–water interfaces.

Biomolecular condensates function as membraneless compartments, and some protein condensates can selectively concentrate single-stranded nucleic acids while excluding double-stranded nucleic acids. Understanding how nucleic acid structure affects partitioning into condensates has important implications for nucleic acid activity and function within condensates. Here, we present a set of coarse-grained two-bead-per-nucleotide models for simulations of double-stranded RNA and DNA in the CALVADOS framework. Our models separately represent the backbone and base, and maintain the helical structures using an elastic network potential tuned to capture chain stiffness. For dsRNA, the base stickiness was tuned using experimental data on differential partitioning of single- and double-stranded RNA into Ddx4N1 condensates in order to account for reduced base accessibility upon duplex formation. This RNA structural selectivity varied with the balance of electrostatic and non-electrostatic interactions, as revealed by simulations of condensates of the CAPRIN1 disordered region at varying ionic concentrations and with an R-to-K sequence variant. Finally, we developed parameters for double-stranded DNA using a similar approach. We envision that the CALVADOS models for double-stranded RNA and DNA will be useful for studying co-condensates of proteins and structured nucleic acids.

Disordered proteins are a heterogeneous group of proteins that play a broad range of functions in biology and display conformational properties ranging from compact globules to expanded chains. Here we describe the results of a data-driven approach to derive a scale that represents the propensity of the 20 amino acids to interact with one another relative to water. The scale is based on biophysical experiments on 115 proteins and can be thought of as a “stickiness” (or hydropathy) scale for disordered proteins. We compare the scale to 70 other previously reported hydropathy scales and find that it is closer to four scales related to membrane proteins or the transition temperatures of elastin-like peptides. We illustrate how the scale may be useful to quantify the role of sequence composition and patterning in disordered proteins, and we expect that it may also be used to understand the driving forces for their interactions with other molecules and their evolutionary conservation.

Parkinson’s disease (PD) is characterized by the aggregation of α-synuclein (aSyn) into amyloid fibrils that seed further aggregation and contribute to pathological spreading. Peptides that bind aggregated aSyn are promising therapeutic leads, but their validation is slow, difficult to standardize, and often relies on structural models limited to the ordered cross-β core. Here, we built models of brain-derived full-length aSyn fibrils by extending the Lewy-fold cryo-EM structure with disordered N- and C-terminal segments and sampling the resulting ensembles with the CALVADOS coarse-grained force field. The resulting fibrils display a dynamic fuzzy coat in which the termini, especially the acidic C-terminal tails, form recurrent transient contacts with the core, including the aggregation-prone β5 and β9 motifs. We then used these full-length fibrils in a standardized in silico assay for peptide binding. Simulations of the validated peptide binders PSMα3 and LL-37 reproduced their relative binding behavior and converged on a common mechanism in which electrostatic capture by the anionic fuzzy coat precedes stabilization on recurrent P2 and P3 hotspots within the structured core. Control simulations with monomeric aSyn or core-only fibrils showed that persistent association is lost in the absence of the full-length architecture, providing a mechanism for selectivity toward aggregated species. Finally, screening 123 peptides from aSynPEP-DB using a relative contact-based binding score yielded a ranked set of candidate binders and identified net positive charge as the dominant determinant of sustained association, with hydrophobicity acting as a secondary modulator. Together, these results establish full-length, brain-derived fibril ensembles as a practical framework for understanding ligand recognition at pathological amyloid surfaces and for prioritizing therapeutic peptide binders targeting aggregated aSyn. Significance: Parkinson’s disease is driven by the assembly of α-synuclein into amyloid fibrils, yet most structural models of these aggregates omit the disordered termini that form the fibrillar “fuzzy coat” in vivo . Here we use coarse-grained simulations to reconstruct full-length, brain-derived α-synuclein fibrils and show that this fuzzy coat transiently contacts the Lewy-fold core, reshaping access to cross-β surface motifs. Using these ensembles in a computational assay, we recapitulate the relative binding behavior of validated peptide inhibitors and reveal a two-step mechanism in which cationic and amphipathic peptides are first captured by the anionic fuzzy coat and then engage recurrent core hotspots. This framework explains selective recognition of aggregated α-synuclein and provides a practical route to prioritize therapeutic peptide binders.

Disordered proteins play essential roles in myriad cellular processes, yet their structural characterization remains a major challenge due to their dynamic and heterogeneous nature. Here we present a community-driven initiative to address this problem by advocating a unified framework for determining conformational ensembles of disordered proteins. Our aim is to integrate state-of-the-art experimental techniques with advanced computational methods, including knowledge-based sampling, enhanced molecular dynamics and machine learning models. The modular framework comprises three interconnected components: experimental data acquisition, computational ensemble generation and validation. The systematic development of this framework will ensure the accurate and reproducible determination of conformational ensembles of disordered proteins. We highlight the open challenges necessary to achieve this goal, including force-field accuracy, efficient sampling, and environmental dependence, advocating for collaborative benchmarking and standardized protocols.

Degrons are short protein segments that direct proteins for degradation via the ubiquitin-proteasome system, ensuring the removal of signaling proteins and clearance of misfolded proteins. We have performed a large-scale screen of more than 200,000 30-residue peptides from more than 5000 human cytosolic proteins, achieving 99.7% coverage. We find that 19% of peptides act as strong degrons, 30% as intermediate, and 51% as non-degrons. We identify both known and previously unidentified degradation signals and show that most depend on the E1 ubiquitin-activating enzyme and the proteasome. Structural mapping shows that many degrons are buried and likely become active upon protein unfolding. Training of a machine learning model allowed us to describe the degron properties and predict the cellular abundance of missense variants that operate by forming degrons in exposed and disordered protein regions, thus providing a mechanism of pathogenicity for germline coding variants at such positions.

Disordered proteins can form biomolecular condensates by demixing from their environment, enabling reversible compartmentalisation of cellular components in the form of membraneless organelles. Multivalent interactions are essential for this type of phase separation behaviour, and for disordered proteins, the potential for multivalent interactions is encoded in the sequence composition and patterning. Mutational studies have been instrumental in helping elucidate this sequence grammar by perturbing the amino acid sequence and quantifying the resulting changes in the driving force for phase separation. While such studies have provided a detailed and predictive understanding of the driving forces for phase separation, they strictly do not inform on the nature of the interactions that drive phase separation. Here, we propose using double mutant cycles to explore molecular interactions and their contributions to condensate properties more directly. We explore the applicability of double mutant cycles for different types of interactions in condensates formed by the low-complexity domain of hnRNPA1 using coarse-grained molecular dynamics simulations. We find that the interactions between arginine and tyrosine residues, as well as between aromatic residues, contribute mostly additively to the propensity for phase separation. However, for the interactions between charged residues, we find that—in an interplay with the net charge of the protein—there is a measurable non-additive contribution to the phase separation propensity. Based on our results, we envisage that double mutant cycles could provide additional insights into protein phase separation, thus expanding the understanding of the sequence grammar and the underlying molecular interactions.