Biology subjects

Tesei, G.

Publications and source records attributed to Tesei, G..

At least 19 recordsLinked to original sources

bioRxiv2026

MEM-CALVADOS: A Residue-Level Model for Flexible Proteins at Membrane Interfaces

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.

biophysics↗

bioRxiv2026

Coarse-grained models for simulations of double-stranded nucleic acids for mixed protein-nucleic acid condensates

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.

biophysics↗

bioRxiv2026

Full-Length Molecular Models of Brain-Derived α-Synuclein Fibrils Reveal a Fuzzy-Coat-Mediated Mechanism for Selective Peptide Binding

Parkinsons disease (PD) is characterized by the aggregation of -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-{beta} 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 {beta}5 and {beta}9 motifs. We then used these full-length fibrils in a standardized in silico assay for peptide binding. Simulations of the validated peptide binders PSM3 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. SignificanceParkinsons disease is driven by the assembly of -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 -synuclein fibrils and show that this fuzzy coat transiently contacts the Lewy-fold core, reshaping access to cross-{beta} 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 -synuclein and provides a practical route to prioritize therapeutic peptide binders.

bioinformatics↗

bioRxiv2026

Single-particle light scattering reveals the dynamic heterogeneity of biomolecular condensates

Biomolecular condensates can be heterogeneous in composition, morphology, and material properties, but resolving this heterogeneity at the level of individual submicrometer condensates remains challenging. Here, we combine off-axis holographic imaging with quantitative light-scattering analysis and particle tracking to simultaneously measure the size, optical properties, interfacial structure, and hydrodynamic mobility of hundreds of individual condensates per minute. Applied to condensates formed by the N-terminal domain of Ddx4 (Ddx4N1), the method reveals two populations with distinct scattering signatures: compact particles with well-defined interfaces and structurally heterogeneous, non-compact assemblies. Their relative abundance varies systematically with ionic strength. Synthetic polymer controls show that pronounced structural heterogeneity is not a generic consequence of phase separation and suggest a role for interaction heterogeneity in promoting competing mesoscale structures. Mass-size scaling and Brownian aggregation simulations further show that aggregation with incomplete fusion of smaller protein-rich units provides a physical model for the non-compact population. Simultaneous optical and diffusion measurements further show that the two populations have distinct hydrodynamic properties. These results demonstrate that chemically homogeneous condensate-forming systems can populate distinct mesoscale structures that are obscured by ensemble-averaged measurements.

biophysics↗

bioRxiv2026

On the Use of Double Mutant Cycles to Probe the Molecular Interactions in Biomolecular Condensates

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.

biophysics↗

bioRxiv2026

A stickiness scale for disordered proteins

Disordered proteins are a heterogeneous group of proteins that play a broad range of functions in biology, and display conformational properties that range from compact globules to expanded chains. We here describe the results of a data-driven approach to derive a scale that represents the propensity of the twenty 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 specific 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 envisage that the new scale will be useful in bioinformatics and machine learning approaches to quantify the role of sequence composition and patterning in disordered proteins, to understand the driving forces for their interactions with other molecules, and their evolutionary conservation.

biophysics↗

bioRxiv2025

A complete map of human cytosolic degrons and their relevance for disease

Degrons are short protein segments that target proteins for degradation via the ubiquitin-proteasome system and thus ensure timely removal of signaling proteins and clearance of misfolded proteins from the intracellular space. Here, we describe a systematic screen for degrons in the human cytosol. We determine degron potency of >200,000 different 30-residue tiles from more than 5,000 cytosolic human proteins with 99.7% coverage. In total, 19.1% of the tiles function as strong degrons, 30.4% as intermediate degrons, while 50.5% did not display degron properties. The vast majority of the degrons are dependent on the E1 ubiquitin-activating enzyme and the proteasome but independent of autophagy. The results reveal both known and novel degron motifs, both internal as well as at the C-terminus. Mapping the degrons onto protein structures, predicted by AlphaFold2, revealed that most of the degrons are located in buried regions, indicating that they only become active upon unfolding or misfolding. Training of a machine learning model allowed us to probe the degron properties further 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.

biochemistry↗

bioRxiv2025

A coarse-grained model for simulations of phosphorylated disordered proteins

Protein phosphorylation is a common and essential post-translational modification that affects biochemical properties and regulates biological activities. Phosphorylation is particularly common for intrinsically disordered proteins and can significantly modulate their function and potential to interact with binding partners. To understand the biophysical origins of how phosphorylation of disordered proteins influences their function, it is valuable to investigate how the modifications lead to changes in their conformational ensembles. Here, we have used a top-down data-driven approach to develop a coarse-grained molecular dynamics model compatible with the CALVADOS protein simulation model to study the effects of serine and threonine phosphorylation on the global structural properties of disordered proteins. We parameterise the model using experimental data on the effects of phosphorylation on global dimensions. By comparing with baseline models and simulations using the phosphomimetics aspartate and glutamate, we show that the effect of phosphorylation on the global dimensions of disordered proteins is mostly driven by the additional charge. We envisage that our model can be applied to study the effects of phosphorylation of disordered proteins at the proteome scale as well as to study the important roles of protein phosphorylation on phase separation.

biophysics↗

bioRxiv2025

A coarse-grained model for disordered proteins under crowded conditions

Macromolecular crowding may strongly affect the dynamics and function of proteins, with intrinsically disordered proteins being particularly sensitive to their crowded environment. To understand the influences of crowding on chain compaction and phase separation behaviour of disordered proteins, both experiments with synthetic crowders--like polyethylene glycol (PEG) and ficoll--and theoretical models and molecular simulation approaches have been applied. Here, we developed a residue-based coarse-grained model for PEG that is compatible with the protein CALVADOS model. To achieve this, we optimised model parameters by comparing simulations with experimental data on single-chain PEG and on PEG-induced compaction of disordered proteins. With our model we show how titrations of PEG can be used to quantify phase separation propensities of proteins that are not prone to phase separate strongly. We illustrate this for both variants of the low-complexity domain of hnRNPA1, and for wild-type and a redesigned variant of -Synuclein. Notably, we observe that the PEG crowding response changes between charge patterning variants of -Synuclein, which is not the case for the variants that vary the number the of aromatic residues in the low-complexity domain of hnRNPA1. We expect that our model will be useful for the interpretation of crowding experiments with disordered proteins, and we envisage it to be a starting point for in-silico explorations of proteins with weak propensities to phase separate.

biophysics↗

bioRxiv2024

A coarse-grained model of disordered RNA for simulations of biomolecular condensates

Protein-RNA condensates are involved in a range of cellular activities. Coarse-grained molecular models of intrinsically disordered proteins have been developed to shed light on and predict single-chain properties and phase separation. An RNA model compatible with such models for disordered proteins would enable the study of complex biomolecular mixtures involving RNA. Here, we present a sequence-independent coarse-grained, two-bead-per-nucleotide model of disordered, flexible RNA based on a hydropathy scale. We parameterize the model, which we term CALVADOS-RNA, using a combination of bottom-up and top-down approaches to reproduce local RNA geometry and intramolecular interactions based on atomistic simulations and in vitro experiments. The model semi-quantitatively captures several aspects of RNA-RNA and RNA-protein interactions. We examined RNA-RNA interactions by comparing calculated and experimental virial coefficients, and non-specific RNA-protein interaction by studying reentrant phase behavior of protein-RNA mixtures. We demonstrate the utility of the model by simulating the formation of mixed condensates consisting of the disordered region of MED1 and RNA chains, and the selective partitioning of disordered regions from transcription factors into these, and compare the results to experiments. Despite the simplicity of our model we show that it captures several key aspects of protein-RNA interactions and may therefore be used as a baseline model to study several aspects of the biophysics and biology of protein-RNA condensates.

biophysics↗

bioRxiv2024

Prediction of phase separation propensities of disordered proteins from sequence

Phase separation is thought to be one possible mechanism governing the selective cellular enrichment of biomolecular constituents for processes such as transcriptional activation, mRNA regulation, and immune signaling. Phase separation is mediated by multivalent interactions of biological macromolecules including intrinsically disordered proteins and regions (IDRs). Despite considerable advances in experiments, theory and simulations, the prediction of the thermodynamics of IDR phase behaviour remains challenging. We combined coarse-grained molecular dynamics simulations and active learning to develop a fast and accurate machine learning model to predict the free energy and saturation concentration for phase separation directly from sequence. We validate the model using both experimental and computational data. We apply our model to all 27,663 IDRs of chain length up to 800 residues in the human proteome and find that 1,420 of these (5%) are predicted to undergo homotypic phase separation with transfer free energies < -2kBT. We use our model to understand the relationship between single-chain compaction and phase separation, and find that changes from charge-to hydrophobicity-mediated interactions can break the symmetry between intra-and inter-molecular interactions. We also analyse the structural preferences at condensate interfaces and find substantial heterogeneity that is determined by the same sequence properties as phase separation. Our work refines the established rules governing the relationships between sequence features and phase separation propensities, and our prediction models will be useful for interpreting and designing cellular experiments on the role of phase separation, and for the design of IDRs with specific phase separation propensities.

biophysics↗

bioRxiv2024

A coarse-grained model for disordered and multi-domain proteins

Many proteins contain more than one folded domain, and such modular multi-domain proteins help expand the functional repertoire of proteins. Because of their larger size and often substantial dynamics, it may be difficult to characterize the conformational ensembles of multi-domain proteins by simulations. Here, we present a coarse-grained model for multi-domain proteins that is both fast and provides an accurate description of the global conformational properties in solution. We show that the accuracy of a one-bead-per-residue coarse-grained model depends on how the interaction sites in the folded domains are represented. Specifically, we find excessive domain-domain interactions if the interaction sites are located at the position of the C atoms. We also show that if the interaction sites are located at the centre of mass of the residue, we obtain good agreement between simulations and experiments across a wide range of proteins. We then optimize our previously described CALVADOS model using this centre-of-mass representation, and validate the resulting model using independent data. Finally, we use our revised model to simulate phase separation of both disordered and multi-domain proteins, and to examine how the stability of folded domains may differ between the dilute and dense phases. Our results provide a starting point for understanding interactions between folded and disordered regions in proteins, and how these regions affect the propensity of proteins to self-associate and undergo phase separation.

biophysics↗

bioRxiv2023

Design of intrinsically disordered protein variants with diverse structural properties

Intrinsically disordered proteins (IDPs) perform a wide range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Designing IDPs with specific structural or functional properties has, however, been diffcult, in part because determining accurate conformational ensembles of IDPs generally requires a combination of computational modelling and experiments. Motivated by recent advancements in effcient physics-based models for simulations of IDPs, we have developed a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs with different levels of compaction and that vary more than 100 fold in their propensity to undergo phase separation, even while keeping a fixed amino acid composition. We experimentally tested designs of variants of the low-complexity domain of hnRNPA1 and find high accuracy in our computational predictions, both in terms of single-chain compaction and propensity to undergo phase separation. We analyze the sequence features that determine changes in compaction and propensity to phase separate and find an overall good agreement with previous findings for naturally occurring sequences. Our general, physics-based method enables the design of disordered sequences with specified conformational properties. Our algorithm thus expands the toolbox for protein design to include also the most flexible proteins and will enable the design of proteins whose functions exploit the many properties afforded by protein disorder.

biophysics↗

bioRxiv2023

Experimental and computational insights into the functional and structural role of the linker region of a two-domain lytic polysaccharide monooxygenase

A considerable number of lytic polysaccharide monooxygenases (LPMOs) and other carbohydrate-active enzymes are modular, with catalytic domains being tethered to additional domains, such as carbohydrate-binding modules, by flexible linkers. While such linkers may affect the structure, function, and stability of the enzyme, their roles remain largely enigmatic, as do the reasons for natural variation in length and sequence. Here, we have explored linker functionality using the two-domain cellulose-active ScLPMO10C from Streptomyces coelicolor as a model system. In addition to investigating the wild-type enzyme, we engineered three linker variants to address the impact of both length and sequence and characterized these using SAXS, NMR, MD simulations, and functional assays. The resulting data revealed that, in the case of ScLPMO10C, linker length is the main determinant of linker conformation and enzyme performance. Both the wild-type and a serine-rich variant, which have the same linker length, demonstrated better performance compared to those with either a shorter or longer linker. A highlight of our findings was the substantial thermostability observed in the serine-rich variant. Importantly, the linker affects thermal unfolding behavior and enzyme stability. In particular, unfolding studies show that the two domains unfold independently when mixed, while the full-length enzyme shows one cooperative unfolding transition, meaning that the impact of linkers in biomass processing enzymes is more complex than mere structural tethering.

biochemistry↗

bioRxiv2023

Conformational ensembles of the human intrinsically disordered proteome: Bridging chain compaction with function and sequence conservation

Intrinsically disordered proteins and regions (collectively IDRs) are pervasive across proteomes in all kingdoms of life, help shape biological functions, and are involved in numerous diseases. IDRs populate a diverse set of transiently formed structures, yet defy commonly held sequence-structure-function relationships. Recent developments in protein structure prediction have led to the ability to predict the three-dimensional structures of folded proteins at the proteome scale, and have enabled large-scale studies of structure-function relationships. In contrast, knowledge of the conformational properties of IDRs is scarce, in part because the sequences of disordered proteins are poorly conserved and because only few have been characterized experimentally. We have developed an efficient model to generate conformational ensembles of IDRs, and thereby to predict their conformational properties from sequence only. Here, we applied this model to simulate all IDRs of the human proteome. Examining conformational ensembles of 29,998 IDRs, we show how chain compaction is correlated with cellular function and localization, including in different types of biomolecular condensates. We train a model to predict compaction from sequence and use this to show conservation of structural properties across orthologs. Our results recapitulate observations from previous studies of individual protein systems, and enable us to study the relationship between sequence, conservation, conformational ensembles, biological function and disease variants at the proteome scale.

biophysics↗

bioRxiv2023

Kinetic stabilization of translation-repression condensates by a neuron-specific microexon

The inclusion of microexons by alternative splicing is frequent in neuronal proteins. The roles of these sequences are in most cases unknown, but changes in their degree of inclusion are associated with neurodevelopmental diseases. We recently found that the decreased inclusion of a 24-nucleotide neuron-specific microexon in CPEB4, an RNA-binding protein that regulates translation through cytoplasmic changes in poly(A) tail length, is linked to idiopathic autism spectrum disorder (ASD). Why this microexon is required and how small changes in its degree of inclusion generate a dominant-negative effect on the expression of ASD-linked genes is not clear. Here we show that neuronal CPEB4 forms condensates that dissolve upon depolarization, a transition associated with a switch from translational repression to activation. Heterotypic intermolecular interactions between the microexon and a cluster of histidine residues kinetically stabilize the condensates by competing with homotypic interactions between clusters, that otherwise lead to the irreversible aggregation of CPEB4. We conclude that the microexon is required in neuronal CPEB4 to preserve the reversible regulation of CPEB4-mediated gene expression in response to neuronal stimulation.

biophysics↗

bioRxiv2023

FRETpredict: A Python package for FRET efficiency predictions using rotamer libraries

Here, we introduce FRETpredict, a Python software program to predict FRET efficiencies from ensembles of protein conformations. FRETpredict uses an established Rotamer Library Approach to describe the FRET probes covalently bound to the protein. The software efficiently operates on large conformational ensembles such as those generated by molecular dynamics simulations to facilitate the validation or refinement of molecular models and the interpretation of experimental data. We demonstrate the performance and accuracy of the software for different types of systems: a relatively structured peptide (polyproline 11), an intrinsically disordered protein (ACTR), and three folded proteins (HiSiaP, SBD2, and MalE). We also describe a general approach to generate new rotamer libraries for FRET probes of interest. FRETpredict is open source (GPLv3) and is available at github.com/KULL-Centre/FRETpredict and as a Python PyPI package at pypi.org/project/FRETpredict. Author SummaryWe present FRETpredict, an open-source software to calculate FRET observables from protein structures. Using a previously developed Rotamer Library Approach, FRETpredict helps place multiple conformations of the selected FRET probes at the labeled sites, and use these to calculate FRET efficiencies. Through several case studies, we illustrate the ability of FRETpredict to interpret experimental results and validate protein conformations. We also explain a methodology for generating new rotamer libraries of FRET probes of interest.

biophysics↗

bioRxiv2022

Improved Predictions of Phase Behaviour of Intrinsically Disordered Proteins by Tuning the Interaction Range

The formation and viscoelastic properties of condensates of intrinsically disordered proteins (IDPs) is dictated by amino acid sequence and solution conditions. Because of the involvement of biomolecular condensates in cell physiology and disease, furthering our understanding of the relationship between protein sequence and phase separation (PS) may have important implications in the formulation of new therapeutic hypotheses. Here, we present CALVADOS 2, a coarse-grained model of IDPs that accurately predicts conformational properties and propensity to undergo PS for diverse sequences and solution conditions. In particular, we systematically study the effect of varying the range of nonionic interactions and use our findings to improve the temperature scale of the model. We further optimize the residue-specific model parameters against experimental data on the conformational properties of 55 proteins, while also leveraging 70 hydrophobicity scales from the literature to avoid overfitting the training data. Extensive testing shows that the model accurately predicts chain compaction and PS propensity for sequences of diverse length and charge patterning, as well as at different temperatures and salt concentrations.

biophysics↗