Biology subjects

Lindorff-Larsen, K.

Publications and source records attributed to Lindorff-Larsen, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Computational characterization of the xanthan gum glycosyltransferase GumK

The activity of GT-B glycosyltransferases depends on their conformational flexibility and high substrate specificity, but the molecular basis of these features is still not well defined. The GT70 family contains a single well-characterized enzyme, GumK, a glucuronosyltransferase from Xanthomonas campestris required for xanthan gum biosynthesis. Here, we applied multiscale molecular simulations and sequence analysis to probe GumK dynamics and substrate specificity. We show that GumK undergoes twisting and bending motions constrained by interdomain contacts and modulated by membrane anchoring. Acceptor-substrate binding within an amphiphilic clamp promotes opening, whereas donor-substrate binding stabilizes closure, defining a substrate-dependent catalytic cycle. Specificity for UDP-glucuronate is mediated by a conserved electrostatic environment centered on Lys307 and a hydrophobic triad that orients the sugar moiety. On the acceptor side, the binding site selectively accommodates polyisoprenyl carriers up to three isoprene units in length and wraps around the substrate, constraining the trisaccharide moiety in a catalytically competent conformation. Comparative analysis highlights GumK-specific motifs that distinguish it from homologous GTs. This work provides mechanistic insight into the GT70 family and the dynamic behavior of GT-B enzymes, establishing principles for the rational engineering of GumK to modify the monosaccharide composition of xanthan gum. Author summaryGlycosyltransferases are enzymes that build many of the sugars and polysaccharides essential for life. One of them, called GumK, is responsible for a key step in producing xanthan gum -- a natural polymer widely used as a thickener in food and industrial materials. Despite its importance, how GumK works at the molecular level has remained unclear. In our study, we used multiscale computer simulation strategies to explore the dynamics of this enzyme and how it interacts with the cell membrane and its sugar substrates. We found that GumK acts like a flexible clamp that opens and closes as it binds two different sugar molecules through specific residues that may control the enzymes selectivity. We also discovered how the surrounding membrane helps the enzyme remain in the correct orientation to perform its function. By identifying the molecular details that determine GumKs selectivity and flexibility, our work provides a foundation for modifying this enzyme to produce xanthan gum with new properties. More broadly, our findings help explain how a large family of related enzymes controls sugar transfer in living organisms, with potential applications in biotechnology and materials science.

biophysics↗

bioRxiv2025

A Bayesian approach to interpret time-resolved experiments using molecular simulations

Time-resolved experiments can provide unique insights into dynamical processes such as protein folding, ligand binding, and many other molecular processes. These experiments are, however, difficult to interpret at the molecular level because they generally report on signals that are averaged over multiple configurational states, and because they often report on processes that are well beyond what can be studied using most simulation methods. Here we describe an approach to use molecular simulations to model and interpret time-resolved experiments. The method, which we term trBME (time-resolved Bayesian/Maximum Entropy), is based on combining a model for the dynamics of the system, with 3D structural models of the protein that can be compared to the experiments. We illustrate the utility of the model using synthetic time-resolved small-angle X-ray scattering data and show how it can be used to extract detailed information from the experimental data on the process of protein unfolding.

biophysics↗

bioRxiv2025

StruCloze: A Unified Framework for Backmapping and Inpainting of Biomolecules

Atomistic resolution is essential for understanding biomolecular structure and function, yet coarse-grained (CG) models remain indispensable for simulating large and dynamic systems. Reconstructing accurate all-atom structures from CG representations, particularly across varied CG schemes and biomolecular types, remains a fundamental challenge. Moreover, flexible or disordered regions may well suffer from failure in structure modeling, making inpainting missing regions another challenging task. Here, we present StruCloze, a deep learning framework for reconstructing atomistic structures from CG models and inpainting missing regions for both proteins and nucleic acids. StruCloze generalizes across various CG levels and biomolecule types on single pretraining, with minimal fine-tuning required for optimal performance on specific representations. It achieves state-of-the-art accuracy in reconstructing both protein and nucleic acid structures, demonstrates superior transferability and speed compared to existing methods. Leveraging masked learning strategy, StruCloze also excels at inpainting structurally missing regions in structures, offering a practical tool for structural refinement and integrative modeling. Our framework provides a general solution for bridging reduced or incomplete representations with full atomistic detail of biomolecular structures, enabling rapid local structure prediction and further analysis on system dynamics.

bioinformatics↗

bioRxiv2025

Comprehensive degron mapping in human transcription factors

Gene expression is regulated by the targeted degradation of transcription factors through the ubiquitin-proteasome system. Transcription factors destined for degradation are recognized by E3 ubiquitin-protein ligases through short motifs termed degrons, embedded within the sequence. In this study, we systematically map degrons in all 1,626 human transcription factors. We find thousands of both known and previously unidentified degrons and characterize their sequence properties. Degrons placed within exposed and intrinsically disordered regions regulate the cellular abundance of the transcription factors, while the most common somatic mutations that are linked to skin cutaneous melanoma lead to unfolding and exposure of a buried degron in zinc fingers. We present examples of compartment specific degrons and demonstrate that variant effects in transcription factors correlate with degron potency. Finally, we show that while >60% of all predicted transcriptional activation domains overlap with strong degrons, acidic residues within the remaining transactivating regions counter the degron potency.

biochemistry↗

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

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2

The flexibility of protein side chains is an essential contributor of conformational entropy and affects processes such as folding, stability and molecular interactions. Structure determination experiments and prediction tools such as AlphaFold generally fail to capture or represent the conformational heterogeneity of proteins in solution. Experiments can be used to study side-chain flexibility, but cannot be applied at scale, and most prediction methods focus on reconstructing the minimum free energy state rather than an ensemble representing side-chain configurations. Here, we use AlphaFold2 and its internal side-chain representations to develop AF2{chi} that predicts side-chain {chi}-angle distributions and generates structural ensembles. We extensively benchmark AF2{chi} predictions using experimental NMR 3J-couplings and s2 order parameters, as well as dihedral angle distributions derived from collections of experimental structures, demonstrating the accuracy of AF2{chi} in generating accurate side-chain ensembles. We also compare the accuracy of AF2{chi} with molecular dynamics simulations and recent machine learning models aimed to generate conformational ensembles and show that AF2{chi} provides state-of-the-art accuracy orders of magnitude faster than molecular simulations. With its speed and accuracy, AF2{chi} offers a strong complementary option to simulations and rotamer library approaches, making it particularly valuable for applications such as protein design, ligand docking and interpretation of biophysical experiments.

biophysics↗

bioRxiv2025

Supervised learning of protein variant effects across large-scale mutagenesis datasets

The increasing availability of data from multiplexed assays of variant effects (MAVEs) enables supervised model training against large quantities of experimental data to learn sequence-function relationships. Variant effect scores from MAVEs can, however, be influenced by the experimental method to create experiment-to-experiment differences in the mapping from molecular level-variant effects to MAVE readout, which presents a challenge for supervised learning across datasets. We here propose a framework for performing supervised learning with MAVE data that takes the influence of the experimental protocol into account, thus enabling variant effects to be learned across datasets produced in independent experiments. We apply the framework to train a model against variant effect scores collected with VAMP-seq, a MAVE technique that quantifies the steady-state cellular abundance of protein variants. We show that mapping variant abundance to VAMP-seq readout in a dataset-specific manner during model training improves the learned abundance model and moreover allows the learned model to predict variant effects on an interpretable scale. Our work highlights the importance of validating MAVE results with low-throughput methods to facilitate MAVE score interpretation and supervised model training.

biophysics↗

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

Proteome-scale quantification of the interactions driving condensate formation of intrinsically disordered proteins

Intrinsically disordered protein regions facilitate cellular organization through phase separation into biomolecular condensates. However, the molecular interactions driving this process remain poorly understood because of experimental limitations. Here, we advance experimental throughput by several orders of magnitude by developing Condensate Partitioning by mRNA-Display (CPmD). The method allows analysis of partitioning of hundred thousand peptides derived from the disordered proteome into reconstituted condensates. Our results demonstrate that the amino acid content, rather than specific sequence, primarily determines partitioning behavior. Importantly, quantification of the partitioning energies of peptides allows us to decipher the molecular grammar of the relevant interactions, allowing accurate prediction of the formation of condensates of diverse full-length disordered protein regions. The results reveal how physicochemical properties of disordered regions encode biological functions through formation of biomolecular condensates.

biophysics↗

bioRxiv2024

Optimized Protein-Excipient Interactions in theMartini 3 Force Field

The high doses of drugs required for biotherapeutics, such as monoclonal antibodies (mAbs), and the small volumes that can be administered to patients by subcutaneous injections pose challenges due to high concentration formulations. The addition of excipients, such as arginine and glutamate, to high concentration protein formulations can increase solubility and reduce the tendency of protein particle formation. Molecular dynamics (MD) simulations can provide microscopic insights into the mode of action of excipients in mAb formulations but require large system sizes and long time scales that are currently beyond reach at the fully atomistic level. Computationally efficient coarse-grained models such as the Martini 3 force field can tackle this challenge but require careful parametrization, testing, and validation. This study extends the popular Martini 3 force field towards realistic protein-excipient interactions of arginine and glutamate excipients, using the Fab domains of the therapeutic mAbs trastuzumab and omalizumab as model systems. A novel all-atom to coarse-grained mapping of the amino acid excipients is introduced, which explicitly captures the zwitterionic character of the backbone. The Fab-excipient interactions of arginine and glutamate are characterized concerning molecular contacts with the Fabs at the single-residue level. The Martini 3 simulations are compared with results from all-atom simulations as a reference. Our findings reveal an overestimation of Fab-excipient contacts with the default interaction parameters of Martini 3, suggesting a too strong attraction between protein residues and excipients. Therefore, we reparametrized the protein-excipient interaction parameters in Martini 3 against all-atom simulations. The excipient interactions obtained with the new Martini 3 mapping and Lennard-Jones (LJ) interaction parameters, coined Martini 3-exc, agree closely with the all-atom reference data. This work presents an improved parameter set for mAb-arginine and mAb-glutamate interactions in the Martini 3 coarse-grained force field, a key step towards large-scale coarse-grained MD simulations of high-concentration mAb formulations and the stabilizing effects of excipients.

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

Tag with Caution - How protein tagging influences the formation of condensates

Fluorescent proteins and peptide tags are essential tools in cellular biology, but can alter the biochemical properties of target proteins. Biomolecular condensates, which have emerged as key principles of cellular organization, are suggested to provide robustness to cells, yet they can also respond sensitively to small changes in environmental conditions--or tagging of their components, as our findings suggest. Here, we investigated the effects of sixteen widely used tags on condensate formation in various model organisms, in vitro, in cells and by computational modelling. We find that tagging strongly influenced condensation for some proteins, while others remained unaffected. Effects varied, with some tags enhancing and others decreasing condensation, and depended on the protein being tagged. Coarse-grained simulations suggest that the charge of the fluorescent protein tags is a critical factor modulating condensation behavior. Together, our results underscore the importance of rigorous experimental design and interpretation in condensate experiments.

molecular biology↗

bioRxiv2024

Effects of residue substitutions on the cellular abundance of proteins

Multiplexed assays of variant effects (MAVEs) make it possible to measure the functional impact of all possible single amino acid residue substitutions in a protein in a single experiment. Combination of variant effect data from several such experiments provides the opportunity to conduct large-scale analyses of variant effect scores measured across proteins, but can be complicated by variations in the phenotypes that are probed across experiments. Thus, using variant effect datasets obtained with similar MAVE techniques can help reveal general rules governing the effects of amino acid variation for a single molecular phenotype. In this work, we accordingly combined data from six individual variant abundance by massively parallel sequencing (VAMP-seq) experiments and analysed a total of 31,614 variant effect scores reporting solely on the impact of single amino acid residue substitutions on the cellular abundance of proteins. Using our combined variant effect dataset, we derived and analysed a collection of amino acid substitution matrices describing the average impact on cellular abundance of all residue substitution types in different structural environments. We found that the substitution matrices predict the cellular abundance of protein variants with surprisingly high accuracy when given structural information only in the form of whether a residue is buried or exposed. We thus propose our substitution matrix-based predictions as strong baselines for future abundance model development.

biophysics↗

bioRxiv2024

Systematic characterization of indel variants using a yeast-based protein folding sensor

Gene variants resulting in insertions or deletions of amino acid residues (indels) have important consequences for evolution and are often linked to disease, yet compared to missense variants the effects of indels are poorly understood and predicted. To approach this issue, we developed a sensitive protein folding sensor based on complementation of uracil auxotrophy in yeast by circular permutated orotate phosphoribosyltransferase (CPOP). The sensor accurately reports on the folding of disease-linked missense variants and de novo designed proteins. Applying the folding sensor to a saturated library of single amino acid indel variants in human DHFR revealed that most regions which tolerate indels are confined to internal loops and the N- and C-termini. Surprisingly, indels are also allowed at a central -helix. Several indels are temperature-sensitive and the folding of most of these indels is rescued upon binding to the competitive DHFR inhibitor methotrexate. Rosetta and AlphaFold2 predictions correlate with the observed effects, suggesting that most indels operate by destabilizing the native fold and that these computational tools may be useful for classification of indels observed in population sequencing.

biochemistry↗

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

Decoding molecular mechanisms for loss of function variants in the human proteome

Proteins play a critical role in cellular function by interacting with other biomolecules; missense variants that cause loss of protein function can lead to a broad spectrum of genetic disorders. While much progress has been made on predicting which missense variants may cause disease, our ability to predict the underlying molecular mechanisms remain limited. One common mechanism is that missense variants cause protein destabilization resulting in decreased protein abundance and loss of function, while other variants directly disrupt key interactions with other molecules. We have here leveraged machine-learning models for protein sequence and structure to disentangle effects on protein function and abundance, and applied our resulting model to all missense variants in the human proteome. We find that approximately half of all missense variants that lead to loss of function and disease do so because they disrupt protein stability. We predicted functionally important positions in all human proteins and found that they cluster on protein structures and are often found on the protein surface. Our work provides a resource for interpreting both predicted and experimental variant effects across the human proteome, and a mechanistic starting point for developing therapies towards genetic diseases.

bioinformatics↗

bioRxiv2024

Predicting absolute protein folding stability using generative models

While there has been substantial progress in our ability to predict changes in protein stability due to amino acid substitutions, progress has been slower in methods to predict the absolute stability of a protein. Here we show how a generative model for protein sequence can be leveraged to predict absolute protein stability. We benchmark our predictions across a broad set of proteins and find a mean error of 1.5 kcal/mol and a correlation coefficient of 0.7 for the absolute stability across a range of natural, small-medium sized proteins up to ca. 150 amino acid residues. We analyse current limitations and future directions including how such model may be useful for predicting conformational free energies. Our approach is simple to use and freely available via an online implementation.

biophysics↗