Biology subjects

Lindorff-Larsen, K.

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

At least 55 records · Page 3Linked to original sources

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

A joint embedding of protein sequence and structure enables robust variant effect predictions

The ability to predict how amino acid changes may affect protein function has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either protein sequences or protein structures. Here, we present a method for integrating information from protein sequences and structures in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments, and we show that by integrating both types of information we obtain a variant effect prediction model that is more robust to cases where sequence information is scarce. Furthermore, we find that SSEmb learns embeddings of the sequence and structural properties that are useful for other downstream tasks. We exemplify this by training a downstream model to predict protein-protein binding sites at high accuracy using only the SSEmb embeddings as input. We envisage that SSEmb may be useful both for zero-shot predictions of variant effects and as a representation for predicting protein properties that depend on protein sequence and structure.

bioinformatics↗

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

Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference

Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced models in which physico-chemical quality is sacrificed for excellent fit to the data. Furthermore, these techniques struggle to represent the conformational heterogeneity averaged out in low-resolution regions of density maps. Here we introduce EMMIVox, a Bayesian inference approach to determine single-structure models as well as structural ensembles from cryo-EM maps. EMMIVox automatically balances experimental information with accurate physico-chemical models of the system and the surrounding environment, including waters, lipids, and ions. Explicit treatment of data correlation and noise as well as inference of accurate B-factors enable determination of structural models and ensembles with both excellent fit to the data and high stereochemical quality, thus outperforming state-of-the-art refinement techniques. EMMIVox represents a flexible approach to determine high-quality structural models that will contribute to advancing our understanding of the molecular mechanisms underlying biological functions.

bioinformatics↗

bioRxiv2023

Deep mutational scanning reveals a tight correlation between protein degradation and toxicity of thousands of non-native aspartoacylase protein variants

When the structural stability of a protein is compromised, the protein may form non-native interactions with other cell proteins and thus becomes a hazard to the cell. To mitigate this danger, destabilized proteins are targeted by the cellular protein quality control (PQC) network, which either corrects the folding defect or targets the protein for degradation. However, the details of how the protein folding and degradation systems collaborate to combat potentially toxic non-native proteins are unknown. To address this issue, we performed systematic studies on destabilized variants of the cytosolic aspartoacylase, ASPA, where loss-of-function variants are linked to Canavans disease, an autosomal recessive and lethal neurological disorder, characterized by the spongy degeneration of the white matter in the brain. Using Variant Abundance by Massively Parallel sequencing (VAMP-seq), we determined the abundance of 6152 out of the 6260 ([~]98%) possible single-site missense and nonsense ASPA variants in cultured human cells. The majority of the low abundance ASPA variants are degraded through the ubiquitin-proteasome system (UPS) and become toxic upon prolonged expression. Variant cellular abundance data correlates with predicted thermodynamic stability, evolutionary conservation, and separates most known disease-linked variants from benign variants. Systematic mapping of degradation signals (degrons) shows that inherent primary degrons in ASPA are located in buried regions, and reveals that the wild-type ASPA C-terminal region functions as a degron. Collectively, our data can be used to interpret Canavans disease variants and also offer mechanistic insight into how ASPA missense variants are targeted by the PQC system. These are essential steps towards future implementation of precision medicine for Canavans disease.

molecular biology↗

bioRxiv2023

A mutational atlas for Parkin proteostasis

The delicate balance of protein homeostasis can be disturbed by mutations that affect folding and stability of the encoded protein. More than half of disease-causing missense variants are thought to lead to protein degradation, but determining which and the molecular mechanisms involved remain enigmatic. To examine these issues, we selected the ubiquitin-protein ligase Parkin, where known missense variants result in an autosomal recessive, early onset Parkinsonism. We used the variant abundance by massively parallel sequencing (VAMP-seq) approach to quantify the abundance of Parkin missense variants in cultured human cells. The resulting mutational map, covering 9219 out of the 9300 possible single-site amino acid substitutions and nonsense Parkin variants, show that most low abundance variants are located within the structured domains of the protein, while the flexible linker regions are more tolerant. The vast majority of low abundance Parkin variants are degraded through the ubiquitin-proteasome system and are stabilized at a lowered temperature. The cellular abundance data correlate with thermodynamic stability, evolutionary conservation, and show that half of the known disease-linked variants are found at low abundance. Systematic mapping of degradation signals (degrons) shows that inherent primary degrons in Parkin largely overlap with regions that are buried, and highly sensitive to mutations. An exposed degron region proximal to the so-called "activation element" is enhanced by substitutions to hydrophobic residues and destroyed by introduction of hydrophilic residues. The data provide examples of how missense variants may cause degradation either via destabilization of the native protein, or by introducing local signals for degradation. Combined with the computational methods based on Parkin structure and conservation, our abundance map sheds light on the mechanisms that cause loss of function, and points to variants where function potentially can be restored.

biochemistry↗

bioRxiv2023

Revisiting the use of dioxane as a reference compound for determination of the hydrodynamic radius of proteins by pulsed field gradient NMR spectroscopy

Measuring the compaction of a protein or complex is key to understand the interactions within and between biomolecules. Experimentally, protein compaction is often probed either by estimating the radius of gyration (Rg) obtained from small-angle X-ray scattering (SAXS) experiments or the hydrodynamic radius (Rh) obtained for example by pulsed field gradient nuclear magnetic resonance (PFG NMR) spectroscopy. PFG NMR experiments generally report on the translational diffusion coefficient, which in turn can be used to estimate Rh using an internal standard. Here, we examine the use of 1,4-dioxane as an internal NMR standard to account for sample viscosity and uncertainty about the gradient strength. Specifically, we revisit the basis for the commonly used reference value for the Rh of dioxane (2.12 [A]) that is used to convert measured diffusion coefficients into a hydrodynamic radius. We follow the same approach that was used to establish the current reference value for the Rh by measuring SAXS and PFG NMR data for a set of seven different proteins and using these as standards. Our analysis shows that the current Rh reference value for 1,4-dioxane Rh (2.12 [A]) is underestimated, and we instead suggest a new value of 2.27 [A] {+/-} 0.04 [A]. Using this updated reference value results in a [~]7% increase in Rh values for proteins whose hydrodynamic radius have been measured by PFG NMR. We discuss the implications for ensemble descriptions of intrinsically disordered proteins and evaluation of effect resulting from for example ligand binding, posttranslational modifications, or changes to the environment.

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

Recalibration of protein interactions in Martini 3

Multidomain proteins with flexible linkers and disordered regions play important roles in many cellular processes, but characterizing their conformational ensembles is diffcult. We have previously shown that the coarse-grained model, Martini 3, produces too compact ensembles in solution, that may in part be remedied by strengthening protein-water interactions. Here, we show that decreasing the strength of protein-protein interactions leads to improved agreement with experimental data on a wide set of systems. We show that the symmetry between rescaling protein-water and protein-protein interactions breaks down when studying interactions with or within membranes; rescaling protein-protein interactions better preserves the binding specificity of proteins with lipid membranes, whereas rescaling protein-water interactions preserves oligomerization of transmembrane helices. We conclude that decreasing the strength of protein-protein interactions improves the accuracy of Martini 3 for IDPs and multidomain proteins, both in solution and in the presence of a lipid membrane.

biophysics↗

bioRxiv2023

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms of human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. We assayed the abundance of 95% of GCK missense and nonsense variants, and found that 43% of hypoactive variants have a decreased cellular abundance. By combining our abundance scores with predictions of protein thermodynamic stability, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

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

MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD [fi]les in generalist data repositories, constituting the dark matter of MD -- data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 [fi]les and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on [fi]les produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identi[fi]ed systems with speci[fi]c molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

bioinformatics↗

bioRxiv2023

Activation and substrate specificity of the human P4-ATPase ATP8B1

Asymmetric distribution of phospholipids in eukaryotic membranes is essential for maintaining cell integrity, signaling pathways, and vesicular trafficking. P4-ATPases, also known as flippases, participate in creating and maintaining this asymmetry through active transport of phospholipids from the exoplasmic to the cytosolic leaflet. In this study, we present a total of nine cryo-electron microscopy structures at a resolution ranging from 2.4 to 3.1 [A], along with functional and computational studies of the human flippase ATP8B1-CDC50A complex, describing the autophosphorylation steps from ATP, substrate recognition and occlusion, as well as its regulation by phosphoinositides. Our findings show that the P4-ATPase transport site is filled with water upon phosphorylation from ATP. Additionally, we identify two different autoinhibited states, a closed and an outward-open conformation. Furthermore, we identified and characterized the PI(3,4,5)P3 binding site of ATP8B1 in an electropositive pocket between transmembrane segments 5, 7, 8, and 10. Our study also highlights the structural basis of ATP8B1 broad specificity for lipids and identifies a new transport substrate for P4-ATPases, phosphatidylinositol (PI). We report the critical role of the sn-2 ester bound of glycerophospholipids in substrate recognition by ATP8B1. These findings provide fundamental insights into ATP8B1 regulation, the catalytic cycle, and substrate recognition in P4-ATPases.

biochemistry↗

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

Combining experiments and simulations to examine the temperature-dependent behaviour of a disordered protein

Intrinsically disordered proteins are a class of proteins that lack stable folded conformations and instead adopt a range of conformations that determine their biochemical functions. The temperature-dependent behaviour of such disordered proteins is complex and can vary depending on the specific protein and environment. Here, we have used molecular dynamics simulations and previously published experimental data to investigate the temperature-dependent behaviour of Histatin 5, a 24-residue-long polypeptide. We examined the hypothesis that Histatin 5 undergoes a loss of polyproline II structure with increasing temperature, leading to more compact conformations. We found that the conformational ensembles generated by the simulations generally agree with small-angle X-ray scattering data for Histatin 5, but show some discrepancies with the hydrodynamic radius as probed by pulsed-field gradient nuclear magnetic resonance spectroscopy, and with the secondary structure information derived from circular dichroism. We attempted to reconcile these differences by reweighting the conformational ensembles against the scattering and NMR data. By doing so, we were in part able to capture the temperature-dependent behaviour of Histatin 5 and to link the observed decrease in hydrodynamic radius with increasing temperature to a loss of polyproline II structure. We were, however, unable to achieve agreement with both the scattering and NMR data within experimental errors. We discuss different possibilities for this outcome including inaccuracies in the force field, differences in conditions of the NMR and scattering experiments, and issues related to the calculation of the hydrodynamic radius from conformational ensembles. Our study highlights the importance of integrating multiple types of experimental data when modelling conformational ensembles of disordered proteins and how environmental factors such as the temperature influence them.

biophysics↗

bioRxiv2023

Fitting Force Field parameters to NMR Relaxation Data

We present an approach to optimise force field parameters using time-dependent data from NMR relaxation experiments. To do so, we scan parameters in the dihedral angle potential energy terms describing the rotation of the methyl groups in proteins, and compare NMR relaxation rates calculated from molecular dynamics simulations with the modified force fields to deuterium relaxation measurements of T4 lysozyme. We find that a small modification of C{gamma} methyl groups improves the agreement with experiments both for the protein used to optimize the force field, and when validating using simulations of CI2 and ubiquitin. We also show that these improvements enable a more effective a posteriori reweighting of the MD trajectories. The resulting force field thus enables more direct comparison between simulations and side-chain NMR relaxation data, and makes it possible to construct ensembles that better represent the dynamics of proteins in solution.

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↗

bioRxiv2023

Rare catechol-O-methyltransferase (COMT) missense variants are structurally unstable proteasome targets

Catechol-O-methyltransferase (COMT) is a key enzyme in the metabolism of catecholamines. Substrates of the enzyme include neurotransmitters such as dopamine and epinephrine, and therefore, COMT plays a central role in neurobiology. Since COMT also metabolises catecholamine drugs such as L-DOPA, variation in COMT activity could affect pharmacokinetics and drug availability. Certain COMT missense variants have been shown to display decreased enzymatic activity. Additionally, studies have shown that such missense variants may lead to loss-of-function induced by impaired structural stability, which results in activation of the protein quality control system and degradation by the ubiquitin-proteasome system. Here, we demonstrate that two rare missense variants of COMT are ubiquitylated and targeted for proteasomal degradation as a result of structural destabilisation and misfolding. This results in strongly reduced intracellular steady-state levels of the enzyme, which for the L135P variant is rescued upon binding to the COMT inhibitors entacapone and tolcapone. Our results reveal that the degradation is independent of the COMT isoform, as both soluble (S-COMT) and ER membrane-bound (MB-COMT) variants are degraded. In silico structural stability predictions identify regions within the protein that are critical for stability overlapping with evolutionarily conserved residues, pointing towards other variants that are likely destabilised and degraded.

biochemistry↗