Biology subjects

Lindorff-Larsen, K.

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

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Slow conformational changes in the rigid and highly stable chymotrypsin inhibitor 2

Slow conformational changes are often directly linked to protein function. It is however less clear how such processes may perturb the overall folding stability of a protein. We previously found that the stabilizing double mutant L49I/I57V in the small protein chymotrypsin inhibitor 2 from barley led to distributed increased nano second and faster dynamics. Here we asked what effect this mutant and the two individual mutants L49I and I57V have on the slow conformational dynamics of CI2. We used 15N CPMG spin relaxation dispersion experiments to measure the kinetics, thermodynamics and structural changes associated with slow conformational change in CI2. These changes result in an excited state that is populated to 4.3% at 1 {degrees}C. As the temperature is increased the population of the excited state decreases. Structural changes in the transition to the excited state are associated with residues that interact with water molecules that have well defined positions and are found at these positions in all crystal structures of CI2. The mutations in CI2 have only little effect on the structure of the excited state whereas the stability of the excited state to some extent follows the stability of the main state. The minor state is thus most populated for the most stable CI2 variant and least populated for the least stable variant. We hypothesize that the interactions between the mutated residues and the well-ordered water molecules links subtle structural changes around the mutated residues to the region in the protein that experience slow conformational changes.

biophysics↗

bioRxiv2022

Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components

In terms of its relative frequency, lysine is a common amino acid in the human proteome. However, by bioinformatics we find hundreds of proteins that contain long and evolutionarily conserved stretches completely devoid of lysine residues. These so-called lysine deserts show a high prevalence in intrinsically disordered proteins with known or predicted functions within the ubiquitin-proteasome system (UPS), including many E3 ubiquitin-protein ligases and UBL domain proteasome substrate shuttles, such as BAG6, RAD23A, UBQLN1 and UBQLN2. We show that introduction of lysine residues into the deserts leads to a striking increase in ubiquitylation of some of these proteins. In case of BAG6, we show that ubiquitylation is catalyzed by the E3 RNF126, while RAD23A is ubiquitylated by E6AP. Despite the elevated ubiquitylation, mutant RAD23A appears stable, but displays a partial loss of function phenotype in fission yeast. In case of UBQLN1 and BAG6, introducing lysine leads to a reduced abundance due to proteasomal degradation of the proteins. For UBQLN1 we show that arginine residues within the lysine depleted region are critical for its ability to form cytosolic inclusions. We propose that selective pressure to avoid lysine residues may be a common evolutionary mechanism to prevent unwarranted ubiquitylation and/or perhaps other lysine post-translational modifications. This may be particularly relevant for UPS components as they closely and frequently encounter the ubiquitylation machinery and are thus more susceptible to non-specific ubiquitylation.

biochemistry↗

bioRxiv2022

WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins

The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, being one ensemble characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.

bioinformatics↗

bioRxiv2022

The prolactin receptor scaffolds Janus kinase 2 via co-structure formation with phosphoinositide-4,5-bisphosphate

Class 1 cytokine receptors transmit signals through the membrane by a single transmembrane helix to an intrinsically disordered cytoplasmic domain that lacks kinase activity. While specific binding to phosphoinositides has been reported for the prolactin receptor (PRLR), the role of lipids in PRLR signalling is unclear. Using an integrative approach combining NMR spectroscopy, cellular signalling experiments, computational modelling and simulation, we demonstrate co-structure formation of the disordered intracellular domain of the human PRLR, the membrane constituent phosphoinositide-4,5-bisphosphate (PI(4,5)P2) and the FERM-SH2 domain of the Janus kinase 2 (JAK2). We find that the complex leads to accumulation of PI(4,5)P2 at the transmembrane helix interface and that mutation of residues identified to interact specifically with PI(4,5)P2 negatively affects PRLR-mediated activation of signal transducer and activator of transcription 5 (STAT5). Facilitated by co-structure formation, the membrane-proximal disordered region arranges into an extended structure. We suggest that the co-structure formed between PRLR, JAK2 and PI(4,5)P2 locks the juxtamembrane disordered domain of the PRLR in an extended structure, enabling signal relay from the extracellular to the intracellular domain upon ligand binding. We find that the co-structure exists in different states which we speculate could be relevant for turning signalling on and off. Similar co-structures may be relevant for other non-receptor tyrosine kinases and their receptors.

biophysics↗

bioRxiv2022

Conformational and oligomeric states of SPOP from small-angle X-ray scattering and molecular dynamics simulations

Speckle-type POZ protein (SPOP) is a substrate adaptor in the ubiquitin proteasome system, and plays important roles in cell-cycle control, development, and cancer. SPOP forms linear higher-order oligomers following an isodesmic self-association model. Oligomerization is essential for SPOPs multivalent interactions with substrates, which facilitate phase separation and localization to biomolecular condensates. Structural characterization of SPOP in its oligomeric state and in solution is, however, challenging due to the inherent conformational and compositional heterogeneity of the oligomeric species. Here, we develop an approach to simultaneously and self-consistently characterize the conformational ensemble and the distribution of oligomeric states of SPOP by combining small-angle X-ray scattering (SAXS) and molecular dynamics simulations. We build initial conformational ensembles of SPOP oligomers using coarse-grained molecular dynamics simulations, and use a Bayesian/maximum entropy approach to refine the ensembles, along with the distribution of oligomeric states, against a concentration series of SAXS experiments. Our results suggest that SPOP oligomers behave as rigid, helical structures in solution, and that a flexible linker region allows SPOPs substrate binding domains to extend away from the core of the oligomers. Additionally, our results are in good agreement with previous characterization of the isodesmic self-association of SPOP. In the future, the approach presented here can be extended to other systems to simultaneously characterize structural heterogeneity and self-assembly.

biophysics↗

bioRxiv2022

Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: comparison of experiments and computation.

Recent advances in DNA sequencing technologies are revealing a large variability in the human genome. Our capacity to establish genotype-phenotype correlations in such large-scale is, however, limited. This task is particularly challenging due to the multifunctional nature of proteins. Here we describe an extensive analysis of the stability and function of naturally-occurring variants (found in the COSMIC and gnomAD databases) of the cancer-associated human NAD(P)H: quinone oxidoreductase 1 (NQO1). First, we performed in silico saturation mutagenesis studies (>5000 substitutions) aimed to identify regions in NQO1 important for stability and function. We then experimentally characterized twenty-two naturally-occurring variants in terms of protein levels during bacterial expression, solubility, thermal stability and coenzyme binding. These studies showed a good overall correlation between experimental analysis and computational predictions; also the magnitude of the effects of the substitutions are similarly distributed in variants from the COSMIC and gnomAD databases. Outliers in these experimental-computational genotype-phenotype correlations remain, and we discuss these on the grounds and limitations of our approaches. Our work represents a further step to characterize the mutational landscape of NQO1 in the human genome and may help to improve high-throughput in silico tools for genotype-phenotype correlations in multifunctional proteins associated with disease.

biophysics↗

bioRxiv2022

Deciphering the alphabet of disorder -- Glu and Asp act differently on local but not global properties

Compared to folded proteins, the sequences of intrinsically disordered proteins (IDPs) are enriched in polar and charged amino acids. Glutamate is one of the most enriched amino acids in IDPs, while the chemically similar amino acid aspartate is less enriched. So far, the underlying functional differences of glutamates and aspartates in IDPs remain poorly understood. In this study, we examine the differential effects of aspartate and glutamates in IDPs by comparing the function and conformational ensemble of glutamate and aspartate variants of the disordered protein Dss1, using a range of assays, including interaction studies, nuclear magnetic resonance spectroscopy, small angle X-ray scattering and molecular dynamics simulation. First, we analyze the sequences of the rapidly growing data base of experimentally verified IDPs (DisProt) and show that the glutamate enrichment is not caused by a taxonomy bias in IDPs. From analyses of local and global structural properties as well as cell growth and protein-protein interactions using a model acidic IDP from yeast and three Glu/Asp variants, we find that while Glu/Asp support similar function and global dimensions, the variants differ in their binding affinities and population of local transient structural elements. We speculate that these local structural differences may play roles in functional diversity where glutamates can support increased helicity important for folding and binding, while aspartates support extended structures and form helical caps, as well as playing more relevant roles in e.g., transactivation domains and ion-binding.

biophysics↗

bioRxiv2022

Discovering functionally important sites in proteins

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

biophysics↗

bioRxiv2022

Rapid protein stability prediction using deep learning representations

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate [~] 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available--including via a Web interface--and enables large-scale analyses of stability in experimental and predicted protein structures.

biophysics↗

bioRxiv2022

Accurate protein stability predictions from homology models

Calculating changes in protein stability ({Delta}{Delta}G) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate {Delta}{Delta}Gs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of {Delta}{Delta}G values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested {Delta}{Delta}Gs and templates for homology modeling across a broad range of sequence identities, and selected three methods for {Delta}{Delta}G calculations to test. We find that {Delta}{Delta}G-values predicted from homology models compare equally well to experimental {Delta}{Delta}Gs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using {Delta}{Delta}Gs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

biophysics↗

bioRxiv2022

Interpreting the molecular mechanisms of disease variants in human membrane proteins

Next-generation sequencing of human genomes reveals millions of missense variants, some of which may lead to loss of protein function and ultimately disease. We here investigate missense variants in membrane proteins -- key drivers in cell signaling and recognition. We find enrichment of pathogenic variants in the transmembrane region across 19,000 functionally classified variants in human membrane proteins. To accurately predict variant consequences, one fundamentally needs to understand the reasons for pathogenicity. A key mechanism underlying pathogenicity in missense variants of soluble proteins has been shown to be loss of stability. Membrane proteins though are widely understudied. We here interpret for the first time on a larger scale variant effects by performing structure-based estimations of changes in thermodynamic stability under the usage of a membrane-specific force-field and evolutionary conservation analyses of 15 transmembrane proteins. We find evidence for loss of stability being the cause of pathogenicity in more than half of the pathogenic variants, indicating that this is a driving factor also in membrane-protein-associated diseases. Our findings show how computational tools aid in gaining mechanistic insights into variant consequences for membrane proteins. To enable broader analyses of disease-related and population variants, we include variant mappings for the entire human proteome. SIGNIFICANCEGenome sequencing is revealing thousands of variants in each individual, some of which may increase disease risks. In soluble proteins, stability calculations have successfully been used to identify variants that are likely pathogenic due to loss of protein stability and subsequent degradation. This knowledge opens up potential treatment avenues. Membrane proteins form about 25% of the human proteome and are key to cellular function, however calculations for disease-associated variants have not systematically been tested on them. Here we present a new protocol for stability calculations on membrane proteins under the usage of a membrane specific force-field and its proof-of-principle application on 15 proteins with disease-associated variants. We integrate stability calculations with evolutionary sequence analysis, allowing us to separate variants where loss of stability is the most likely mechanism from those where other protein properties such as ligand binding are affected.

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↗

bioRxiv2022

Assessment of models for calculating the hydrodynamic radius of intrinsically disordered proteins

Diffusion measurements by pulsed field gradient NMR and fluorescence correlation spectroscopy can be used to probe the hydrodynamic radius of proteins, which contains information about the overall dimension of a protein in solution. The comparison of this value with structural models of intrinsically disordered proteins is nonetheless impaired by the uncertainty of the accuracy of the methods for computing the hydrodynamic radius from atomic coordinates. To tackle this issue, we here build conformational ensembles of 11 intrinsically disordered proteins that we ensure are in agreement with measurements of compaction by small-angle X-ray scattering. We then use these ensembles to identify the forward model that more closely fits the radii derived from pulsed field gradient NMR diffusion experiments. Of the models we examined, we find that the Kirkwood-Riseman equation provides the best description of the hydrodynamic radius probed by pulsed field gradient NMR experiments. While some minor discrepancies remain, our results enable better use of measurements of the hydrodynamic radius in integrative modelling and for force field benchmarking and parameterization. SIGNIFICANCEAccurate models of the conformational properties of intrinsically disordered proteins rely on our ability to interpret experimental data that reports on the conformational ensembles of these proteins in solution. Methods to calculate experimental observables from conformational ensembles are central to link experiments and computation, for example in integrative modelling or the assessment of molecular force fields. Benchmarking such methods is, however, difficult for disordered proteins because it is difficult to construct accurate ensembles without using the data. We here circumvent this problem by combining independent measures of protein compaction to test several methods to calculate the hydrodynamic radius of a disordered protein, as measured by pulsed field gradient NMR diffusion experiments, and find the Kirkwood-Riseman model to be most accurate.

biophysics↗

bioRxiv2022

Increasing protein stability by inferring substitution effects from high-throughput experiments

Protein stability is an important parameter in almost all protein-engineering efforts. Evaluating the effects of the many possible amino acid changes to guide such projects is a significant task, even with recent advances in experimental and computational approaches. Here, we apply a computational model, GMMA, to extract substitution effects from a cost-effective genetic screen of a randomly mutated protein library. Using a high mutation frequency, the method can map stability effects of even very stable proteins for which conventional selection systems have reached their limit. Thus, we screened a mutant library of a highly stable and optimised model protein using an in vivo genetic sensor for folding and assigned a stability effect to 374 of 912 possible single amino acid substitutions. Combining the top 9 substitutions increased the thermodynamic stability by almost 50% in a single engineering step. This illustrates the capability of the method, which is applicable to any screen for protein function.

biochemistry↗

bioRxiv2022

A multiplexed assay of human glucokinase reveals thousands of potential disease variants with both decreased and increased activity

Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia (HH) or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants. Here we exploited a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlated with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants were concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shifted the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation. Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.

genetics↗

bioRxiv2022

Prediction of quality-control degradation signals in yeast proteins

Effective proteome homeostasis is key to cellular and organismal survival, and cells therefore contain efficient quality control systems to monitor and remove potentially toxic misfolded proteins. Such general protein quality control to a large extent relies on the efficient and robust delivery of misfolded or unfolded proteins to the ubiquitin-proteasome system. This is achieved via recognition of so-called degradation motifs--degrons--that are assumed to become exposed as a result of protein misfolding. Despite their importance, the nature and sequence properties of quality-control degrons remain elusive. Here, we have used data from a yeast-based screen of 23,600 17-residue peptides to build a predictor of quality-control degrons. The resulting model, QCDPred (Quality Control Degron Prediction), achieves good accuracy using only the sequence composition of the peptides as input. Our analysis reveals that strong degrons are enriched in hydrophobic amino acids and depleted in negatively charged amino acids, in line with the expectation that they are buried in natively folded proteins. We applied QCDPred to the yeast proteome, enabling us to analyse more widely the potential effects of degrons. As an example, we show a correlation between cellular abundance and degron potential in disordered regions of proteins. Together with recent results on membrane proteins, our work suggest that the recognition of exposed hydrophobic residues is a key and generic mechanism for proteome homeostasis. QCDPred is freely available as open source code and via a web interface.

bioinformatics↗

bioRxiv2022

Conserved degronome features governing quality control-associated proteolysis

The eukaryotic proteome undergoes constant surveillance by quality control systems that either sequester, refold, or eliminate aberrant proteins by ubiquitin-dependent mechanisms. Ubiquitin-conjugation necessitates the recognition of degradation determinants, termed degrons, by their cognate E3 ubiquitin-protein ligases. To learn about the distinctive properties of quality control degrons, we performed an unbiased peptidome stability screen in yeast. The search identified a large cohort of proteome-derived degrons, some of which exhibited broad E3 ligase specificity. Consequent application of a machine-learning algorithm established constraints governing degron potency, including the amino acid composition and secondary structure propensities. According to the set criteria, degrons with transmembrane domain-like characteristics are the most probable sequences to act as degrons. Similar quality control degrons were identified in viral and human proteins, suggesting conserved degradation mechanisms. Altogether, the emerging data indicate that transmembrane domain-like degron features have been preserved in evolution as key quality control determinants of proteins half-life.

cell biology↗

bioRxiv2021

HSP70-binding motifs function as protein quality control degrons

Protein quality control (PQC) degrons are short protein segments that target misfolded proteins for proteasomal degradation, and thus protect cells against the accumulation of potentially toxic non-native proteins. Studies have shown that PQC degrons are hydrophobic and rarely contain negatively charged residues, features which are shared with chaperone-binding regions. Here we explore the notion that chaperone-binding regions may function as PQC degrons. When directly tested, we found that a canonical Hsp70-binding motif (the APPY peptide) functioned as a dose-dependent PQC degron both in yeast and in human cells. In yeast, Hsp70, Hsp110, Fes1, and the E3 Ubr1 target the APPY degron. Screening revealed that the sequence space within the chaperone-binding region of APPY that is compatible with degron function is vast. We find that the number of exposed Hsp70-binding sites in the yeast proteome correlates with a reduced protein abundance and half-life. Our results suggest that when protein folding fails, chaperone-binding sites may operate as PQC degrons, and PQC-linked degradation therefore overlaps in specificity with chaperone binding. This sheds new light on how the PQC system has evolved to exploit the intrinsic capacity of chaperones to recognize misfolded proteins, thereby placing them at the nexus of protein folding and degradation. Significance StatementIt is broadly accepted that misfolded proteins are often rapidly degraded by the ubiquitin-proteasome system, but how cells specifically recognize this immensely diverse group of proteins is largely unknown. Here we show that upon uncoupling of protein folding from protein degradation, a canonical chaperone-binding motif doubles as a degradation signal (degron), and that within the context of a Hsp70-binding region, many sequences are compatible with degron function. We find that degradation is correlated with the number of Hsp70-binding sites within a protein, and that the number of exposed Hsp70-binding sites in the yeast proteome correlates with more rapid degradation.

biochemistry↗