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Stein, A.

Publications and source records attributed to Stein, A..

At least 37 records · Page 2Linked to original sources

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↗

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↗

MutationExplorer - a webserver for mutation of proteins and 3D visualization of energetic impacts

AO_SCPLOWBSTRACTC_SCPLOWThe possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW strives for efficiency in user experience. For example, we have prepared 45,000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW is available at: http://proteinformatics.org/mutation_explorer/

bioinformatics↗

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↗

Post-pubertal developmental trajectories of laryngeal shape and size in humans

Laryngeal morphotypes have been hypothesized related to both phonation and to laryngeal pathologies. Morphotypes have not been validated or demonstrated quantitatively and sources of shape and size variation are incompletely understood but could be critical for the explanation of behavioral changes (e.g., changes of physical properties of a voice) and for therapeutic approaches to the larynx. Therefore results are likely to have implications for surgeons and speech language pathologists. A stratified human sample was interrogated for phenotypic variation of the vocal organ. First, computed tomography image stacks were used to generate three-dimensional reconstructions of the thyroid cartilage. Then cartilage shapes were quantified using multivariate statistical analysis of high dimensional shape data from margins and surfaces of the thyroid cartilage. The effects of sex, age, body mass index (BMI) and body height on size and shape differences were analyzed. We found that sex, age, BMI and the age-sex interaction showed significant effects on the mixed sex sample. Among males, only age showed a strong effect. The thyroid cartilage increased in overall size, and the angulation between left and right lamina decreased in older males. Age, BMI and the age-height interaction were statistically significant factors within females. The angulation between left and right lamina increased in older females and was smaller in females with greater BMI. A cluster analysis confirmed the strong age effect on larynx shape in males and a complex interaction between the age, BMI and height variables in the female sample. The investigation demonstrated that age and BMI, two risk factors in a range of clinical conditions, are associated with shape and size variation of the human larynx. The effects influence shape differently in female and male larynges. The male-female shape dichotomy is partly size-dependent but predominantly size-independent.

developmental biology↗

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↗

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↗

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↗

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↗

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↗

Overcoming the Reproducibility Crisis - Results of the first Community Survey of the German National Research Data Infrastructure for Neuroscience.

The lack of reproducibility of research results is a serious problem - known as "the reproducibility crisis". The German National Research Data Infrastructure (NFDI) initiative implemented by the German Research Foundation (DFG) aims to help overcoming this crisis by developing sustainable solutions for research data management (RDM). NFDI comprises domain specific consortia across all science disciplines. In the field of neuroscience, NFDI Neuroscience (NFDI-Neuro) contributes to the strengthening of systematic and standardized RDM in its research communities. NFDI-Neuro conducted a comprehensive survey amongst the neuroscience community to determine the current needs, challenges, and opinions with respect to RDM. The outcomes of this survey are presented here. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, 3) lack of a privacy preserving research infrastructure for sensitive data (4) lack of RDM literacy and (5) lack of required time and resources for proper RDM. NFDI-Neuro aims to systematically address these barriers by leading and contributing to the development of standards, tools, and infrastructure and by providing training, education, and support, as well as additional resources for RDM to its research community. The RDM work of NFDI-Neuro is conducted in close collaboration with its partner EBRAINS AISBL, the coordinating entity of the EU Flagship Human Brain Project, and its Research Infrastructure (RI) EBRAINS with more than 5000 registered users and developers from more than 70 countries of all continents. While NFDI-Neuro aims to address the German national needs, it closely aligns with the international community and the topics of the Digital Europe Program and EU Data Spaces. Significance StatementA comprehensive survey amongst the neuroscience community in Germany determined the current needs, challenges, and opinions with respect to standardized research data management (RDM) to overcome the reproducibility crisis. Significant deficits were pointed out concerning the perceived lack of standards for data and metadata, lack of provenance tracking and versioning of data, lack of protected digital research infrastructure for sensitive data and the lack of education and resources for proper RDM. Yet, at the same time, an overwhelming majority of community members indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Thus, the survey results suggest that training, the provision of standards, tools, infrastructure and additional resources for RDM holds the potential to significantly facilitate reproducible research in neuroscience.

neuroscience↗

Machine learning improves global models of plant diversity

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is incomplete, hampering basic research and biodiversity conservation. Here, we used machine learning (random forests, extreme gradient boosting, neural networks) and conventional statistical methods (generalised linear models, generalised additive models) to model species richness and phylogenetic richness of vascular plants worldwide based on 830 regional plant inventories including c. 300,000 species and predictors of past and present environmental conditions. Machine learning showed an outstanding performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity. Finally, we combined predictions from multiple modelling techniques (ensemble predictions) to reveal global patterns and centres of plant diversity at multiple resolutions down to 7,774 km2. Our predictive maps provide the most accurate estimates of global plant diversity available to date at grain sizes relevant for conservation and macroecology.

ecology↗

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↗

Disease-linked mutations trigger exposure of a protein quality control degron in the DHFR protein

Degrons are short stretches of amino acids or structural motifs that are embedded in proteins. They mediate recognition by E3 ubiquitin-protein ligases and thus confer protein degradation via the ubiquitin-proteasome system. Well-described degrons include the N-degrons, destruction boxes, and the PIP degrons, which mediate the controlled degradation of various proteins including signaling components and cell cycle regulators. In comparison, the so-called protein quality control (PQC) degrons that mediate the degradation of structurally destabilized or misfolded proteins are not well described. Here, we show that disease-linked DHFR missense variants are structurally destabilized and chaperone-dependent proteasome targets. We systematically mapped regions within DHFR to assess those that act as cytosolic PQC degrons in yeast cells. Two regions, DHFR-Deg13-36 (here Deg1) and DHFR-Deg61-84 (here Deg2), act as degrons and conferred degradation to unrelated fusion partners. The proteasomal turnover of Deg2 was dependent on the molecular chaperone Hsp70. Structural analyses by NMR and hydrogen/deuterium exchange revealed that Deg2 is buried in wild-type DHFR, but becomes transiently exposed in the disease-linked missense variants.

biochemistry↗

A structural biology community assessment of AlphaFold 2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

biophysics↗

Predicting and interpreting large scale mutagenesis data using analyses of protein stability and conservation

Understanding and predicting the functional consequences of single amino acid is central in many areas of protein science. Here we collected and analysed experimental measurements of effects of >150,000 variants in 29 proteins. We used biophysical calculations to predict changes in stability for each variant, and assessed them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability, and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments, and show how the two sources of information are able to support one another. Together our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.

biophysics↗

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

bioinformatics↗

Mapping Twenty Years of Antimicrobial Resistance Research Trends

BackgroundAntimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses. MethodsStructural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space. FindingsIn total, 88 topics were identified in 158616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor. InterpretationThis study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research. FundingINTERREG-VA EurHealth-1Health (202085); European Commission Horizon 2020 Framework Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrior to this study, PubMed, Web of Science, Scopus, and IEEE Xplore were queried to find studies providing a conceptual overview of antimicrobial resistance (AMR) research over time and space. The search string included keywords ("antimicrobial" OR antibiotic*) AND (resistan*) AND ("science mapping" OR bibliometric OR scientometric) in the title and abstract and focused on articles published before 2019 without language restrictions. Few studies were found relying on scientometric and bibliometric methods to assess either subfields of AMR research (e.g., AMR among uropathogens) or AMR-related fields (e.g., microbiology). No studies were found that focus on the entire AMR field. Therefore, this science mapping study using topic modelling was performed to provide an overview of the AMR field by identifying and assessing topics, trends, and geographical differences over time. Added value of this studyTo the best of our knowledge, this study is the first to use a science mapping approach to provide a comprehensive overview of the entire AMR research field, covering over 150 thousand articles published between 1999 and 2018. Our findings revealed important (e.g., Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship) and emerging (e.g., Water and environment, and Sequencing) topics in AMR research. Lastly, this study resulted in an interactive user interface where all data are presented for further analyses. Implications of all the available evidenceOur comprehensive overview of the AMR field, including its conceptual structure, and temporal and geographical trends revealed the response of the research community to the AMR burden. The results and the openly available supporting data provide the base to guide future funding and research directions to tackle AMR.

microbiology↗