Biology subjects

Johansson, K. E.

Publications and source records attributed to Johansson, K. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Comprehensive Variant Effect Map of Parkin-Mediated Mitophagy in Parkinson's Disease

The development of Parkinsons disease (PD) has a substantial genetic basis. Variants in the PRKN gene account for roughly half of autosomal-recessive PD cases, yet most variants remain classified as variants of uncertain significance. PRKN encodes the E3 ubiquitin-protein ligase, Parkin, that plays a key role in mitochondrial quality control by initiating mitophagy. In this work, we introduce a multiplexed mitophagy assay and measure the activity of more than 99% of all possible single-amino acid substitution and nonsense variants of Parkin. We also demonstrate that misfolded, rapidly degraded variants autonomously trigger Parkin-independent mitophagy. The obtained activity landscape closely reflects known structural and functional aspects of Parkin while revealing new insights into specific functional effects across the protein. This dataset near-perfectly distinguishes pathogenic from benign variants and surpasses both computational predictors and abundance-based metrics in pathogenicity assessment. Finally, the data pinpoint hypomorphic variants that could be amenable to rescue in future personalized therapeutic approaches for PD.

genetics↗

bioRxiv2026

BAG6 and RNF126 are broadly involved in protein quality control of non-native missense protein variants

Protein quality control (PQC) degradation is a vigorous and selective system that limits the accumulation of harmful, non-native and aggregation-prone proteins. Specific PQC pathways have been described for misfolded ER proteins, aberrant translational products and mislocalized proteins. However, many components involved in PQC of structurally unstable cytosolic proteins are unknown despite such proteins being a common consequence of pathogenic coding variants. Here, we present results from a genome-wide CRISPR knockout screen to identify components involved in PQC of the Parkin R42P variant. We find that HSP90 and STIP1 stabilize Parkin, while the BAG6 chaperone and E3 ubiquitin ligase RNF126 are critical for PQC degradation. Variant abundance by massively parallel sequencing (VAMP-seq) reveal >1000 Parkin variants, including several variants linked to autosomal recessive juvenile Parkinsonism, are BAG6 targets. Pathogenic missense variants in phenylalanine hydroxylase (PAH) and the tumor suppressor FLCN are also stabilized in BAG6 and RNF126 knockout cells. We propose that BAG6 and RNF126 are broadly involved in PQC and may represent targets for the development of therapeutics for protein misfolding diseases.

biochemistry↗

bioRxiv2025

AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level

Deep-learning methods have transformed our ability to predict the three-dimensional structures of folded proteins from sequence, and coarse-grained simulations have made it possible to study intrinsically disordered proteins at the proteome scale. More than half of human proteins, however, contain mixtures of disordered regions and one or more folded domains, and the biological function of these multi-domain proteins depends on the interplay between the folded and disordered regions. Here, we developed AF-CALVADOS, a coarse-grained simulation model that is informed by AlphaFold to model the dynamics of intrinsically disordered proteins and multi-domain proteins containing mixtures of folded and disordered regions. AF-CALVADOS leverages information from AlphaFold 2 to model folded regions that we then integrate with the coarse-grained CALVADOS model. Our automated framework makes it possible to perform simulations of any soluble folded or disordered protein without manually defining the folded regions, enabling scaling to the proteome level. We validate AF-CALVADOS using experimental SAXS data for more than 400 proteins and find that it performs well across proteins with varying amounts of ordered and disordered regions. We demonstrate the scalability of AF-CALVADOS by performing simulations of 12,483 intracellular human proteins and make the data freely available; we envisage that large-scale simulation data generated by AF-CALVADOS can be used to benchmark or train machine learning models for flexible, multi-domain proteins. The conformational ensembles can also be used to study sequence-dynamics-function relationships at scale, and can shed light on the interplay between folded and disordered regions. We exemplify this by analysing the disordered regions in 1,487 human transcription factors.

biophysics↗

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

A highly stable engineered disulfide bond in the dimer interface of E. coli orotate phosphoribosyl transferase

Structural disulfides are very rarely found in cytoplasmic proteins of mesophilic organisms. Nevertheless, disulfides can form in the cytosol if these are stabilized sufficiently by the supporting protein structure. To investigate the redox properties of structural disulfide bonds, we introduced disulfide bonds into the cytosolic enzyme from E. coli, orotate phosphoribosyl transferase (OPRT). Because OPRT is a homodimer the introduction of opposing cysteine residues (R44C and D92C) into separate monomers of the enzyme meant that disulfide bond formation could easily be followed by non-reducing SDS-PAGE. Redox titration experiments in DTT buffers revealed that the designed disulfide bond was exceedingly stable with a redox potential of -314 mV, close to that of dithiothreitol (DTT). This interchain disulfide bond improved global thermostability by 5.9 {degrees}C relative to wild type protein and 21.4 {degrees}C above its reduced form. The catalytic activity of the enzyme in either its thiol or disulfide state was not substantially affected, indicating structural integrity. Combining an inactive subunit with the asymmetric nature of the disulfide bond enabled determining the rates of subunit rearrangement and disulfide formation. The R44C/D92C double mutant resulted in the formation of a symmetric homodimer with two interchain disulfides. However, the global stability of the two-disulfide dimer was not substantially increased relative to the dimer with the single disulfide bond, suggesting that the stabilizing effect was gained by preventing dimer dissociation. Finally, we show that the engineered disulfide bonds are formed to a significant degree in the cytosol of yeast cells, allowing us to determine the redox potential of the cytosol in equilibrium with this substrate to -300 mV. This is slightly lower than that previously determined using a GFP-based sensor, rxYFP in yeast. We find that disulfide bond formation is particularly enhanced in mutants lacking the glutathione reductase, Glr1. Thus, the engineered disulfide provides an alternative method for determining intracellular redox potential in living cells with an extended dynamic range relative to GFP-based sensors.

biochemistry↗

bioRxiv2024

GMMA can stabilize proteins across different functional constraints

Stabilizing proteins without otherwise hampering their function is a central task in protein engineering and design. PYR1 is a plant hormone receptor that has been engineered to bind diverse small molecule ligands. We sought a set of generalized mutations that would provide stability without affecting functionality for PYR1 variants with diverse ligand-binding capabilities. To do this we used a global multi-mutant analysis (GMMA) approach, which can identify substitutions that have stabilizing effects and do not lower function. GMMA has the added benefit of finding substitutions that are stabilizing in different sequence contexts and we hypothesized that applying GMMA to PYR1 with different functionalities would identify this set of generalized mutations. Indeed, conducting FACS and deep sequencing of libraries for PYR1 variants with two different functionalities and applying a GMMA analysis identified 5 substitutions that, when inserted into four PYR1 variants that each bind a unique ligand, provided 2-6{degrees}C in increased thermal stability and no decrease in functionality.

biochemistry↗

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

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↗

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

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

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↗

bioRxiv2021

Computational and experimental assessment of backbone templates for computational protein design

Computational protein design has taken big strides over the recent years, however, the tools available are still not at a state where a sequence can be designed to fold into a given protein structure at will and with high probability. We have here applied a recent release of Rosetta Design to redesign a set of structurally very similar proteins belonging to the Thioredoxin fold. We determined design success using a combination of a genetic screening tool to assay folding/stability in E. coli and selecting the best hits from this for further biochemical characterization. We have previously used this set of template proteins for redesign and found that success was highly dependent on template structure, a trait which was also found in this study. Nevertheless, state of the art design software is now able to predict the best template, most likely due to the introduction of the cart_bonded energy term. The template that led to the greatest fraction of successful designs was the same (a Thioredoxin from spinach) as that identified in our previous study. Our previously described redesign of Thioredoxin, which also used the spinach protein as template, however also performed well. In the present study, both these templates yielded proteins with compact folded structures, and enforces the conclusion that any design project must carefully consider different design templates. Fortunately, selecting designs using the cart_bonded energy term appears to correctly identify such templates.

biochemistry↗

bioRxiv2020

Global analysis of multi-mutants to discover stabilizing amino acid substitutions

The identification of amino acid substitutions that both enhance the stability and function of a protein is a key challenge in protein engineering. Technological advances have enabled assaying thousands of protein variants in a single high-throughput experiment, and more recent studies use such data in protein engineering. We present a Global Multi-Mutant Analysis (GMMA) that exploits the presence of multiply-substituted variants to identify individual amino acid substitutions that are beneficial for the stability and function across a large library of protein variants. We have applied GMMA to >54,000 variants of green fluorescent protein (GFP), each with known fluorescence output, and each carrying 1-15 amino acid substitutions. The GMMA method achieves a good fit to the data while being analytically transparent. The six top-ranking substitutions are demonstrated to progressively enhance GFP and in general, our analysis recovers nearly all the substitutions previously reported to be beneficial for GFP folding and function, only using a single experiment as input. Significance StatementProtein engineering is carried out to improve proteins for practical applications by changing one or more amino acid residues in a protein. We present a method termed global multi-mutant analysis (GMMA) that helps solve two problems in protein engineering. First, because many proteins are already highly optimized, it can be difficult to identify individual variants that further improve function. Second, while it is possible to combine variants with small individual effects, such approaches may be hampered by non-additivity. GMMA identifies combinable effects of single substitutions from a large set of variants each carrying multiple substitutions. We demonstrate the approach on a set of 54,000 variants of green fluorescent protein and identify many enhancing single-substitutions from a single experiment.

biophysics↗

bioRxiv2020

Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance

Understanding and predicting how amino acid substitutions affect proteins is key to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analysed 6749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects, and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyse the structural and mechanistic origins of loss of function, and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.

biophysics↗