The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins

The Legionella collagen-like protein employs a distinct binding mechanism for the recognition of host glycosaminoglycans

Bacterial adhesion is a fundamental process which enables colonisation of niche environments and is key for infection. However, in Legionella pneumophila, the causative agent of Legionnaires' disease, these processes are not well understood. The Legionella collagen-like protein (Lcl) is an extracellular peripheral membrane protein that recognises sulphated glycosaminoglycans on the surface of eukaryotic cells, but also stimulates bacterial aggregation in response to divalent cations. Here we report the crystal structure of the Lcl C-terminal domain (Lcl-CTD) and present a model for intact Lcl. Our data reveal that Lcl-CTD forms an unusual trimer arrangement with a positively charged external surface and negatively charged solvent exposed internal cavity. Through molecular dynamics simulations, we show how the glycosaminoglycan chondroitin-4-sulphate associates with the Lcl-CTD surface via distinct binding modes. Our findings show that Lcl homologs are present across both the Pseudomonadota and Fibrobacterota-Chlorobiota-Bacteroidota phyla and suggest that Lcl may represent a versatile carbohydrate-binding mechanism.

Structure and interactions of prion-like domains in transcription factor Efg1 phase separation

Candida albicans, a prominent member of the human microbiome, can make an opportunistic switch from commensal coexistence to pathogenicity accompanied by an epigenetic shift between the white and opaque cell states. This transcriptional switch is under precise regulation by a set of transcription factors (TFs), with Enhanced Filamentous Growth Protein 1 (Efg1) playing a central role. Previous research has emphasized the importance of Efg1's prion-like domain (PrLD) and the protein's ability to undergo phase separation for the white-to-opaque transition of C. albicans. However, the underlying molecular mechanisms of Efg1 phase separation have remained underexplored. In this study, we delved into the biophysical basis of Efg1 phase separation, revealing the significant contribution of both N-terminal (N) and C-terminal (C) PrLDs. Through NMR structural analysis, we found that Efg1 N-PrLD and C-PrLD are mostly disordered but have prominent partial α-helical secondary structures in both domains. NMR titration experiments suggest that the partially helical structures in N-PrLD act as hubs for self-interaction as well as Efg1 interaction with RNA. Using condensed-phase NMR spectroscopy, we uncovered diverse amino acid interactions underlying Efg1 phase separation. Particularly, we highlight the indispensable role of tyrosine residues within the transient α-helical structures of PrLDs particularly in the N-PrLD compared to the C-PrLD in stabilizing phase separation. Our study provides evidence that the transient α-helical structure is present in the phase-separated state and highlights the particular importance of aromatic residues within these structures for phase separation. Together, these results enhance the understanding of C. albicans transcription factor interactions that lead to virulence and provide a crucial foundation for potential antifungal therapies targeting the transcriptional switch.

The Legionella collagen-like protein employs a unique binding mechanism for the recognition of host glycosaminoglycans

Bacterial adhesion is a fundamental process which enables colonisation of niche environments and is key for infection. However, in Legionella pneumophila, the causative agent of Legionnaires' disease, these processes are not well understood. The Legionella collagen-like protein (Lcl) is an extracellular peripheral membrane protein that recognises sulphated glycosaminoglycans (GAGs) on the surface of eukaryotic cells, but also stimulates bacterial aggregation in response to divalent cations. Here we report the crystal structure of the Lcl C-terminal domain (Lcl-CTD) and present a model for intact Lcl. Our data reveal that Lcl-CTD forms an unusual dynamic trimer arrangement with a positively charged external surface and a negatively charged solvent exposed internal cavity. Through Molecular Dynamics (MD) simulations, we show how the GAG chondroitin-4-sulphate associates with the Lcl-CTD surface via unique binding modes. Our findings show that Lcl homologs are present across both the Pseudomonadota and Fibrobacterota-Chlorobiota-Bacteroidota phyla and suggest that Lcl may represent a versatile carbohydrate binding mechanism.

Evidence of DISC1 as an arsenic binding protein and implications regarding its role as a translational activator

Disrupted-in-schizophrenia-1 (DISC1) is a scaffolding protein that plays a pivotal role in orchestrating signaling pathways involved in neurodevelopment, neural migration, and synaptogenesis. Among those, it has recently been reported that the role of DISC1 in the Akt/mTOR pathway can shift from a global translational repressor to a translational activator in response to oxidative stress induced by arsenic. In this study we provide evidence that DISC1 can directly bind arsenic via a C-terminal cysteine motif (C-X-C-X-C). A series of fluorescence-based binding assays were conducted with a truncated C-terminal domain construct of DISC1 and a series of single, double, and triple cysteine mutants. We found that arsenous acid, a trivalent arsenic derivative, specifically binds to the C-terminal cysteine motif of DISC1 with low micromolar affinity. All three cysteines of the motif are required for high-affinity binding. Electron microscopy experiments combined with in silico structural predictions reveal that the C-terminal of DISC1 forms an elongated tetrameric complex. The cysteine motif is consistently predicted to be located within a loop, fully exposed to solvent, providing a simple molecular framework to explain the high-affinity of DISC1 toward arsenous acid. This study sheds light on a novel functional facet of DISC1 as an arsenic binding protein and highlights its potential role as both a sensor and translational modulator within Akt/mTOR pathway.

Backbone 1H, 13C and 15N assignments of the apo-acyl carrier protein (ACP1) of Pseudomonas aeruginosa

The N-acyl-L-homoserine lactone (AHL) quorum sensing regulates virulence in the opportunistic pathogen, Pseudomonas aeruginosa. The LasI and RhlI AHL synthases use acyl carrier protein substrates to synthesize, respectively, the 3-oxododecanoyl-L-homoserine lactone (3-oxoC12-HSL) and butyryl-L-homoserine lactone (C4-HSL) QS signals for this bacterium. Although P. aeruginosa genome contains three open reading frames to encode three acyl carrier proteins, namely the ACP1, ACP2 and ACP3, microarray and gene replacement studies show that only the ACP1 carrier protein is under quorum sensing regulation. In this study, we isotopically enriched one of the acyl carrier proteins, ACP1 from P. aeruginosa and describe the backbone resonance assignments for this protein to delineate the structural and molecular basis of ACP1 recognition in P. aeruginosa AHL quorum sensing signal synthesis.

Prediction of folding patterns for intrinsic disordered protein

The conformation flexibility of natural protein causes both complexity and difficulty to understand the relationship between structure and function. The prediction of intrinsically disordered protein primarily is focusing on to disclose the regions with structural flexibility involving relevant biological functions and various diseases. The order of amino acids in protein sequence determines possible conformations, folding flexibility and biological function. Although many methods provided the information of intrinsically disordered protein (IDP), but the results are mainly limited to determine the locations of regions without knowledge of possible folding conformations. Here, the developed protein folding fingerprint adopted the protein folding variation matrix (PFVM) to reveal all possible folding patterns for the intrinsically disordered protein along its sequence. The PFVM integrally exhibited the intrinsically disordered protein with disordering regions, degree of disorder as well as folding pattern. The advantage of PFVM will not only provide rich information for IDP, but also may promote the study of protein folding problem.

Structure and position-specific interactions of prion-like domains in transcription factor Efg1 phase separation

Candida albicans, a prominent member of the human microbiome, can make an opportunistic switch from commensal coexistence to pathogenicity accompanied by an epigenetic shift between the white and opaque cell states. This transcriptional switch is under precise regulation by a set of transcription factors (TFs), with Enhanced Filamentous Growth Protein 1 (Efg1) playing a central role. Previous research has emphasized the importance of Egf1's prion-like domain (PrLD) and the protein's ability to undergo phase separation for the white-to-opaque transition of C. albicans. However, the underlying molecular mechanisms of Efg1 phase separation have remained underexplored. In this study, we delved into the biophysical basis of Efg1 phase separation, revealing the significant contribution of both N-terminal (N) and C-terminal (C) PrLDs. Through NMR structural analysis, we found that Efg1 N-PrLD and C-PrLD are mostly disordered though have prominent partial α-helical secondary structures in both domains. NMR titration experiments suggest that the partially helical structures in N-PrLD act as hubs for self-interaction as well as Efg1 interaction with RNA. Using condensed-phase NMR spectroscopy, we uncovered diverse amino acid interactions underlying Efg1 phase separation. Particularly, we highlight the indispensable role of tyrosine residues within the transient α-helical structures of PrLDs particularly in the N-PrLD compared to the C-PrLD in stabilizing phase separation. Our study provides evidence that the transient α-helical structure is present in the phase separated state and highlights the particular importance of aromatic residues within these structures for phase separation. Together, these results enhance the understanding of C. albicans TF interactions that lead to virulence and provide a crucial foundation for potential antifungal therapies targeting the transcriptional switch.

Evidence of Disrupted-in Schizophrenia 1 (DISC1) as an arsenic binding protein and implications regarding its role as a translational activator

Disrupted-in-schizophrenia-1 (DISC1) is a scaffold protein that plays a pivotal role in orchestrating signaling pathways involved in neurodevelopment, neural migration, and synaptogenesis. Among those, it has recently been reported that the role DISC1 in the Akt/mTOR pathway can shift from a global translational repressor to a translational activator in response to oxidative stress induced by arsenic. In this study we are providing evidence that DISC1 can directly bind arsenic via a C-terminal cysteine motif (C-X-C-X-C). A series of fluorescence-based binding assays were conducted with a truncated C-terminal domain construct of DISC1 and a of series of single, double, and triple cysteine mutants. We found that arsenous acid, a trivalent arsenic derivative, specifically binds to the C-terminal cysteine motif of DISC1 with low micromolar affinity. All three cysteines of the motif are required for high-affinity binding. Electron microscopy experiments combined with in silico structural predictions revealed that that the C-terminal of DISC1 forms an elongated tetrameric complex. The cysteine motif is consistently predicted to be located within a loop, fully exposed to solvent, providing a simple molecular framework to explain the high-affinity of DISC1 toward arsenous acid. This study sheds light on a novel functional facet of DISC1 as an arsenic binding protein and highlights its potential role as both a sensor and translational modulator within the Akt/mTOR pathway.

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

Protein intrinsic disorder is increasingly recognized for its biological and disease-driven functions. However, it represents significant challenges for biophysical studies due to its high conformational flexibility. In addressing these challenges, we highlight the complementary and distinct capabilities of a range of experimental and computational methods and further describe integrative strategies available for combining these techniques. Integrative biophysics methods provide valuable insights into the sequence–structure–function relationship of disordered proteins, setting the stage for protein intrinsic disorder to become a promising target for drug discovery. Finally, we briefly summarize recent advances in the development of new small molecule inhibitors targeting the disordered N-terminal domains of three vital transcription factors.

SETH predicts nuances of residue disorder from protein embeddings

Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of AlphaFold2 results for UniProt. However, many proteins have so-called intrinsically disordered regions (IDRs) that do not adopt unique structures in isolation. These IDRs are associated with several diseases, including Alzheimer’s Disease. We showed that three recent disorder measures of AlphaFold2 predictions (pLDDT, “experimentally resolved” prediction and “relative solvent accessibility”) correlated to some extent with IDRs. However, expert methods predict IDRs more reliably by combining complex machine learning models with expert-crafted input features and evolutionary information from multiple sequence alignments (MSAs). MSAs are not always available, especially for IDRs, and are computationally expensive to generate, limiting the scalability of the associated tools. Here, we present the novel method SETH that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input. Thereby, our method, relying on a relatively shallow convolutional neural network, outperformed much more complex solutions while being much faster, allowing to create predictions for the human proteome in about 1 hour on a consumer-grade PC with one NVIDIA GeForce RTX 3060. Trained on a continuous disorder scale (CheZOD scores), our method captured subtle variations in disorder, thereby providing important information beyond the binary classification of most methods. High performance paired with speed revealed that SETH’s nuanced disorder predictions for entire proteomes capture aspects of the evolution of organisms. Additionally, SETH could also be used to filter out regions or proteins with probable low-quality AlphaFold2 3D structures to prioritize running the compute-intensive predictions for large data sets. SETH is freely publicly available at: https://github.com/Rostlab/SETH.

Developing Bonded Potentials for a Coarse-Grained Model of Intrinsically Disordered Proteins

Recent advances in residue-level coarse-grained (CG) computational models have enabled molecular-level insights into biological condensates of intrinsically disordered proteins (IDPs), shedding light on the sequence determinants of their phase separation. The existing CG models that treat protein chains as flexible molecules connected via harmonic bonds cannot populate common secondary-structure elements. Here, we present a CG dihedral angle potential between four neighboring beads centered at Cα atoms to faithfully capture the transient helical structures of IDPs. In order to parameterize and validate our new model, we propose Cα-based helix assignment rules based on dihedral angles that succeed in reproducing the atomistic helicity results of a polyalanine peptide and folded proteins. We then introduce sequence-dependent dihedral angle potential parameters (εd) and use experimentally available helical propensities of naturally occurring 20 amino acids to find their optimal values. The single-chain helical propensities from the CG simulations for commonly studied prion-like IDPs are in excellent agreement with the NMR-based α-helix fraction, demonstrating that the new HPS-SS model can accurately produce structural features of IDPs. Furthermore, this model can be easily implemented for large-scale assembly simulations due to its simplicity.

Solution structure and dynamics of the mitochondrial-targeted GTPase-activating protein (GAP) VopE by an integrated NMR/SAXS approach

The bacterial pathogen Vibrio cholerae use a type III secretion system to inject effector proteins into a host cell. Recently, a putative Toxic GTPase Activating Protein (ToxGAP) called Vibrio outer protein E (VopE) was identified as a T3SS substrate and virulence factor that affected host mitochondrial dynamics and immune response. However, biophysical and structural characterization has been absent. Here, we describe solution NMR structure of the putative GTPase-activating protein (GAP) domain (73-204) of VopE. Using size exclusion chromatography coupled with small-angle x-ray scattering and residual dipolar coupling data, we restrained the MD process to efficiently determine the overall fold and improve the quality of the output calculated structures. Comparing the structure of VopE with other ToxGAP's revealed a similar overall fold with several features unique to VopE. Specifically, the "Bulge 1," α1 helix, and noteworthy "backside linker" elements on the N-terminus are dissimilar to the other ToxGAP's. By using NMR relaxation dispersion experiments, we demonstrate that these regions undergo motions on a > 6 s-1 timescale. Based on the disposition of these mobile regions relative to the putative catalytic arginine residue, we hypothesize that the protein may undergo structural changes to bind cognate GTPases.

Deep learning in prediction of intrinsic disorder in proteins

Intrinsic disorder prediction is an active area that has developed over 100 predictors. We identify and investigate a recent trend towards the development of deep neural network (DNN)-based methods. The first DNN-based method was released in 2013 and since 2019 deep learners account for majority of the new disorder predictors. We find that the 13 currently available DNN-based predictors are diverse in their topologies, sizes of their networks and the inputs that they utilize. We empirically show that the deep learners are statistically more accurate than other types of disorder predictors using the blind test dataset from the recent community assessment of intrinsic disorder predictions (CAID). We also identify several well-rounded DNN-based predictors that are accurate, fast and/or conveniently available. The popularity, favorable predictive performance and architectural flexibility suggest that deep networks are likely to fuel the development of future disordered predictors. Novel hybrid designs of deep networks could be used to adequately accommodate for diversity of types and flavors of intrinsic disorder. We also discuss scarcity of the DNN-based methods for the prediction of disordered binding regions and the need to develop more accurate methods for this prediction.

Predicting Protein Conformational Disorder and Disordered Binding Sites

In the last two decades it has become increasingly evident that a large number of proteins adopt either a fully or a partially disordered conformation. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded by the amino acid sequence, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting protein disorder and identifying intrinsically disordered binding sites.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Pöllänen M, Johansson A, Permi P, Ihalin R. Decreased temperature increases the expression of a disordered bacterial late embryogenesis abundant (LEA) protein that enhances natural transformation

Late embryogenesis abundant (LEA) proteins are important players in the management of responses to stressful conditions, such as drought, high salinity, and changes in temperature. Many LEA proteins do not have defined three-dimensional structures, so they are intrinsically disordered proteins (IDPs) and are often highly hydrophilic. Although LEA-like sequences have been identified in bacterial genomes, the functions of bacterial LEA proteins have been studied only recently. Sequence analysis of outer membrane interleukin receptor I (BilRI) from the oral pathogen Aggregatibacter actinomycetemcomitans indicated that it shared sequence similarity with group 3/3b/4 LEA proteins. Comprehensive nuclearcgq magnetic resonance (NMR) studies confirmed its IDP nature, and expression studies in A. actinomycetemcomitans harboring a red fluorescence reporter protein-encoding gene revealed that bilRI promoter expression was increased at decreased temperatures. The amino acid backbone of BilRI did not stimulate either the production of reactive oxygen species from human leukocytes or the production of interleukin-6 from human macrophages. Moreover, BilRI-specific IgG antibodies could not be detected in the sera of A. actinomycetemcomitans culture-positive periodontitis patients. Since the bilRI gene is located near genes involved in natural competence (i.e., genes associated with the uptake of extracellular (eDNA) and its incorporation into the genome), we also investigated the role of BilRI in these events. Compared to wild-type cells, the ΔbilRI mutants showed a lower transformation efficiency, which indicates either a direct or indirect role in natural competence. In conclusion, A. actinomycetemcomitans might express BilRI, especially outside the host, to survive under stressful conditions and improve its transmission potential.

Semaphorin 3A-Glycosaminoglycans Interaction as Therapeutic Target for Axonal Regeneration

Semaphorin 3A (Sema3A) is a cell-secreted protein that participates in the axonal guidance pathways. Sema3A acts as a canonical repulsive axon guidance molecule, inhibiting CNS regenerative axonal growth and propagation. Therefore, interfering with Sema3A signaling is proposed as a therapeutic target for achieving functional recovery after CNS injuries. It has been shown that Sema3A adheres to the proteoglycan component of the extracellular matrix (ECM) and selectively binds to heparin and chondroitin sulfate-E (CS-E) glycosaminoglycans (GAGs). We hypothesize that the biologically relevant interaction between Sema3A and GAGs takes place at Sema3A C-terminal polybasic region (SCT). The aims of this study were to characterize the interaction of the whole Sema3A C-terminal polybasic region (Sema3A 725–771) with GAGs and to investigate the disruption of this interaction by small molecules. Recombinant Sema3A basic domain was produced and we used a combination of biophysical techniques (NMR, SPR, and heparin affinity chromatography) to gain insight into the interaction of the Sema3A C-terminal domain with GAGs. The results demonstrate that SCT is an intrinsically disordered region, which confirms that SCT binds to GAGs and helps to identify the specific residues involved in the interaction. NMR studies, supported by molecular dynamics simulations, show that a new peptoid molecule (CSIC02) may disrupt the interaction between SCT and heparin. Our structural study paves the way toward the design of new molecules targeting these protein–GAG interactions with potential therapeutic applications.

Nrf2, the Major Regulator of the Cellular Oxidative Stress Response, is Partially Disordered

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription regulator that plays a pivotal role in coordinating the cellular response to oxidative stress. Through interactions with other proteins, such as Kelch-like ECH-associated protein 1 (Keap1), CREB-binding protein (CBP), and retinoid X receptor alpha (RXRα), Nrf2 mediates the transcription of cytoprotective genes critical for removing toxicants and preventing DNA damage, thereby playing a significant role in chemoprevention. Dysregulation of Nrf2 is linked to tumorigenesis and chemoresistance, making Nrf2 a promising target for anticancer therapeutics. However, despite the physiological importance of Nrf2, the molecular details of this protein and its interactions with most of its targets remain unknown, hindering the rational design of Nrf2-targeted therapeutics. With this in mind, we used a combined bioinformatics and experimental approach to characterize the structure of full-length Nrf2 and its interaction with Keap1. Our results show that Nrf2 is partially disordered, with transiently structured elements in its Neh2, Neh7, and Neh1 domains. Moreover, interaction with the Kelch domain of Keap1 leads to protection of the binding motifs in the Neh2 domain of Nrf2, while the rest of the protein remains highly dynamic. This work represents the first detailed structural characterization of full-length Nrf2 and provides valuable insights into the molecular basis of Nrf2 activity modulation in oxidative stress response.

Phase separation of the LINE-1 ORF1 protein is mediated by the N-terminus and coiled-coil domain

Long interspersed nuclear element-1 (L1) is a retrotransposable element that autonomously replicates in the human genome, resulting in DNA damage and genomic instability. Activation of L1 in senescent cells triggers a type I interferon response and age-associated inflammation. Two open reading frames encode an ORF1 protein functioning as messenger RNA chaperone and an ORF2 protein providing catalytic activities necessary for retrotransposition. No function has been identified for the conserved, disordered N-terminal region of ORF1. Using microscopy and NMR spectroscopy, we demonstrate that ORF1 forms liquid droplets in vitro in a salt-dependent manner and that interactions between its N-terminal region and coiled-coil domain are necessary for phase separation. Mutations disrupting blocks of charged residues within the N-terminus impair phase separation, whereas some mutations within the coiled-coil domain enhance phase separation. Demixing of the L1 particle from the cytosol may provide a mechanism to protect the L1 transcript from degradation.

A new type of flexible CP12 protein in the marine diatom Thalassiosira pseudonana

Background

CP12 is a small chloroplast protein that is widespread in various photosynthetic organisms and is an actor of the redox signaling pathway involved in the regulation of the Calvin Benson Bassham (CBB) cycle. The gene encoding this protein is conserved in many diatoms, but the protein has been overlooked in these organisms, despite their ecological importance and their complex and still enigmatic evolutionary background.

Methods

A combination of biochemical, bioinformatics and biophysical methods including electrospray ionization-mass spectrometry, circular dichroism, nuclear magnetic resonance spectroscopy and small X ray scattering, was used to characterize a diatom CP12.

Results

Here, we demonstrate that CP12 is expressed in the marine diatom Thalassiosira pseudonana constitutively in dark-treated and in continuous light-treated cells as well as in all growth phases. This CP12 similarly to its homologues in other species has some features of intrinsically disorder protein family: it behaves abnormally under gel electrophoresis and size exclusion chromatography, has a high net charge and a bias amino acid composition. By contrast, unlike other known CP12 proteins that are monomers, this protein is a dimer as suggested by native electrospray ionization-mass spectrometry and small angle X-ray scattering. In addition, small angle X-ray scattering revealed that this CP12 is an elongated cylinder with kinks. Circular dichroism spectra indicated that CP12 has a high content of α-helices, and nuclear magnetic resonance spectroscopy suggested that these helices are unstable and dynamic within a millisecond timescale. Together with in silico predictions, these results suggest that T. pseudonana CP12 has both coiled coil and disordered regions.

Conclusions

These findings bring new insights into the large family of dynamic proteins containing disordered regions, thus increasing the diversity of known CP12 proteins. As it is a protein that is more abundant in many stresses, it is not devoted to one metabolism and in particular, it is not specific to carbon metabolism. This raises questions about the role of this protein in addition to the well-established regulation of the CBB cycle.

Choregraphy of metabolism by CP12 proteins in Viridiplantae and Heterokonta.

While the monomeric CP12 in Viridiplantae is involved in carbon assimilation, regulating phosphoribulokinase (PRK) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) through the formation of a ternary complex, in Heterokonta studied so far, the dimeric CP12 is associated with Ferredoxin-NADP reductase (FNR) and GAPDH. The Viridiplantae CP12 can bind metal ions and can be a chaperone, the Heterokonta CP12 is more abundant in all stresses (C, N, Si, P limited conditions) and is not specific to a metabolism.

Liquid-Liquid Phase Separation of Tau Protein Is Encoded at the Monomeric Level

Liquid-liquid phase separation (LLPS) is involved in both physiological and pathological processes. The intrinsically disordered protein Tau and its K18 construct can undergo LLPS in a distinct temperature-dependent manner, and the LLPS of Tau protein can initiate Tau aggregation. However, the underlying mechanism driving Tau LLPS remains largely elusive. To understand the temperature-dependent LLPS behavior of Tau at the monomeric level, we explored the conformational ensemble of Tau at different temperatures by performing all-atom replica-exchange molecular dynamic simulation on K18 monomer with an accumulated simulation time of 26.4 μs. Our simulation demonstrates that the compactness, β-structure propensity, and intramolecular interaction of K18 monomer exhibit nonlinear temperature-dependent behavior. ²⁹⁵DNIKHV³⁰⁰/³²⁶GNIHHK³³¹/³³⁷VEVKSE³⁴² make significant contributions to the temperature dependence of the β propensity of K18 monomer, while the two fibril-nucleating cores display relatively high β propensity at all temperatures. At a specific temperature, K18 monomer adopts the most collapsed state with exposed sites for both persistent and transient interactions. Given that more collapsed polypeptide chains were reported to be more prone to phase separate, our results suggest that K18 monomer inherently possesses conformational characteristics favoring LLPS. Our simulation predicts the importance of ²⁹⁵DNIKHV³⁰⁰/³²⁶GNIHHK³³¹/³³⁷VEVKSE³⁴² to the temperature-dependent conformational properties of K18, which is corroborated by CD spectra, turbidity assays, and DIC microscopy. Taken together, we offer a computational and experimental approach to comprehend the structural basis for LLPS by amyloidal building blocks.

The intrinsically disordered N-terminus of the voltage-dependent anion channel

The voltage-dependent anion channel (VDAC) is a critical β-barrel membrane protein of the mitochondrial outer membrane, which regulates the transport of ions and ATP between mitochondria and the cytoplasm. In addition, VDAC plays a central role in the control of apoptosis and is therefore of great interest in both cancer and neurodegenerative diseases. Although not fully understood, it is presumed that the gating mechanism of VDAC is governed by its N-terminal region which, in the open state of the channel, exhibits an α-helical structure positioned midway inside the pore and strongly interacting with the β-barrel wall. In the present work, we performed molecular simulations with a recently developed force field for disordered systems to shed new light on known experimental results, showing that the N-terminus of VDAC is an intrinsically disordered region (IDR). First, simulation of the N-terminal segment as a free peptide highlighted its disordered nature and the importance of using an IDR-specific force field to properly sample its conformational landscape. Secondly, accelerated dynamics simulation of a double cysteine VDAC mutant under applied voltage revealed metastable low conducting states of the channel representative of closed states observed experimentally. Related structures were characterized by partial unfolding and rearrangement of the N-terminal tail, that led to steric hindrance of the pore. Our results indicate that the disordered properties of the N-terminus are crucial to properly account for the gating mechanism of VDAC.

The voltage-dependent anion channel (VDAC) is a membrane protein playing a pivotal role in the transport of ions or ATP across the mitochondrial outer membrane as well as in the induction of apoptosis. At high enough membrane potential, VDAC is known to transition from an open state to multiple closed states, reducing the flow of ions through the channel and blocking the passage of large metabolites. While the structure of the open state was resolved more than a decade ago, a molecular description of the gating mechanism of the channel is still missing. Here we show that the N-terminus of VDAC is an intrinsically disordered region and that such a property has a profound impact on its dynamics either as a free peptide or as part of the channel. By taking disordered properties of the N-terminus into account, we managed to generate long-lived closed conformations of the channel at experimental values of the membrane potential. Our results provide new insights into the molecular mechanism driving the gating of VDAC.

Reduced proteasome activity in the aging brain results in ribosome stoichiometry loss and aggregation

A progressive loss of protein homeostasis is characteristic of aging and a driver of neurodegeneration. To investigate this process quantitatively, we characterized proteome dynamics during brain aging in the short-lived vertebrate Nothobranchius furzeri combining transcriptomics and proteomics. We detected a progressive reduction in the correlation between protein and mRNA, mainly due to post-transcriptional mechanisms that account for over 40% of the age-regulated proteins. These changes cause a progressive loss of stoichiometry in several protein complexes, including ribosomes, which show impaired assembly/disassembly and are enriched in protein aggregates in old brains. Mechanistically, we show that reduction of proteasome activity is an early event during brain aging and is sufficient to induce proteomic signatures of aging and loss of stoichiometry in vivo. Using longitudinal transcriptomic data, we show that the magnitude of early life decline in proteasome levels is a major risk factor for mortality. Our work defines causative events in the aging process that can be targeted to prevent loss of protein homeostasis and delay the onset of age-related neurodegeneration.

Rapid Biophysical Characterization and NMR Spectroscopy Structural Analysis of Small Proteins from Bacteria and Archaea

Proteins encoded by small open reading frames (sORFs) have a widespread occurrence in diverse microorganisms and can be of high functional importance. However, due to annotation biases and their technically challenging direct detection, these small proteins have been overlooked for a long time and were only recently rediscovered. The currently rapidly growing number of such proteins requires efficient methods to investigate their structure-function relationship. Herein, a method is presented for fast determination of the conformational properties of small proteins. Their small size makes them perfectly amenable for solution-state NMR spectroscopy. NMR spectroscopy can provide detailed information about their conformational states (folded, partially folded, and unstructured). In the context of the priority program on small proteins funded by the German research foundation (SPP2002), 27 small proteins from 9 different bacterial and archaeal organisms have been investigated. It is found that most of these small proteins are unstructured or partially folded. Bioinformatics tools predict that some of these unstructured proteins can potentially fold upon complex formation. A protocol for fast NMR spectroscopy structure elucidation is described for the small proteins that adopt a persistently folded structure by implementation of new NMR technologies, including automated resonance assignment and nonuniform sampling in combination with targeted acquisition.

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory (LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone, but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features (MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.

The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease.

Intrinsically disordered HAX-1 regulates Ca2+ cycling by interacting with lipid membranes and the phospholamban cytoplasmic region

Hematopoietic-substrate-1 associated protein X-1 (HAX-1) is a 279 amino acid protein expressed ubiquitously. In cardiac muscle, HAX-1 was found to modulate the sarcoendoplasmic reticulum calcium ATPase (SERCA) by shifting its apparent Ca2+ affinity (pCa). It has been hypothesized that HAX-1 binds phospholamban (PLN), enhancing its inhibitory function on SERCA. HAX-1 effects are reversed by cAMP-dependent protein kinase A that phosphorylates PLN at Ser16. To date, the molecular mechanisms for HAX-1 regulation of the SERCA/PLN complex are still unknown. Using enzymatic, in cell assays, circular dichroism, and NMR spectroscopy, we found that in the absence of a binding partner HAX-1 is essentially disordered and adopts a partial secondary structure upon interaction with lipid membranes. Also, HAX-1 interacts with the cytoplasmic region of monomeric and pentameric PLN as detected by NMR and in cell FRET assays, respectively. We propose that the regulation of the SERCA/PLN complex by HAX-1 is mediated by its interactions with lipid membranes, adding another layer of control in Ca2+ homeostatic balance in the heart muscle.

Inherent Biophysical Properties Modulate the Toxicity of Soluble Amyloidogenic Light Chains

In light chain amyloidosis (AL), fibrillar deposition of monoclonal immunoglobulin light chains (LCs) in vital organs, such as heart, is associated with their severe dysfunction. In addition to the cellular damage caused by fibril deposition, direct toxicity of soluble prefibrillar amyloidogenic proteins has been reported, in particular, for cardiotoxicity. However, the molecular bases of proteotoxicity by soluble LCs have not been clarified. Here, to address this issue, we rationally engineered the amino acid sequence of the highly cardiotoxic LC H6 by introducing three residue mutations, designed to reduce the dynamics of its native state. The resulting mutant (mH6) is less toxic than its parent H6 to human cardiac fibroblasts and C. elegans. The high sequence and structural similarity, together with the different toxicity, make H6 and its non-toxic designed variant mH6 a test case to shed light on the molecular properties underlying soluble toxicity. Our comparative structural and biochemical study of H6 and mH6 shows closely matching crystal structures, whereas spectroscopic data and limited proteolysis indicate that H6 displays poorly cooperative fold, higher flexibility, and kinetic instability, and a higher dynamic state in its native fold. Taken together, the results of this study show a strong correlation between the overall conformational properties of the native fold and the proteotoxicity of cardiotropic LCs.

Structural approaches for the DNA binding motifs prediction in Bacillus thuringiensis sigma-E transcription factor (σETF)

The sigma-E transcription factor (σ^ETF) can be found in most of the bacteria cells including Bacillus thuringiensis. However, the cellular regulatory mechanisms of these transcription factors in the mass production of δ-endotoxins during sporulation stage are yet to be revealed. In addition, the recognition of DNA towards σ^ETF DNA binding motifs that led to the transcription activities is also being poorly studied. Therefore, this work studied the possible DNA binding motifs of σ^ETF by utilising in silico approaches. The structure of σ^ETF was first built via three different computational methods. A cognate DNA sequence was then docked to the predicted σ^ETF DNA-binding motifs. The binding free energy calculated using molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) for triplicate 50 ns simulation of σ^ETF-DNA complex revealed favourable binding energy of DNA to σ^ETF (average ∆G_bind = -34.57 kcal/mol) mainly driven by non-polar interactions. This study revealed that σ^ETF LYS131, ARG133, PHE138, TRP146, ARG222, LYS225 and ARG226 are most likely the key residues upon the binding and recognition of DNA prior to transcription actives. Since determination of genome-regulating protein which recognises specific DNA sequence is important to discriminate between the proteins preferences for different genes, this study might provide some understanding on the possible σ^ETF-DNA recognition prior to transcription initiated for the δ-endotoxins production.

Intrinsically disordered proteins in various hypotheses on the pathogenesis of Alzheimer's and Parkinson's diseases

Amyloid-β (Aβ) and α-synuclein (αS) are two intrinsically disordered proteins (IDPs) at the centers of the pathogenesis of Alzheimer's and Parkinson's diseases, respectively. Different hypotheses have been proposed for explanation of the molecular mechanisms of the pathogenesis of these two diseases, with these two IDPs being involved in many of these hypotheses. Currently, we do not know, which of these hypothesis is more accurate. Experiments face challenges due to the rapid conformational changes, fast aggregation processes, solvent and paramagnetic effects in studying these two IDPs in detail. Furthermore, pathological modifications impact their structures and energetics. Theoretical studies using computational chemistry and computational biology have been utilized to understand the structures and energetics of Aβ and αS. In this chapter, we introduce Aβ and αS in light of various hypotheses, and discuss different experimental and theoretical techniques that are used to study these two proteins along with their weaknesses and strengths. We suggest that a promising solution for studying Aβ and αS at the center of varying hypotheses could be provided by developing new techniques that link quantum mechanics, statistical mechanics, thermodynamics, bioinformatics to machine learning. Such new developments could also lead to development in experimental techniques.

Molecular basis of the scalp-ear-nipple syndrome unraveled by the characterization of disease-causing KCTD1 mutants

The scalp-ear-nipple (SEN) syndrome is an autosomal-dominant disorder characterized by cutis aplasia of the scalp and malformations of breast, external ears, digits, and nails. Genetic analyses have shown that the disease is caused by missense mutations of the KCTD1 protein, although the functional/structural basis of SEN insurgence is hitherto unknown. With the aim of unravelling the molecular basis of the SEN syndrome associated with KCTD1 mutations we here expressed and characterized several disease causing mutants. A preliminary dissection of the protein provides insights into the role that individual domains play in KCTD1 stability. The characterization of SEN-causing mutants indicates that, although the mutation sites are located in distant regions of the BTB domain or of the pre-BTB region, all of them are unable to interact with the transcription factor AP-2α, a well-known KCTD1 biological partner. Notably, all mutations, including the one located in the pre-BTB region, produce a significant destabilization of the protein. The structural role of the pre-BTB region in KCTD1 and other proteins of the family is corroborated by its sequence conservation in orthologs and paralogs. Interestingly, SEN-causing mutations also favor the tendency of KCTD1 to adopt structural states that are characterized by the ability to bind the β-amyloid fluorescent dye thioflavin T. The formation of aggregation-prone species may have important implications for the disease etiology. Collectively, these findings provide an intriguing picture of the functional and structural alterations induced by KCTD1 mutations that ultimately lead to disease.

Entropy and Information within Intrinsically Disordered Protein Regions

Bioinformatics and biophysical studies of intrinsically disordered proteins and regions (IDRs) note the high entropy at individual sequence positions and in conformations sampled in solution. This prevents application of the canonical sequence-structure-function paradigm to IDRs and motivates the development of new methods to extract information from IDR sequences. We argue that the information in IDR sequences cannot be fully revealed through positional conservation, which largely measures stable structural contacts and interaction motifs. Instead, considerations of evolutionary conservation of molecular features can reveal the full extent of information in IDRs. Experimental quantification of the large conformational entropy of IDRs is challenging but can be approximated through the extent of conformational sampling measured by a combination of NMR spectroscopy and lower-resolution structural biology techniques, which can be further interpreted with simulations. Conformational entropy and other biophysical features can be modulated by post-translational modifications that provide functional advantages to IDRs by tuning their energy landscapes and enabling a variety of functional interactions and modes of regulation. The diverse mosaic of functional states of IDRs and their conformational features within complexes demands novel metrics of information, which will reflect the complicated sequence-conformational ensemble-function relationship of IDRs.

Quality and bias of protein disorder predictors

Disorder in proteins is vital for biological function, yet it is challenging to characterize. Therefore, methods for predicting protein disorder from sequence are fundamental. Currently, predictors are trained and evaluated using data from X-ray structures or from various biochemical or spectroscopic data. However, the prediction accuracy of disordered predictors is not calibrated, nor is it established whether predictors are intrinsically biased towards one of the extremes of the order-disorder axis. We therefore generated and validated a comprehensive experimental benchmarking set of site-specific and continuous disorder, using deposited NMR chemical shift data. This novel experimental data collection is fully appropriate and represents the full spectrum of disorder. We subsequently analyzed the performance of 26 widely-used disorder prediction methods and found that these vary noticeably. At the same time, a distinct bias for over-predicting order was identified for some algorithms. Our analysis has important implications for the validity and the interpretation of protein disorder, as utilized, for example, in assessing the content of disorder in proteomes.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

A Protein Intrinsic Disorder Approach for Characterising Differentially Expressed Genes in Transcriptome Data: Analysis of Cell-Adhesion Regulated Gene Expression in Lymphoma Cells

Conformational protein properties are coupled to protein functionality and could provide a useful parameter for functional annotation of differentially expressed genes in transcriptome studies. The aim was to determine whether predicted intrinsic protein disorder was differentially associated with proteins encoded by genes that are differentially regulated in lymphoma cells upon interaction with stromal cells, an interaction that occurs in microenvironments, such as lymph nodes that are protective for lymphoma cells during chemotherapy. Intrinsic disorder protein properties were extracted from the Database of Disordered Protein Prediction (D²P²), which contains data from nine intrinsic disorder predictors. Proteins encoded by differentially regulated cell-adhesion regulated genes were enriched in intrinsically disordered regions (IDRs) compared to other genes both with regard to IDR number and length. The enrichment was further ascribed to down-regulated genes. Consistently, a higher proportion of proteins encoded by down-regulated genes contained at least one IDR or were completely disordered. We conclude that down-regulated genes in stromal cell-adherent lymphoma cells encode proteins that are characterized by elevated levels of intrinsically disordered conformation, indicating the importance of down-regulating functional mechanisms associated with intrinsically disordered proteins in these cells. Further, the approach provides a generally applicable and complementary alternative to classification of differentially regulated genes using gene ontology or pathway enrichment analysis.

Bacillus subtilis HelD, an RNA Polymerase Interacting Helicase, Forms Amyloid-Like Fibrils

HelD, an RNA polymerase binding protein from Bacillus subtilis, stimulates transcription and helps in timely adaptation of cells under diverse environmental conditions. At present, no structural information is available for HelD. In the current study, we performed size exclusion chromatography coupled to small angle X-ray scattering (SEC-SAXS) which suggests that HelD is predominantly monomeric and globular in solution. Using combination of size exclusion chromatography and analytical ultracentrifugation, we also show that HelD has a tendency to form higher order oligomers in solution. CD experiments suggest that HelD has both α-helical (∼35%) and β sheet (∼26%) secondary structural elements. Thermal melting experiments suggest that even at 90°C, there is only about 30% loss in secondary structural contents with T_m of 44°C. However, with the increase in temperature, there was a gain in the β-sheet content and significant irreversible loss of α-helical content. Using a combination of X-ray fiber diffraction analysis, and dye based assays including Thioflavin-T based fluorescence and Congo red binding assays, we discovered that HelD forms amyloid-like fibrils at physiologically relevant conditions in vitro. Using confocal imaging, we further show that HelD forms amyloid inclusions in Escherichia coli. Bioinformatics-based sequence analysis performed using three independent web-based servers suggests that HelD has more than 20 hot-spots spread across the sequence that may aid the formation of amyloid-like fibrils. This discovery adds one more member to the growing list of amyloid or amyloid-like fibril forming cytosolic proteins in bacteria. Future studies aimed at resolving the function of amyloid-like fibrils or amyloid inclusions may help better understand their role, if any, in the bacterial physiology.

SVM-dependent pairwise HMM: an application to protein pairwise alignments

Motivation

Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions.

Results

Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences.

Availability and implementation

A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo.

Contact

wim.vranken@vub.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

A method for partitioning the information contained in a protein sequence between its structure and function

Proteins employ the information stored in the genetic code and translated into their sequences to carry out well-defined functions in the cellular environment. The possibility to encode for such functions is controlled by the balance between the amount of information supplied by the sequence and that left after that the protein has folded into its structure. We study the amount of information necessary to specify the protein structure, providing an estimate that keeps into account the thermodynamic properties of protein folding. We thus show that the information remaining in the protein sequence after encoding for its structure (the 'information gap') is very close to what needed to encode for its function and interactions. Then, by predicting the information gap directly from the protein sequence, we show that it may be possible to use these insights from information theory to discriminate between ordered and disordered proteins, to identify unknown functions, and to optimize artificially-designed protein sequences.

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82-84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88-90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.

Peptide Level Turnover Measurements Enable the Study of Proteoform Dynamics

The coordination of protein synthesis and degradation regulating protein abundance is a fundamental process in cellular homeostasis. Today, mass spectrometry-based technologies allow determination of endogenous protein turnover on a proteome-wide scale. However, standard dynamic SILAC (Stable Isotope Labeling in Cell Culture) approaches can suffer from missing data across pulse time-points limiting the accuracy of such analysis. This issue is of particular relevance when studying protein stability at the level of proteoforms because often only single peptides distinguish between different protein products of the same gene. To address this shortcoming, we evaluated the merits of combining dynamic SILAC and tandem mass tag (TMT)-labeling of ten pulse time-points in a single experiment. Although the comparison to the standard dynamic SILAC method showed a high concordance of protein turnover rates, the pulsed SILAC-TMT approach yielded more comprehensive data (6000 proteins on average) without missing values. Replicate analysis further established that the same reproducibility of turnover rate determination can be obtained for peptides and proteins facilitating proteoform resolved investigation of protein stability. We provide several examples of differentially turned over splice variants and show that post-translational modifications can affect cellular protein half-lives. For example, N-terminally processed peptides exhibited both faster and slower turnover behavior compared with other peptides of the same protein. In addition, the suspected proteolytic processing of the fusion protein FAU was substantiated by measuring vastly different stabilities of the cleavage products. Furthermore, differential peptide turnover suggested a previously unknown mechanism of activity regulation by post-translational destabilization of cathepsin D as well as the DNA helicase BLM. Finally, our comprehensive data set facilitated a detailed evaluation of the impact of protein properties and functions on protein stability in steady-state cells and uncovered that the high turnover of respiratory chain complex I proteins might be explained by oxidative stress.

Conformational sampling of the intrinsically disordered dsRBD-1 domain from Arabidopsis thaliana DCL1

Partial folding and stability of DCL1-dsRBD1.

Non-Native α-Helices in the Initial Folding Intermediate Facilitate the Ordered Assembly of the β-Barrel in β-Lactoglobulin

The roles of non-native α-helices frequently observed in the initial folding stage of β-sheet proteins have been examined for many years. We herein investigated the residue-level structures of several mutants of bovine β-lactoglobulin (βLG) in quenched-flow pH-pulse labeling experiments. βLG assumes a collapsed intermediate with a non-native α-helical structure (I₀) in the early stage of folding, although its native form is predominantly composed of β-structures. The protection profile in I₀ of pseudo-wild type (WT*) βLG was found to deviate from the pattern of the "average area buried upon folding" (AABUF). In particular, the level of protection at the region of strand A, at which non-native α-helices form in the I₀ state, was significantly low compared to AABUF. G17E, the mutant with an increased helical propensity, showed a similar protection pattern. In contrast, the protection pattern for I₀ of E44L, the mutant with an increased β-sheet propensity, was distinct from that of WT* and resembled the AABUF pattern. Transverse relaxation measurements demonstrated that the positions of the residual structures in the unfolded states of these mutants were consistent with those of the protected residues in the respective I₀ states. On the basis of the slower conversion of I₀ to the native state for E44L to that for WT*, non-native α-helices facilitate the ordered assembly of the β-barrel by preventing interactions that trap folding.

A Biologically-validated HCV E1E2 Heterodimer Structural Model

The design of vaccine strategies and the development of drugs targeting the early stages of Hepatitis C virus (HCV) infection are hampered by the lack of structural information about its surface glycoproteins E1 and E2, the two constituents of HCV entry machinery. Despite the recent crystal resolution of limited versions of both proteins in truncated form, a complete picture of the E1E2 complex is still missing. Here we combined deep computational analysis of E1E2 secondary, tertiary and quaternary structure with functional and immunological mutational analysis across E1E2 in order to propose an in silico model for the ectodomain of the E1E2 heterodimer. Our model describes E1-E2 ectodomain dimerization interfaces, provides a structural explanation of E1 and E2 immunogenicity and sheds light on the molecular processes and disulfide bridges isomerization underlying the conformational changes required for fusion. Comprehensive alanine mutational analysis across 553 residues of E1E2 also resulted in identifying the epitope maps of diverse mAbs and the disulfide connectivity underlying E1E2 native conformation. The predicted structure unveils E1 and E2 structures in complex, thus representing a step towards the rational design of immunogens and drugs inhibiting HCV entry.