From protein sequence to dynamics and disorder with DynaMine

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://github.com/YyinGong/MR2CPPIS.

Deciphering the dual nature of nesfatin-1: a tale of zinc ion's Janus-faced influence

BACKGROUND

Nucleobindin-2 (Nucb2) and nesfatin-1 (N1) are widely distributed hormones that regulate numerous physiological processes, from energy homeostasis to carcinogenesis. However, the role of nesfatin-2 (N2), the second product of Nucb2 proteolytic processing, remains elusive. To elucidate the relationship between the structure and function of nesfatins, we investigated the properties of chicken and human homologs of N1, as well as a fragment of Nucb2 consisting of N1 and N2 conjoined in a head-to-tail manner (N1/2).

RESULTS

Our findings indicate that Zn(II) sensing, in the case of N1, is conserved between chicken and human species. However, the data presented here reveal significant differences in the molecular features of the analyzed peptides, particularly in the presence of Zn(II). We demonstrated that Zn(II) has a Janus effect on the M30 region (a crucial anorexigenic core) of N1 and N1/2. In N1 homologs, Zn(II) binding results in the concealment of the M30 region driven by a disorder-to-order transition and adoption of the amyloid fold. In contrast, in N1/2 molecules, Zn(II) binding causes the exposure of the M30 region and its destabilization, resulting in strong exposure of the region recognized by prohormone convertases within the N1/2 molecule.

CONCLUSIONS

In conclusion, we found that Zn(II) binding is conserved between chicken and human N1. However, despite the high homology of chicken and human N1, their interaction modes with Zn(II) appear to differ. Furthermore, Zn(II) binding might be essential for regulating the function of nesfatins by spatiotemporally hindering the N1 anorexigenic M30 core and concomitantly facilitating N1 release from Nucb2.

Prediction of order parameters based on protein NMR structure ensemble and machine learning

The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. The most used NMR approach to study fast protein dynamics is the model free method, which uses order parameter S2 to describe the amplitude of the internal motion of local group. However, to obtain order parameter through NMR experiments is quite complex and lengthy. In this paper, we present a machine learning approach for predicting backbone 1H-15N order parameters based on protein NMR structure ensemble. A random forest model is used to learn the relationship between order parameters and structural features. Our method achieves high accuracy in predicting backbone 1H-15N order parameters for a test dataset of 10 proteins, with a Pearson correlation coefficient of 0.817 and a root-mean-square error of 0.131.

Leucine Motifs Stabilize Residual Helical Structure in Disordered Proteins

Many examples are known of regions of intrinsically disordered proteins that fold into α-helices upon binding to their targets. These helical binding motifs (HBMs) can be partially helical also in the unbound state, and this so-called residual structure can affect binding affinity and kinetics. To investigate the underlying mechanisms governing the formation of residual helical structure, we assembled a dataset of experimental helix contents of 65 peptides containing HBM that fold-upon-binding. The average residual helicity is 17% and increases to 60% upon target binding. The helix contents of residual and target-bound structures do not correlate, however the relative location of helix elements in both states shows a strong overlap. Compared to the general disordered regions, HBMs are enriched in amino acids with high helix preference and these residues are typically involved in target binding, explaining the overlap in helix positions. In particular, we find that leucine residues and leucine motifs in HBMs are the major contributors to helix stabilization and target-binding. For the two model peptides, we show that substitution of leucine motifs to other hydrophobic residues (valine or isoleucine) leads to reduction of residual helicity, supporting the role of leucine as helix stabilizer. From the three hydrophobic residues only leucine can efficiently stabilize residual helical structure. We suggest that the high occurrence of leucine motifs and a general preference for leucine at binding interfaces in HBMs can be explained by its unique ability to stabilize helical elements.

Recent Advances and Challenges in Protein Structure Prediction

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Phase separation propensity of the intrinsically disordered AB region of human RXRβ

RXRβ is one of three subtypes of human retinoid X receptor (RXR), a transcription factor that belongs to the nuclear receptor superfamily. Its expression can be detected in almost all tissues. In contrast to other subtypes - RXRα and RXRγ - RXRβ has the longest and unique N-terminal sequence called the AB region, which harbors a ligand-independent activation function. In contrast to the functional properties of this sequence, the molecular properties of the AB region of human RXRβ (AB_hRXRB) have not yet been characterized. Here, we present a systematic biochemical and biophysical analysis of recombinant AB_hRXRB, along with in silico examinations, which demonstrate that AB_hRXRB exhibits properties of a coil-like intrinsically disordered region. AB_hRXRB possesses a flexible structure that is able to adopt a more ordered conformation under the influence of different environmental factors. Interestingly, AB_hRXRB promotes the formation of liquid-liquid phase separation (LLPS), a phenomenon previously observed for the AB region of another human subtype of RXR - RXRγ (AB_hRXRG). Although both AB regions seem to be similar in terms of their ability to induce phase separation, they clearly differ in the sensitivity to factors driving and regulating LLPS. This distinct LLPS response to environmental factors driven by the unique amino acid compositions of AB_hRXRB and AB_hRXRG can be significant for the specific modulation of the transcriptional activation of target genes by different subtypes of RXR. Video Abstract.

Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA-binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA-binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA-binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°-120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

Alternatively spliced exon regulates context-dependent MEF2D higher-order assembly during myogenesis

During muscle cell differentiation, the alternatively spliced, acidic β-domain potentiates transcription of Myocyte-specific Enhancer Factor 2 (Mef2D). Sequence analysis by the FuzDrop method indicates that the β-domain can serve as an interaction element for Mef2D higher-order assembly. In accord, we observed Mef2D mobile nuclear condensates in C2C12 cells, similar to those formed through liquid-liquid phase separation. In addition, we found Mef2D solid-like aggregates in the cytosol, the presence of which correlated with higher transcriptional activity. In parallel, we observed a progress in the early phase of myotube development, and higher MyoD and desmin expression. In accord with our predictions, the formation of aggregates was promoted by rigid β-domain variants, as well as by a disordered β-domain variant, capable of switching between liquid-like and solid-like higher-order states. Along these lines, NMR and molecular dynamics simulations corroborated that the β-domain can sample both ordered and disordered interactions leading to compact and extended conformations. These results suggest that β-domain fine-tunes Mef2D higher-order assembly to the cellular context, which provides a platform for myogenic regulatory factors and the transcriptional apparatus during the developmental process.

N-Glycan on the Non-Consensus N-X-C Glycosylation Site Impacts Activity, Stability, and Localization of the Sda Synthase B4GALNT2

The Sd^a carbohydrate epitope and its biosynthetic B4GALNT2 enzyme are expressed in the healthy colon and down-regulated to variable extents in colon cancer. The human B4GALNT2 gene drives the expression of a long and a short protein isoform (LF-B4GALNT2 and SF-B4GALNT2) sharing identical transmembrane and luminal domains. Both isoforms are trans-Golgi proteins and the LF-B4GALNT2 also localizes to post-Golgi vesicles thanks to its extended cytoplasmic tail. Control mechanisms underpinning Sd^a and B4GALNT2 expression in the gastrointestinal tract are complex and not fully understood. This study reveals the existence of two unusual N-glycosylation sites in B4GALNT2 luminal domain. The first atypical N-X-C site is evolutionarily conserved and occupied by a complex-type N-glycan. We explored the influence of this N-glycan using site-directed mutagenesis and showed that each mutant had a slightly decreased expression level, impaired stability, and reduced enzyme activity. Furthermore, we observed that the mutant SF-B4GALNT2 was partially mislocalized in the endoplasmic reticulum, whereas the mutant LF-B4GALNT2 was still localized in the Golgi and post-Golgi vesicles. Lastly, we showed that the formation of homodimers was drastically impaired in the two mutated isoforms. An AlphaFold2 model of the LF-B4GALNT2 dimer with an N-glycan on each monomer corroborated these findings and suggested that N-glycosylation of each B4GALNT2 isoform controlled their biological activity.

Does one plus one always equal two? Structural differences between nesfatin-1, -2, and nesfatin-1/2

Nesfatin-1 and -2 are produced from a reaction in which the N-terminus of human Nucleobindin-2 undergoes proteolytical processing. To date, Nucleobindin-2 and/or nesfatin-1 have only been shown to act as peptide hormones. On the other hand, the purpose of nesfatin-2 remains unknown. Since Nucleobindin-2/nesfatin-1 is thought impact the control of a wide range of physiological processes, including energy homeostasis, neurodegenerative processes and carcinogenesis, its ligands/interactions deserve special studies and attention. However, there are no reports about the molecular properties of the proteolytical products of human Nucleobindin-2 in the literature. Hence, this study aimed to analyze the effect of Zn(II) and Ca(II) on human nesfatin-1, -2, and -1/2 structures. Herein, we report that human nesfatin-1 is a member of the intrinsically disordered protein family, as indicated by circular dichroism and analytical ultracentrifugation experiments. In contrast, we found that the human nesfatin-2 and nesfatin-1/2 structures were globular with intrinsically disordered regions. Under Zn(II) treatment, we observed concentration-dependent structurization and compaction of intrinsically disordered nesfatin-1 and its propensity for oligomerization, as well as destabilization of both nesfatin-2 and nesfatin-1/2. Furthermore, dissociation constants for Zn(II) binding by nesfatin-1, nesfatin-2, and nesfatin-1/2 were also reported. Moreover, structurally distinct nesfatin-1 and -2 seem to be interdependent when linked together, as indicated by the observed molecular properties of nesfatin-1/2, which in turn are not a simple sum of the properties exhibited by the former peptides. Thus, herein, we shed new light on the molecular behavior of human nesfatins, which might help to elucidate the complex function of those peptides. Video abstract.

Structural chemistry and molecular-level interactome reveals histidine kinase EvgS to subvert both antimicrobial resistance and virulence in Shigella flexneri 2a str. 301

Multi-drug resistant (MDR) Shigella flexneri 2a, one of the leading bacterial agents of diarrhoeal mortality, has posed challenges in treatment strategies. The present study was conducted to identify potential therapeutic biomarkers using gene interaction network (GIN) in order to understand the cellular and molecular level interactions of both antimicrobial resistance (AMR) and virulence genes through topological and clustering metrics. Statistically significant differential gene expression (DGE), structural chemistry and dynamics were incorporated to elucidate biomarker for sustainable therapeutic regimen against MDR S. flexneri. Functional enrichments and topological metrics revealed evgS, ybjZ, tolC, gyrA, parC and their direct interactors to be associated with diverse AMR mechanisms. Histidine kinase EvgS was considered as the hub protein due to its highest prevalence in the molecular interactome profiles of both the AMR (71.6%) and virulence (45.8%) clusters interconnecting several genes concerning two-component system (TCS). DGE profiles of ΔPhoPQ (deleted regulatory PhoP and sensor PhoQ) led to the upregulation of TCS comprising EvgSA thereby validating EvgS as a promising therapeutic biomarker. Druggability and structural stability of EvgS was assessed through thermal shifts, backbone stability and coarse dynamics refinement. Structure-function relationship was established revealing the C-terminal extracellular domain as the drug-binding site which was further validated through molecular dynamics simulation. Structure elucidation of identified biomarker followed by secondary and tertiary structural validation would prove pivotal for future therapeutic interventions against subverting both AMR and virulence posed by this strain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03325-w.

Evaluating the role of trypsin in silk degumming: An in silico approach

The trypsin being universal enzyme forming family of proteases catalyzes the hydrolysis of proteins into amino acids and regenerates the serine hydroxyl an active site. The trypsin enzyme from D. saccharalis, uses sericin as its preferred substrate. Presence of catalytic triad (serine, aspartic acid and histidine) at the substrate binding site of this enzyme is very important for the catalytic activity. In the current study, the interacting mechanism between the substrate sericin protein and enzyme trypsin protein were explored by integrating various computational approaches including physico-chemical properties, biophysical properties, dynamics, gene ontology, molecular docking, protein - protein interactions, binding free energy calculation and structural motifs were studied. The evolutionary study performed by MEGA X showed that trypsin protein sequence (ALE15212.1) is closely related to cocoonase protein sequence (ADG26770.1) from Antheraea pernyi. 3-D models of trypsin and sericin proteins were predicted using I-TASSER and further validated by PROCHECK, and ProSAweb softwares. The predicted trypsin structure model was assigned E.C. no. 3.4.21.4 which refers hydrolytic mechanism. Gene Ontology predicted by QuickGO showed that trypsin has serine hydrolase activity (GO: 00017171), and part of proteolysis (GO: 0006508) as well as protein metabolic process (GO:0019538) actvity. Molecular docking studies between trypsin and sericin proteins were conducted by the HADDOCK 2.4 having best docked protein complex with Z-score - 1.9. 2D and 3D protein-protein interaction was performed with LIGPLOT⁺ and HAWKDOCK, PDBsum, respectively. The amino acid residues interacting across proteins interface are sericin_chain A representing "Ser133, Tyr214, Thr188, Thr243, Ser225, Ser151, Ser156, His294, Arg293, Gly296″ and trypsin_chain B "Lys120, Tyr246, Asn119, Glu239, Ser62, Tyr194, Ile197, Ser171, Tyr169, Gly170″. Based on our results trypsin shows similarity with cocoonase and presumably trypsin can be used as an alternative source in cocoon degumming.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

Panoramic Perspective on Human Phosphosites

Protein phosphorylation is the most common reversible post-translational modification of proteins and is key in the regulation of many cellular processes. Due to this importance, phosphorylation is extensively studied, resulting in the availability of a large amount of mass spectrometry-based phospho-proteomics data. Here, we leverage the information in these large-scale phospho-proteomics data sets, as contained in Scop3P, to analyze and characterize proteome-wide protein phosphorylation sites (P-sites). First, we set out to differentiate correctly observed P-sites from false-positive sites using five complementary site properties. We then describe the context of these P-sites in terms of the protein structure, solvent accessibility, structural transitions and disorder, and biophysical properties. We also investigate the relative prevalence of disease-linked mutations on and around P-sites. Moreover, we assess the structural dynamics of P-sites in their phosphorylated and unphosphorylated states. As a result, we show how large-scale reprocessing of available proteomics experiments can enable a more reliable view on proteome-wide P-sites. Furthermore, adding the structural context of proteins around P-sites helps uncover possible conformational switches upon phosphorylation. Moreover, by placing sites in different biophysical contexts, we show the differential preference in protein dynamics at phosphorylated sites when compared to the nonphosphorylated counterparts.

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.

Resurrecting Golgi proteins to grasp Golgi ribbon formation and self-association under stress

The Golgi complex is an essential organelle of the eukaryotic exocytic pathway. A subfamily of Golgi matrix proteins, called GRASPs, is central in stress-induced unconventional secretion, Golgi dynamics during mitosis/apoptosis, and Golgi ribbon formation. The Golgi ribbon is vertebrate-specific and correlates with the appearance of two GRASP paralogues and two Golgins (GM130/Golgin45), which form specific GRASP-Golgin pairs. The molecular details of their appearance only in Metazoans are unknown. Moreover, despite new functionalities supported by GRASP paralogy, little is known about their structural and evolutionary differences. Here, we used ancestor sequence reconstruction and biophysical/biochemical approaches to assess the evolution of GRASPs structure/dynamics, fibrillation, and how they started anchoring their Golgin partners. Our data showed that a GRASP ancestor anchored Golgins before gorasp gene duplication in Metazoans. After gene duplication, variations within the GRASP binding pocket determined which paralogue would recruit which Golgin. These interactions are responsible for their specific Golgi location and Golgi ribbon appearance. We also suggest that GRASPs have a long-standing capacity to form supramolecular structures, affecting their participation in stress-induced processes.

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.

Transient local secondary structure in the intrinsically disordered C-term of the Albino3 insertase

Albino3 (Alb3) is an integral membrane protein fundamental to the targeting and insertion of light-harvesting complex (LHC) proteins into the thylakoid membrane. Alb3 contains a stroma-exposed C-terminus (Alb3-Cterm) that is responsible for binding the LHC-loaded transit complex before LHC membrane insertion. Alb3-Cterm has been reported to be intrinsically disordered, but precise mechanistic details underlying how it recognizes and binds to the transit complex are lacking, and the functional roles of its four different motifs have been debated. Using a novel combination of experimental and computational techniques such as single-molecule fluorescence resonance energy transfer, circular dichroism with deconvolution analysis, site-directed mutagenesis, trypsin digestion assays, and all-atom molecular dynamics simulations in conjunction with enhanced sampling techniques, we show that Alb3-Cterm contains transient secondary structure in motifs I and II. The excellent agreement between the experimental and computational data provides a quantitatively consistent picture and allows us to identify a heterogeneous structural ensemble that highlights the local and transient nature of the secondary structure. This structural ensemble was used to predict both the inter-residue distance distributions of single molecules and the apparent unfolding free energy of the transient secondary structure, which were both in excellent agreement with those determined experimentally. We hypothesize that this transient local secondary structure may play an important role in the recognition of Alb3-Cterm for the LHC-loaded transit complex, and these results should provide a framework to better understand protein targeting by the Alb3-Oxa1-YidC family of insertases.

Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here develop a method, SECRiFY, to simultaneously assess the secretability of >10⁵ protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50-100 amino acids, we generate datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. The SECRiFY methodology generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability patterns. The finding that secretability is indeed a learnable feature of protein sequences provides a solid base for application-focused studies.

Reducing Hinge Flexibility of CAR-T Cells Prolongs Survival In Vivo With Low Cytokines Release

Chimeric antigen receptor (CAR)-modified T cells targeting CD19 demonstrate unparalleled responses in B cell malignancies. However, high tumor burden limits clinical efficacy and increases the risk of cytokine release syndrome and neurotoxicity, which is associated with over-activation of the CAR-T cells. The hinge domain plays an important role in the function of CAR-T cells. We hypothesized that deletion of glycine, an amino acid with good flexibility, may reduce the flexibility of the hinge region, thereby mitigating CAR-T cell over-activation. This study involved generating a novel CAR by deletion of two consecutive glycine residues in the CD8 hinge domain of second-generation (2nd) CAR, thereafter named 2nd-GG CAR. The 2nd-GG CAR-T cells showed similar efficacy of CAR expression but lower hinge flexibility, and its protein affinity to CD19 protein was lower than that of 2nd CAR-T cells. Compared to the 2nd CAR-T cells, 2nd-GG CAR-T cells reduced proinflammatory cytokine secretion without diminishing the specific cytotoxicity toward tumor cells in vitro. Furthermore, 2nd-GG CAR-T cells prolonged overall survival in an immunodeficient mouse model bearing NALM-6 when tumor burden was high. This study demonstrated that a lower-flexibility of CD8α hinge improved survival under high tumor burden and reduced proinflammatory cytokines in preclinical studies. While there is potential for improved safety and efficacy, yet this needs validation with clinical trials.

b2bTools: online predictions for protein biophysical features and their conservation

We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata β-sheet aggregation. These predictions, several of which were previously not available online, capture 'emergent' properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). In addition, upload of a multiple sequence alignment (MSA) in a variety of formats enables exploration of the biophysical variation observed in homologous proteins. The associated plots indicate the biophysical limits of functionally relevant protein behaviour, with unusual residues flagged by a Gaussian mixture model analysis. The prediction results are available as JSON or CSV files and directly accessible via an API. Online visualisation is available as interactive plots, with brief explanations and tutorial pages included. The server and API employ an email-free token-based system that can be used to anonymously access previously generated results.

The C-terminal tail extension of myosin 16 acts as a molten globule, including intrinsically disordered regions, and interacts with the N-terminal ankyrin

The lesser-known unconventional myosin 16 protein is essential in proper neuronal functioning and has been implicated in cell cycle regulation. Its longer Myo16b isoform contains a C-terminal tail extension (Myo16Tail), which has been shown to play a role in the neuronal phosphoinositide 3-kinase signaling pathway. Myo16Tail mediates the actin cytoskeleton remodeling, downregulates the actin dynamics at the postsynaptic site of dendritic spines, and is involved in the organization of the presynaptic axon terminals. However, the functional and structural features of this C-terminal tail extension are not well known. Here, we report the purification and biophysical characterization of the Myo16Tail by bioinformatics, fluorescence spectroscopy, and CD. Our results revealed that the Myo16Tail is functionally active and interacts with the N-terminal ankyrin domain of myosin 16, suggesting an intramolecular binding between the C and N termini of Myo16 as an autoregulatory mechanism involving backfolding of the motor domain. In addition, the Myo16Tail possesses high structural flexibility and a solvent-exposed hydrophobic core, indicating the largely unstructured, intrinsically disordered nature of this protein region. Some secondary structure elements were also observed, indicating that the Myo16Tail likely adopts a molten globule-like structure. These structural features imply that the Myo16Tail may function as a flexible display site particularly relevant in post-translational modifications, regulatory functions such as backfolding, and phosphoinositide 3-kinase signaling.

Ubiquitin Interacting Motifs: Duality Between Structured and Disordered Motifs

Ubiquitin is a small protein at the heart of many cellular processes, and several different protein domains are known to recognize and bind ubiquitin. A common motif for interaction with ubiquitin is the Ubiquitin Interacting Motif (UIM), characterized by a conserved sequence signature and often found in multi-domain proteins. Multi-domain proteins with intrinsically disordered regions mediate interactions with multiple partners, orchestrating diverse pathways. Short linear motifs for binding are often embedded in these disordered regions and play crucial roles in modulating protein function. In this work, we investigated the structural propensities of UIMs using molecular dynamics simulations and NMR chemical shifts. Despite the structural portrait depicted by X-crystallography of stable helical structures, we show that UIMs feature both helical and intrinsically disordered conformations. Our results shed light on a new class of disordered UIMs. This group is here exemplified by the C-terminal domain of one isoform of ataxin-3 and a group of ubiquitin-specific proteases. Intriguingly, UIMs not only bind ubiquitin. They can be a recruitment point for other interactors, such as parkin and the heat shock protein Hsc70-4. Disordered UIMs can provide versatility and new functions to the client proteins, opening new directions for research on their interactome.

Residue interaction networks of K-Ras protein with water molecules identifies the potential role of switch II and P-loop

The mutant K-Ras with aberrant signaling is the primary cause of several cancers. The proposed study investigated the influence of water molecules in K-Ras crystal structure, where they have a significant function by understanding their residue interaction networks (RINs). We analyzed the RINs of K-Ras with and without water molecules and determined their interaction properties. RINs were developed with the help of StructureViz2 and RINspector; further, the changes in K-Ras backbone flexibility were predicted with the DynaMine. We found that the residues K42, I142, and L159 are the hotspots from water, including the K-Ras-GTP complex with the highest residue centrality analysis (RCA) Z-score. The DynaMine prediction calculated the NMR S² value for the frequently mutated positions G12, G13, and Q61 showing a minor shift in flexibility, which make up the P-Loop and switch II of the K-Ras protein. This flexibility shift can account for changes in conformational activity and the protein's GTPase activity, making it difficult to recognize by the effectors and exchange factors. Taken together, our study helps in understanding the functional importance of the water molecules in K-Ras protein and the impact of mutation that modulate the conformational state of the protein.

Human myelin protein P2: from crystallography to time-lapse membrane imaging and neuropathy-associated variants

Peripheral myelin protein 2 (P2) is a fatty acid-binding protein expressed in vertebrate peripheral nervous system myelin, as well as in human astrocytes. Suggested functions of P2 include membrane stacking and lipid transport. Mutations in the PMP2 gene, encoding P2, are associated with Charcot-Marie-Tooth disease (CMT). Recent studies have revealed three novel PMP2 mutations in CMT patients. To shed light on the structure and function of these P2 variants, we used X-ray and neutron crystallography, small-angle X-ray scattering, circular dichroism spectroscopy, computer simulations and lipid binding assays. The crystal and solution structures of the I50del, M114T and V115A variants of P2 showed minor differences to the wild-type protein, whereas their thermal stability was reduced. Vesicle aggregation assays revealed no change in membrane stacking characteristics, while the variants showed altered fatty acid binding. Time-lapse imaging of lipid bilayers indicated formation of double-membrane structures induced by P2, which could be related to its function in stacking of two myelin membrane surfaces in vivo. In order to better understand the links between structure, dynamics and function, the crystal structure of perdeuterated P2 was refined from room temperature data using neutrons and X-rays, and the results were compared to simulations and cryocooled crystal structures. Our data indicate similar properties for all known human P2 CMT variants; while crystal structures are nearly identical, thermal stability and function of CMT variants are impaired. Our data provide new insights into the structure-function relationships and dynamics of P2 in health and disease.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

Motivation

Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein–protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

Results

We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody–antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research.

Availability and implementation

Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Online biophysical predictions for SARS-CoV-2 proteins

BACKGROUND

The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. MAIN: We present a website ( https://bio2byte.be/sars2/ ) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour.

CONCLUSION

The https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action.

In Silico Analysis of Huntingtin Homologs in Lower Eukaryotes

Huntington’s disease is a rare neurodegenerative and autosomal dominant disorder. HD is caused by a mutation in the gene coding for huntingtin (Htt). The result is the production of a mutant Htt with an abnormally long polyglutamine repeat that leads to pathological Htt aggregates. Although the structure of human Htt has been determined, albeit at low resolution, its functions and how they are performed are largely unknown. Moreover, there is little information on the structure and function of Htt in other organisms. The comparison of Htt homologs can help to understand if there is a functional conservation of domains in the evolution of Htt in eukaryotes. In this work, through a computational approach, Htt homologs from lower eukaryotes have been analysed, identifying ordered domains and modelling their structure. Based on the structural models, a putative function for most of the domains has been predicted. A putative C. elegans Htt-like protein has also been analysed following the same approach. The results obtained support the notion that this protein is a orthologue of human Htt.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

MEDUSA: Prediction of Protein Flexibility from Sequence

Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.

MobiDB: intrinsically disordered proteins in 2021

The MobiDB database (URL: https://mobidb.org/) provides predictions and annotations for intrinsically disordered proteins. Here, we report recent developments implemented in MobiDB version 4, regarding the database format, with novel types of annotations and an improved update process. The new website includes a re-designed user interface, a more effective search engine and advanced API for programmatic access. The new database schema gives more flexibility for the users, as well as simplifying the maintenance and updates. In addition, the new entry page provides more visualisation tools including customizable feature viewer and graphs of the residue contact maps. MobiDB v4 annotates the binding modes of disordered proteins, whether they undergo disorder-to-order transitions or remain disordered in the bound state. In addition, disordered regions undergoing liquid-liquid phase separation or post-translational modifications are defined. The integrated information is presented in a simplified interface, which enables faster searches and allows large customized datasets to be downloaded in TSV, Fasta or JSON formats. An alternative advanced interface allows users to drill deeper into features of interest. A new statistics page provides information at database and proteome levels. The new MobiDB version presents state-of-the-art knowledge on disordered proteins and improves data accessibility for both computational and experimental users.

Identification of a conserved N-terminal domain in the first module of ACV synthetases

The l-δ-(α-aminoadipoyl)-l-cysteinyl-d-valine synthetase (ACVS) is a trimodular nonribosomal peptide synthetase (NRPS) that provides the peptide precursor for the synthesis of β-lactams. The enzyme has been extensively characterized in terms of tripeptide formation and substrate specificity. The first module is highly specific and is the only NRPS unit known to recruit and activate the substrate l-α-aminoadipic acid, which is coupled to the α-amino group of l-cysteine through an unusual peptide bond, involving its δ-carboxyl group. Here we carried out an in-depth investigation on the architecture of the first module of the ACVS enzymes from the fungus Penicillium rubens and the bacterium Nocardia lactamdurans. Bioinformatic analyses revealed the presence of a previously unidentified domain at the N-terminus which is structurally related to condensation domains, but smaller in size. Deletion variants of both enzymes were generated to investigate the potential impact on penicillin biosynthesis in vivo and in vitro. The data indicate that the N-terminal domain is important for catalysis.

Asymmetric dynamic coupling promotes alternative evolutionary pathways in an enzyme dimer

The importance of dynamic factors in enzyme evolution is gaining recognition. Here we study how the evolution of a new enzymatic activity exploits conformational tinkering and demonstrate that conversion of a dimeric phosphotriesterase to an arylesterase in Pseudomonas diminuta is accompanied by structural divergence between the two subunits. Deviations in loop conformations increase with promiscuity, leading to functionally distinct states, while they decrease during specialisation for the new function. We show that opposite loop movements in the two subunits are due to a dynamic coupling with the dimer interface, the importance of which is also corroborated by the co-evolution of the loop and interface residues. These results illuminate how protein dynamics promotes conformational heterogeneity in a dimeric enzyme, leading to alternative evolutionary pathways for the emergence of a new function.

ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values

Nuclear magnetic resonance (NMR) spectroscopy data provides valuable information on the behaviour of proteins in solution. The primary data to determine when studying proteins are the per-atom NMR chemical shifts, which reflect the local environment of atoms and provide insights into amino acid residue dynamics and conformation. Within an amino acid residue, chemical shifts present multi-dimensional and complexly cross-correlated information, making them difficult to analyse. The ShiftCrypt method, based on neural network auto-encoder architecture, compresses the per-amino acid chemical shift information in a single, interpretable, amino acid-type independent value that reflects the biophysical state of a residue. We here present the ShiftCrypt web server, which makes the method readily available. The server accepts chemical shifts input files in the NMR Exchange Format (NEF) or NMR-STAR format, executes ShiftCrypt and visualises the results, which are also accessible via an API. It also enables the ”biophysically-based” pairwise alignment of two proteins based on their ShiftCrypt values. This approach uses Dynamic Time Warping and can optionally include their amino acid code information, and has applications in, for example, the alignment of disordered regions. The server uses a token-based system to ensure the anonymity of the users and results. The web server is available at www.bio2byte.be/shiftcrypt.

Exploring Protein Intrinsic Disorder with MobiDB

Nowadays, it is well established that many proteins or regions under physiological conditions lack a fixed three-dimensional structure and are intrinsically disordered. MobiDB is the main repository of protein disorder and mobility annotations, combining different data sources to provide an exhaustive overview of intrinsic disorder. MobiDB includes curated annotations from other databases, indirect disorder evidence from structural data, and disorder predictions from protein sequences. It provides an easy-to-use web server to visualize and explore disorder information. This chapter describes the data available in MobiDB, emphasizing how to use and access the intrinsic disorder data. MobiDB is available at URL http://mobidb.bio.unipd.it .

Sequence-based prediction of protein binding mode landscapes

Interactions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disorder (DD) transitions, when the conformational heterogeneity is maintained in the bound states. Furthermore, systematic studies of these interactions are revealing that proteins may exhibit different binding modes with different partners. Proteins that exhibit this context-dependent binding can be referred to as fuzzy proteins. Here we investigate amino acid code for fuzzy binding in terms of the entropy of the probability distribution of transitions towards decreasing order. We implement these entropy calculations into the FuzPred (http://protdyn-fuzpred.org) algorithm to predict the range of context-dependent binding modes of proteins from their amino acid sequences. As we illustrate through a variety of examples, this method identifies those binding sites that are sensitive to the cellular context or post-translational modifications, and may serve as regulatory points of cellular pathways.

Great advances have been made in the last several decades in deciphering how the behavior of proteins is encoded in their amino acid sequences. A variety of sequence-based prediction methods have been developed to estimate a wide range of properties of proteins, including secondary structure propensity, native state structures, preference for being disordered and tendency to aggregate. Much less is known, however, about the rules that regulate the conformational changes of proteins upon binding. In particular, many proteins change their binding modes upon interacting with different partners, or as a consequence of post-translational modifications or changes in the cellular milieu. Here we address the problem of how amino acid sequences can encode different binding modes depending on their binding partners, and describe the FuzPred method of predicting context-dependent binding modes.

Interaction between the scaffold proteins CBP by IQGAP1 provides an interface between gene expression and cytoskeletal activity

Crosstalk between cellular pathways is often mediated through scaffold proteins that function as platforms for the assembly of signaling complexes. Based on yeast two-hybrid analysis, we report here the interaction between two complex scaffold proteins, CREB-binding protein (CBP) and the Ras GTPase-activating-like protein 1 (IQGAP1). Dissection of the interaction between the two proteins reveals that the central, thus far uncharacterized, region of IQGAP1 interacts with the HAT domain and the C-terminal intrinsically disordered region of CBP (termed ID5). Structural analysis of ID5 by solution NMR spectroscopy and SAXS reveals the presence of two regions with pronounced helical propensity. The ID5 region(s) involved in the interaction of nanomolar affinity were delineated by solution NMR titrations and pull-down assays. Moreover, we found that IQGAP1 acts as an inhibitor of the histone acetyltransferase (HAT) activity of CBP. In in vitro assays, the CBP-binding region of IQGAP1 positively and negatively regulates the function of HAT proteins of different families including CBP, KAT5 and PCAF. As many signaling pathways converge on CBP and IQGAP1, their interaction provides an interface between transcription regulation and the coordination of cytoskeleton. Disruption or alteration of the interaction between these scaffold proteins may lead to cancer development or metastatic processes, highlighting the importance of this interaction.

Ordered structure-forming properties of the intrinsically disordered AB region of hRXRγ and its ability to promote liquid-liquid phase separation

The retinoid X receptor (RXR) is a member of the nuclear receptor (NR) superfamily that occupies the central position among other NRs by forming both homodimers and heterodimers with other representatives of the family. RXR shares similar structural domains with other members of NRs. The major differences in the subtypes and isoforms of RXR are in the AB region. To date, there have been no data concerning the molecular properties of the AB region of hRXRγ (AB_hRXG). Here, we describe the biochemical and biophysical properties of the recombinant AB_hRXG. The results indicate that AB_hRXG shows the structural and functional characteristics of the pre-molten globule-like (PMG-like) group of intrinsically disordered proteins (IDPs) and also has a significant propensity for folding. We also present the first experimental evidence showing that the AB region of NRs promotes the formation of liquid-liquid phase separation (LLPS).

Calcium ions modulate the structure of the intrinsically disordered Nucleobindin-2 protein

Nucleobindin-2 (Nucb2) is a widely expressed multi-domain protein. Nucb2 participates in many physiological processes, i.e. calcium level maintenance, feeding regulation in the hypothalamus, emotion and stress regulation, and many others. To date, this protein has not been structurally characterized. We describe the first comparative structural analysis of two homologs, a Gallus gallus and a Homo sapiens Nucb2. The in silico analysis suggested that apo-Nucb2s contain a mosaic-like structure, consisting of intertwined disordered and ordered regions. Surprisingly, the hydrogen-deuterium exchange mass spectrometry results revealed that Nucb2 is divided into two parts: an N-terminal half with a stable mosaic-like structure and a disordered C-terminal half. However, the presence of Ca²⁺ induces the formation of a mosaic-like structure in the C-terminal half of the Nucb2s. The Ca²⁺ also affects the tertiary and quaternary structure of Nucb2s. The presence of Ca²⁺ leads to an overall compaction of the Nucb2 molecule, resulting in structural change that is propagated along the molecule, which in turn affects the quaternary structure of the protein. Intrinsic disorder, and the mosaic-like Ca²⁺ dependent structure of Nucb2s, might be seen as the molecular factors responsible for their multifunctionality. Thus, Nucb2s might function as the versatile Ca²⁺ sensor involved in signal transduction.

PDBe-KB: a community-driven resource for structural and functional annotations

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession.

The Salt Tolerance Related Protein (STRP) Mediates Cold Stress Responses and Abscisic Acid Signalling in Arabidopsis thaliana

Low temperature stress is one of the major causes of crop yield reduction in agriculture. The alteration of gene expression pattern and the accumulation of stress-related proteins are the main strategies activated by plants under this unfavourable condition. Here we characterize the Arabidopsis thaliana Salt Tolerance Related Protein (STRP). The protein rapidly accumulates under cold treatment, and this effect is not dependent on transcriptional activation of the STRP gene, but on the inhibition of proteasome-mediated degradation. Subcellular localization of STRP was determined by the transient expression of STRP-YFP in A. thaliana protoplasts. STRP is localized into the cytosol, nucleus, and associated to the plasma membrane. Under cold stress, the membrane-associated fraction decreases, while in the cytosol and in the nucleus STRP levels strongly increase. STRP has high similarity with WCI16, a wheat Late Embryogenesis Abundant (LEA)-like protein. Despite no canonical LEA motifs in the STRP sequence are present, physicochemical characterization demonstrated that STRP shares common features with LEA proteins, being a high hydrophilic unstructured protein, highly soluble after boiling and with cryoprotectant activity. To clarify the physiological function of STRP, we characterized the phenotype and the response to low temperature stress of the strp knockout mutant. The mutation causes an equal impairment of plant growth and development both in physiological and cold stress conditions. The strp mutant is more susceptible to oxidative damage respect to the wild type, showing increased lipid peroxidation and altered membrane integrity. Furthermore, the analysis of Abscisic acid (ABA) effects on protein levels demonstrated that the hormone induces the increase of STRP levels, an effect in part ascribable to its ability to activate STRP expression. ABA treatments showed that the strp mutant displays an ABA hyposensitive phenotype in terms of seed germination, root development, stomata closure and in the expression of ABA-responsive genes. In conclusion, our results demonstrate that STRP acts as a multifunctional protein in the response mechanisms to low temperature, suggesting a crucial role for this protein in stress perception and in the translation of extracellular stimuli in an intracellular response.

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.

Accurate prediction of protein beta-aggregation with generalized statistical potentials

Motivation

Protein beta-aggregation is an important but poorly understood phenomena involved in diseases as well as in beneficial physiological processes. However, while this task has been investigated for over 50 years, very little is known about its mechanisms of action. Moreover, the identification of regions involved in aggregation is still an open problem and the state-of-the-art methods are often inadequate in real case applications.

Results

In this article we present AgMata, an unsupervised tool for the identification of such regions from amino acidic sequence based on a generalized definition of statistical potentials that includes biophysical information. The tool outperforms the state-of-the-art methods on two different benchmarks. As case-study, we applied our tool to human ataxin-3, a protein involved in Machado–Joseph disease. Interestingly, AgMata identifies aggregation-prone residues that share the very same structural environment. Additionally, it successfully predicts the outcome of in vitro mutagenesis experiments, identifying point mutations that lead to an alteration of the aggregation propensity of the wild-type ataxin-3.

Availability and implementation

A python implementation of the tool is available at https://bitbucket.org/bio2byte/agmata.

Supplementary information

Supplementary data are available at Bioinformatics online.

StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map

Protein folding and structure prediction are two sides of the same coin. Contact maps and the related techniques of constraint-based structure reconstruction can be considered as unifying aspects of both processes. We present the Structural Relevance (SR) score which quantifies the information content of individual contacts and residues in the context of the whole native structure. The physical process of protein folding is commonly characterized with spatial and temporal resolution: some residues are Early Folding while others are Highly Stable with respect to unfolding events. We employ the proposed SR score to demonstrate that folding initiation and structure stabilization are subprocesses realized by distinct sets of residues. The example of cytochrome c is used to demonstrate how StructureDistiller identifies the most important contacts needed for correct protein folding. This shows that entries of a contact map are not equally relevant for structural integrity. The proposed StructureDistiller algorithm identifies contacts with the highest information content; these entries convey unique constraints not captured by other contacts. Identification of the most informative contacts effectively doubles resilience toward contacts which are not observed in the native contact map. Furthermore, this knowledge increases reconstruction fidelity on sparse contact maps significantly by 0.4 Å.