Toward optimal fragment generations for ab initio protein structure assembly

Hairpin protein partitioning from the ER to lipid droplets involves major structural rearrangements

Lipid droplet (LD) function relies on proteins partitioning between the endoplasmic reticulum (ER) phospholipid bilayer and the LD monolayer membrane to control cellular adaptation to metabolic changes. It has been proposed that these hairpin proteins integrate into both membranes in a similar monotopic topology, enabling their passive lateral diffusion during LD emergence at the ER. Here, we combine biochemical solvent-accessibility assays, electron paramagnetic resonance spectroscopy and intra-molecular crosslinking experiments with molecular dynamics simulations, and determine distinct intramembrane positionings of the ER/LD protein UBXD8 in ER bilayer and LD monolayer membranes. UBXD8 is deeply inserted into the ER bilayer with a V-shaped topology and adopts an open-shallow conformation in the LD monolayer. Major structural rearrangements are required to enable ER-to-LD partitioning. Free energy calculations suggest that such structural transition is unlikely spontaneous, indicating that ER-to-LD protein partitioning relies on more complex mechanisms than anticipated and providing regulatory means for this trans-organelle protein trafficking.

A novel missense mutation in the AIRE gene underlying autoimmune polyglandular syndrome type 1

The immune regulator gene AIRE plays an essential role in the establishment of immune tolerance and the prevention of autoimmunity. This transcription factor plays a critical role in promoting self-tolerance in the thymus by regulating the expression of a large number of self-antigens that share the common feature of being tissue-restricted in their expression pattern in the periphery. Dysfunction of AIRE in humans causes a rare disease, autoimmune polyglandular syndrome type 1 (APS1), characterized by an autoimmune response against peripheral tissues, particularly endocrine tissues. Although a few dominant mutations have been described, the inactivation of AIRE is usually caused by recessive mutations. Recent data suggests that alterations in AIRE function contribute not only to APS1 but also to more common forms of autoimmune disease. Here, we present a previously unreported missense mutation (NM_000383.2:c.260 T > C) in exon 2 of the AIRE gene, predicted to cause the substitution (p.(Leu87Pro)) in the CARD domain of the AIRE protein. When inherited in conjunction with another dysfunctional AIRE allele, this mutation was associated with immune dysregulation in a pediatric patient. The presence of hypergammaglobulinemia, malabsorption syndrome, ectodermal dysplasia, mucocutaneous candidiasis, vitiligo, and hypothyroidism as well as the presence of multiple autoantibodies allowed us to confirm an APS1 diagnosis.

Structural Space of the Duffy Antigen/Receptor for Chemokines' Intrinsically Disordered Ectodomain 1 Explored by Temperature Replica-Exchange Molecular Dynamics Simulations

Plasmodium vivax malaria affects 14 million people each year. Its invasion requires interactions between the parasitic Duffy-binding protein (PvDBP) and the N-terminal extracellular domain (ECD1) of the host's Duffy antigen/receptor for chemokines (DARC). ECD1 is highly flexible and intrinsically disordered, therefore it can adopt different conformations. We computationally modeled the challenging ECD1 local structure. With T-REMD simulations, we sampled its dynamic behavior and collected its most representative conformations. Our results suggest that most of the DARC ECD1 domain remains in a disordered state during the simulated time. Globular local conformations are found in the analyzed local free-energy minima. These globular conformations share an α-helix spanning residues Ser18 to Ser29 and in many cases they comprise an antiparallel β-sheet, whose β-strands are formed around residues Leu10 and Ala49. The formation of a parallel β-sheet is almost negligible. So far, progress in understanding the mechanisms forming the basis of the P. vivax malaria infection of reticulocytes has been hampered by experimental difficulties, along with a lack of DARC structural information. Our collection of the most probable ECD1 structural conformations will help to advance modeling of the DARC structure and to explore DARC-ECD1 interactions with a range of physiological and pathological ligands.

PLIN5 interacts with FATP4 at membrane contact sites to promote lipid droplet-to-mitochondria fatty acid transport

Cells adjust their metabolism by remodeling membrane contact sites that channel metabolites to different fates. Lipid droplet (LD)-mitochondria contacts change in response to fasting, cold exposure, and exercise. However, their function and mechanism of formation have remained controversial. We focused on perilipin 5 (PLIN5), an LD protein that tethers mitochondria, to probe the function and regulation of LD-mitochondria contacts. We demonstrate that efficient LD-to-mitochondria fatty acid (FA) trafficking and ß-oxidation during starvation of myoblasts are promoted by phosphorylation of PLIN5 and require an intact PLIN5 mitochondrial tethering domain. Using human and murine cells, we further identified the acyl-CoA synthetase, FATP4 (ACSVL4), as a mitochondrial interactor of PLIN5. The C-terminal domains of PLIN5 and FATP4 constitute a minimal protein interaction capable of inducing organelle contacts. Our work suggests that starvation leads to phosphorylation of PLIN5, lipolysis, and subsequent channeling of FAs from LDs to FATP4 on mitochondria for conversion to fatty-acyl-CoAs and subsequent oxidation.

Exploring the 'N-terminal arm' & 'Convex surface' binding Interfaces of the T3SS Chaperone-Translocator Complexes from P. aeruginosa

One infection method widely used by many gram-negative bacteria involves a protein nanomachine called the Type Three Secretion System (T3SS). The T3SS enables the transportation of bacterial "toxins" via a proteinaceous channel that directly links the cytosol of the bacteria and host cell. The channel from the bacteria is completed by a translocon pore formed by two proteins named the major and minor translocators. Prior to pore formation, the translocator proteins are bound to a small chaperone within the bacterial cytoplasm. This interaction is crucial to effective secretion. Here we investigated the specificity of the binding interfaces of the translocator-chaperone complexes from Pseudomonas aeruginosa via the selection of peptide and protein libraries based on its chaperone PcrH. Five libraries encompassing PcrH's N-terminal and central α-helices were panned, using ribosome display, against both the major (PopB) and minor (PopD) translocator. Both translocators were shown to significantly enrich a similar pattern of WT and non-WT sequences from the libraries. This highlighted key similarities/differences between the interactions of the major and minor translocators with their chaperone. Moreover, as the enriched non-WT sequences were specific to each translocator, it would suggest that PcrH can be adapted to bind each translocator individually. The ability to evolve such proteins indicates that these molecules may provide promising anti-bacterial candidates.

Relating Molecular Dynamics Simulations to Functional Activity for Gly-Rich Membranolytic Helical Kiadin Peptides

Kiadins are in silico designed peptides with a strong similarity to diPGLa-H, a tandem sequence of PGLa-H (KIAKVALKAL) and with single, double or quadruple glycine substitutions. They were found to show high variability in their activity and selectivity against Gram-negative and Gram-positive bacteria, as well as cytotoxicity against host cells, which are influenced by the number and placing of glycine residues along the sequence. The conformational flexibility introduced by these substitutions contributes differently peptide structuring and to their interactions with the model membranes, as observed by molecular dynamics simulations. We relate these results to experimentally determined data on the structure of kiadins and their interactions with liposomes having a phospholipid membrane composition similar to simulation membrane models, as well as to their antibacterial and cytotoxic activities, and also discuss the challenges in interpreting these multiscale experiments and understanding why the presence of glycine residues in the sequence affected the antibacterial potency and toxicity towards host cells in a different manner.

Function and dynamics of the intrinsically disordered carboxyl terminus of β2 adrenergic receptor

Advances in structural biology have provided important mechanistic insights into signaling by the transmembrane core of G-protein coupled receptors (GPCRs); however, much less is known about intrinsically disordered regions such as the carboxyl terminus (CT), which is highly flexible and not visible in GPCR structures. The β2 adrenergic receptor's (β2AR) 71 amino acid CT is a substrate for GPCR kinases and binds β-arrestins to regulate signaling. Here we show that the β2AR CT directly inhibits basal and agonist-stimulated signaling in cell lines lacking β-arrestins. Combining single-molecule fluorescence resonance energy transfer (FRET), NMR spectroscopy, and molecular dynamics simulations, we reveal that the negatively charged β2AR-CT serves as an autoinhibitory factor via interacting with the positively charged cytoplasmic surface of the receptor to limit access to G-proteins. The stability of this interaction is influenced by agonists and allosteric modulators, emphasizing that the CT plays important role in allosterically regulating GPCR activation.

Binding by calmodulin is coupled to transient unfolding of the third FF domain of Prp40A

Human pre-mRNA processing protein 40 homolog A (hPrp40A) is a splicing factor that interacts with the Huntington's disease protein huntingtin (Htt). Evidence has accumulated that both Htt and hPrp40A are modulated by the intracellular Ca2+ sensor calmodulin (CaM). Here we report characterization of the interaction of human CM with the third FF domain (FF3 ) of hPrp40A using calorimetric, fluorescence and structural approaches. Homology modeling, differential scanning calorimetry and small angle X-ray scattering (SAXS) data show FF3 forms a folded globular domain. CaM was found to bind FF3 in a Ca2+ -dependent manner with a 1:1 stoichiometry and a dissociation constant (Kd ) of 25 ± 3 μM at 25°C. NMR studies showed that both domains of CaM are engaged in binding and SAXS analysis of the FF3 -CaM complex revealed CaM occupies an extended configuration. Analysis of the FF3 sequence showed that the anchors for CaM binding must be buried in its hydrophobic core, suggesting that binding to CaM requires unfolding of FF3 . Trp anchors were proposed based on sequence analysis and confirmed by intrinsic Trp fluorescence of FF3 upon binding of CaM and substantial reductions in affinity for Trp-Ala FF3 mutants. The consensus model of the complex showed that binding to CaM binding occurs to an extended, non-globular state of the FF3 , consistent with coupling to transient unfolding of the domain. The implications of these results are discussed in the context of the complex interplay of Ca2+ signaling and Ca2+ sensor proteins in modulating Prp40A-Htt function.

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

De novo protein fold design through sequence-independent fragment assembly simulations

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Modeling of SARS-CoV-2 Virus Proteins: Implications on Its Proteome

COronaVIrus Disease 19 (COVID-19) is a severe acute respiratory syndrome (SARS) caused by a group of beta coronaviruses, SARS-CoV-2. The SARS-CoV-2 virus is similar to previous SARS- and MERS-causing strains and has infected nearly six hundred and fifty million people all over the globe, while the death toll has crossed the six million mark (as of December, 2022). In this chapter, we look at how computational modeling approaches of the viral proteins could help us understand the various processes in the viral life cycle inside the host, an understanding of which might provide key insights in mitigating this and future threats. This understanding helps us identify key targets for the purpose of drug discovery and vaccine development.

PPDPF Promotes the Development of Mutant KRAS-Driven Pancreatic Ductal Adenocarcinoma by Regulating the GEF Activity of SOS1

The guanine nucleotide exchange factor (GEF) SOS1 catalyzes the exchange of GDP for GTP on RAS. However, regulation of the GEF activity remains elusive. Here, the authors report that PPDPF functions as an important regulator of SOS1. The expression of PPDPF is significantly increased in pancreatic ductal adenocarcinoma (PDAC), associated with poor prognosis and recurrence of PDAC patients. Overexpression of PPDPF promotes PDAC cell growth in vitro and in vivo, while PPDPF knockout exerts opposite effects. Pancreatic-specific deletion of PPDPF profoundly inhibits tumor development in KRASG12D -driven genetic mouse models of PDAC. PPDPF can bind GTP and transfer GTP to SOS1. Mutations of the GTP-binding sites severely impair the tumor-promoting effect of PPDPF. Consistently, mutations of the critical amino acids mediating SOS1-PPDPF interaction significantly impair the GEF activity of SOS1. Therefore, this study demonstrates a novel model of KRAS activation via PPDPF-SOS1 axis, and provides a promising therapeutic target for PDAC.

Functional Testing of Bone Morphogenetic Protein (BMP) Pathway Variants Identified on Whole-Exome Sequencing in a Patient with Delayed-Onset Fibrodysplasia Ossificans Progressiva (FOP) Using ACVR1R206H -Specific Human Cellular and Zebrafish Models

Bone morphogenetic protein (BMP) signaling is critical in skeletal development. Overactivation can trigger heterotopic ossification (HO) as in fibrodysplasia ossificans progressiva (FOP), a rare, progressive disease of massive HO formation. A small subset of FOP patients harboring the causative ACVR1^R206H mutation show strikingly mild or delayed-onset HO, suggesting that genetic variants in the BMP pathway could act as disease modifiers. Whole-exome sequencing of one such patient identified BMPR1A^R443C and ACVR2A^V173I as candidate modifiers. Molecular modeling predicted significant structural perturbations. Neither variant decreased BMP signaling in ACVR1^R206H HEK 293T cells at baseline or after stimulation with BMP4 or activin A (AA), ligands that activate ACVR1^R206H signaling. Overexpression of BMPR1A^R443C in a Tg(ACVR1-R206Ha) embryonic zebrafish model, in which overactive BMP signaling yields ventralized embryos, did not alter ventralization severity, while ACVR2A^V173I exacerbated ventralization. Co-expression of both variants did not affect dorsoventral patterning. In contrast, BMPR1A knockdown in ACVR1^R206H HEK cells decreased ligand-stimulated BMP signaling but did not affect dorsoventral patterning in Tg(ACVR1-R206Ha) zebrafish. ACVR2A knockdown decreased only AA-stimulated signaling in ACVR1^R206H HEK cells and had no effect in Tg(ACVR1-R206Ha) zebrafish. Co-knockdown in ACVR1^R206H HEK cells decreased basal and ligand-stimulated signaling, and co-knockdown/knockout (bmpr1aa/ab; acvr2aa/ab) decreased Tg(ACVR1-R206Ha) zebrafish ventralization phenotypes. Our functional studies showed that knockdown of wild-type BMPR1A and ACVR2A could attenuate ACVR1^R206H signaling, particularly in response to AA, and that ACVR2A^V173I unexpectedly increased ACVR1^R206H -mediated signaling in zebrafish. These studies describe a useful strategy and platform for functionally interrogating potential genes and genetic variants that may impact the BMP signaling pathway. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Acetylated HOXB13 Regulated Super Enhancer Genes Define Therapeutic Vulnerabilities of Castration-Resistant Prostate Cancer

PURPOSE

Androgen receptor (AR) antagonism is exacerbated by HOXB13 in castration-resistant prostate cancers (CRPC). However, it is unclear when and how HOXB13 primes CRPCs for AR antagonism. By mass-spectrometry analysis of CRPC extract, we uncovered a novel lysine 13 (K13) acetylation in HOXB13 mediated by CBP/p300. To determine whether acetylated K13-HOXB13 is a clinical biomarker of CRPC development, we characterized its role in prostate cancer biology.

EXPERIMENTAL DESIGN

We identified tumor-specific acK13-HOXB13 signal enriched super enhancer (SE)-regulated targets. We analyzed the effect of loss of HOXB13K13-acetylation on chromatin binding, SE proximal target gene expression, self-renewal, enzalutamide sensitivity, and CRPC tumor growth by employing isogenic parental and HOXB13K13A mutants. Finally, using primary human prostate organoids, we evaluated whether inhibiting an acK13-HOXB13 target, ACK1, with a selective inhibitor (R)-9b is superior to AR antagonists in inhibiting CRPC growth.

RESULTS

acK13-HOXB13 promotes increased expression of lineage (AR, HOXB13), prostate cancer diagnostic (FOLH1), CRPC-promoting (ACK1), and angiogenesis (VEGFA, Angiopoietins) genes early in prostate cancer development by establishing tumor-specific SEs. acK13-HOXB13 recruitment to key SE-regulated targets is insensitive to enzalutamide. ACK1 expression is significantly reduced in the loss of function HOXB13K13A mutant CRPCs. Consequently, HOXB13K13A mutants display reduced self-renewal, increased sensitivity to enzalutamide, and impaired xenograft tumor growth. Primary human prostate tumor organoids expressing HOXB13 are significantly resistant to AR antagonists but sensitive to (R)-9b.

CONCLUSIONS

In summary, acetylated HOXB13 is a biomarker of clinically significant prostate cancer. Importantly, PSMA-targeting agents and (R)-9b could be new therapeutic modalities to target HOXB13-ACK1 axis regulated prostate cancers.

Computational Structural and Functional Analyses of ORF10 in Novel Coronavirus SARS-CoV-2 Variants to Understand Evolutionary Dynamics

Introduction:

In an effort to combat SARS-CoV-2 through multi-subunit vaccine design, during studies using whole genome and immunome, ORF10, located at the 3′ end of the genome, displayed unique features. It showed no homology to any known protein in other organisms, including SARS-CoV. It was observed that its nucleotide sequence is 100% identical in the SARS-CoV-2 genomes sourced worldwide, even in the recent-most VoCs and VoIs of B.1.1.529 (Omicron), B.1.617 (Delta), B.1.1.7 (Alpha), B.1.351 (Beta), and P.1 (Gamma) lineages, implicating its constant nature throughout the evolution of deadly variants.

Aim:

The structure and function of SARS-CoV-2 ORF10 and the role it may play in the viral evolution is yet to be understood clearly. The aim of this study is to predict its structure, function, and understand evolutionary dynamics on the basis of mutations and likely heightened immune responses in the immunopathogenesis of this deadly virus.

Methods:

Sequence analysis, ab-initio structure modeling and an understanding of the impact of likely substitutions in key regions of protein was carried out. Analyses of viral T cell epitopes and primary anchor residue mutations was done to understand the role it may play in the evolution as a molecule with likely enhanced immune response and consequent immunopathogenesis.

Results:

Few amino acid substitution mutations are observed, most probably due to the ribosomal frameshifting, and these mutations may not be detrimental to its functioning. As ORF10 is observed to be an expressed protein, ab-initio structure modeling shows that it comprises mainly an α-helical region and maybe an ER-targeted membrane mini-protein. Analyzing the whole proteome, it is observed that ORF10 presents amongst the highest number of likely promiscuous and immunogenic CTL epitopes, specifically 11 out of 30 promiscuous ones and 9 out of these 11, immunogenic CTL epitopes. Reactive T cells to these epitopes have been uncovered in independent studies. Majority of these epitopes are located on the α-helix region of its structure, and the substitution mutations of primary anchor residues in these epitopes do not affect immunogenicity. Its conserved nucleotide sequence throughout the evolution and diversification of virus into several variants is a puzzle yet to be solved.

Conclusions:

On the basis of its sequence, structure, and epitope mapping, it is concluded that it may function like those mini-proteins used to boost immune responses in medical applications. Due to the complete nucleotide sequence conservation even a few years after SARS-CoV-2 genome was first sequenced, it poses a unique puzzle to be solved, in view of the evolutionary dynamics of variants emerging in the populations worldwide.

Biocomputational Assessment of Natural Compounds as a Potent Inhibitor to Quorum Sensors in Ralstonia solanacearum

Ralstonia solanacearum is among the most damaging bacterial phytopathogens with a wide number of hosts and a broad geographic distribution worldwide. The pathway of phenotype conversion (Phc) is operated by quorum-sensing signals and modulated through the (R)-methyl 3-hydroxypalmitate (3-OH PAME) in R. solanacearum. However, the molecular structures of the Phc pathway components are not yet established, and the structural consequences of 3-OH PAME on quorum sensing are not well studied. In this study, 3D structures of quorum-sensing proteins of the Phc pathway (PhcA and PhcR) were computationally modeled, followed by the virtual screening of the natural compounds library against the predicted active site residues of PhcA and PhcR proteins that could be employed in limiting signaling through 3-OH PAME. Two of the best scoring common ligands ZINC000014762512 and ZINC000011865192 for PhcA and PhcR were further analyzed utilizing orbital energies such as HOMO and LUMO, followed by molecular dynamics simulations of the complexes for 100 ns to determine the ligands binding stability. The findings indicate that ZINC000014762512 and ZINC000011865192 may be capable of inhibiting both PhcA and PhcR. We believe that, after further validation, these compounds may have the potential to disrupt bacterial quorum sensing and thus control this devastating phytopathogenic bacterial pathogen.

Core amino acid substitutions in HCV-3a isolates from Pakistan and opportunities for multi-epitopic vaccines

Hepatitis C virus (HCV), which infected 71 million worldwide and about 5%-6% are from Pakistan, is an ssRNA virus, responsible for end-stage liver disease. To date, no effective therapy is available to cure this disease. Hence, it is important to study the most prevalent genotypes infecting human population and design novel vaccine or small molecule inhibitors to control the infections associated with HCV. Therefore, in this study clinical samples (n = 35; HCV-3a) from HCV patients were subjected to Sanger sequencing method. The sequencing of the core gene, which is generally considered as conserved, involved in the detection, quantitation and genotyping of HCV was performed. Multiple mutations, that is, R46C, R70Q, L91C, G60E, N/S105A, P108A, N110I, S116V, G90S, A77G and G145R that could be linked with response to antiviral therapies were detected. Phylogenetic analysis suggests emerging viral isolates are circulating in Pakistan. Using ab initio modelling technique, we predicted the 3D structure of core protein and subjected to molecular dynamics simulation to extract the most stable conformation of the structure for further analysis. Immunoinformatic approaches were used to propose a multi-epitopes vaccine against HCV by using core protein. The vaccine constructs consist of nine CTL and three HTL epitopes joined by different linkers were docked against the two reported Toll-like receptors (TLR-3 and TLR-8). Docking of vaccine construct with TLR-3 and TLR-8 shows proper binding and in silico expression of the vaccine resulted in a CAI value of 0.93. These analyses suggest that specific immune responses may be produced by the proposed vaccine.Communicated by Ramaswamy H. Sarma.

proANP Metabolism Provides New Insights Into Sacubitril/Valsartan Mode of Action

BACKGROUND

Sacubitril/valsartan (S/V) treatment is associated with clinical benefits in patients with heart failure with reduced ejection fraction (HFrEF), but its mode of action remains elusive, although it involves the increase of ANP (atrial natriuretic peptide).

METHODS AND RESULTS

Using a cohort of 73 HFrEF patients treated with S/V and controls, we deciphered the proteolytic cascade that converts proANP into 4 vasoactive peptides, including ANP, which exert vasodilatory actions. We found that proANP processing is sequential and involved meprin B, ECE (endothelin-converting enzyme) 1, and ANPEP (aminopeptidase N). This processing is limited in HFrEF patients when compared with controls via the downregulation of proANP production, corin, and meprin B activities by miR-425 and miR1-3p, resulting in limited production of proANP-derived bioactive peptides. S/V restored or compensated proANP processing by downregulating miR-425 and miR1-3p beyond levels observed in controls, hence increasing levels of proANP-derived bioactive peptides and vasodilation. In contrast, S/V directly and indirectly partially inhibited ECE1 and ANPEP. Consequently, ECE1 partial inhibition resulted in a lower-than-expected increase in ET1 (endothelin 1), tilting the vasoactive balance toward vasodilation, possibly explaining the hypotensive action of S/V. Finally, we show that proANP glycosylation interferes with the midregional proANP assay-a clinical surrogate for proANP production, preventing any pathophysiological interpretation of the results. Finally, the analysis of S/V dose escalation with respect to baseline treatments suggests S/V-specific effects.

CONCLUSIONS

These findings offer mechanistic evidence to the natriuretic peptide-defective state in HFrEF, which is improved by S/V. These data also strongly suggest that S/V increases plasma ANP by multiple mechanisms that involve the indirect regulation of 2 microRNAs, besides its protection from NEP (neprilysin) cleavage. Altogether, these data provide new insights on HFrEF pathophysiology and the mode of action of S/V.

Dimerization Activity of a Disordered N-Terminal Domain from Drosophila CLAMP Protein

In Drosophila melanogaster, CLAMP is an essential zinc-finger transcription factor that is involved in chromosome architecture and functions as an adaptor for the dosage compensation complex. Most of the known Drosophila architectural proteins have structural N-terminal homodimerization domains that facilitate distance interactions. Because CLAMP performs architectural functions, we tested its N-terminal region for the presence of a homodimerization domain. We used a yeast two-hybrid assay and biochemical studies to demonstrate that the adjacent N-terminal region between 46 and 86 amino acids is capable of forming homodimers. This region is conserved in CLAMP orthologs from most insects, except Hymenopterans. Biophysical techniques, including nuclear magnetic resonance (NMR) and small-angle X-ray scattering (SAXS), suggested that this domain lacks secondary structure and has features of intrinsically disordered regions despite the fact that the protein structure prediction algorithms suggested the presence of beta-sheets. The dimerization domain is essential for CLAMP functions in vivo because its deletion results in lethality. Thus, CLAMP is the second architectural protein after CTCF that contains an unstructured N-terminal dimerization domain.

Equilibrium Between Dimeric and Monomeric Forms of Human Epidermal Growth Factor is Shifted Towards Dimers in a Solution

An interplay between monomeric and dimeric forms of human epidermal growth factor (EGF) affecting its interaction with EGF receptor (EGFR) is poorly understood. While EGF dimeric structure was resolved at pH 8.1, the possibility of EGF dimerization under physiological conditions is still unclear. This study aimed to describe the oligomeric state of EGF in a solution at physiological pH value. With centrifugal ultrafiltration followed by blue native gel electrophoresis, we showed that synthetic human EGF in a solution at a concentration of 0.1 mg/ml exists mainly in the dimeric form at pH 7.4 and temperature of 37 °C, although a small fraction of its monomers was also observed. Based on bioinformatics predictions, we introduced the D46G substitution to examine if EGF C-terminal part is directly involved in the intermolecular interface formation of the observed dimers. We found a reduced ability of the resulting EGF D46G dimers to dissociate at temperatures up to 50 °C. The D46G substitution also increased the intermolecular antiparallel β-structure content within the EGF peptide in a solution according to the CD spectra analysis that was confirmed by HATR-FTIR results. Additionally, the energy transfer between Tyr and Trp residues was detected by fluorescence spectroscopy for the EGF D46G mutant, but not for the native EGF. This allowed us to suggest the elongation and rearrangement of the intermolecular β-structure that leads to the observed stabilization of EGF D46G dimers. The results imply EGF dimerization under physiological pH value and temperature and the involvement of EGF C-terminal part in this process.

Construct a variable-length fragment library for de novo protein structure prediction

Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this work, we developed a variable-length fragment library (VFlib). In VFlib, a master structure database was first constructed from the Protein Data Bank through sequence clustering. The hidden Markov model (HMM) profile of each protein in the master structure database was generated by HHsuite, and the secondary structure of each protein was calculated by DSSP. For the query sequence, the HMM-profile was first constructed. Then, variable-length fragments were retrieved from the master structure database through dynamically variable-length profile-profile comparison. A complete method for chopping the query HMM-profile during this process was proposed to obtain fragments with increased diversity. Finally, secondary structure information was used to further screen the retrieved fragments to generate the final fragment library of specific query sequence. The experimental results obtained with a set of 120 nonredundant proteins show that the global precision and coverage of the fragment library generated by VFlib were 55.04% and 94.95% at the RMSD cutoff of 1.5 Å, respectively. Compared with the benchmark method of NNMake, the global precision of our fragment library had increased by 62.89% with equivalent coverage. Furthermore, the fragments generated by VFlib and NNMake were used to predict structure models through fragment assembly. Controlled experimental results demonstrate that the average TM-score of VFlib was 16.00% higher than that of NNMake.

In silico modelling and virtual screening for identification of inhibitors for spore wall protein-5 in Nosema bombycis

Bombyx mori is an insect of economic importance in the production of silk. It often gets infected by Nosema bombycis, an intracellular parasite. The infection causes a fatal disease known as a Pebrine which affects the development of the worm. The infected larvae of silkworms are coated with brown spots and are unable to spin the silkworm thread. They lose appetite, become sluggish, opaque and ultimately die. The Spore Wall Protein 5 is an exospore protein in N. bombycis and interacts with the polar tube proteins PTP2 and PTP3, a part of the extrusion apparatus that facilitates infection of the host. SWP5 also plays an essential part in maintaining the structural integrity of the spore wall and could possibly regulate the route of the infection in N. bombycis. In the present study, the homology modelling of three protein structures SWP5, PTP2 and PTP3 were performed. The protein-protein interaction was studied and a complete complex of SWP5, PTP2 and PTP3 was generated to understand the discharge of the penetrating polar tube. Virtual screening and molecular dynamics simulation was performed and a potential lead-like molecule is identified.Communicated by Ramaswamy H. Sarma.

Modeling the MreB-CbtA Interaction to Facilitate the Prediction and Design of Candidate Antibacterial Peptides

Protein-protein interactions (PPIs) have emerged as promising targets for PPI modulators as alternative drugs because they are essential for most biochemical processes in living organisms. In recent years, a spotlight has been put on the development of peptide-based PPI inhibitors as the next-generation therapeutics to combat antimicrobial resistance taking cognizance of protein-based PPI-modulators that interact with target proteins to inhibit function. Although protein-based PPI inhibitors are not effective therapeutic agents because of their high molecular weights, they could serve as sources for peptide-based pharmaceutics if the target-inhibitor complex is accessible and well characterized. The Escherichia coli (E. coli) toxin protein, CbtA, has been identified as a protein-based PPI modulator that binds to the bacterial actin homolog MreB leading to the perturbation of its polymerization dynamics; and consequently has been suggested to have antibacterial properties. Unfortunately, however, the three-dimensional structures of CbtA and the MreB-CbtA complex are currently not available to facilitate the optimization process of the pharmacological properties of CbtA. In this study, computer modeling strategies were used to predict key MreB-CbtA interactions to facilitate the design of antiMreB peptide candidates. A model of the E. coli CbtA was built using the trRosetta software and its stability was assessed through molecular dynamics (MD) simulations. The modeling and simulations data pointed to a model with reasonable quality and stability. Also, the HADDOCK software was used to predict a possible MreB-CbtA complex, which was characterized through MD simulations and compared with MreB-MreB dimmer. The results suggest that CbtA inhibits MreB through the competitive mechanism whereby CbtA competes with MreB monomers for the interprotofilament interface leading to interference with double protofilament formation. Additionally, by using the antiBP software to predict antibacterial peptides in CbtA, and the MreB-CbtA complex as the reference structure to determine important interactions and contacts, candidate antiMreB peptides were suggested. The peptide sequences could be useful in a rational antimicrobial peptide hybridization strategy to design novel antibiotics. All-inclusive, the data reveal the molecular basis of MreB inhibition by CbtA and can be incorporated in the design/development of the next-generation antibacterial peptides targeting MreB.

ProFitFun: a protein tertiary structure fitness function for quantifying the accuracies of model structures

MOTIVATION

An accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are used to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins.

RESULTS

The approach was extensively validated on a large test dataset (n = 25 005) of protein structures, comprising 23 661 models of 82 non-homologous proteins and 1344 non-homologous experimental structures. In addition, an external dataset of 40 000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further used for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state-of-the-art methods in terms of Spearman's and Pearson's correlation coefficients, average GDT-TS loss, sum of z-scores and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design.

AVAILABILITY AND IMPLEMENTATION

http://github.com/KYZ-LSB/ProTerS-FitFun.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

De novo Protein Structure Prediction by Coupling Contact With Distance Profile

De novo protein structure prediction is a challenging problem that requires both an accurate energy function and an efficient conformation sampling method. In this study, a de novo structure prediction method, named CoDiFold, is proposed. In CoDiFold, contacts and distance profiles are organically combined into the Rosetta low-resolution energy function to improve the accuracy of energy function. As a result, the correlation between energy and root mean square deviation (RMSD) is improved. In addition, a population-based multi-mutation strategy is designed to balance the exploration and exploitation of conformation space sampling. The average RMSD of the models generated by the proposed protocol is decreased by 49.24 and 45.21 percent in the test set with 43 proteins compared with those of Rosetta and QUARK de novo protocols, respectively. The results also demonstrate that the structures predicted by proposed CoDiFold are comparable to the state-of-the-art methods for the 10 FM targets of CASP13. The source code and executable versions are freely available at http://github.com/iobio-zjut/CoDiFold.

Visualising Viruses

Viruses pose a challenge to our imaginations. They exert a highly visible influence on the world in which we live, but operate at scales we cannot directly perceive and without a clear separation between their own biology and that of their hosts. Communication about viruses is therefore typically grounded in mental images of virus particles. Virus particles, as the infectious stage of the viral replication cycle, can be used to explain many directly observable properties of transmission, infection and immunity. In addition, their often striking beauty can stimulate further interest in virology. The structures of some virus particles have been determined experimentally in great detail, but for many important viruses a detailed description of the virus particle is lacking. This can be because they are challenging to describe with a single experimental method, or simply because of a lack of data. In these cases, methods from medical illustration can be applied to produce detailed visualisations of virus particles which integrate information from multiple sources. Here, we demonstrate how this approach was used to visualise the highly variable virus particles of influenza A viruses and, in the early months of the COVID-19 pandemic, the virus particles of the then newly characterised and poorly described SARS-CoV-2. We show how constructing integrative illustrations of virus particles can challenge our thinking about the biology of viruses, as well as providing tools for science communication, and we provide a set of science communication resources to help visualise two viruses whose effects are extremely apparent to all of us.

A C-term truncated EIF2Bγ protein encoded by an intronically polyadenylated isoform introduces unfavorable EIF2Bγ-EIF2γ interactions

Eukaryotic translation initiates upon recruitment of the EIF2-GTP·Met-tRNA_i ternary complex (TC) to the ribosomes. EIF2 (α, β, γ subunits) is a GTPase. The GDP to GTP exchange within EIF2 is facilitated by the guanine nucleotide exchange factor EIF2B (α-ε subunits). During stress-induced conditions, phosphorylation of the α-subunit of EIF2 turns EIF2 into an inhibitor of EIF2B. In turn, inhibition of EIF2B decreases TC formation and triggers the internal stress response (ISR), which determines the cell fate. Deregulated ISR has been linked to neurodegenerative disorders and cancer, positioning EIF2B as a promising therapeutic target. Hence, a better understanding of the mechanisms/factors that regulate EIF2B activity is required. Here, combining transcript and protein level analyses, we describe an intronically polyadenylated (IPA) transcript of EIF2B's γ-subunit. We show that the IPA mRNA isoform is translated into a C-terminus truncated protein. Using structural modeling, we predict that the truncated EIF2Bγ protein has unfavorable interactions with EIF2γ, leading to a potential decrease in the stability of the nonproductive EIF2:EIF2B complex. While we discovered and confirmed the IPA mRNA isoform in breast cancer cells, the expression of this isoform is not cancer-specific and is widely present in normal tissues. Overall, our data show that a truncated EIF2Bγ protein co-exists with the canonical protein and is an additional player to regulate the equilibrium between productive and nonproductive states of the EIF2:EIF2B complex. These results may have implications in stress-induced translation control in normal and disease states. Our combinatorial approach demonstrates the need to study noncanonical mRNA and protein isoforms to understand protein interactions and intricate molecular mechanisms.

From molecular dynamics to quantum mechanics of misfolded proteins and amyloid-like macroaggregates applied to neurodegenerative diseases

A misfolded protein compared with its native state lacks its biological function resulting in cell dysregulations and often death. Outdated hypotheses on protein folding must be revised: More realistic molecular models, focusing not only on classical molecular dynamics (MD) but also on ab initio quantum mechanics (QM) at the molecular orbitals (MOs) scale, which is not experimentally achievable, are presented to improve our understanding of the thermodynamics of the protein-protein interactions leading to misfolding and neurodegenerative diseases for future drug design. Protein misfolding is characterized by the formation of highly reactive beta-sheets oligomers leading to fibrillar macroscopic aggregates, which are studied with the models given herein that can be useful for the development of new immunotherapies against the Alzheimer's disease and prion, e.g. The example of the prion - an intrinsically disordered protein - is studied, but the models can be generalized to other misfolding diseases. The binding free energy and interactions in a complex of a misfolded prion with a native prion are first analyzed by MD and compared to a complex of two native conformers. A conversion of residues to toxic beta-sheets is observed in the optimized misfolded complex. Then, QM is used to compute, with a much better accuracy than that of MD, the binding free energy of the hydrophobic binding site, responsible of the aggregation, between the bound misfolded and native conformers in the misfolded complex. The latter quantity is significantly negative, so that aggregation is strong and fast. The frontier MOs from QM are used for docking to determine how the first repetitive beta-sheets building blocks of the nanofibrils can be assembled from initial cleaved complexes of the native and misfolded proteins. Successive aggregation of multiple monomers leads to an amyloid-like nanofibril that grows along a principal elongation direction, as also observed experimentally.

In silico evaluation of molecular virus-virus interactions taking place between Cotton leaf curl Kokhran virus- Burewala strain and Tomato leaf curl New Delhi virus

Background

Cotton leaf curl disease (CLCuD) is a disease of cotton caused by begomoviruses, leading to a drastic loss in the annual yield of the crop. Pakistan has suffered two epidemics of this disease leading to the loss of billions in annual exports. The speculation that a third epidemic of CLCuD may result as consequence of the frequent occurrence of Tomato leaf curl New Delhi virus (ToLCNDV) and Cotton leaf curl Kokhran Virus-Burewala Strain (CLCuKoV-Bu) in CLCuD infected samples, demand that the interactions taking between the two viruses be properly evaluated. This study is designed to assess virus-virus interactions at the molecular level and determine the type of co-infection taking place.

Methods

Based on the amino acid sequences of the gene products of both CLCuKoV-Bu and ToLCNDV, protein structures were generated using different software, i.e., MODELLER, I-TASSER, QUARKS, LOMETS and RAPTORX. A consensus model for each protein was selected after model quality assessment using ERRAT, QMEANDisCo, PROCHECK Z-Score and Ramachandran plot analysis. The active and passive residues in the protein structures were identified using the CPORT server. Protein–Protein Docking was done using the HADDOCK webserver, and 169 Protein–Protein Interaction (PPIs) were performed between the proteins of the two viruses. The docked complexes were submitted to the PRODIGY server to identify the interacting residues between the complexes. The strongest interactions were determined based on the HADDOCK Score, Desolvation energy, Van der Waals Energy, Restraint Violation Energy, Electrostatic Energy, Buried Surface Area and Restraint Violation Energy, Binding Affinity and Dissociation constant (K_d). A total of 50 ns Molecular Dynamic simulations were performed on complexes that exhibited the strongest affinity in order to validate the stability of the complexes, and to remove any steric hindrances that may exist within the structures.

Results

Our results indicate significant interactions taking place between the proteins of the two viruses. Out of all the interactions, the strongest were observed between the Replication Initiation protein (Rep) of CLCuKoV-Bu with the Movement protein (MP), Nuclear Shuttle Protein (NSP) of ToLCNDV (DNA-B), while the weakest were seen between the Replication Enhancer protein (REn) of CLCuKoV-Bu with the REn protein of ToLCNDV. The residues identified to be taking a part in interaction belonged to domains having a pivotal role in the viral life cycle and pathogenicity. It maybe deduced that the two viruses exhibit antagonistic behavior towards each other, and the type of infection may be categorised as a type of Super Infection Exclusion (SIE) or homologous interference. However, further experimentation, in the form of transient expression analysis, is needed to confirm the nature of these interactions and increase our understanding of the direct interactions taking place between two viruses.

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.

The bacteriophage LUZ24 "Igy" peptide inhibits the Pseudomonas DNA gyrase

The bacterial DNA gyrase complex (GyrA/GyrB) plays a crucial role during DNA replication and serves as a target for multiple antibiotics, including the fluoroquinolones. Despite it being a valuable antibiotics target, resistance emergence by pathogens including Pseudomonas aeruginosa are proving problematic. Here, we describe Igy, a peptide inhibitor of gyrase, encoded by Pseudomonas bacteriophage LUZ24 and other members of the Bruynoghevirus genus. Igy (5.6 kDa) inhibits in vitro gyrase activity and interacts with the P. aeruginosa GyrB subunit, possibly by DNA mimicry, as indicated by a de novo model of the peptide and mutagenesis. In vivo, overproduction of Igy blocks DNA replication and leads to cell death also in fluoroquinolone-resistant bacterial isolates. These data highlight the potential of discovering phage-inspired leads for antibiotics development, supported by co-evolution, as Igy may serve as a scaffold for small molecule mimicry to target the DNA gyrase complex, without cross-resistance to existing molecules.

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions

Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions.

Assigning secondary structure in proteins using AI

Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the 1980s, various methods based on hydrogen bond analysis and atomic coordinate geometry, followed by machine learning, have been employed in protein structure assignment. However, the assignment process becomes challenging when missing atoms are present in the protein files. Our method proposed a multi-class classifier program named DLFSA for assigning protein secondary structure elements (SSE) using convolutional neural networks (CNNs). A fast and efficient GPU-based parallel procedure extracts fragments from protein files. The model implemented in this work is trained with a subset of the protein fragments and achieves 88.1% and 82.5% train and test accuracy, respectively. The model uses only C_α coordinates for secondary structure assignments. The model has been successfully tested on a few full-length proteins also. Results from the fragment-based studies demonstrate the feasibility of applying deep learning solutions for structure assignment problems.

Unveiling the structure of GPI-anchored protein of Malassezia globosa and its pathogenic role in pityriasis versicolor

Glycosylphosphatidylinositols (GPI)-anchored proteins (GpiPs) are related to the cell wall biogenesis, adhesion, interactions, protease activity, mating, etc. These proteins have been identified in many organisms, including fungi such as Neurospora crassa, Candida albicans, Saccharomyces cerevisiae, and Fusarium graminearum. MGL-3153 gene of Malassezia globosa (M. globosa) encodes a protein which is homologous of the M. restricta, M. sympodialis, M. Pachydermatis, and U. maydis GpiPs. Real-time PCR assay showed that the expression of MGL_3153 gene was significantly up-regulated among M. globosa isolated from patients with pityriasis versicolor (PV) compared to a healthy individual, suggesting the contribution of this gene in the virulence of M. globosa. Accordingly, the sequence of this protein was analyzed by bioinformatics tools to evaluate the structure of that. The conservation analysis of MGL-3153 protein showed that the C-terminal region of this protein, which is responsible for GPI-anchor ligation, was highly conserved during evolution while the N-terminal region just conserved in Malassezia species. Moreover, the predicted tertiary structure of this protein by homology modeling showed that this protein almost has alpha helix structure and represented a stable structure during 150 ns of molecular dynamic simulation. Our results revealed that this protein potentially belongs to GPI-anchored proteins and may contribute to the virulence of M. globosa which warrants further investigations in this area.

Activation of the type VI secretion system in the squid symbiont Vibrio fischeri requires the transcriptional regulator TasR and the structural proteins TssM and TssA

Bacteria have evolved diverse strategies to compete for a niche, including the type VI secretion system (T6SS), a contact-dependent killing mechanism. T6SSs are common in bacterial pathogens, commensals, and beneficial symbionts, where they affect the diversity and spatial structure of host-associated microbial communities. Although T6SS gene clusters are often located on genomic islands (GIs), which may be transferred as a unit, the regulatory strategies that promote gene expression once the T6SS genes are transferred into a new cell are not known. We used the squid symbiont, Vibrio fischeri, to identify essential regulatory factors that control expression of a strain-specific T6SS encoded on a GI. We found that a transcriptional reporter for this T6SS is active only in strains that contain the T6SS-encoding GI, suggesting the GI encodes at least one essential regulator. A transposon screen identified seven mutants that could not activate the reporter. These mutations mapped exclusively to three genes on the T6SS-containing GI that encode two essential structural proteins (a TssA-like protein and TssM) and a transcriptional regulator (TasR). Using T6SS reporters, RT-PCR, competition assays, and differential proteomics, we found that all three genes are required for expression of many T6SS components, except for the TssA-like protein and TssM, which are constitutively expressed. Based on these findings, we propose a model whereby T6SS expression requires conserved structural proteins, in addition to the essential regulator TasR, and this ability to self-regulate may be a strategy to activate T6SS expression upon transfer of T6SS-encoding elements into a new bacterial host. Importance Interbacterial weapons like the T6SS are often located on mobile genetic elements and their expression is highly regulated. We found that two conserved structural proteins are required for T6SS expression in Vibrio fischeri. These structural proteins also contain predicted GTPase and GTP binding domains, suggesting their role in promoting T6SS expression may involve sensing the energetic state of the cell. Such a mechanism would provide a direct link between T6SS activation and cellular energy levels, providing a "checkpoint" to ensure the cell has sufficient energy to build such a costly weapon. Because these regulatory factors are encoded within the T6SS gene cluster, they are predicted to move with the genetic element to activate T6SS expression in a new host cell.

Membrane interaction and disulphide-bridge formation in the unconventional secretion of Tau

Misfolded, pathological tau protein propagates from cell to cell causing neuronal degeneration in Alzheimer's disease and other tauopathies. The molecular mechanisms of this process have remained elusive. Unconventional secretion of tau takes place via several different routes, including direct penetration through the plasma membrane. Here, we show that tau secretion requires membrane interaction via disulphide bridge formation. Mutating residues that reduce tau interaction with membranes or formation of disulphide bridges decrease both tau secretion from cells, and penetration through artificial lipid membranes. Our results demonstrate that tau is indeed able to penetrate protein-free membranes in a process independent of active cellular processes and that both membrane interaction and disulphide bridge formation are needed for this process. QUARK-based de novo modelling of the second and third microtubule-binding repeat domains (MTBDs), in which the two cysteine residues of 4R isoforms of tau are located, supports the concept that this region of tau could form transient amphipathic helices for membrane interaction.

Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14

In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

Toward the solution of the protein structure prediction problem

Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.

Structural and functional characterization of a plant alpha-actinin

The Australian tree malletwood (Rhodamnia argentea) is unique. The genome of malletwood is the only known plant genome that contains a gene coding for an α‐actinin‐like protein. Several organisms predating the animal‐plant bifurcation express an α‐actinin or α‐actinin‐like protein. Therefore, it appears that plants in general, but not malletwood, have lost the α‐actinin or α‐actinin‐like gene during evolution. In order to characterize its structure and function, we synthesized the gene and expressed the recombinant R. argentea protein. The results clearly show that this protein has all properties of genuine α‐actinin. The N‐terminal actin‐binding domain (ABD), with two calponin homology motifs, is very similar to the ABD of any α‐actinin. The C‐terminal calmodulin‐like domain, as well as the intervening rod domain, are also similar to the corresponding regions in other α‐actinins. The R. argentea α‐actinin‐like protein dimerises in solution and thereby can cross‐link actin filaments. Based on these results, we believe the R. argentea protein represents a genuine α‐actinin, making R. argentea unique in the plant world.

Deep learning techniques have significantly impacted protein structure prediction and protein design

Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.

The stabilization of yes-associated protein by TGFβ-activated kinase 1 regulates the self-renewal and oncogenesis of gastric cancer stem cells

Gastric cancer (GC) is the most frequent digestive system malignant tumour and the second most common cause of cancer death globally. Cancer stem cell (CSC) is a small percentage of cancer cells in solid tumours that have differentiation, self‐renewal and tumorigenic capabilities. They have an active participation in the initiation, development, metastasis, recurrence and resistance of tumours to chemotherapy and radiotherapy. Gastric cancer stem cells (GCSCs) have been shown to be correlated with GC initiation and metastasis. In this study, we found that TAK1 expression level in GC tissues was significantly increased compared to the adjacent non‐cancerous tissues by RT‐qPCR, Western blot and immunohistochemistry. TAK1 has been identified as a critical molecule that promoted a variety of malignant GC phenotypes both in vivo and in vitro and promoted the self‐renewal of GCSCs. Mechanistically, TAK1 was up‐regulated by IL‐6 and prevented the degradation of yes‐associated protein (YAP) in the cytoplasm by binding to YAP. Thus, TAK1 promoted the SOX2 and SOX9 transcription and the self‐renewal and oncogenesis of GCSCs. Our findings provide insights into the mechanism of self‐renewal and tumorigenesis of TAK1 in GCSCs and have broad implications for clinical therapies.

Current directions in combining simulation-based macromolecular modeling approaches with deep learning

Introduction: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as drugs. Success rates are limited for any of these tasks, however. Recently, deep neural network-based methods have greatly enhanced the accuracy of predictions of protein structure from sequence, generating excitement about the potential impact of deep learning.Areas covered: This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design. Particular focus is given to the interplay between simulation-based and neural network-based approaches.Expert opinion: As deep learning grows integral to macromolecular modeling, simulation- and neural network-based approaches must grow more tightly interconnected. Modular software architecture must emerge allowing both types of tools to be combined with maximal versatility. Open sharing of code under permissive licenses will be essential. Although experiments will remain the gold standard for reliable information to guide drug discovery, we may soon see successful drug development projects based on high-accuracy predictions from algorithms that combine simulation with deep learning - the ultimate validation of this combination's power.

Genetic analysis of sinonasal undifferentiated carcinoma discovers recurrent SWI/SNF alterations and a novel PGAP3-SRPK1 fusion gene

BACKGROUND

Sinonasal Undifferentiated Carcinoma (SNUC) is a rare and aggressive skull base tumor with poor survival and limited treatment options. To date, targeted sequencing studies have identified IDH2 and SMARCB1 as potential driver alterations, but the molecular alterations found in SMARCB1 wild type tumors are unknown.

METHODS

We evaluated survival outcomes in a cohort of 46 SNUC patients treated at an NCI designated cancer center and identify clinical and disease variables associated with survival on Kaplan-Meier and Cox multivariate survival analysis. We performed exome sequencing to characterize a series of SNUC tumors (n = 5) and cell line (MDA8788-6) to identify high confidence mutations, copy number alterations, microsatellite instability, and fusions. Knockdown studies using siRNA were utilized for validation of a novel PGAP3-SRPK1 gene fusion.

RESULTS

Overall survival analysis revealed no significant difference in outcomes between patients treated with surgery +/- CRT and CRT alone. Tobacco use was the only significant predictor of survival. We also confirmed previously published findings on IDH and SMARC family mutations and identified novel recurrent aberrations in the JAK/STAT and PI3K pathways. We also validated a novel PGAP3-SRPK1 gene fusion in the SNUC cell line, and show that knockdown of the fusion is negatively associated with EGFR, E2F and MYC signaling.

CONCLUSION

Collectively, these data demonstrate recurrent alterations in the SWI/SNF family as well as IDH, JAK/STAT, and PI3K pathways and discover a novel fusion gene (PGAP3-SRPK1). These data aim to improve understanding of possible driver mutations and guide future therapeutic strategies for this disease.

Protein Structure Prediction: Conventional and Deep Learning Perspectives

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein-protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.