RaptorX server: a resource for template-based protein structure modeling

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Structure elucidation of a multi-modular recombinant endoglucanase, AtGH9C-CBM3A-CBM3B from Acetivibrio thermocellus ATCC 27405 and its substrate binding analysis

Cellulases from GH9 family show endo-, exo- or processive endocellulase activity, but the reason behind the variation is unclear. A GH9 recombinant endoglucanase, AtGH9C-CBM3A-CBM3B from Acetivibrio thermocellus was structurally characterized for conformation, binding and dynamics assessment. Modeled AtGH9C-CBM3A-CBM3B depicted (α/α)6-barrel structure with Asp98, Asp101 and Glu489 acting as catalytic triad. CD results revealed 25.2 % α-helix, 18.4 % β-sheet and rest 56.4 % of random coils, corroborating with predictions from PSIPRED and SOPMA. MD simulation of AtGH9C-CBM3A-CBM3B bound cellotetraose showed structural stability and global compactness with lowered RMSD values (1.5 nm) as compared with only AtGH9C-CBM3A-CBM3B (1.8 nm) for 200 ns. Higher fluctuation in RMSF values in far-positioned CBM3B pointed to its redundancy in substrate binding. Docking studies showed maximum binding with cellotetraose (ΔG = -5.05 kcal/mol), with reduced affinity towards ligands with degree of polymerization (DP) lower (DP < 4) or higher than 4 (DP > 4). Processivity index displayed the enzyme to be processive with loop 3 (342-379 aa) possibly blocking the non-reducing end of cellulose chain, resulting in cellotetraose release. SAXS analysis of AtGH9C-CBM3A-CBM3B at 5 mg/mL displayed monodispersed state with fist-and-elbow shape in solution. Negative zeta potential of -24 mV at 5 mg/mL indicated stability and free from aggregation.

Assembly of the Tn7 targeting complex by a regulated stepwise process

The Tn7 family of transposons is notable for its highly regulated integration mechanisms, including programmable RNA-guided transposition. The targeting pathways rely on dedicated target selection proteins from the TniQ family and the AAA+ adaptor TnsC to recruit and activate the transposase at specific target sites. Here, we report the cryoelectron microscopy (cryo-EM) structures of TnsC bound to the TniQ domain of TnsD from prototypical Tn7 and unveil key regulatory steps stemming from unique behaviors of ATP- versus ADP-bound TnsC. We show that TnsD recruits ADP-bound dimers of TnsC and acts as an exchange factor to release one protomer with exchange to ATP. This loading process explains how TnsC assembles a heptameric ring unidirectionally from the target site. This unique loading process results in functionally distinct TnsC protomers within the ring, providing a checkpoint for target immunity and explaining how insertions at programmed sites precisely occur in a specific orientation across Tn7 elements.

The multifunction Coxiella effector Vice stimulates macropinocytosis and interferes with the ESCRT machinery

Intracellular bacterial pathogens divert multiple cellular pathways to establish their niche and persist inside their host. Coxiella burnetii, the causative agent of Q fever, secretes bacterial effector proteins via its Type 4 secretion system to generate a Coxiella-containing vacuole (CCV). Manipulation of lipid and protein trafficking by these effectors is essential for bacterial replication and virulence. Here, we have characterized the lipid composition of CCVs and found that the effector Vice interacts with phosphoinositides and membranes enriched in phosphatidylserine and lysobisphosphatidic acid. Remarkably, eukaryotic cells ectopically expressing Vice present compartments that resemble early CCVs in both morphology and composition. We found that the biogenesis of these compartments relies on the double function of Vice. The effector protein initially localizes at the plasma membrane of eukaryotic cells where it triggers the internalization of large vacuoles by macropinocytosis. Then, Vice stabilizes these compartments by perturbing the ESCRT machinery. Collectively, our results reveal that Vice is an essential C. burnetii effector protein capable of hijacking two major cellular pathways to shape the bacterial replicative niche.

Template-Based Modelling of the Structure of Fungal Effector Proteins

The discovery of new fungal effector proteins is necessary to enable the screening of cultivars for disease resistance. Sequence-based bioinformatics methods have been used for this purpose, but only a limited number of functional effector proteins have been successfully predicted and subsequently validated experimentally. A significant obstacle is that many fungal effector proteins discovered so far lack sequence similarity or conserved sequence motifs. The availability of experimentally determined three-dimensional (3D) structures of a number of effector proteins has recently highlighted structural similarities amongst groups of sequence-dissimilar fungal effectors, enabling the search for similar structural folds amongst effector sequence candidates. We have applied template-based modelling to predict the 3D structures of candidate effector sequences obtained from bioinformatics predictions and the PHI-BASE database. Structural matches were found not only with ToxA- and MAX-like effector candidates but also with non-fungal effector-like proteins-including plant defensins and animal venoms-suggesting the broad conservation of ancestral structural folds amongst cytotoxic peptides from a diverse range of distant species. Accurate modelling of fungal effectors were achieved using RaptorX. The utility of predicted structures of effector proteins lies in the prediction of their interactions with plant receptors through molecular docking, which will improve the understanding of effector-plant interactions.

New Genetic Variants of RUNX2 in Mexican Families Cause Cleidocranial Dysplasia

Cleidocranial dysplasia (CCD) is an autosomal dominant skeletal dysplasia characterized by persistent open skull sutures with bulging calvaria, hypoplasia, or aplasia of clavicles permitting abnormal opposition of the shoulders; wide public symphysis; short middle phalanx of the fifth fingers; and vertebral, craniofacial, and dental anomalies. It is a rare disease, with a prevalence of 1-9/1,000,000, high penetrance, and variable expression. The gene responsible for CCD is the Runt-related transcription factor 2 (RUNX2) gene. We characterize the clinical, genetic, and bioinformatic results of four CCD cases: two cases within Mexican families with six affected members, nine asymptomatic individuals, and two sporadic cases with CCD, with one hundred healthy controls. Genomic DNA analyses of the RUNX2 gene were performed for Sanger sequencing. Bioinformatics tools were used to predict the function, stability, and structural changes of the mutated RUNX2 proteins. Three novel heterozygous mutations (c.651_652delTA; c.538_539delinsCA; c.662T>A) and a previously reported mutation (c.674G>A) were detected. In silico analysis showed that all mutations had functional, stability-related, and structural alterations in the RUNX2 protein. Our results show novel mutations that enrich the pool of RUNX2 gene mutations with CCD. Moreover, the proband 1 presented clinical data not previously reported that could represent an expanded phenotype of severe expression.

Structural Analysis of Breast-Milk αS1-Casein: An α-Helical Conformation Is Required for TLR4-Stimulation

Breast-milk αS1-casein is a Toll-like receptor 4 (TLR4) agonist, whereas phosphorylated αS1-casein does not bind TLR4. The objective of this study was to analyse the structural requirements for these effects. In silico analysis of αS1-casein indicated high α-helical content with coiled-coil characteristics. This was confirmed by CD-spectroscopy, showing the α-helical conformation to be stable between pH 2 and 7.4. After in vitro phosphorylation, the α-helical content was significantly reduced, similar to what it was after incubation at 80 °C. This conformation showed no in vitro induction of IL-8 secretion via TLR4. A synthetic peptide corresponding to V77-E92 of αS1-casein induced an IL-8 secretion of 0.95 ng/mL via TLR4. Our results indicate that αS1-casein appears in two distinct conformations, an α-helical TLR4-agonistic and a less α-helical TLR4 non-agonistic conformation induced by phosphorylation. This is to indicate that the immunomodulatory role of αS1-casein, as described before, could be regulated by conformational changes induced by phosphorylation.

Characterization of Novel Trypanosoma cruzi-Specific Antigen with Potential Use in the Diagnosis of Chagas Disease

Chagas disease is caused by the parasite Trypanosoma cruzi. In humans, it evolves into a chronic disease, eventually resulting in cardiac, digestive, and/or neurological disorders. In the present study, we characterized a novel T. cruzi antigen named Tc323 (TcCLB.504087.20), recognized by a single-chain monoclonal antibody (scFv 6B6) isolated from the B cells of patients with cardiomyopathy related to chronic Chagas disease. Tc323, a ~323 kDa protein, is an uncharacterized protein showing putative quinoprotein alcohol dehydrogenase-like domains. A computational molecular docking study revealed that the scFv 6B6 binds to an internal domain of Tc323. Immunofluorescence microscopy and Western Blot showed that Tc323 is expressed in the main developmental forms of T. cruzi, localized intracellularly and exhibiting a membrane-associated pattern. According to phylogenetic analysis, Tc323 is highly conserved throughout evolution in all the lineages of T. cruzi so far identified, but it is absent in Leishmania spp. and Trypanosoma brucei. Most interestingly, only plasma samples from patients infected with T. cruzi and those with mixed infection with Leishmania spp. reacted against Tc323. Collectively, our findings demonstrate that Tc323 is a promising candidate for the differential serodiagnosis of chronic Chagas disease in areas where T. cruzi and Leishmania spp. infections coexist.

Thermococcus kodakarensis TK0353 is a novel AP lyase with a new fold

Hyperthermophilic organisms thrive in extreme environments prone to high levels of DNA damage. Growth at high temperature stimulates DNA base hydrolysis resulting in apurinic/apyrimidinic (AP) sites that destabilize the genome. Organisms across all domains have evolved enzymes to recognize and repair AP sites to maintain genome stability. The hyperthermophilic archaeon Thermococcus kodakarensis encodes several enzymes to repair AP site damage including the essential AP endonuclease TK endonuclease IV. Recently, using functional genomic screening, we discovered a new family of AP lyases typified by TK0353. Here, using biochemistry, structural analysis, and genetic deletion, we have characterized the TK0353 structure and function. TK0353 lacks glycosylase activity on a variety of damaged bases and is therefore either a monofunctional AP lyase or may be a glycosylase-lyase on a yet unidentified substrate. The crystal structure of TK0353 revealed a novel fold, which does not resemble other known DNA repair enzymes. The TK0353 gene is not essential for T. kodakarensis viability presumably because of redundant base excision repair enzymes involved in AP site processing. In summary, TK0353 is a novel AP lyase unique to hyperthermophiles that provides redundant repair activity necessary for genome maintenance.

In Silico Characterization of Intracellular Localization Signals and Structural Features of Mosquito Densovirus (MDV) Viral Proteins

As entomopathogenic viruses, mosquito densoviruses (MDVs) are widely studied for their potential as biocontrol agents and molecular laboratory tools for mosquito manipulation. The nucleus of the mosquito cell is the site for MDV genome replication and capsid assembly, however the nuclear localization signals (NLSs) and nuclear export signals (NES) for MDV proteins have not yet been identified. We carried out an in silico analysis to identify putative NLSs and NESs in the viral proteins of densoviruses that infect diverse mosquito genera (Aedes, Anopheles, and Culex) and identified putative phosphorylation and glycosylation sites on these proteins. These analyses lead to a more comprehensive understanding of how MDVs are transported into and out of the nucleus and lay the foundation for the potential use of densoviruses in mosquito control and basic research.

In silico discovery of epitopes of gag and env proteins for the development of a multi-epitope vaccine candidate against Maedi Visna Virus using reverse vaccinology approach

Maedi Visna Virus (MVV) causes a chronic viral disease in sheep. Since there is no specific therapeutic drug that targets MVV, development of a vaccine against the MVV is inevitable. This study aimed to analyze the gag and env proteins as vaccine candidate proteins and to identify epitopes in these proteins. In addition, it was aimed to construct a multi-epitope vaccine candidate. According to the obtained results, the gag protein was detected to be more conserved and had a higher antigenicity value. Also, the number of alpha helix in the secondary structure was higher and transmembrane helices were not detected. Although many B cell and MHC-I/II epitopes were predicted, only 19 of them were detected to have the properties of antigenic, non-allergenic, non-toxic, soluble, and non-hemolytic. Of these epitopes, five were remarkable due to having the highest antigenicity value. However, the final multi-epitope vaccine was constructed with 19 epitopes. A strong affinity was shown between the final multi-epitope vaccine and TLR-2/4. In conclusion, the gag protein was a better antigen. However, both proteins had epitopes with high antigenicity value. Also, the final multi-epitope vaccine construct had a potential to be used as a peptide vaccine due to its immuno-informatics results.

A Legionella toxin exhibits tRNA mimicry and glycosyl transferase activity to target the translation machinery and trigger a ribotoxic stress response

A widespread strategy employed by pathogens to establish infection is to inhibit host-cell protein synthesis. Legionella pneumophila, an intracellular bacterial pathogen and the causative organism of Legionnaires' disease, secretes a subset of protein effectors into host cells that inhibit translation elongation. Mechanistic insights into how the bacterium targets translation elongation remain poorly defined. We report here that the Legionella effector SidI functions in an unprecedented way as a transfer-RNA mimic that directly binds to and glycosylates the ribosome. The 3.1 Å cryo-electron microscopy structure of SidI reveals an N-terminal domain with an 'inverted L' shape and surface-charge distribution characteristic of tRNA mimicry, and a C-terminal domain that adopts a glycosyl transferase fold that licenses SidI to utilize GDP-mannose as a sugar precursor. This coupling of tRNA mimicry and enzymatic action endows SidI with the ability to block protein synthesis with a potency comparable to ricin, one of the most powerful toxins known. In Legionella-infected cells, the translational pausing activated by SidI elicits a stress response signature mimicking the ribotoxic stress response, which is activated by elongation inhibitors that induce ribosome collisions. SidI-mediated effects on the ribosome activate the stress kinases ZAKα and p38, which in turn drive an accumulation of the protein activating transcription factor 3 (ATF3). Intriguingly, ATF3 escapes the translation block imposed by SidI, translocates to the nucleus and orchestrates the transcription of stress-inducible genes that promote cell death, revealing a major role for ATF3 in the response to collided ribosome stress. Together, our findings elucidate a novel mechanism by which a pathogenic bacterium employs tRNA mimicry to hijack a ribosome-to-nuclear signalling pathway that regulates cell fate.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

Identification of conserved immunogenic peptides of SARS-CoV-2 nucleocapsid protein

The emergence of the new SARS-CoV-2 variants has led to major concern regarding the efficacy of approved vaccines. Nucleocapsid is a conserved structural protein essential for replication of the virus. This study focuses on identifying conserved epitopes on the nucleocapsid (N) protein of SARS-CoV-2. Using 510 unique amino acid sequences of SARS-CoV-2 N protein, two peptides (193 and 215 aa) with 90% conservancy were selected for T cell epitope prediction. Three immunogenic peptides containing multiple T cell epitopes were identified which were devoid of autoimmune and allergic immune response. These peptides were also conserved (100%) in recent Omicron variants reported in Jan-August 2023. HLA analysis reveals that these peptides are predicted as binding to large number of HLA alleles and 71-90% population coverage in six continents. Identified peptides displayed good binding score with both HLA class I and HLA class II molecules in the docking study. Also, a vaccine construct docked with TLR-4 receptor displays strong interaction with 20 hydrogen bonds and molecular simulation analysis reveals that docked complex are stable. Additionally, the immunogenicity of these N protein peptides was confirmed using SARS-CoV-2 convalescent serum samples. We conclude that the identified N protein peptides contain highly conserved and antigenic epitopes which could be used as a target for the future vaccine development against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

ComplexQA: a deep graph learning approach for protein complex structure assessment

MOTIVATION

In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints.

RESULTS

We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes.

AVAILABILITY

https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu.

Molecular dynamics-based structural insights of the first putative endoglucanase, PsGH5A of glycoside hydrolase family 5 from Pseudopedobacter saltans

CONTEXT

The putative endoglucanase, PsGH5A, from Pseudopedobacter saltans of family GH5 contains a catalytic module, PsGH5 (β/α)₈-TIM barrel), at N-terminal followed by a family 6 carbohydrate-binding module (CBM6, β-sandwich). Superposition of PsGH5A with PDB homologs revealed Glu220 and Glu318 as evolutionarily conserved and catalytic residues performing the hydrolysis through retaining-type mechanism, a canonical property of GH5 family. PsGH5A showed higher affinity for longer cellooligosaccharides, as long as cellodecaose with binding free energy (∆G) of - 13.72 kcal/mol upon the molecular docking, thereby indicating the endo-mode of hydrolysis. The radius of gyration, Rg (2.7 nm), and solvent accessible surface area, SASA (229.6 nm²), of PsGH5A-Cellotetraose complex were determined by MD simulation which was lower than that of PsGH5A (Rg, 2.8 nm, SASA, 267 nm²) demonstrating the compactness and affinity of PsGH5A with the cellulosic ligands. Cellulose compatibility of PsGH5A was further confirmed by MMPBSA and per-residue decomposition analysis, where notable ∆G of - 54.38 kcal/mol for PsGH5A-Cellotetraose complex was observed. Thus, PsGH5A could be potentially an efficient endoglucanase as it accommodated larger cellooligosaccharides at its active-site. PsGH5A is the first putative endoglucanase studied here from P. saltans which could be genome-mined for lignocellulosic biomass saccharification in the renewable energy sector.

METHODS

The 3-D structure of PsGH5A generated by AlphaFold2, RaptorX, SwissModel, Phyre2 and Robetta tool; YASARA was used for energy minimization of built models. UCLA SAVES-v6 was used for quality assessment of models. Molecular Docking was performed using SWISS-DOCK server and Chimera software. Molecular Dynamics simulations and MMPBSA analysis of PsGH5A and PsGH5A-Cellotetraose complex were performed on GROMACS 2019.6.

Insights on Novel Effectors and Characterization of Metacaspase (RS107_6) as a Potential Cell Death-Inducing Protein in Rhizoctonia solani

Effectors play an important role in host–pathogen interactions. Though an economically significant disease in rice, knowledge regarding the infection strategy of Rhizoctonia solani is obscure. In this study, we performed a genome-wide identification of the effectors in R. solani based on the characteristics of previously reported effector proteins. A total of seven novel effectors (designated as RS107_1 to RS107_7) in the disease mechanism of R. solani were identified and were predicted to be non-classically secreted proteins with functionally conserved domains. The function, reactivity, and stability of these proteins were evaluated through physiochemical characterization. The target proteins involved in the regulation of rice defense mechanisms were identified. Furthermore, the effector genes were cloned and RS107_6 (metacaspase) was heterologously expressed in Escherichia coli to obtain a purified protein of ~36.5 kDa. The MALD-TOF characterization confirmed that the protein belonged to a metacaspase of the Peptidase_C14 protein family, 906 bp in size, and encoded a polypeptide of 301 amino acids. These findings suggest that the identified effectors can potentially serve as a virulence factor and can be targeted for the management of sheath blight in rice.

Recombinant T-cell epitope conjugation: A new approach for Dermatophagoides hypoallergen design

BACKGROUND

Allergen-specific immunotherapy (AIT) is the only clinical approach that can potentially cure some allergic diseases by inducing immunological tolerance. Dermatophagoides pteronyssinus is considered as the most important source of mite allergens worldwide, with high sensitization rates for the major allergens Der p 1, Der p 2 and Der p 23. The aim of this work is to generate a hypoallergenic hybrid molecule containing T-cell epitopes from these three major allergens.

METHODS

The hybrid protein termed Der p 2231 containing T-cell epitopes was purified by affinity chromatography. The human IgE reactivity was verified by comparing those with the parental allergens. The hybrid was also characterized immunologically through an in vivo mice model.

RESULTS

The hybrid rDer p 2231 stimulated in peripheral blood mononuclear cells (PBMCs) isolated from allergic patients with higher levels of IL- 2, IL-10, IL-15 and IFN-γ, as well as lower levels of IL-4, IL-5, IL-13, TNF-α and GM-CSF. The use of hybrid molecules as a therapeutic model in D. pteronyssinus allergic mice led to the reduction of IgE production and lower eosinophilic peroxidase activity in the airways. We found increased levels of IgG antibodies that blocked the IgE binding to the parental allergens in the serum of allergic patients. Furthermore, the stimulation of splenocytes from mice treated with rDer p 2231 induced higher levels of IL-10 and IFN-γ and decreased the secretion of IL-4 and IL-5, when compared with parental allergens and D. pteronyssinus extract.

CONCLUSIONS

rDer p 2231 has the potential to be used in AIT in patients co-sensitized with D. pteronyssinus major allergens, once it was able to reduce IgE production, inducing allergen-specific blocking antibodies, restoring and balancing Th1/Th2 immune responses, and inducing regulatory T-cells.

In Silico Structural and Functional Analyses of NLRP3 Inflammasomes to Provide Insights for Treating Neurodegenerative Diseases

Inflammasomes are cytoplasmic intracellular multiprotein complexes that control the innate immune system's activation of inflammation in response to derived chemicals. Recent advancements increased our molecular knowledge of activation of NLRP3 inflammasomes. Although several studies have been done to investigate the role of inflammasomes in innate immunity and other diseases, structural, functional, and evolutionary investigations are needed to further understand the clinical consequences of NLRP3 gene. The purpose of this study is to investigate the structural and functional impact of the NLRP3 protein by using a computational analysis to uncover putative protein sites involved in the stabilization of the protein-ligand complexes with inhibitors. This will allow for a deeper understanding of the molecular mechanism underlying these interactions. It was found that human NLRP3 gene coexpresses with PYCARD, NLRC4, CASP1, MAVS, and CTSB based on observed coexpression of homologs in other species. The NACHT, LRR, and PYD domain-containing protein 3 is a key player in innate immunity and inflammation as the sensor subunit of the NLRP3 inflammasome. The inflammasome polymeric complex, consisting of NLRP3, PYCARD, and CASP1, is formed in response to pathogens and other damage-associated signals (and possibly CASP4 and CASP5). Comprehensive structural and functional analyses of NLRP3 inflammasome components offer a fresh approach to the development of new treatments for a wide variety of human disorders.

A novel fold for acyltransferase-3 (AT3) proteins provides a framework for transmembrane acyl-group transfer

Acylation of diverse carbohydrates occurs across all domains of life and can be catalysed by proteins with a membrane bound acyltransferase-3 (AT3) domain (PF01757). In bacteria, these proteins are essential in processes including symbiosis, resistance to viruses and antimicrobials, and biosynthesis of antibiotics, yet their structure and mechanism are largely unknown. In this study, evolutionary co-variance analysis was used to build a computational model of the structure of a bacterial O-antigen modifying acetyltransferase, OafB. The resulting structure exhibited a novel fold for the AT3 domain, which molecular dynamics simulations demonstrated is stable in the membrane. The AT3 domain contains 10 transmembrane helices arranged to form a large cytoplasmic cavity lined by residues known to be essential for function. Further molecular dynamics simulations support a model where the acyl-coA donor spans the membrane through accessing a pore created by movement of an important loop capping the inner cavity, enabling OafB to present the acetyl group close to the likely catalytic resides on the extracytoplasmic surface. Limited but important interactions with the fused SGNH domain in OafB are identified, and modelling suggests this domain is mobile and can both accept acyl-groups from the AT3 and then reach beyond the membrane to reach acceptor substrates. Together this new general model of AT3 function provides a framework for the development of inhibitors that could abrogate critical functions of bacterial pathogens.

Current Update on Rotavirus in-Silico Multiepitope Vaccine Design

Rotavirus gastroenteritis is one of the leading causes of pediatric morbidity and mortality worldwide in infants and under-five populations. The World Health Organization (WHO) recommended global incorporation of the rotavirus vaccine in national immunization programs to alleviate the burden of the disease. Implementation of the rotavirus vaccination in certain regions of the world brought about a significant and consistent reduction of rotavirus-associated hospitalizations. However, the efficacy of licensed vaccines remains suboptimal in low-income countries where the incidences of rotavirus gastroenteritis continue to happen unabated. The problem of low efficacy of currently licensed oral rotavirus vaccines in low-income countries necessitates continuous exploration, design, and development of new rotavirus vaccines. Traditional vaccine development is a complex, expensive, labor-intensive, and time-consuming process. Reverse vaccinology essentially utilizes the genome and proteome information on pathogens and has opened new avenues for in-silico multiepitope vaccine design for a plethora of pathogens, promising time reduction in the complete vaccine development pipeline by complementing the traditional vaccinology approach. A substantial number of reviews on licensed rotavirus vaccines and those under evaluation are already available in the literature. However, a collective account of rotavirus in-silico vaccines is lacking in the literature, and such an account may further fuel the interest of researchers to use reverse vaccinology to expedite the vaccine development process. Therefore, the main focus of this review is to summarize the research endeavors undertaken for the design and development of rotavirus vaccines by the reverse vaccinology approach utilizing the tools of immunoinformatics.

Two new Scianna variants causing loss of high prevalence antigens: ERMAP model and 3D analysis of the antigens

BACKGROUND

Scianna (Sc) antigens, seven high and two of low prevalence, are expressed on erythrocyte membrane-associated protein (ERMAP). We investigated SC (ERMAP) in individuals who made antibodies to high prevalence Scianna antigens, and propose a 3D model for ERMAP to precisely localize the residues associated with the known antigens.

METHODS

Serological testing and DNA sequencing was performed by standard methods. A 3D structural model was built using a multi-template homology approach. Protein structures representing missense variants associated with the loss or gain of an antigen were generated. Residue accessibility and intraprotein interactions were compared with the wild-type protein.

RESULTS

Two new SC alleles, one with c.349C > T (p.Arg117Cys) in a woman from South India with anti-Sc3 in her plasma, and a c.217_219delinsTGT (p.Arg73Cys) in an African-American woman with an antibody to a new high prevalence antigen, termed SCAC, were identified. Six structural templates were used to model ERMAP. 3D analysis showed that residues key for Scianna antigen expression were all exposed at the surface of the extracellular domain. The p.Arg117Cys change was predicted to abolish interactions between residues 93 and 117, with no compensating interactions.

CONCLUSION

We confirm the extracellular location of Scianna residues responsible for antigen expression which predicts direct accessibility to antibodies. Loss of intraprotein interactions appear to be responsible for a Sc null and production of anti-Sc3 with p.117Cys, SC*01 N.03, and for loss of a high prevalence antigen with p.73Cys, termed SCAC for Sc Arg to Cys. Comparative modeling aids our understanding of new alleles and Scianna antigen expression.

In-Silico Designing of a Multi-Epitope Vaccine against SARS-CoV2 and Studying the Interaction of the Vaccine with Alpha, Beta, Delta and Omicron Variants of Concern

BACKGROUND

The sudden appearance of the SARS-CoV2 virus has almost changed the future of vaccine development. There have been many different approaches to vaccination; among them, computational vaccinology in the form of multi-epitope vaccines with excellent immunological properties and minimal contamination or other adverse reactions has emerged as a promising strategy with a lot of room for further study in this area.

OBJECTIVE

Designing a multi-epitope vaccine from the spike protein of SARS-CoV2 based on immunoinformatics and in-silico techniques. Evaluating the binding affinity of the constructed vaccine against the major variants of concern (alpha, beta, delta, and omicron) using docking studies.

METHODS

The potential antigenic, immunogenic, and non-allergic T-cell epitopes were thoroughly explored using IEDB, NetCTL1.2, and NetMHCII pan 3.2 servers. The best suitable linker was identified using the ExPASy Protparam tool and VERIFY 3D. The 3D model of the vaccine was developed by RaptorX and the model was validated using ERRAT, Z-score, and Ramachandran Plot. Docking studies of the vaccine with TLR-2, 3, 4, and 7 and alpha, beta, delta, and omicron variants were performed using HADDOCK 2.4.

RESULTS

The vaccine construct showed good antigenic and immunogenic scores and was non-allergic as well. The model was capable of binding to all four selected Toll-like receptors. Docking scores with variants were also promising.

CONCLUSION

All the variants showed good binding ability with the vaccine construct. Interaction with the alpha variant was found to be the most intense, followed by delta, beta, and omicron.

From Genome Mining to Protein Engineering: A Structural Bioinformatics Route

This chapter outlines applications in genome mining, along with computational methods to predict protein structure and protein-ligand docking. It offers a simple computational route to rapidly identify proteins of interest from genomic and proteomic data, to accurately predict their three-dimensional structures, and to dock small molecules to their binding pockets and strategies to improve their biophysical properties depending on the needs of the experimental researcher.

In Silico Evaluation of Nonsynonymous SNPs in Human ADAM33: The Most Common Form of Genetic Association to Asthma Susceptibility

ADAM33 is a zinc-dependent metalloprotease of the ADAM family, which plays a vital biological role as an activator of Th2 cytokines and growth factors. Moreover, this protein is crucial for the normal development of the lung in the fetus two months after gestation leading to determining lung functions all over life. In this regard, mutations in ADAM33 have been linked with asthma risk factors. Consequently, identifying ADAM33 pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) can be very important in asthma treatment. In the present study, 1055 nsSNPs of human ADAM33 were analyzed using biocomputational software, 31 of which were found to be detrimental mutations. Precise structural and stability analysis revealed D219V, C669G, and C606S as the most destabilizing SNPs. Furthermore, MD simulations disclosed higher overall fluctuation and alteration in intramolecular interactions compared with the wild-type structure. Overall, the results suggest D219V, C669G, and C606S detrimental mutations as a starting point for further case-control studies on the ADAM33 protein as well as an essential source for future targeted mechanisms.

Structure-based virtual screening and molecular dynamics of potential inhibitors targeting sodium-bile acid co-transporter of carcinogenic liver fluke Clonorchis sinensis

Background

Clonorchis sinensis requires bile acid transporters as this fluke inhabits bile juice-filled biliary ducts, which provide an extreme environment. Clonorchis sinensis sodium-bile acid co-transporter (CsSBAT) is indispensable for the fluke’s survival in the final host, as it circulates taurocholate and prevents bile toxicity in the fluke; hence, it is recognized as a useful drug target.

Methodology and principal findings

In the present study, using structure-based virtual screening approach, we presented inhibitor candidates targeting a bile acid-binding pocket of CsSBAT. CsSBAT models were built using tertiary structure modeling based on a bile acid transporter template (PDB ID: 3zuy and 4n7x) and were applied into AutoDock Vina for competitive docking simulation. First, potential compounds were identified from PubChem (holding more than 100,000 compounds) by applying three criteria: i) interacting more favorably with CsSBAT than with a human homolog, ii) intimate interaction to the inward- and outward-facing conformational states, iii) binding with CsSBAT preferably to natural bile acids. Second, two compounds were identified following the Lipinski’s rule of five. Third, other two compounds of molecular weight higher than 500 Da (Mr > 500 Da) were presumed to efficiently block the transporter via a feasible rational screening strategy. Of these candidates, compound 9806452 exhibited the least hepatotoxicity that may enhance drug-likeness properties.

Conclusions

It is proposed that compound 9806452 act as a potential inhibitor toward CsSBAT and further studies are warranted for drug development process against clonorchiasis.

Bioinformatics approach for the construction of multiple epitope vaccine against omicron variant of SARS-CoV-2

The World Health Organization categorized SARS-CoV-2 as a variant of concern, having numerous mutations in spike protein, which have been found to evade the effect of antibodies stimulated by the COVID-19 vaccine. The susceptibility to omicron variant by immunization-induced antibodies are direly required for risk evaluation. To avoid the risk of arising viral illness, the construction of a specific vaccine that triggers the production of targeted antibodies to combat infection remains highly imperative. The aim of the present study is to develop a particular vaccine exploiting bioinformatics approaches which can target B- and T-cells epitopes. Through this approach, novel epitopes of the S protein-SARS-CoV-2 were predicted for the development of a multiple epitope vaccine. Multiple epitopes were selected on the basis of toxicity, immunogenicity and antigenicity, and vaccine subunit was constructed having potential immunogenic properties. The epitopes were linked with 3 types of linker EAAAK, AAY and GPGPG for vaccine construction. Subsequently, vaccine structure was docked with the receptor and cloned in a pET-28a (+) vector. The constructed vaccine was ligated in pET-28a (+) vector in E. coli using the SnapGene tool for the expression study and a good immune response was observed. Several computational tools were used to predict and analyze the vaccine constructed by using spike protein sequence of omicrons. The current study identified a Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that could potentially help the global world to combat SARS-CoV-2 infections.

Insight into the first multi-epitope-based peptide subunit vaccine against avian influenza A virus (H5N6): An immunoinformatics approach

The rampant spread of highly pathogenic avian influenza A (H5N6) virus has drawn additional concerns along with ongoing Covid-19 pandemic. Due to its migration-related diffusion, the situation is deteriorating. Without an existing effective therapy and vaccines, it will be baffling to take control measures. In this regard, we propose a revers vaccinology approach for prediction and design of a multi-epitope peptide based vaccine. The induction of humoral and cell-mediated immunity seems to be the paramount concern for a peptide vaccine candidate; thus, antigenic B and T cell epitopes were screened from the surface, membrane and envelope proteins of the avian influenza A (H5N6) virus, and passed through several immunological filters to determine the best possible one. Following that, the selected antigenic with immunogenic epitopes and adjuvant were linked to finalize the multi-epitope-based peptide vaccine by appropriate linkers. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLR8). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, molecular dynamics simulation was performed within the highest binding affinity complex to observe the stability, and minimize the designed vaccine's high mobility region to order to increase its stability. Then, Codon optimization and other physicochemical properties were performed to reveal that the vaccine would be suitable for a higher expression at cloning level and satisfactory thermostability condition. In conclusion, predicting the overall in silico assessment, we anticipated that our designed vaccine would be a plausible prevention against avian influenza A (H5N6) virus.

Escherichia coli transcription factors of unknown function: sequence features and possible evolutionary relationships

Organisms need mechanisms to perceive the environment and respond accordingly to environmental changes or the presence of hazards. Transcription factors (TFs) are required for cells to respond to the environment by controlling the expression of genes needed. Escherichia coli has been the model bacterium for many decades, and still, there are features embedded in its genome that remain unstudied. To date, 58 TFs remain poorly characterized, although their binding sites have been experimentally determined. This study showed that these TFs have sequence variation at the third codon position G+C content but maintain the same Codon Adaptation Index (CAI) trend as annotated functional transcription factors. Most of these transcription factors are in areas of the genome where abundant repetitive and mobile elements are present. Sequence divergence points to groups with distinctive sequence signatures but maintaining the same type of DNA binding domain. Finally, the analysis of the promoter sequences of the 58 TFs showed A+T rich regions that agree with the features of horizontally transferred genes. The findings reported here pave the way for future research of these TFs that may uncover their role as spare factors in case of lose-of-function mutations in core TFs and trace back their evolutionary history.

Proteome Exploration of Legionella pneumophila To Identify Novel Therapeutics: a Hierarchical Subtractive Genomics and Reverse Vaccinology Approach

Legionella pneumophila is a human pathogen distributed worldwide, causing Legionnaires’ disease (LD), a severe form of pneumonia and respiratory tract infection. L. pneumophila is emerging as an antibiotic-resistant strain, and controlling LD is now difficult. Hence, developing novel drugs and vaccines against L. pneumophila is a major research priority.

Immunoinformatics Approach Toward the Introduction of a Novel Multi-Epitope Vaccine Against Clostridium difficile

Clostridium difficile (C.difficile) is an exclusively anaerobic, spore-forming, and Gram-positive pathogen that is the most common cause of nosocomial diarrhea and is becoming increasingly prevalent in the community. Because C. difficile is strictly anaerobic, spores that can survive for months in the external environment contribute to the persistence and diffusion of C. difficile within the healthcare environment and community. Antimicrobial therapy disrupts the natural intestinal flora, allowing spores to develop into propagules that colonize the colon and produce toxins, thus leading to antibiotic-associated diarrhea and pseudomembranous enteritis. However, there is no licensed vaccine to prevent Clostridium difficile infection (CDI). In this study, a multi-epitope vaccine was designed using modern computer methods. Two target proteins, CdeC, affecting spore germination, and fliD, affecting propagule colonization, were chosen to construct the vaccine so that it could simultaneously induce the immune response against two different forms (spore and propagule) of C. difficile. We obtained the protein sequences from the National Center for Biotechnology Information (NCBI) database. After the layers of filtration, 5 cytotoxic T-cell lymphocyte (CTL) epitopes, 5 helper T lymphocyte (HTL) epitopes, and 7 B-cell linear epitopes were finally selected for vaccine construction. Then, to enhance the immunogenicity of the designed vaccine, an adjuvant was added to construct the vaccine. The Prabi and RaptorX servers were used to predict the vaccine's two- and three-dimensional (3D) structures, respectively. Additionally, we refined and validated the structures of the vaccine construct. Molecular docking and molecular dynamics (MD) simulation were performed to check the interaction model of the vaccine-Toll-like receptor (TLR) complexes, vaccine-major histocompatibility complex (MHC) complexes, and vaccine-B-cell receptor (BCR) complex. Furthermore, immune stimulation, population coverage, and in silico molecular cloning were also conducted. The foregoing findings suggest that the final formulated vaccine is promising against the pathogen, but more researchers are needed to verify it.

Cold-adaptive traits identified by comparative genomic analysis of a lipase-producing Pseudomonas sp. HS6 isolated from snow-covered soil of Sikkim Himalaya and molecular simulation of lipase for wide substrate specificity

The genomic analysis of industrially important bacteria can help in understanding their capability to withstand extreme environments and shed light on their metabolic capabilities. The whole genome of a previously reported broad temperature active lipase-producing Pseudomonas sp. HS6, isolated from snow-covered soil of the Sikkim Himalayan Region, was analyzed to understand the capability of the bacterium to withstand cold temperatures and study its lipolytic nature. Pseudomonas sp. HS6 was found to be psychrotolerant with an optimal growth temperature ranging between 25 and 30 °C, with the ability to grow at 5 °C. The genome harbours various cold-adaptation genes, such as cold-shock proteins, fatty acid alteration, and cold stress-tolerance genes, supporting the psychrotolerant nature of the organism. The comparative analysis of Pseudomonas sp. HS6 genome showed the presence of amino acid substitutions in genes that favor efficient functioning and flexibility at cold temperatures. Genome mining revealed the presence of four triacylglycerol lipases, among which the putative lipase 3 was highly similar to the broad temperature-active lipase purified and characterized in our previous study. In silico studies of putative lipase 3 revealed broad substrate specificity with partial and no inhibition of the enzyme activity in the presence of PMSF and orlistat. The presence of genes associated with cold adaptations and true lipases with activity at broad temperature and substrate specificity in the genome of Pseudomonas sp. HS6 makes this bacterium a suitable candidate for industrial applications.

PyPAn: An Automated Graphical User Interface for Protein Sequence and Structure Analyses

Background:

Protein sequence and structure analyses have been essential components of bioinformatics and structural biology. They provide a deeper insight into the physicochemical properties, structure, and subsequent functions of a protein. Advanced computational approaches and bioinformatics utilities help solve several issues related to protein analysis. Still, beginners and non-professional may struggle when encountering a wide variety of computational tools and the sheer number of input parameter variables required by each tool.

Methods:

We introduce a free-to-access graphical user interface (GUI) named PyPAn 'Python-based Protein Analysis' for varieties of protein sequence/structure analyses. PyPAn serves as a universal platform to analyze protein sequences, structure, and their properties. PyPAn facilitates onboard analysis of each task in just a single click. It can be used to calculate the physicochemical properties, including instability index and molar extinction coefficient, for a protein. PyPAn is one of the few computational tools that allow users to generate a Ramachandran plot and calculate solvent accessibility and the radius of gyration (Rg) of proteins at once. In addition, it can refine the protein model along with computation and minimization of its energy.

Results:

PyPAn can generate a recommendation for an appropriate structure modelling method to employ for a query protein sequence. PyPAn is one of the few, if not the only, Python-based computational GUI tools with an array of options for the user to employ as they see fit.

Conclusion:

PyPAn aims to unify many successful academically significant proteomic applications and is freely available for academic and industrial research uses at https://hassanlab.org/pypan.

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.

VHH Structural Modelling Approaches: A Critical Review

V_HH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved V_HH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most V_HHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given V_HH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of V_HHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved V_HH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of V_HH from its sequence.

Development of peptide vaccine candidate using highly antigenic PE-PGRS family proteins to stimulate the host immune response against Mycobacterium tuberculosis H37Rv: an immuno-informatics approach

Tuberculosis (TB) is a fast spreading; transmissible disease caused by the Mycobacterium tuberculosis (M. tuberculosis). M. tuberculosis has a high death rate in its endemic regions due to a lack of appropriate treatment and preventative measures. We have used a vaccinomics strategy to create an effective multi-epitope vaccine against M. tuberculosis. The antigenic proteins with the highest antigenicity were utilised to predict cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes. CTL and HTL epitopes were covered in 99.97% of the population. Seven epitopes each of CTL, HTL, and LBL were ultimately selected and utilised to develop a multi-epitope vaccine. A vaccine design was developed by combining these epitopes with suitable linkers and LprG adjuvant. The vaccine chimera was revealed to be highly immunogenic, non-allergenic, and non-toxic. To ensure a better expression within the Escherichia coli K12 (E. coli K12) host system, codon adaptation and in silico cloning were accomplished. Following that, various validation studies were conducted, including molecular docking, molecular dynamics simulation, and immunological simulation, all of which indicated that the designed vaccine would be stable in the biological environment and effective against M. tuberculosis infection. The immune simulation revealed higher levels of T-cell and B-cell activity, which corresponded to the actual immune response. Exposure simulations were repeated several times, resulting in increased clonal selection and faster antigen clearance. These results suggest that, if proposed vaccine chimera would test both in-vitro and in-vivo, it could be a viable treatment and preventive strategy for TB.Communicated by Ramaswamy H. Sarma.

GPU-I-TASSER: a GPU accelerated I-TASSER protein structure prediction tool

MOTIVATION

Accurate and efficient predictions of protein structures play an important role in understanding their functions. Iterative Threading Assembly Refinement (I-TASSER) is one of the most successful and widely used protein structure prediction methods in the recent community-wide CASP experiments. Yet, the computational efficiency of I-TASSER is one of the limiting factors that prevent its application for large-scale structure modeling.

RESULTS

We present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelerated I-TASSER protein structure prediction tool for fast and accurate protein structure prediction. Our implementation is based on OpenACC parallelization of the replica-exchange Monte Carlo simulations to enhance the speed of I-TASSER by extending its capabilities to the GPU architecture. On a benchmark dataset of 71 protein structures, GPU-I-TASSER achieves on average a 10× speedup with comparable structure prediction accuracy compared to the CPU version of the I-TASSER.

AVAILABILITY AND IMPLEMENTATION

The complete source code for GPU-I-TASSER can be downloaded and used without restriction from https://zhanggroup.org/GPU-I-TASSER/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Are Methionine Sulfoxide-Containing Proteins Related to Seed Longevity? A Case Study of Arabidopsisthaliana Dry Mature Seeds Using Cyanogen Bromide Attack and Two-Dimensional-Diagonal Electrophoresis

In recent years, several reports pointed out the role of protein oxidation in seed longevity, notably regarding the oxidation of methionine (Met) residues to methionine sulfoxide (MetO) in proteins. To further consider this question, we present a handy proteomic method based on the use of two-dimensional diagonal electrophoresis (2Dd) and cyanogen bromide (CNBr) cleavage, which we refer to as 2Dd-CNBr. CNBr treatment of proteins causes the non-enzymatic hydrolysis of peptide bonds on the carboxyl side of reduced Met residues. However, Met oxidation causes a lack of cleavage, thus modifying the electrophoretic mobility of CNBr-induced peptides. This approach was first validated using bovine serum albumin as a model protein, which confirmed the possibility of distinguishing between oxidized and non-oxidized forms of Met-containing peptides in gels. Then, the 2Dd-CNBr method was applied to the Arabidopsis thaliana seed protein extract in a control (non-oxidized) condition and in an oxidized one (as obtained following hypochlorous acid treatment). Twenty-four oxidized Met residues in 19 proteins identified by mass spectrometry were found to be surface exposed in these proteins. In the three-dimensional environment of the oxidized Met, we detected amino acid residues that could be converted by oxidation (carbonylation) or by phosphorylation, suggesting a possible interplay between Met oxidation and the other protein modifications. The identification of the proteins oxidatively modified in Met residues revealed the finding that MetO-containing proteins are related to seed longevity. Based on these results, we suggest that the method presently described also has the potential for wider applications.

Targeted Mass Spectrometry-Based Proteomics Method to Quantify Placental Extracellular Vesicles

Extracellular vesicles (EVs) carry a wide range of molecules, such as proteins, RNAs, and DNA. EVs are secreted from a wide range of cells, including placental cells. Interestingly, EVs secreted from placental cells have been identified in maternal circulation as early as 6 weeks of gestation, and their concentration increases with the gestational age. While there is growing interest in elucidating the role of exosomes during normal and complicated pregnancies, progress in the field has been delayed because of the inability to quantify placental EVs from the maternal circulation. Recent reports have demonstrated the presence of placental-type alkaline phosphatase (PLAP) EVs only in the blood of pregnant women, indicating that PLAP is a marker to identify EVs secreted from the placenta. Therefore, here we describe a workflow to quantify placental EVs from maternal circulation using a targeted proteomics approach based on selecting specific peptides identified in the PLAP protein.

Structural model of human PORCN illuminates disease-associated variants and drug-binding sites

Wnt signaling is essential for normal development and is a therapeutic target in cancer. The enzyme PORCN, or porcupine, is a membrane-bound O-acyltransferase (MBOAT) that is required for the post-translational modification of all Wnts, adding an essential mono-unsaturated palmitoleic acid to a serine on the tip of Wnt hairpin 2. Inherited mutations in PORCN cause focal dermal hypoplasia, and therapeutic inhibition of PORCN slows the growth of Wnt-dependent cancers. Based on homology to mammalian MBOAT proteins, we developed and validated a structural model of human PORCN. The model accommodates palmitoleoyl-CoA and Wnt hairpin 2 in two tunnels in the conserved catalytic core, shedding light on the catalytic mechanism. The model predicts how previously uncharacterized human variants of uncertain significance can alter PORCN function. Drugs including ETC-159, IWP-L6 and LGK-974 dock in the PORCN catalytic site, providing insights into PORCN pharmacologic inhibition. This structural model enhances our mechanistic understanding of PORCN substrate recognition and catalysis, as well as the inhibition of its enzymatic activity, and can facilitate the development of improved inhibitors and the understanding of disease-relevant PORCN mutants. This article has an associated First Person interview with the joint first authors of the paper.

The evolution of metazoan shelterin

In this study, Myler et al. investigated the evolutionary origins of shelterin complex, which is comprised of TRF1, TRF2, Rap1, TIN2, TPP1, and POT1; blocks the DNA damage response at chromosome ends; and interacts with telomerase and the CST complex to regulate telomere length. They describe the evolution of metazoan shelterin, showing that TRF1 emerged in vertebrates upon duplication of a TRF2-like ancestor, and providing insights into the biology of shelterin and its evolution from ancestral telomeric DNA-binding proteins.

The mammalian telomeric shelterin complex—comprised of TRF1, TRF2, Rap1, TIN2, TPP1, and POT1—blocks the DNA damage response at chromosome ends and interacts with telomerase and the CST complex to regulate telomere length. The evolutionary origins of shelterin are unclear, partly because unicellular organisms have distinct telomeric proteins. Here, we describe the evolution of metazoan shelterin, showing that TRF1 emerged in vertebrates upon duplication of a TRF2-like ancestor. TRF1 and TRF2 diverged rapidly during vertebrate evolution through the acquisition of new domains and interacting factors. Vertebrate shelterin is also distinguished by the presence of an HJRL domain in the split C-terminal OB fold of POT1, whereas invertebrate POT1s carry inserts of variable nature. Importantly, the data reveal that, apart from the primate and rodent POT1 orthologs, all metazoan POT1s are predicted to have a fourth OB fold at their N termini. Therefore, we propose that POT1 arose from a four-OB-fold ancestor, most likely an RPA70-like protein. This analysis provides insights into the biology of shelterin and its evolution from ancestral telomeric DNA-binding proteins.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Evaluation of FRET X for single-molecule protein fingerprinting

Single-molecule protein identification is an unrealized concept with potentially ground-breaking applications in biological research. We propose a method called FRET X (Förster Resonance Energy Transfer via DNA eXchange) fingerprinting, in which the FRET efficiency is read out between exchangeable dyes on protein-bound DNA docking strands and accumulated FRET efficiencies constitute the fingerprint for a protein. To evaluate the feasibility of this approach, we simulated fingerprints for hundreds of proteins using a coarse-grained lattice model and experimentally demonstrated FRET X fingerprinting on model peptides. Measured fingerprints are in agreement with our simulations, corroborating the validity of our modeling approach. In a simulated complex mixture of >300 human proteins of which only cysteines, lysines, and arginines were labeled, a support vector machine was able to identify constituents with 95% accuracy. We anticipate that our FRET X fingerprinting approach will form the basis of an analysis tool for targeted proteomics.

•

We propose a FRET-based single-molecule protein identification method

•

Peptides are experimentally distinguishable by their fingerprints

•

Our approach can classify the constituents of complex samples with 95% accuracy

Evolution of the Cytokinin Dehydrogenase (CKX) Domain

Plant hormone cytokinins are important regulators of plant development, response to environmental stresses and interplay with other plant hormones. Cytokinin dehydrogenases (CKXs) are proteins responsible for the irreversible break-down of cytokinins to the adenine and aldehyde. Even though plant CKXs have been extensively studied, homologous proteins from other taxa remain mainly uncharacterised. Here we present our study on the molecular evolution and divergence of the CKX from bacteria, fungi, amoeba and viridiplantae. Although CKXs are present in eukaryotes and prokaryotes, they are missing in algae and metazoan taxa. The prevalent domain architecture consists of the FAD-binding and cytokinin binding domains, whereas some bacteria appear to have only cytokinin binding domain proteins. The CKXs play important role in the various aspects of plant life including control of plant development, response to biotic and abiotic stress, influence nutrition. Results of our study suggested that CKX originates from the FAD-linked C-terminal oxidase and has a defence-oriented function. The obtained results significantly extend the current understanding of the cytokinin dehydrogenases structure-function from the relationship to homologues from other taxa and provide a starting point baseline for their future functional characterization.

Salmonella delivered Lawsonia intracellularis novel epitope-fusion vaccines enhance immunogenicity and confers protection against Lawsonia intracellularis in mice

Attenuated Salmonella-mediated vaccine constructs were designed by employing selected discontinuous immunodominant epitopes of LatA, FliC, and PAL antigens of Lawsonia intracellularis to create vaccines against porcine proliferative enteropathy (PPE). Whole protein sequences were subjected to in silico prediction of dominant epitopes, the stability of fusions, and hydropathicity and to ensure that the fused epitopes were feasible for expression in a Salmonella system. Two fusion constructs, one comprising LatA epitopes and the other FliC-PAL-FliC epitopes, were built into a prokaryotic constitutive expression system and transformed into the auxotrophic Salmonella host strain JOL1800. Epitope selection eliminated the majority of less immunodominant regions of target proteins and resulted in an efficient secretion platform that induced significant protective responses. Overall, our results demonstrated that the Salmonella-mediated LI- multi-epitope vaccines elicited significant humoral and cellular immune responses. Additionally, the challenge study suggested that the vaccinated mice were protected against experimental Lawsonia intracellularis infection. Based on the outcomes of the study, Salmonella-mediated LI- multi-epitope vaccines have the potential to prevent PPE.