Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

Toxoplasma gondii (GRA10): A Closer Glance Into Immunogenic and Biochemical Characteristics Using In Silico Approach

Toxoplasma gondii, an obligate, intracellular, protozoan parasite, is known to infect a wide range of warm-blooded animals, including livestock, marine mammals and humans leading to the development of toxoplasmosis. The dense granule antigens (GRAs) have garnered crucial role in parasite survival, virulence and the formation of the parasitophorous vacuole. The study focusing on the GRA10 protein of T. gondii aims to elucidate its features further to support its potential inclusion in future vaccine formulations. The present study provided an exhaustive elucidation of the key characteristics of the GRA10 protein, encompassing its presence of a transmembrane domain, physico-chemical properties, subcellular localization, potential epitopes recognised by B-cells and T-cells, secondary and tertiary structures, as well as other significant attributes of this protein. The results indicated that the GRA10 protein possesses a total of 192 possible post-translational modification sites, with no transmembrane domains being detected within its structure. In terms of secondary structure, the GRA10 protein is composed of 27.74% alpha-helix, 13.53% extended strand and 58.72% random coil elements. Additionally, various potential B- and T-cell epitopes were pinpointed for the GRA10 protein, suggesting its immunogenic properties. The assessment of antigenicity and allergenicity further confirmed that GRA10 is immunogenic but non-allergenic, making it a promising candidate for further study. Furthermore, the induction of IFN-γ and IL-4 highlighted the ability of related MHC-II molecules to interact with GRA10, indicating its potential role in immune responses. These findings shed light on the multifaceted nature of the GRA10 protein and its significance in immunological processes. The study presented crucial fundamental and theoretical information regarding GRA10 in order to facilitate the creation of a potent vaccine against both acute and chronic toxoplasmosis, warranting additional in vivo examinations.

In silico analysis and structural vaccinology prediction of Toxoplasma gondii ROP41 gene via immunoinformatics methods as a vaccine candidate

INTRODUCTION

Toxoplasma gondii (T. gondii) infects all warm-blooded animals, including humans. Currently, no effective treatments exist to prevent the generation of chronic tissue cysts in infected hosts. Therefore, developing a vaccine to protect to deal with toxoplasmosis is a promising strategy, as a single immunization could provide lifelong protective immunity. Rhoptry proteins (ROPs) play a vital role for the parasite's survival within host cells and perform critical functions during different phases of parasite invasion. Little is known about ROP41 gene. Nevertheless, Understanding the characteristics of ROP41 will enhance diagnostic and vaccine research.

MATERIALS AND METHODS

The current article provides a comprehensive analysis of the essential components of the ROP41 protein, including its transmembrane domain, physico-chemical properties, subcellular location, tertiary and secondary structures, and potential T- and B-cell epitopes. These features were determined by many bioinformatics approaches to identify possible epitopes for developing a highly effective vaccine.

RESULTS

ROP41 protein showed 36 possible post-translational modification regions. The ROP41 protein secondary structure contains 17.35 % extended strand, 33.47 % alpha-helix, and 49.18 % random coil. Also, ROP41 showed many possible B- and T-cell epitopes. According to the Ramachandran plot, 90.78 % of amino acid residues had been placed in favored, 3.28 % in outlier, and 5.94 % in allowed areas. Also, the allergenicity and antigenicity evaluation indicated that ROP41 is non-allergenic and immunogenic.

CONCLUSION

The current study offered critical basic and conceptual information on ROP41 to increase a successful vaccine in opposition to continual and acute toxoplasmosis for in addition in vivo assessments. Further research is necessary for the development of vaccines utilizing ROP41 alone or combined with various antigens.

In Silico Conotoxin Studies: Progress and Prospects

Cone snails of the genus Conus have evolved to produce structurally distinct and functionally diverse venom peptides for defensive and predatory purposes. This nature-devised delicacy enlightened drug discovery and for decades, the bioactive cone snail venom peptides, known as conotoxins, have been widely explored for their therapeutic potential, yet we know very little about them. With the augmentation of computational algorithms from the realms of bioinformatics and machine learning, in silico strategies have made substantial contributions to facilitate conotoxin studies although still with certain limitations. In this review, we made a bibliometric analysis of in silico conotoxin studies from 2004 to 2024 and then discussed in silico strategies to not only efficiently classify conotoxin superfamilies but also speed up drug discovery from conotoxins, reveal binding modes of known conotoxin-ion channel interactions at a microscopic level and relate the mechanisms of ion channel modulation to its underlying molecular structure. We summarized the current progress of studies in this field and gave an outlook on prospects.

A rare germline BMP15 missense mutation causes hereditary ovarian immature teratoma in human

Ovarian immature teratomas (OITs) are malignant tumors originating from the ovarian germ cells that mainly occur during the first 30 y of a female's life. Early age of onset strongly suggests the presence of susceptibility gene mutations for the disease yet to be discovered. Whole exon sequencing was used to screen pathogenic mutations from pedigrees with OITs. A rare missense germline mutation (C262T) in the first exon of the BMP15 gene was identified. In silico calculation suggested that the mutation could impair the formation of mature peptides. In vitro experiments on cell lines confirmed that the mutation caused an 84.7% reduction in the secretion of mature BMP15. Clinical samples from OIT patients also showed a similar pattern of decrease in the BMP15 expression. In the transgenic mouse model, the spontaneous parthenogenetic activation significantly increased in oocytes carrying the T allele. Remarkably, a mouse carrying the T allele developed the phenotype of OIT. Oocyte-specific RNA sequencing revealed that abnormal activation of the H-Ras/MAPK pathway might contribute to the development of OIT. BMP15 was identified as a pathogenic gene for OIT which improved our understanding of the etiology of OIT and provided a potential biomarker for genetic screening of this disorder.

An in-silico approach to understand the potential role of Wnt inhibitory factor-1 (WIF-1) in the inhibition of the Wnt signalling pathway

WIF1 (Wnt inhibitory factor 1) is a potent tumour suppressor gene which is epigenetically silenced in numerous malignancies. The associations of WIF1 protein with the Wnt pathway molecules have not been fully explored, despite their involvement in the downregulation of several malignancies. In the present study, a computational approach encompassing the expression, gene ontology analysis and pathway analysis is employed to obtain an insight into the role of the WIF1 protein. Moreover, the interaction of the WIF1 domain with the Wnt pathway molecules was carried out to ascertain the tumour-suppressive role of the domain, along with the determination of their plausible interactions. Initially, the protein-protein interaction network analysis endowed us with the Wnt ligands (such as Wnt1, Wnt3a, Wnt4, Wnt5a, Wnt8a and Wnt9a), along with the Frizzled receptors (Fzd1 and Fzd2) and the low-density lipoprotein complex (Lrp5/6) as the foremost interactors of the protein. Further, the expression analysis of the aforementioned genes and proteins was determined using The Cancer Genome Atlas to comprehend the significance of the signalling molecules in the major cancer subtypes. Moreover, the associations of the aforementioned macromolecular entities with the WIF1 domain were explored using the molecular docking studies, whereas the dynamics and stability of the assemblage were investigated using 100 ns molecular dynamics simulations. Therefore, providing us insights into the plausible roles of WIF1 in inhibiting the Wnt pathways in various malignancies.Communicated by Ramaswamy H. Sarma.

Computational insights of the molecular recognition between volatile molecules and odorant binding proteins from the red palm weevil Rhynchophorus ferrugineus

The red palm weevil Rhynchophorus ferrugineus (Coleoptera: Curculionidae) is one of the most harmful pests for palm trees, causing serious economic damage worldwide. The present work aims to model and study the 3D structures of highly expressed odorant binding proteins from R. ferrugineus (RferOBPs) and identify possible binding modes and ligand release mechanism by docking and molecular dynamics. Highly confident 3D structures of a total of 11 odorant binding proteins (OBPs) were obtained with AlphaFold2. All 3D RferOBPs modeled structures displayed six characteristic α-helices, except for RfeOBP7 and RfeOBP10, which had an extra terminal α-helix. Among the eleven modeled RferOBPs, RferOBP4 was highly expressed in the antennae and subsequently selected for further analyses. Molecular docking analyses demonstrated that ferruginol, α-pinene, DEET, and picaridin can favorably bind the RferOBP4 cavity with low affinity energies. Molecular dynamic simulations of RferOBP4 bound to ferruginol at different pH values showed that low pH environments dictate a structural change into an apo-state that modifies the number of tunnels where the ligand can coexist, further triggering ligand release by a pH-dependent mechanism. This is the first report concerning the modelling and study of ligand binding modes and release mechanism of R. ferrugineus OBPs.Communicated by Ramaswamy H. Sarma.

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.

Increased EGFRvIII Epitope Accessibility after Tyrosine Kinase Inhibitor Treatment of Glioblastoma Cells Creates More Opportunities for Immunotherapy

The number of glioblastoma (GB) cases is increasing every year, and the currently available therapies remain ineffective. A prospective antigen for GB therapy is EGFRvIII, an EGFR deletion mutant containing a unique epitope that is recognized by the L8A4 antibody used in CAR-T (chimeric antigen receptor T cell) therapy. In this study, we observed that the concomitant use of L8A4 with particular tyrosine kinase inhibitors (TKIs) does not impede the interaction between L8A4 and EGFRvIII; moreover, in this case, the stabilization of formed dimers results in increased epitope display. Unlike in wild-type EGFR, a free cysteine at position 16 (C16) is exposed in the extracellular structure of EGFRvIII monomers, leading to covalent dimer formation in the region of L8A4-EGFRvIII mutual interaction. Following in silico analysis of cysteines possibly involved in covalent homodimerization, we prepared constructs containing cysteine-serine substitutions of EGFRvIII in adjacent regions. We found that the extracellular part of EGFRvIII possesses plasticity in the formation of disulfide bridges within EGFRvIII monomers and dimers due to the engagement of cysteines other than C16. Our results suggest that the EGFRvIII-specific L8A4 antibody recognizes both EGFRvIII monomers and covalent dimers, regardless of the cysteine bridging structure. To summarize, immunotherapy based on the L8A4 antibody, including CAR-T combined with TKIs, can potentially increase the chances of success in anti-GB therapy.

Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field

Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit-explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring "the state of the art" in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI-PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI-PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI-PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the "state of the art" on research in the AI-PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.

In silico characterization of the GH5-cellulase family from uncultured microorganisms: physicochemical and structural studies

BACKGROUND

Hydrolysis of cellulose-based biomass by cellulases produce fermented sugar for making biofuels, such as bioethanol. Cellulases hydrolyze the β-1,4-glycosidic linkage of cellulose and can be obtained from cultured and uncultured microorganisms. Uncultured microorganisms are a source for exploring novel cellulase genes through the metagenomic approach. Metagenomics concerns the extraction, cloning, and analysis of the entire genetic complement of a habitat without cultivating microbes. The glycoside hydrolase 5 family (GH5) is a cellulase family, as the largest group of glycoside hydrolases. Numerous variants of GH5-cellulase family have been identified through the metagenomic approach, including CelGH5 in this study. University-CoE-Research Center for Biomolecule Engineering, Universitas Airlangga successfully isolated CelGH5 from waste decomposition of oil palm empty fruit bunches (OPEFB) soil by metagenomics approach. The properties and structural characteristics of GH5-cellulases from uncultured microorganisms can be studied using computational tools and software.

RESULTS

The GH5-cellulase family from uncultured microorganisms was characterized using standard computational-based tools. The amino acid sequences and 3D-protein structures were retrieved from the GenBank Database and Protein Data Bank. The physicochemical analysis revealed the sequence length was roughly 332-751 amino acids, with the molecular weight range around 37-83 kDa, dominantly negative charges with pI values below 7. Alanine was the most abundant amino acid making up the GH5-cellulase family and the percentage of hydrophobic amino acids was more than hydrophilic. Interestingly, ten endopeptidases with the highest average number of cleavage sites were found. Another uniqueness demonstrated that there was also a difference in stability between in silico and wet lab. The II values indicated CelGH5 and ACA61162.1 as unstable enzymes, while the wet lab showed they were stable at broad pH range. The program of SOPMA, PDBsum, ProSA, and SAVES provided the secondary and tertiary structure analysis. The predominant secondary structure was the random coil, and tertiary structure has fulfilled the structure quality of QMEAN4, ERRAT, Ramachandran plot, and Z score.

CONCLUSION

This study can afford the new insights about the physicochemical and structural properties of the GH5-cellulase family from uncultured microorganisms. Furthermore, in silico analysis could be valuable in selecting a highly efficient cellulases for enhanced enzyme production.

Combination Processing Method Reduced IgE-Binding Activity of Litopenaeus vannamei by Modifying Lysine, Arginine, and Cysteine on Multiple Allergen Epitopes

Allergic reactions occur after the whole food is ingested, rather than the purified allergen. The present study explores the low-allergenic food processing for Litopenaeus vannamei by analysis of macrostructure, digestibility, and immunoreactivity. Furthermore, the presence of modified amino acids on the reported IgE epitopes was analyzed by mass spectrometry. Results showed that the combination processing of Maillard reaction (shrimp meat with galactose) with high temperature-pressure at 115 °C obviously changed the macrostructure and increased the digestibility for the shrimp meat. Meanwhile, the processing significantly reduced the IgG/IgE-binding activity of the shrimp meat. The hypo-IgE-binding activity in processed shrimp may be due to the modification of lysine, arginine, and cysteine residues in antigen epitopes. This is a comprehensive assessment of the specific amino acid residues modified by glycation of multiple allergens in processed L. vannamei, which provides a new research method to explore the hypo-IgE-binding activity in food.

Immunoinformatic analysis of immunogenic B- and T-cell epitopes of MIC4 protein to designing a vaccine candidate against Toxoplasma gondii through an in-silico approach

Purpose

Toxoplasmosis, transmitted by Toxoplasma gondii, is a worldwide parasitic disease that affects approximately one-third of the world's inhabitants. Today, there are no appropriate drugs to deter tissue cysts from developing in infected hosts. So, developing an effective vaccine would be valuable to avoid from toxoplasmosis. Considering the role of microneme antigens such as microneme protein 4 (MIC4) in T. gondii pathogenesis, it can be used as potential candidates for vaccine against T. gondii.

Materials and Methods

In this study several bioinformatics methods were used to assess the different aspects of MIC4 protein such as secondary and tertiary structure, physicochemical characteristics, the transmembrane domains, subcellular localization, B-cell, helper-T lymphocyte, cytotoxic-T lymphocyte epitopes, and other notable characteristic of this protein design a suitable vaccine against T. gondii.

Results

The studies revealed that MIC4 protein includes 59 potential post-translational modification sites without any transmembrane domains. Moreover, several probable epitopes of B- and T-cells were detected for MIC4. The secondary structure comprised 55.69% random coil, 5.86% beta-turn, 19.31% extended strand, and 19.14% alpha helix. According to the Ramachandran plot results, 87.42% of the amino acid residues were located in the favored, 9.44% in allowed, and 3.14% in outlier regions. The protein allergenicity and antigenicity revealed that it was non-allergenic and antigenic.

Conclusion

This study gives vital basic on MIC4 protein for further research and also established an effective vaccine with different techniques against acute and chronic toxoplasmosis.

Folding of Truncated Granulin Peptides

Granulins are a family of unique protein growth factors which are found in a range of species and have several bioactivities that include cell proliferation and wound healing. They typically contain six disulfide bonds, but the sequences, structures and bioactivities vary significantly. We have previously shown that an N-terminally truncated version of a granulin from the human liver fluke, Opisthorchis viverrini, can fold independently into a “mini-granulin” structure and has potent wound healing properties in vivo. The incorporation of a non-native third disulfide bond, with respect to the full-length granulin module, was critical for the formation of regular secondary structure in the liver fluke derived peptide. By contrast, this third disulfide bond is not required for a carp granulin-1 truncated peptide to fold independently. This distinction led us to explore granulins from the zebrafish model organism. Here we show that the mini-granulin fold occurs in a naturally occurring paragranulin (half-domain) from zebrafish, and is also present in a truncated form of a full-length zebrafish granulin, suggesting this structure might be a common property in either naturally occurring or engineered N-terminally truncated granulins and the carp granulin-1 folding is an anomaly. The in vitro folding yield is significantly higher in the naturally occurring paragranulin, but only the truncated zebrafish granulin peptide promoted the proliferation of fibroblasts consistent with a growth factor function, and therefore the function of the paragranulin remains unknown. These findings provide insight into the folding and evolution of granulin domains and might be useful in the elucidation of the structural features important for bioactivity to aid the design of more potent and stable analogues for the development of novel wound healing agents.

Antigenic properties of dense granule antigen 12 protein using bioinformatics tools in order to improve vaccine design against Toxoplasma gondii

PURPOSE

Toxoplasma gondii is an opportunistic parasite infecting all warm-blooded animals including humans. The dense granule antigens (GRAs) play an important role in parasite survival and virulence and in forming the parasitophorous vacuole. Identification of protein characteristics increases our knowledge about them and leads to develop the vaccine and diagnostic studies.

MATERIALS AND METHODS

This paper gave a comprehensive definition of the important aspects of GRA12 protein, including physico-chemical features, a transmembrane domain, subcellular position, secondary and tertiary structure, potential epitopes of B-cells and T-cells, and other important features of this protein using different and reliable bioinformatics methods to determine potential epitopes for designing of a high-efficient vaccine.

RESULTS

The findings showed that GRA12 protein had 53 potential post-translational modification sites. Also, only one transmembrane domain was recognized for this protein. The secondary structure of GRA12 protein comprises 35.55% alpha-helix, 19.50% extended strand, and 44.95% random coil. Moreover, several potential B- and T-cell epitopes were identified for GRA12. Based on the results of the Ramachandran plot, 79.26% of amino acid residues were located in favored, 11.85% in allowed and 8.89% in outlier regions. Furthermore, the results of the antigenicity and allergenicity assessment noted that GRA12 is immunogenic and non-allergenic.

CONCLUSION

This research provided important basic and conceptual data on GRA12 to develop an effective vaccine against acute and chronic toxoplasmosis for further in vivo investigations. More studies are required on vaccine development using the GRA12 alone or combined with other antigens in the future.

Prediction of disulfide bond engineering sites using a machine learning method

Disulfide bonds are covalently bonded sulfur atoms from cysteine pairs in protein structures. Due to the importance of disulfide bonds in protein folding and structural stability, artificial disulfide bonds are often engineered by cysteine mutation to enhance protein structural stability. To facilitate the experimental design, we implemented a method based on neural networks to predict amino acid pairs for cysteine mutations to form engineered disulfide bonds. The designed neural network was trained with high-resolution structures curated from the Protein Data Bank. The testing results reveal that the proposed method recognizes 99% of natural disulfide bonds. In the test with engineered disulfide bonds, the algorithm achieves similar accuracy levels with other state-of-the-art algorithms in published dataset and better performance for two comprehensively studied proteins with 70% accuracy, demonstrating potential applications in protein engineering. The neural network framework allows exploiting the full features in distance space, and therefore improves accuracy of the disulfide bond engineering site prediction. The source code and a web server are available at http://liulab.csrc.ac.cn/ssbondpre.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

Antimicrobial and structural insights of a new snakin-like peptide isolated from Peltophorum dubium (Fabaceae)

Snakins are antimicrobial peptides (AMPs) found, so far, exclusively in plants, and known to be important in the defense against a wide range of pathogens. Like other plant AMPs, they contain several positively charged amino acids, and an even number of cysteine residues forming disulfide bridges which are considered important for their usual function. Despite its importance, studies on snakin tertiary structure and mode of action are still scarce. In this study, a new snakin-like gene was isolated from the native plant Peltophorum dubium, and its expression was verified in seedlings and adult leaves. The deduced peptide (PdSN1) shows 84% sequence identity with potato snakin-1 mature peptide, with the 12 cysteines characteristic from this peptide family at the GASA domain. The mature PdSN1 coding sequence was successfully expressed in Escherichia coli. The purified recombinant peptide inhibits the growth of important plant and human pathogens, like the economically relevant potato pathogen Streptomyces scabies and the opportunistic fungi Candida albicans and Aspergillus niger. Finally, homology and ab initio modeling techniques coupled to extensive molecular dynamics simulations were used to gain insight on the 3D structure of PdSN1, which exhibited a helix-turn-helix motif conserved in both native and recombinant peptides. We found this motif to be strongly coded in the sequence of PdSN1, as it is stable under different patterns of disulfide bonds connectivity, and even when the 12 cysteines are considered in their reduced form, explaining the previous experimental evidences.

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue-residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A _TMH, 87.1% of A _P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/.

A molecular dynamics strategy for CSαβ peptides disulfide-assisted model refinement

Many cysteine-stabilized antimicrobial peptides from a variety of living organisms could be good candidates for the development of anti-infective agents. In the absence of experimentally obtained structural data, peptide modeling is an essential tool for understanding structure-activity relationships and for optimizing the bioactive moieties. Focusing on cysteine-rich peptide structures, we reproduced the case of structure predictions in the so-called midnight zone. We developed our protocol on a training set derived by clustering the available cysteine-stabilized αβ (CSαβ) structures in nine different representative families and tested it on peptides randomly selected from each family. Starting from draft models, we tested a structure-based disulfide predictor and we used cysteine distances as constraints during molecular dynamics. Finally, we proposed an analysis for final structure selection. Accordingly, we obtained a mean root mean square deviation improvement of 21% for the test set. Our findings demonstrate that it is possible to predict the network of disulfide bridges in cysteine-stabilized peptides and to use this result to improve the accuracy of structural predictions. Finally, we applied the methods to predict the structure of royalisin, a cysteine-rich peptide with unknown structure.

Cysteine-Rich Atrial Secretory Protein from the Snail Achatina achatina: Purification and Structural Characterization

Despite extensive studies of cardiac bioactive peptides and their functions in molluscs, soluble proteins expressed in the heart and secreted into the circulation have not yet been reported. In this study, we describe an 18.1-kDa, cysteine-rich atrial secretory protein (CRASP) isolated from the terrestrial snail Achatina achatina that has no detectable sequence similarity to any known protein or nucleotide sequence. CRASP is an acidic, 158-residue, N-glycosylated protein composed of eight alpha-helical segments stabilized with five disulphide bonds. A combination of fold recognition algorithms and ab initio folding predicted that CRASP adopts an all-alpha, right-handed superhelical fold. CRASP is most strongly expressed in the atrium in secretory atrial granular cells, and substantial amounts of CRASP are released from the heart upon nerve stimulation. CRASP is detected in the haemolymph of intact animals at nanomolar concentrations. CRASP is the first secretory protein expressed in molluscan atrium to be reported. We propose that CRASP is an example of a taxonomically restricted gene that might be responsible for adaptations specific for terrestrial pulmonates.